Utilizing AI - The Enterprise AI Podcast: Recent Episodes
Utilizing AI
AI is getting real, moving out of academia and hyperscale and into the enterprise. Businesses are adopting AI in strategically, and IT companies are deploying AI technologies in their products. This podcast focuses on practical applications of artificial intelligence and machine learning in the modern enterprise datacenter and cloud infrastructure. Hosted by Stephen Foskett of GestaltIT.com, Chris Grundemann of chrisgrundemann.com, and Frederic Van Haren, of HighFens Inc.
As practical applications of AI are rolled out, they are increasingly being deployed on-premises at scale. We are wrapping up this season of Utilizing Tech with Solidigm focused on AI Data Infrastructure by discussing practical deployment considerations with Ariel Pisetzky, VP of Information Technology and Cyber at Taboola in a discussion with Jeniece Wnorowski and Stephen Foskett. Companies like Taboola are built on data and have been deploying AI-driven applications for years. Generative AI brings new capabilities but is part of a spectrum of solutions that leverage data to produce results for customers. As applications mature, many companies are looking to bring them back on-premises, and this trend will likely accelerate given the cost of AI infrastructure as-a-service offerings. Owned infrastructure can also deliver beyond expected lifespans, representing a potential windfall for businesses that can continue to use deprerciated hardware. This is especially true of large flash drives, which have proven much more reliable than initially predicted. Although it is tempting to buy the biggest, fastest infrastructure to extend the lifespan of equipment, Pisetzky recommends focusing on equipment that is flexible and can be re-purposed in other ways in the future. Server storage is unique in that it is easy to upgrade and replace it in place, even hot-swapping drives, and large lives have a very long lifespan.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Jeniece Wnorowski, Datacenter Product Marketing Manager at Solidigm: https://www.linkedin.com/in/jeniecewnorowski/
Guest: Ariel Pisetzky, VP of Information Technology and Cyber, Taboola: https://www.linkedin.com/in/ariel-pisetzky/
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Tags: #UtilizingTech, #Sponsored, #AIDataInfrastructure, #AI, @SFoskett, @TechFieldDay, @UtilizingTech, @Solidigm,
Modern AI infrastructure has exposed the importance of reliability and predictability of storage in addition to performance. This episode of Utilizing Tech, presented by Solidigm, features Kelley Osburn of Graid Technology discussing the challenges of maximizing performance and resiliency of storage for AI with Jeniece Wnorowski and Stephen Foskett. AI servers are optimized for machine learning processing, and Graid Technology SupremeRAID offloads processing to GPUs similarly to the way these massively-parallel processors offload ML processing. They also have a peer-to-peer DMA feature to direct the data directly to the processor rather than forcing all data to pass through a single processor or channel. There is a need for RAID software at many spots in the data pipeline, from ingestion and preparation to processing and consolidation, and each requires performance and availability. There are many applications that require maximum performance and capacity without impacting the host CPU, including military, medical research and diagnostics, and financial, in addition to AI processing.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Jeniece Wnorowski, Datacenter Product Marketing Manager at Solidigm: https://www.linkedin.com/in/jeniecewnorowski/
Guest: Kelley Osburn, Senior Director at Graid Technology: https://www.linkedin.com/in/kelleyosburn/
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Tech Field DayWebsite: https://www.TechFieldDay.comLinkedIn: https://www.LinkedIn.com/company/Tech-Field-Day X/Twitter: https://www.Twitter.com/TechFieldDay
Tags: #UtilizingTech, #Sponsored, #AIDataInfrastructure, #AI, @SFoskett, @TechFieldDay, @UtilizingTech, @Solidigm,
Many of the largest-scale data storage environments use Ceph, an open source storage system, and are now connecting this to AI. This episode of Utilizing Tech, sponsored by Solidigm, features Dan van der Ster, CTO of Clyso, discussing Ceph for AI Data with Jeniece Wnorowski and Stephen Foskett. Ceph began in research and education but today is widely used as well in finance, entertainment, and commerce. All of these use cases require massive scalability and extreme reliability despite using commodity storage components, but Ceph is increasingly able to deliver high performance as well. AI workloads require scalable metadata performance as well, which is an area that Ceph developers are making great strides. The software has also proved itself adaptable to advanced hardware, including today’s large NVMe SSDs. As data infrastructure development has expanded from academia to HPC to the cloud and now AI, it’s important to see how the community is embracing and improving the software that underpins today’s compute stack.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Jeniece Wnorowski, Datacenter Product Marketing Manager at Solidigm: https://www.linkedin.com/in/jeniecewnorowski/
Guest: Dan van der Ster, CTO at CLYSO and Ceph Executive Council Member: https://www.linkedin.com/in/dan-vanderster/
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As the volume of data supporting AI applications grows ever larger, it's critical to deliver scalable performance without overlooking power efficiency. This episode of Utilizing Tech, sponsored by Solidigm, brings Chris Gladwin, CEO and co-founder of Ocient, to talk about scalable and efficient data platforms for AI with Jeniece Wnorowski and Stephen Foskett. Ocient has developed a new data analytics stack focused on scalability with energy efficiency for ultra-large data analytics applications. At scale, applications need to incorporate trillions of data points, and it is not just desirable but necessary to enable this without losing sight of energy consumption. Ocient leverages flash storage to reduce power consumption and increase performance but also moves data processing closer to the storage to reduce power consumption further. This type of integrated storage and compute would not be possible without flash, and reflects the architecture of modern processors, which locate memory on-package with compute. Ocient is already popular in telco, e-commerce, and automotive, and the scale of data required by AI applications is similar, especially as concepts like retrieval-augmented generation are implemented. The conversation around datacenter, cloud, and AI energy usage is coming to the fore, and companies must address the environmental impact of everything we do.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Jeniece Wnorowski, Datacenter Product Marketing Manager at Solidigm: https://www.linkedin.com/in/jeniecewnorowski/
Guest: Chris Gladwin, CEO and Cofounder, Ocient: https://www.linkedin.com/in/chris-gladwin-7ba42b/
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Tags: #UtilizingTech, #Sponsored, #AIDataInfrastructure, #AI, @SFoskett, @TechFieldDay, @UtilizingTech, @Solidigm,
Cutting-edge AI infrastructure needs all the performance it can get, but these environments must also be efficient and reliable. This episode of Utilizing Tech, brought to you by Solidigm, features Davide Villa of Xinnor discussing the value of modern software RAID and NVMe SSDs with Ace Stryker and Stephen Foskett. Xinnor xiRAID leverages the resources of the server, including the AVX instruction set found on modern CPUs, to combine NVMe SSDs, providing high performance and reliability inside the box. Modern servers have multiple internal drive slots, and all of these drives must be managed and protected in the event of failure. This is especially important in AI servers, since an ML training run can take weeks, amplifying the risk of failure. Software RAID can be used in many different implementations, with various file systems, including NFS and high-performance networks like InfiniBand. And it can be tuned to maximize performance for each workload. Xinnor can help customers to tune the software to maximize reliability of SSDs, especially with QLC flash, by adapting the chunk size and minimizing write amplification. Xinnor also produces a storage platform solution called xiSTORE that combines xiRAID with the Lustre FS clustered file system, which is already popular in HPC environments. Although many environments can benefit from a full-featured storage platform, others need a software RAID solution to combine NVMe SSDs for performance and reliability.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Ace Stryker, Director of Product Marketing, AI Product Marketing at Solidigm: https://www.linkedin.com/in/acestryker/
Davide Villa, Chief Revenue Officer at Xinnor: https://www.linkedin.com/in/davide-villa-b1256a2/
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Organizations seeking to build an infrastructure stack for AI training need to know how the data platform is going to perform. This episode of Utilizing Tech, presented by Solidigm, includes Curtis Anderson, Co-Chair of the Storage Working Group at MLCommons, discussing storage benchmarking with Ace Stryker and Stephen Foskett. MLCommons is an industry consortium seeking to improve AI solutions through joint engineering. The organization publishes the well-known MLPerf benchmark, which now includes practical metrics for storage solutions. The goal of MLPerf Storage is to answer the key question: Will a given data infrastructure support AI training of a given scale. The organization encourages storage vendors to run the benchmarks against their solutions to prove the suitability to support specific workloads. The AI industry is already shifting its focus from maximum scale and performance to more-balances infrastructure using alternative GPUs, accelerators, and even CPUs, and is increasingly concerned about price and environmental impact. The question of data preparation is also rising, and this generally uses a different CPU-focused solution. MLPerf Storage is focused on training today and will soon address data preparation, though this can be quite different for each data set. The next MLPerf Storage benchmark opens soon, and we encourage all data infrastructure companies to get involved and submit their own performance numbers.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Ace Stryker, Director of Product Marketing, AI Product Marketing at Solidigm: https://www.linkedin.com/in/acestryker/
Guest: Curtis Anderson, Co-Chair MLCommons Storage Working Group: https://www.linkedin.com/in/curtis-anderson-174aa/
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Model training seriously stresses data infrastructure, but preparing that data to be used is a much more difficult challenge. This episode of Utilizing Tech features Subramanian Kartik of VAST Data discussing the broad data pipeline with Jeniece Wnorowski of Solidigm and Stephen Foskett. The first step in building an AI model is collecting, organizing, tagging, and transforming data. Yet this data is spread around the organization in databases, data lakes, and unstructured repositories. The challenge of building a data pipeline is familiar to most businesses, since a similar process is required in analytics, business intelligence, observability, and simulation, but generative AI applications have an insatiable appetite for data. These applications also demand extreme levels of storage performance, and only flash SSDs can meet this demand. A side benefit is the improvements in power consumption and cooling versus hard disk drives, and this is especially true as massive SSDs come to market. Ultimately the success of generative AI will drive greater collection and processing of data on the inferencing side, perhaps at the edge, and this will drive AI data infrastructure further.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Jeniece Wnorowski, Datacenter Product Marketing Manager at Solidigm: https://www.linkedin.com/in/jeniecewnorowski/
Guest: Subramanian Kartik, Ph. D, Global Systems Engineering Lead at VAST Data: https://www.linkedin.com/in/subramanian-kartik-ph-d-1880835/
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Analysts and press spend a lot of time talking about specs and performance numbers, so it's always a treat when we get to talk to people who are testing and using these products. This episode of Utilizing Tech is focused on AI Data Infrastructure and features Jordan Ranous from StorageReview and is co-hosted by Stephen Foskett and Ace Stryker from our sponsor, Solidigm. StorageReview has constricted an experimental environment focused on astrophotography as a way to demonstrate AI applications in challenging edge environments. Their setup included a ruggedized Dell server, NVIDIA GPU, and Solidigm SSDs. This is the same sort of setup found at edge compute environments in retail, manufacturing, and remote use cases. StorageReview benchmarks storage devices by profiling real-world applications and building representative infrastructure to test. When it comes to GPUs, the goal is to keep these expensive processors operating at maximum capacity through optimal network and storage throughput.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Ace Stryker, Director of Product Marketing, AI Product Marketing at Solidigm: https://www.linkedin.com/in/acestryker/
Guest: Jordan Ranous, AI, Hardware, & Advanced Workloads Specialist at StorageReview.com: https://www.linkedin.com/in/jranous/
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Great AI needs excellent data infrastructure, in terms of capacity, performance, and efficiency. This episode of Utilizing Tech serves as a preview of season 7, brought to you by Solidigm, and features co-hosts Jeniece Wnorowski and Ace Stryker along with Stephen Foskett. Solidigm's partners are discovering just how important it is to optimize every element of the Ai infrastructure stack. With ever-larger AI datacenters being built, efficient storage can make a big difference, from power and cooling to physical density to performance. As we will hear throughout season 7, different AI environments will need specialized data infrastructure, from the edge to the cloud. With retrieval-augmented generation (RAG) emerging as a new trend in AI, it makes high-performance even more important at run-time.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Jeniece Wnorowski, Datacenter Product Marketing Manager at Solidigm: https://www.linkedin.com/in/jeniecewnorowski/Ace Stryker, Director of Product Marketing at Solidigm: https://www.linkedin.com/in/acestryker/
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Tech Field DayWebsite: https://www.TechFieldDay.comLinkedIn: https://www.LinkedIn.com/company/Tech-Field-Day X/Twitter: https://www.Twitter.com/TechFieldDay
Tags: #UtilizingTech, #AIDataInfrastructure, #AI, @SFoskett, @TechFieldDay, @UtilizingTech, @Solidigm,
We knew that 2024 would be the year of AI right from the start, but this season of the podcast has seen incredible development and change. This final episode of Utilizing Tech Season 6 features hosts Frederic Van Haren, Allyson Klein, and Stephen Foskett discussing the current state of AI infrastructure half-way through 2024. In addition to AI Field Day, we experienced NVIDIA GTC and numerous product introductions over the last few months. It's truly an ecosystem play now, with every company showing how well they can partner to build AI infrastructure. At the same time, a few superusers of AI are responsible for the basic models, including Google, Amazon, and Microsoft, and of course OpenAI and the other dedicated generative AI firms. The key to bringing this to the enterprise market is transfer learning, which will see a few base models tuned and trained for specific use cases. This season saw a range of guests discussing storage, data platforms, connectivity, and application development, and every one is focused on delivering practical AI solutions in the enterprise.
Hosts: Stephen Foskett: https://www.linkedin.com/in/sfoskett/Allyson Klein: https://www.linkedin.com/in/allysonklein/Frederic Van Haren: https://www.linkedin.com/in/fredericvharen/
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Tags: #UtilizingAI, #UtilizingTech, #YearofAI, @UtilizingTech, @TechFieldDay, @TheFuturumGroup, @SFoskett, @FredericVHaren, @TechAllyson,
Both AI and quantum computing seemed entirely theoretical just a few years ago, yet generative AI is everywhere today. This episode of Utilizing Tech considers the promise of quantum computing generally and the applicability of this technology in AI with Dr. Bob Sutor of The Futurum Group, Alastair Cooke, and Stephen Foskett. One big challenge for quantum computing is the difficulty of storing data for calculations, a severe limitation for using the technology in AI. But there is the possibility of pairing classical computers with quantum processors to bring the best of both concepts. Both AI and quantum computing deal with linear algebra, so there is an affinity between the technologies, but it is likely that each will find use cases in different areas. Ultimately there is still a lot of development to do but quantum technology shows great promise in AI and beyond.
Hosts: Stephen Foskett: https://www.linkedin.com/in/sfoskett/Alastair Cooke: https://www.linkedin.com/in/alastaircooke/
Guest:Bob Sutor, Vice President and Practice Lead od Emerging Technologies at The Futurum Group: https://www.linkedin.com/in/bobsutor/
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As we consider the impact of AI on modern applications, we should consider the ways that this technology will improve these products. This episode of Utilizing Tech brings Matthew Wallace, CTO of Kamiwaza AI, to discuss the adoption of AI in application development with Allyson Klein and Stephen Foskett. Large SaaS providers were the first to add AI-powered features but this technology is rapidly coming to market across the spectrum of applications. Matthew likens this to the evolution from spreadsheets to SaaS tools and cloud, which was a similar revolution. He mentions tools like ?, Cursor, Llama2, Mixtral, and more. We also discuss retrieval-automated generation (RAG), which enables LLMs to bring in external data at run time. We must also consider the source of the data, both in training and RAG, and questions of sovereignty, privacy, copyright, and safety. Looking forward, Matthew expects companies to customize their own models based on use case specific data. Looking forward, we expect new frameworks and models to be adopted rapidly to bring maturity and reliability to AI in enterprise applications throughout 2024.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Allyson Klein: https://www.linkedin.com/in/allysonklein/
Guest: Matthew Wallace, CTO and Cofounder of KamiwazaAI: https://www.linkedin.com/in/matthewwallaceco/
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AI is accelerating application development and modernization in many ways, but developers are just ramping up their use of the technology. This episode of Utilizing Tech includes Paul Nashawaty, who focuses on application development at The Futurum Group, discussing this topic with Allyson Klein and Stephen Foskett. Use cases for AI include documentation, chatbots, data integration, programming co-pilots, and more. Regardless of how AI is used, organizations must accept that they are ultimately responsible for the products and outputs produced. Accelerating testing and thus the entire DevOps release cycle is one area where AI is making incredible growth. Another popular concept is retrieval-augmented generation (RAG) which brings existing datasets to generative AI to improve results. There is currently a lack of confidence in AI-based solutions and concern about the complexity and level of effort required to bring them to market, so vendors must help make deployment easier and better integrated. Product vendors are addressing this by delivering solutions with partners and popular platforms and frameworks. Open source tools and open data are also helping to move AI technologies forward. Developers want invisible infrastructure and platforms that just work, and the emerging AI PC segment promises more processing power on the desktop. 2024 is shaping up to be the year of AI in application development.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Allyson Klein: https://www.linkedin.com/in/allysonklein/
Paul Nashawaty, Practice Lead, Application Development Modernization at The Futurum Group: https://www.linkedin.com/in/paulnashawaty/
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Tags: #UtilizingAI, #AppDev, #AI, @TheFuturumGroup, @GestaltIT, @TechFieldDay, @SFoskett, @TechAllyson, @PNashawaty,
We are at a turning point, as AI has matured from theoretical experimentation to practical application. In this episode of Utilizing Tech, Neeloy Bhattacharyya joins Allyson Klein and Stephen Foskett to discuss how VAST Data's customers and partners are making practical use of AI. Data is the key to successful AI-powered applications, and VAST Data supports both unstructured and structured data sets. Neeloy emphasizes the interactive nature of AI application development and the flexibility required to support this. He also discusses the need for structured data to support LLMs and the challenges of keeping these up to date and synchronized. One of the biggest issues in deploying AI applications is the complexity inherent in these systems. That's why it's heartening to see companies working together to created integrations and standardized platforms to make AI easier to deploy. Collaboration is the key to making AI practical.Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Allyson Klein: https://www.linkedin.com/in/allysonklein/Guest: Neeloy Bhattacharyya, Director of AI/HPC Solutions Engineering at VAST Data: https://www.linkedin.com/in/neeloybhattacharyya/Follow Utilizing TechWebsite: https://www.UtilizingTech.com/X/Twitter: https://www.twitter.com/UtilizingTech Tech Field DayWebsite: https://www.TechFieldDay.comLinkedIn: https://www.LinkedIn.com/company/Tech-Field-Day X/Twitter: https://www.Twitter.com/TechFieldDay Tags: #UtilizingAI, #AI, #Data, @VAST_Data,
Ultra Ethernet promises to tune Ethernet for the needs of specialized workloads, including HPC and AI, from the lowest hardware to the software stack. This episode of Utilizing Tech features Dr. J Metz, Steering Committee Chair of the Ultra Ethernet Consortium, discussing this new technology with Frederic Van Haren and Stephen Foskett. The process of tuning Ethernet begins with a study of the profile and workloads to be served to identify the characteristics needed to support it. The group focuses on scale-out networks for large-scale applications like AI and HPC. Considerations include security, latency, ordering, and scalability. The goal is not to replace PCIe, CXL, or fabrics like NVLink but to extend Ethernet to address the connectivity and performance needs in an open standardized way. But Ultra Ethernet is more than hardware; the group is also building software features including a Libfabric interface and are working with OCP, DMTF, SNIA, and other industry groups.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Guest:
J Metz, Chair of the Ultra Ethernet Consortium and SNIA, Technical Director at AMD: https://www.linkedin.com/in/jmetz/
Everyone uses AI today, whether they know it or not, and it's critical for users of this technology to understand its capability and limitations. This episode of Utilizing Tech features Chris Grundemann, a fellow podcast host and Tech Field Day delegate, talking the many ways we use AI every day. Like Frederic Van Haren and Stephen Foskett, Chris uses AI to assist with content creation in many ways, from summarization and organization of data to image generation. Content creators are using AI tools like ChatGPT, Stable Diffusion, and more to process data, but we all agree that it's best to spend time and effort to refine the output, especially when it comes to tone and voice. We are also using AI based tools for coding and structuring data, especially interactively. Although some have suggested that AI will replace content creators or coders, the technology is instead democratizing access and making it easier to use on a daily basis. It also makes computing more available to those who were previously locked out, expanding the impact of the technology we have spent decades creating.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Guest:
Chris Grundemann, Managing Director at Grundemann Solutions: https://www.linkedin.com/in/cgrundemann/
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Many people involved in artificial intelligence don't spend much time considering storage infrastructure. That's the topic of this episode of Utilizing AI, which features Ace Stryker of Solidigm discussing the role of flash storage in AI infrastructure with Frederic Van Haren and Stephen Foskett. Considering the cost of GPUs and the rest of the AI stack, idle time is the enemy. That's why it's critical to have a low-latency storage layer to support tasks like training.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Guest: Ace Stryker, Director of Product Marketing at Solidigm: https://www.linkedin.com/in/acestryker/
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AI training is a uniquely data-hungry application, and it requires a special data pipeline to keep expensive GPUs fed. This episode of Utilizing Tech focuses on the data platform for machine learning, featuring Molly Pressley of Hammerspace along with Frederic Van Haren and Stephen Foskett. Nothing is worse than idle hardware, especially when it comes to expensive GPUs intended for ML training. Performance is important, but parallel access and access to multiple systems is just as important. Building an AI training environment requires identifying and eliminating bottlenecks at every layer, but many systems are simply not capable of scaling to the extent required by the largest GPU clusters. But a data pipeline goes way beyond storage: Training requires checkpoints, metadata, and access to different data points. And different models have unique requirements as well. Ultimately, AI applications require a flexible data pipeline not just high-performance storage.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Guest: Molly Presley, Head of Global Marketing at Hammerspace: https://www.linkedin.com/in/mollyjpresley/
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AI is powering breakthroughs across all domains. In this episode of Utilizing AI Podcast brought to you by Tech Field Day, part of The Futurum Group, David Kanter, Founder and Executive Director of MLCommons, joins hosts, Stephen Foskett and Frederic Van Haren, to talk about MLCommons’ role in driving valuable AI solutions, and helping organizations overcome the challenges around AI safety. MLCommons’ set of benchmarks provides transparent ratings and reviews of a wide range of products guiding buyers towards better purchase decisions.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Guest: David Kanter, Founder and Executive Director, MLCommons: https://www.linkedin.com/in/kanterd/
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Tags: #UtilizingTech #AI #MLPerf #UtilizingAI #AISafety @MLCommons @UtilizingTech
AI is all about data, so it is no surprise that enterprises are deploying their own private AI inside the firewall. This episode of Utilizing Tech brings Chris Wolf, Global Head of AI and Advanced Services at VMware by Broadcom, to discuss private AI with Frederic Van Haren and Stephen Foskett. Companies looking to deploy AI are finding that it doesn't require nearly as much hardware as expected, software is widely available, and are reluctant to trust sensitive data to a service provider. VMware by Broadcom is deploying their own private AI code assist, keeping proprietary software and standards inside the firewall. But the solution also helps the AI team be more agile and responsive to the needs of the business and customers. One of the first use cases they found for private AI was customer support, and this is tightly integrated with internal documentation and sources to ensure valid responses. The biggest challenge is to integrate unstructured data, which can be spread across many locations, and this is actively being investigated by companies like Broadcom as well as projects like LlamaIndex. VMware has contributed back to the open source community, notably with the Ray workload scheduler, open source models, and related projects. It's important to build community and long-term engagement and support for open source as well, and this is in keeping with the overall trends in the AI community. Organizations looking to get started with private AI should consider the VMware by Broadcom reference architecture, which incorporates best practices at a smaller scale and pick a use case that provides immediate value.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Chris Wolf, Global Head of AI and Advanced Services at VMware by Broadcom: https://www.linkedin.com/in/cswolf/
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Tags: #UtilizingAI, #AI, #PrivateAI, @VMware, @Broadcom, @UtilizingTech, @GestaltIT, @SFoskett, @FredericVHaren,
Data is the foundation on which AI models are built, and integration of enterprise data will be the key to generative AI applications. This episode of Utilizing Tech brings Nick Magnuson and Clive Bearman from Qlik to discuss the integration of data and AI with Frederic Van Haren and Stephen Foskett. Enterprises sometimes worry that their data will never be ready for AI or that they will feed models with too much low-quality data, and overcoming this issue is one of the first hurdles. Another application for machine learning is improving data quality, organizing and tagging unstructured data for applications. The concept of curated data is an interesting one, since it promises to elevate the value of enterprise data. But what if a flood of data causes the model to make the wrong connections? If data is to be a product it must be profiled, tagged, and organized, and ML can help make this happen. The trend of generative AI is driving budgets and priorities to make data more useful and organized, but even unstructured data streams can be valuable. The application of large language models to structured data is promising as well, since it enables people to query these data sets even if they lack the background and skills to construct queries.
Hosts: Stephen Foskett, Organizer of Tech Field Day: https://www.linkedin.com/in/sfoskett/Frederic Van Haren, CTO and Founder of HighFens, Inc.: https://www.linkedin.com/in/fredericvharen/
Guests: Nick Magnuson, Head of AI, Qlik: https://www.linkedin.com/in/nick-magnuson-0a253931/ Clive Bearman, Senior Director of Product Marketing, Qlik: https://www.linkedin.com/in/clivebearman/
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In the two years since we focused on AI on this podcast, OpenAI added a simple conversational interface to their deep-learning model and AI has exploded on society. This season of Utilizing Tech focuses on practical applications for artificial intelligence and features co-hosts Frederic Van Haren and Mark Beccue along with Stephen Foskett. In this episode we return to AI with a look back at the incredible impact that generative AI has had on society. Humans traditionally interfaced with machines using keyboard and mouse, touch and gesture, but ChatGPT changed all that by enabling people to communicate with computers verbally. But this is just one of many potential AI model components that can be used to build business applications. The true power of generative AI will be realized when these other components appear, and when they are able to integrate custom data. We will also see innovation in the AI infrastructure stack, from GPUs to NPUs to CPUs, storage and data platforms, and even client devices.
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Tags: @UtilizingTech, @GestaltIT, @TheFuturumGroup, @SFoskett, @FredericVHaren, @FutureBec, #UtilizingAI, #AI, #GenerativeAI, #AINetworking,
As we've discussed all season on Utilizing Edge, innovation is coming from all directions, including hardware, software, and applications. This special crossover episode of the On-Premise IT and Utilizing Tech podcasts features Edge Field Day delegates Brian Knudtson, Ned Bellavance, and Jody Lemoine discussing their perspectives about edge innovation with Stephen Foskett. The primary drivers at the edge are integration, efficiency, and connectivity, as well as the unique needs of the applications there. Starting with hardware, customers are headed in two directions, with more enterprise availability features deployed in some locations and less-capable hardware in others, both in terms of compute and networking. At the software level, most edge infrastructure is hyper-converged, meaning that multiple layers of the stack are integrated in software and managed as one. Although intended as an application platform, Kubernetes is being deployed as a packaging abstraction and distribution solution at the edge.
Host:
Stephen Foskett: https://www.linkedin.com/in/sfoskett/
Guests:
Ned Bellavance: https://www.linkedin.com/in/ned-bellavance/
Brian Knudtson: https://www.linkedin.com/in/bknudtson/
Jody Lemoine: https://www.linkedin.com/in/jodyl/
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Tags: #EFD2, #Edge, #UtilizingEdge, @UtilizingTech, @GestaltIT, @SFoskett, @Ned1313, @GhostInTheNet, @BKnudtson,
Edge is leading the hardware industry into a new era of innovation. In this episode of Utilizing Tech, Stephen Foskett, and co-hosts, Allyson Klein and Alistair Cooke, sit down to dissect this. In the wake of Intel’s discontinuation of its NUC product line, a question that is in everyone’s mind is, what’s next. Intel, and many behemoths like it, have a proud legacy of knowing how to break a stalemate and preserve the churn, and even if that means stepping up and pulling the plug on an old product. It oxygenates the marketplace, welcoming new solutions and keeping the wheel of innovation moving. In the context of the emerging paradigm of edge, this change will likely propel the market towards a new breed of powerful, low-cost, pocket-size hardware that delivers breakthrough energy-efficiency and compute performance with little infrastructure.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZ
Allyson Klein: https://www.twitter.com/TechAllyson
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Tags: #UtilizingEdge, #Edge, #NUC, @UtilizingTech, @GestaltIT, @SFoskett, @TechAllyson, @DemitasseNZ,
Personal devices are increasingly being used across enterprise IT, from smartphones to wearable devices, and these are becoming the true edge. This episode of Utilizing Edge brings Field Day delegates Mark Houtz and Jim Czuprynski together with Stephen Foskett to discuss the personal side of the edge. Mobile Device Management (MDM) has been used to manage smartphones and similar technology is used for personal computers, but it seems inevitable that there will be a mixing of business and private data. The mix and match of personal devices at the edge is sure to be a topic of future focus.
Host:
Stephen Foskett: https://www.Twitter.com/SFoskett
Guests:
Jim Czuprynski:
https://mastodon.social/@jimthewhyguy@techfieldday.net
Matt Houtz: https://www.Twitter.com/Marko_With_A_K
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Tags: #UtilizingEdge, #EdgeComputing, Edge, #RaspberryPi, @UtilizingTech, @SFoskett, @JimTheWhyGuy, @Marko_With_A_Why, @GestaltIT, #EFD2,
Although blockchain technology has been tainted by scammers, the core idea of distributed consensus is relevant for certain edge applications. This episode of Utilizing Edge focuses on practical application of blockchain technology in edge and IoT, featuring Jason Benedicic, Alastair Cooke, and Stephen Foskett. Two key concepts in blockchain are the distributed ledger and consensus approach. There is a lot of work being done to apply blockchain in medical, finance, IoT, and proof of provenance. Although many applications can rely on authority or quorum, larger and more heterogeneous applications might benefit from consensus instead. The other aspect of blockchain, chain of custody and immutability of data, is potentially relevant in preventing supply chain attacks and dealing with transient devices. It's important to remember that many of the things that have put people off blockchain, from financialization to public exposure of transactions, are not necessarily required in all blockchains.Guest:
Jason Benedicic: https://www.twitter.com/JBenedicicHosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZFollow Gestalt IT and Utilizing Tech
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Tags: #UtilizingEdge, #Edge, #Blockhain, #IoT, @GestaltIT, @UtilizingTech, @SFoskett, @DemitasseNZ, @JBenedicic,
Computing and sensors have been deployed at the edge for decades, and this is certainly true in the agriculture sector. This episode of Utilizing Edge features Edge Field Day delegates Ben Young and Alastair Cooke discussing with Stephen Foskett the evolution of connectivity and orchestration at the edge. Governmental policies can have a huge impact on the deployment of technology, from connectivity to environmental regulations, as has the emergence of inexpensive sensors, batteries, and processors thanks to the mobile phone industry, along with solar power. Cloud-inspired automation and orchestration is another key enabler of technology in agriculture and at the edge, with zero-touch provisioning, management, and central maintenance. Containerized applications, caching, and cloud-derived control planes are very popular at the edge, though not exactly the same way as in the cloud.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZ
Guest:
Ben Young: https://www.twitter.com/BenYoungNZ
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Tags: #Orchestration, #Edge, #UtilizingEdge,
VMware Explore 2023 featured a broad set of products and technologies, all of which could be included under the banner of edge. This episode of Utilizing Edge considers this dynamic and varied world with Roy Chua and Brian Knudtson, along with host Stephen Foskett. Virtualization, networking, storage, and orchestration are all key edge technologies, and VMware and its partners are deeply involved in bringing these technologies to the edge. VMware announced Edge Cloud Orchestrator, new security capabilities, integrated networking, client integration, and the exciting potential of private 5G networks. Dive into this episode to unravel the complexities and promising future of edge computing.
Host:
Stephen Foskett: https://www.twitter.com/SFoskett
Guests:
Brian Knudtson: https://www.twitter.com/BKnudtson
Roy Chua: https://www.twitter.com/WireRoyFollow Gestalt IT
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https://www.UtilizingTech.com/
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Industrial control and operational technology systems face the same forces as IT applications and are rapidly being integrated into comprehensive edge infrastructure. This episode of Utilizing Edge brings Andy Foster of IOTech to discuss standardization of industrial IoT with Allyson Klein and Stephen Foskett. There are many more similarities between industrial control systems and information technology systems than many people realize. Both are impacted by standards in commodity hardware, networking and communications protocols, and cloud integration. And OT and IT are colliding at the edge, with a massive opportunity to leverage data.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Allyson Klein: https://www.twitter.com/TechAllyson
Guest:
Andy Foster, Product Director and Cofounder of IOTech
https://www.linkedin.com/in/andrew-foster-673b268/
https://www.iotechsys.com/Follow Gestalt IT
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Tags: #EdgeComputing, #IoT, #UtilizingEdge, @UtilizingTech, @SFoskett, @TechAllyson, @IOTechSystems, @GestaltIT,
Media and entertainment is one industry generating and moving vast amounts of data at the edge. This episode of Utilizing Edge brings Jimmy Fusil of Tsecond into a discussion with Alastair Cooke and Stephen Foskett about the unique data challenges in media and entertainment. There is a great need for local processing, secure data transport, and efficient storage utilization in managing the ever-increasing volume of data generated during film and TV production. The podcast explores the continued relevance of physical media, like tape, for long-term archiving, while also emphasizing the security measures necessary to protect creative privacy and prevent potential plot leaks in the industry. Fusil also discusses Tsecond’s groundbreaking product, Bryck, a portable NVMe storage device capable of holding up to a petabyte of data with a remarkable 40GBps data transfer speed. The discussion highlights how Bryck’s high-performance storage facilitates on-device data processing, ensuring quick backups and maintaining data integrity. It’s critical to consider edge storage solutions like Bryck in many data-intensive sectors beyond media and entertainment as well.Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZGuest:
Jimmy Fusil, M&E Expert and Business Development Manager, Tsecond
https://www.linkedin.com/in/jimmyfusil/Follow Gestalt IT and Utilizing Tech
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Tags: #UtilizingEdge #EdgeComputing #Bryck #EdgeStorage
Edge computing comes in many forms and brings many challenges, including bandwidth limitations, network reliability issues, and limited space. This episode of Utilizing Edge brings Brian Chambers, Alastair Cooke, and Stephen Foskett together to discuss the state of the edge in 2023. Industries like retail, multi-tenant environments, and industrial IoT find practical applications, but defining the edge remains an ongoing exploration. Implementation varies, from repurposing existing technologies to adopting modern approaches like containers and function as a service. The debate between virtual machines and containers continues, driven by organizational comfort. Despite constraints, edge environments offer greater control and accountability. The future promises more innovation and adoption, cementing edge computing’s significance in the tech landscape.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZ
Brian Chambers: https://www.twitter.com/BriChamb
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LinkedIn: https://www.linkedin.com/company/Gestalt-ITTags: #UtilizingEdge, #EdgeDiversity, #EdgeComputing, #EdgeSolutions, @UtilizingTech, @SFoskett, @DemitasseNZ, @BriChamb, @GestaltIT,
In this episode of the Utilizing Tech podcast, Stephen Foskett, Alison Klein, and Gina Rosenthal discuss dark data in edge computing. Dark data is unutilized or unknown data collected by organizations. The distributed nature and use of third-party apps can make it challenging to handle dark data, limiting insights and posing security risks. Establishing a stronger IT-business connection is crucial. Observability solutions and data analytics can aid in discovering and centralizing dark data. AI has potential for data hygiene improvement, but human-driven cleaning is still necessary. Despite challenges, edge computing offers better data management due to controlled deployments.Host:Stephen Foskett: https://www.twitter.com/SFoskettPanelists:Allyson Klein: https://www.twitter.com/TechAllysonGina Rosenthal: https://www.twitter.com/Digi_Sunshine Follow Gestalt IT and Utilizing TechWebsite: https://www.GestaltIT.com/Utilizing Tech: https://www.UtilizingTech.com/Twitter: https://www.twitter.com/GestaltITTwitter: https://www.twitter.com/UtilizingTech LinkedIn: https://www.linkedin.com/company/Gestalt-IT
Tags: #UtilizingEdge, #DarkData, #EdgeComputing, #Edge,
Although we use Intel's NUC as a shorthand for the type of hardware deployed at the far edge, this recently-cancelled platform isn't all there is. This episode of Utilizing Edge looks beyond the NUC, to platforms from Lenovo, Nvidia, and more, with Julian Chesterfield of Sunlight, Andrew Green, and Stephen Foskett. ARM-based solutions, many using the Nvidia Jetson platform, are particularly interesting given their low cost and power consumption and strong GPUs for edge AI. A hyperconverged stack runs all of the components required for high availability, including storage and networking, in software spanning all of the nodes in a cluster, and this is commonly deployed on low-cost devices at the far edge. The trend to deploying applications at the edge is driven both by new hardware and software capabilities and the changing expectation of consumers and businesses.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Andrew Green, Analyst at GigaOm: https://www.linkedin.com/in/andrew-green-tech/Guest:
Julian Chesterfield, CTO and Founder, Sunlight.io: https://www.linkedin.com/in/julian-chesterfield-3B74951Follow Gestalt IT and Utilizing Tech
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Edge infrastructure is susceptible to many of the same security risks as datacenter and cloud, but is often run in less protected environments. This episode of Utilizing Edge features Craig Nunes, Co-Founder and COO of Nebulon, talking to Brian Chambers and Stephen Foskett about the provision of reliable infrastructure services at the edge. Nebulon's product presents storage to servers in a managed way, monitoring and protecting storage in real time. Edge servers must have a known-good system image to ensure that they are secure, yet this is difficult to achieve in remote devices.Hosts: Stephen Foskett: https://www.twitter.com/SFoskettBrian Chambers: https://www.twitter.com/BriChambGuest: Craig Nunes, COO and Co-founder, Nebulon: https://www.linkedin.com/in/craig-nun... Follow Gestalt ITWebsite: https://www.GestaltIT.com/Twitter: https://www.twitter.com/GestaltITLinkedIn: https://www.linkedin.com/company/Gestalt-IT
Tags: #UtilizingEdge, #EdgeComputing, #ResilientInfrastructure, #Security, #EdgeSecurity,
Perhaps no company is more important to the datacenter than VMware, but how are the company’s technologies applied at the edge? This episode of Utilizing Edge features Saadat Malik, VP and GM of Edge Computing at VMware, discussing the evolution of VMware at the edge with Brian Chambers and Stephen Foskett. The discussion delves into the evolution of VMware’s presence at the edge, highlighting the differences between datacenter and edge environments in terms of people and technology. Malik emphasizes the importance of outcomes and product-focused mindsets in edge environments, as well as the constraints posed by limited physical resources. VMware’s technologies in connectivity, storage, security, and management are showcased as key enablers of successful edge computing. The episode also touches upon the growing significance of AI and machine learning at the edge and the need for standardized solutions to drive edge growth and transformation.Hosts: Stephen Foskett: https://www.twitter.com/SFoskettBrian Chambers: https://www.twitter.com/BriChambGuest from VMware:Saadat Malik, VP and GM of Edge Computing at VMwarehttps://www.linkedin.com/in/saadatmalik/Follow Gestalt ITWebsite: https://www.GestaltIT.com/Twitter: https://www.twitter.com/GestaltITLinkedIn: https://www.linkedin.com/company/Gestalt-IT
Tags: #UtilizingEdge, #EdgeComputing, #Edge, #CustomEdge,
Datacenter IT is used to having tight control over infrastructure and applications, but this is challenging to maintain at the edge. This episode of Utilizing Edge features Pierluca Chiodelli of Dell Technology discussing the modern edge application platform with Allyson Klein and Stephen Foskett. A typical edge environment features many different platforms, devices, and connections that must be deployed, managed, and controlled remotely. When looking at the modern edge, Chiodelli recognizes the different personas and needs and constructs a plan to achieve the required outcome at this location. Modern applications need specialized hardware and connectivity that must be supported, deployed, and managed.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Allyson Klein: https://www.twitter.com/TechAllyson
Guests:
Pierluca Chiodelli: https://www.linkedin.com/in/pierluca-chiodelli-3b743a4/
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Tags: #UtilizingEdge, #EdgeComputing, #Edge
Between the so-called last mile and first mile lies the middle mile, the realm of colocation and network service providers. This episode of Utilizing Tech features Roy Chua and Allyson Klein, discussing the middle mile with Stephen Foskett. This middle area includes content delivery services like Varnish and Akamai, as well as companies like Cloudflare that are delivering content and compute there. The middle network includes providers like Equinix, Digital Realty, and Megaport, which provide connectivity to the cloud and service providers, the hyperscalers themselves, and some interesting networking startups like Packet Fabric and Graphiant. We must also consider observability, with companies like cPacket and Kentik as well as companies like Cisco and Juniper Networks.
Host: Stephen Foskett: https://www.twitter.com/SFoskett
Guests:
Allyson Klein: https://www.twitter.com/TechAllyson
Roy Chua: https://www.twitter.com/WireRoy
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Tags: #UtilizingEdge, #EdgeComputing, #Edge
When it comes to edge computing, money is not limitless. Joining us for this episode of Utilizing Edge is Carlo Daffara of NodeWeaver, who discusses the unique economic challenges of edge with Alastair Cooke and Stephen Foskett. Cost is always a factor for technology decisions, but every decision is multiplied when designing edge infrastructure with hundreds or thousands of nodes. Total Cost of Ownership is a critical consideration, especially operations and deployment on-site at remote locations, and the duration of deployment must also be taken into consideration. Part of the solution is designing a very compact and flexible system, but the system must also work with nearly any configuration, from virtual machines to Kubernetes. Another issue is the fact that technology will change over time and the system must be adaptable to different hardware platforms. It is critical to consider not just the cost of hardware but also the cost of maintenance and long-term operation.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZ
NodeWeaver Representative:
Carlo Daffara, CEO of NodeWeaver: https://www.linkedin.com/in/cdaffara/
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Tags: #EdgeEconomy, #EdgeComputing, #Edge, @NodeWeaver
Although everyone wants high availability from IT systems, the cost to achieve it must be weighed against the benefits. This episode of Utilizing Edge focuses on HA solutions at the edge with Bruce Kornfeld of StorMagic, Alastair Cooke, and Stephen Foskett. Although it might be tempting to build the same infrastructure at the edge as in the data center, but this can get very expensive. Thinking about multi-node server clusters and RAID storage, the risk of a so-called split brain means not just two nodes but three must be deployed in most cases. StorMagic addresses this issue in a novel way, with a remote node providing a quorum witness and reducing the need for on-site hardware. Edge infrastructure also relies on so-called hyperconverged systems, which use software to create advanced services on simple and inexpensive hardware.Hosts: Stephen Foskett: https://www.twitter.com/SFoskettAlastair Cooke: https://www.twitter.com/DemitasseNZStorMagic Representative:Bruce Kornfeld, Chief Marketing and Product Officer at StorMagic: https://www.linkedin.com/in/brucekornfeld/Tags: #UtilizingEdge, #EdgeStorage, #EdgeComputing, @StorMagic
The edge isn't the same thing to everyone: Some talk about equipment for use outside the datacenter, while others talk about equipment that lives in someone else's location. The difference between this far edge and near edge is the topic of Utilizing Edge, with Andrew Green and Alastair Cooke, Research Analysts at Gigaom, and Stephen Foskett. Andrew is drawing a line at 20 ms roundtrip, the point at which a user feels that a resource is remote rather than local. From the perspective of an application or service, this limit requires a different approach to delivery. One approach is to distribute points of presence around the world closer to users, including compute and storage, not just caching. This would entail deploying hundreds of points of presence around the world, and perhaps even more. Technologies like Kubernetes, serverless, and function-as-a-service are being used today, and these are being deployed even beyond service provider locations.Hosts: Stephen Foskett: https://www.twitter.com/SFoskettAlastair Cooke: https://www.twitter.com/DemitasseNZGuest: Andrew Green, Analyst at GigaOm: https://www.linkedin.com/in/andrew-green-tech/Follow Gestalt IT and Utilizing TechWebsite: https://www.GestaltIT.com/Twitter: https://www.twitter.com/UtilizingTechLinkedIn: https://www.linkedin.com/company/1789Tags: #UtilizingTech #EdgeComputing #UtilizingEdge @UtilizingTech @GestaltIT
One of the main differentiators for edge computing is developing a scalable architecture that works everywhere, from deployment to support to updates. This episode of Utilizing Edge welcomes Dave Demlow of Scale Computing discussing the need for scalable architecture at the edge. Scale Computing discussed Zero-Touch Provisioning and Disposable Units of Compute at their Edge Field Day presentation, and we kick off the discussion with these concepts. We also consider the undifferentiated heavy lifting of cloud infrastructure and the tools for infrastructure as code and patch management in this different environment. Ultimately the differentiator is scale, and the key challenge for designing infrastructure for the edge is making sure it can be deployed and supported at hundreds or thousands of sites.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Brian Chambers: https://www.twitter.com/BriChamb
Scale Computing Representative:
Dave Demlow: https://www.linkedin.com/in/ddemlow/
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Website: https://www.GestaltIT.com/
Twitter: https://www.twitter.com/GestaltIT
LinkedIn: https://www.linkedin.com/company/gestalt-it/
Tags: #UtilizingEdge, #Edge, #EdgeComputing, #EdgeTechnology, #ScalableEdge, #EdgeInfrastructure, @UtilizingTech, @GestaltIT, @AvassaSystems,
There is a long-standing gulf between developers and operations, let alone infrastructure, and this is made worse by the scale and limitations of edge computing. This episode of Utilizing Edge features Carl Moberg of Avassa discussing the application-first mindset of developers with Brian Chambers and Stephen Foskett. As we've been discussing, it's critical to standardize infrastructure to make them supportable at the edge, yet we also must make platforms that are attractive to application owners.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Brian Chambers: https://www.twitter.com/BriChamb
Carl Moberg, CTO and co-founder at Avassahttps://www.linkedin.com/in/carlmoberg/
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Tags: #UtilizingEdge, #Edge, #EdgeComputing, #EdgeTechnology, #EdgeApplications, @UtilizingTech, @GestaltIT, @AvassaSystems,
Edge has many different stakeholders, applications, and needs, and this is especially true in distributed retail environments. This episode of Utilizing Edge features Simon Gamble of Mako Networks talking with Brian Chambers and Stephen Foskett about the complexities of technology at the retail edge. The key according to Gamble is segmentation of brands, franchisees, and technical applications. In some cases a single location might even include multiple separate companies or tenants under the same roof. Video, sensors, IoT, and location-based services are coming to retail locations as well, and some of these leverage outside service providers as well. Although sharing infrastructure is desirable, segmentation and security is key. Retail edge environments are increasingly complicated, but there are many ways to consolidate, converge, and standardize to make them practical to implement.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Brian Chamber: https://www.twitter.com/BriChamb
Mako Networks Representative:Simon Gamble, President of Mako Networks: https://www.linkedin.com/in/simongnz/
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Tags: #UtilizingEdge, #EdgeComputing, #RetailEdge, #Edge, @GestaltIT, @MakoNetworks
Edge environments were historically very specialized, but virtualization and cloud technology is enabling companies to deploy commodity platforms at the edge. This episode of Utilizing Edge features Raghu Vatte of ZEDEDA discussing this commoditization with Alastair Cooke and Stephen Foskett. Although the transition is still getting started, standard compute platforms are rapidly being exploited at edge locations, from warehouses to retail to industrial. Even is some specialized hardware is still needed, a unified platform can increasingly absorb a majority of applications at the edge. Another factor contributing to commoditization is the standardization of application requirements, with most now virtualized or containerized with standards developing for I/O and shared hardware resources.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZ
ZEDEDA Representative:Raghu Vatte, VP of Product Management and Customer Success, ZEDEDA: https://www.linkedin.com/in/raghushankar-vatte-970a9214/
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Tags: #UtilizingEdge #Commoditization #EdgeComputing #EdgeInfrastructure @UtilizingTech @GestaltIT @ZededaEdge
Although the technology is roughly similar to datacenter or cloud, the unique challenges of edge computing require new approaches to storage, networking, orchestration, deployment, and more. We were kicking off a new season of Utilizing Tech focused on edge computing, featuring Alastair Cooke and Brian Chambers as co-hosts along with Stephen Foskett. One of the key differences for edge computing is the strictly constrained resources there as well as the massive scale. What we now call edge has existed for decades but the proprietary hardware previously used has been largely replaced by commodity and internet-connected systems that inherit technologies like virtualization, containerization, and hyperconvergence. Businesses and consumers expect more interactivity and capability in retail, restaurants, and hospitality environments, and the IoT revolution is strongly affecting manufacturing, military, and industrial settings. But what is the edge really, and how do we deliver services there? That's the question we hope to answer in this season of Utilizing Edge.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Alastair Cooke: https://www.twitter.com/DemitasseNZ
Brian Chambers: https://www.twitter.com/BriChamb
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Website: https://www.GestaltIT.com/
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LinkedIn: https://www.linkedin.com/company/gestalt-it/
Tags: #UtilizingEdge #Edge #EdgeComputing #EdgeTechnology @UtilizingTech @GestaltIT
We're wrapping up this season of Utilizing Tech by asking the key question: Why would a system architect choose to utilize CXL in their designs? This episode of Utilizing CXL features Stephen Foskett, Nathan Bennett, and Craig Rodgers discussing the practical prospects and benefits of CXL.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Nathan Bennett: https://www.twitter.com/vNathanBennett
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Website: https://www.GestaltIT.com/
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LinkedIn: https://www.linkedin.com/company/1789
Tags: #UtilizingCXL #CXLFabric #CXLMemoryExpansion @UtilizingTech
Perhaps no one can tell the story of Compute Express Link (CXL) better than Jim Pappas, who was involved in the development of nearly every related technology, from PCI to UCIe. This episode wraps up the season of Utilizing Tech with Stephen Foskett and Craig Rodgers discussing the evolution of CXL with Jim Pappas, Director of Technology Initiatives at Intel and Chairman of the CXL Consortium. No matter how good the technology is, it needs widespread industry support, backwards and forwards compatibility, and open cooperation, and that's what made technologies like PCI, PCI Express, USB, and now CXL successful.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest:
Jim Pappas, Director of Technology Initiatives, Intel and Chairman of the CXL Consortium: https://www.linkedin.com/in/jim-pappas-3624442/
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Tags: #CXL #CXLConsortium #UtilizingCXL @Intel @UtilizingTech
Moving memory and other resources off the system bus to CXL is exciting, but how do we ensure that these systems will be reliable, available, and serviceable? This episode of Utilizing Tech features Mark Orthodoxou, VP of Strategic Marketing for Datacenter Products at Rambus discussing with Stephen Foskett and Craig Rodgers the technology and standards required for mass adoption of CXL-attached memory. Rambus brings decades of experience and a breadth of technology to the deployment of memory in high-performance and highly-available systems. As a CXL Consortium member, Rambus is bringing this experience to CXL, enabling the technology across the ecosystem. Memory expansion with CXL is being deployed today, and memory pooling over CXL fabrics is coming, but it is disaggregation and rack-scale architecture that will ultimately be the result of the adoption of CXL.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Rambus Representative:
Mark Orthodoxou, VP of Strategic Marketing - Datacenter Products: https://www.linkedin.com/in/mark-orthodoxou-94b189/
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Tags: #UtilizingCXL, #CXLAttachedMemory, #Datacenter, @RambusInc, @UtilizingTech
CXL grew out of a long history of Intel platform technology and promises to revolutionize this platform. This episode of Utilizing CXL features Allyson Klein of TechArena, who shares her vision for the future of disaggregated servers with Craig Rodgers and Stephen Foskett. The foundation for CXL was laid with coherent interconnects and fabrics in previous decades, and the concept of tiered memory draws heavily on the history of Intel and Micron 3D XPoint and Optane Persistent Memory. As was the case for virtualization, CXL has an initial value proposition and a revolutionary future. CXL opens up virtually unlimited memory, pools of processors and offload engines, and unprecedented flexibility. Ultimately CXL will allow us to deliver compute resources that are truly balanced and matched to the needs of each workload. We also discussed UCIe, which promises to enable mix and match chiplets within future processors in a way that is compatible with CXL.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest Host:
Allyson Klein: https://www.twitter.com/TechAllyson
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Tags: #UtilizingCXL #DisaggregatedServers #CXLFabrics #MemoryExpansion #CXL @UtilizingTech
Memory expansion is the first application for CXL, and memory pooling is coming next, but this technology will eventually support storage, Ethernet, and more. This episode of Utilizing CXL brings Ronen Hyatt, CEO of UnifabriX, to discuss memory and more over CXL with Nathan Bennett and Stephen Foskett. UnifabriX demonstrated high-performance compute benchmarks using their Smart Memory Node at Super Computing 22 and claims to be the first to show a CXL 3.0 fabric. But the company is also promising NVMe storage and Ethernet connectivity over the CXL fabric. This enables each server to have the right type and capacity of connectivity, from basic Ethernet to DPU, with dynamic reconfiguration. The fabric can also contain an NVMe storage target that combines DRAM and flash and can be dynamically allocated.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Nathan Bennett: https://www.twitter.com/vNathanBennett
Guest:
Ronen Hyatt, CEO and Founder of UnifabriX: https://www.linkedin.com/in/execute/
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Website: https://www.GestaltIT.com/
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LinkedIn: https://www.linkedin.com/company/1789
Tags: #UtilizingCXL #CXLStorage #Ethernet @UtilizingTech @UnifabriX
Tiered memory will have different performance, so operating systems will need to incorporate techniques to adapt to pages with different characteristics. This episode of Utilizing CXL features Hasan Al Maruf, part of a team that developed transparent page placement for Linux. He began his work enabling transparent page placement for InfiniBand-connected peers before applying the concept to NUMA nodes and now CXL memory.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest Host:
Hasan Al Maruf, Researcher at the University of Michigan:
https://www.linkedin.com/in/hasanalmaruf/
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Tags: #UtilizingCXL #TieringMemory #CXL #Linux @UtilizingTech @GestaltIT @UMich
Bringing CXL to market requires a wide variety of components, and Marvell is a key supplier to datacenter and cloud. This episode of Utilizing CXL features Shalesh Thusoo of Marvell, who discusses the many solutions they are creating to bring CXL to market. Current products are directly connecting memory to CPUs via CXL but soon we will see products enabling the sharing of memory between hosts to enable new applications.
From Marvell: This podcast contains forward-looking statements within the meaning of the federal securities laws that involve risks and uncertainties. Forward-looking statements include, without limitation, any statement that may predict, forecast, indicate or imply future events or achievements. Actual events or results may differ materially from those contemplated in this podcast. Forward-looking statements speak only as of the date they are made. Listeners are cautioned not to put undue reliance on forward-looking statements, and no person assumes any obligation to update or revise any such forward-looking statements, whether as a result of new information, future events or otherwise.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest:
Shalesh Thusoo, Vice President CXL Product Development, Marvell
https://www.linkedin.com/in/shaleshthusoo/
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Tags: #UtilizingCXL #CPU #CXL #Memory @Marvell @GestaltIT @UtilizingTech
The CXL Consortium is an open industry standard group responsible for technical specifications and standards. This episode of Utilizing CXL brings Siamak Tavallaei, CXL president, to discuss the emergence of Compute Express Link. The goal of CXL is to disaggregate servers, moving memory away from the CPU and enabling users to compose servers more flexibly. We have already disaggregated storage from compute, and breaking the fixed memory interconnect will have a similar effect. But we also need the system to be manageable, with software to configure and orchestrate resources. The CXL Consortium is facilitating the development of all this in association with nearly every company in the IT industry.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Nathan Bennett: https://www.twitter.com/vNathanBennett
Guest:
Siamak Tavallaei of the CXL Consortium: https://www.linkedin.com/in/siamaktavallaei/
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Tags: #UtilizingCXL #CXLConsortium #CXL #SapphireRapids #Genoa #Epyc #CXLDevelopment
Although the emergence of CXL in server CPUs is big news, the inclusion of this technology in ARM processor IP is just as important. In this episode of Utilizing CXL, Eddie Ramirez of ARM joins Craig Rodgers and Stephen Foskett to discuss CXL in the ARM-powered ecosystem. ARM develops processor IP that is used in CPUs as well as supporting processors throughout the datacenter. We begin with a discussion of CXL 1.1, which brings memory expansion to ARM CPUs. But ARM is also delivering CXL 2.0 which would allow memory pooling to increase the utilization of memory, and thus overall system efficiency. The next step is true heterogeneous compute, with accelerators like GPU and DPU sharing memory with CPUs in a flexible fabric that can leverage CXL.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest:
Eddie Ramirez, VP of Marketing at Arm: https://www.linkedin.com/in/eddie-ramirez-41233a1/
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With both AMD and Intel supporting CXL in their newly-launched server platforms, it's worth taking a moment to consider what has been delivered to date. This special episode of Utilizing CXL brings hosts Stephen Foskett, Craig Rodgers, and Nathan Bennett together to look back at 6 months of CXL announcements now that real products are shipping. We were pleased to see the support delivered by Intel and AMD, but also from Astera Labs, Samsung, SK Hynix, and MemVerge, as well as promising developments from ARM, Elastics Cloud, IntelliProp, Marvell, and many others.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Nathan Bennett: https://www.twitter.com/vNathanBennett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
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Tags: #UtilizingCXL #CXLServerPlatforms #CXL #SapphireRapids #Genoa #Epyc
CXL is being rolled out in production and more reliability, scalability, and security features are being added all the time. This episode of Utilizing Tech focuses on enterprise-grade CXL with John Spiers, CEO of IntelliProp, talking about the ongoing evolution of CXL. IntelliProp is bringing a CXL fabric solution to market the enables memory expansion outside the server over a fabric. CXL 3.0 introduces memory fabrics, but it will take more development to bring features like high availability, routing, peer-to-peer, failover, and re-routing while preserving cache coherency and enabling management, and IntelliProp is working to bring all this to the spec.
As we discussed in our last episode with Dan Ernst from Microsoft Azure, CXL delivers acceptable memory latency, and John expects that this will continue with fabrics to some extent. IntelliProp will profile memory and enable tiered memory that matches application needs. The company is also working to enable advanced features, including sharing memory between systems, in association with the CXL consortium.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Nathan Bennett: https://www.twitter.com/vNathanBennett
Guest Host:
John Spiers, CEO of IntelliProp: https://www.linkedin.com/in/johnwspiers/
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Tags: #UtilizingCXL #CXLFabric #MemoryLatency @UtilizingTech #CXL @Intelliprop
Cloud architects have long wished for more flexibility in how memory is provisioned in servers, and CXL is finally delivering on a decade of promises. In this episode of Utilizing Tech, Stephen Foskett and Craig Rodgers talk to Dan Ernst of Microsoft, who is deeply involved in bringing CXL-attached memory to fruition within Azure. Dan was previously involved in the Gen-Z effort, and feels that CXL picks up the baton and brings valuable real-world benefits to cloud server architecture. Memory has a bigger impact on overall IT platforms than many are aware, and is already the costliest component in large servers. Per Amdahl's Law, it makes sense to look for cost savings in memory. And this is doubly the case because DIMMs only come in certain sizes so memory is usually over-specified. CXL memory modules cost a bit more than a DIMM, but this is offset by efficient right-sizing, and CXL is already cheaper than 3D stacked DIMMs.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest Host:
Dan Ernst, Principal Architect working on Future Azure Cloud Systems, Microsoft Azure
https://www.linkedin.com/in/danernst/
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Tags: #CXL #DIMMs #Azure #UtilizingCXL @Microsoft @Azure
As 2023 arrives, so do two server platforms that support CXL, along with associated chipsets, software, and memory expansion cards. This special episode of Utilizing Tech features the three hosts of this season, Stephen Foskett, Craig Rodgers, and Nathan Bennett, discussing the prospects for CXL in 2023. AMD recently introduced Genoa Epyc, featuring CXL and PCIe 5, and it is widely expected that Intel will introduce Sapphire Rapids Xeon very soon with similar support. We expect these new server platforms to be adopted quickly by hyperscalers and to reach the enterprise datacenter throughout the year. We wonder what CXL memory expansion might bring, from other types of DRAM to persistent memory and possible even Optane. The prospect for shared and pooled memory is perhaps a little further off, but we have already heard that this capability might come to CXL 1.1 via device-specific features. What could go wrong? Enterprise server vendors might not embrace composability for various reasons, and this could derail CXL in the datacenter. Another concern is security, especially for shared memory and devices. The big differentiator will be software that enables systems to take advantage of features from memory expansion to pooling to disaggregation to composability.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Nathan Bennett: https://www.twitter.com/vNathanBennett
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Website: https://www.GestaltIT.com/
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LinkedIn: https://www.linkedin.com/company/1789
Tags: #UtilizingCXL #Genoa #Epyc #SapphireRapids #DRAM #CXL
The key to building an ideal server is balance, and this is why CXL is important, since it helps overcome the inflexible nature of modern server architectures. In this episode of Utilizing CXL, Stephen Foskett and Craig Rodgers talk with Ahmad Danesh of Astera Labs about the company's CXL-based memory expansion technology. Although there are many CXL memory expansion chips coming to market, the industry is keen on interoperability testing to make sure everything works as expected. These products are differentiated based on their reliability, performance, and security, including trusted hardware and encryption. This is especially important with AMD Genoa having recently been launched and Intel Sapphire Rapids coming very soon. Security is very important as memory moves further from the CPU and is shared and pooled among multiple servers. Ahmad also suggests that CXL memory can perform as well as local memory, so specialized memory tiering software might not always be needed. But this technology also allows other types of memory to be used, including non-DDR5 DRAM and potentially future non-DRAM memory.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest Host:
Ahmad Danesh, Sr. Director, Product Management, Astera Labs
https://www.linkedin.com/in/ahmaddanesh/
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Tags: #UtilizingCXL #MemoryExpansion #Genoa #Epyc #AMD #CXL
CXL couldn't be utilized until there was a server platform that supported it, so we're very excited to see AMD launch their next-generation Epyc server platform, code-name Genoa. In this episode of Utilizing CXL, Stephen Foskett and Craig Rodgers talk with Mahesh Wagh of AMD and the CXL Consortium about this important release. The first step to bring CXL to market is to prove it is functional and performs well with a mainstream platform like AMD 4th-generation Epyc. AMD is bringing CXL to market as a tiered memory solution that performs similar to as memory on a remote NUMA socket without any special configuration or software. But AMD also supports other memory technologies, including hierarchical memory with software, security, pooling, and even memory sharing with specialized software. Although Epyc is said to only support CXL 1.1, later spec devices will be backwards-compatible and the platform also supports some CXL 2.0 features including global flush for persistent memory, firmware-first error handling, and device-specific capabilities.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest Host:
Mahesh Wagh, Senior Fellow at AMD, Server Systems Architect, Co-chair CXL Consortium Board Technical Task Force https://www.linkedin.com/in/maheshwagh/
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Tags: #UtilizingCXL #Genoa #Epyc #AMD #CXL
A lot of VMware’s initiatives are in the same sphere as CXL, and now everybody is wondering if VMware is officially camp CXL. In this episode of Utilizing CXL, Stephen Foskett and co-host Craig Rodgers join Arvind Jagannath from VMware to hear it straight from the horse’s mouth. Learn if CXL is on the horizon for VMware as it is for a lot of IT companies of its stature, and what VMware’s role is in the enablement of the technology. Watch the full episode to find out how VMware is participating in the CXL revolution and the way its most recent projects support and enable implementation of CXL.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest Host:
Arvind Jagannath, Product Management Lead at VMware https://www.linkedin.com/in/arvindjagannath/
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UtilizingCXL #CXL #VMware
Today's AI and ML systems use proprietary interconnects, which limits the choices available to customers. CXL technology promises to enable greater interoperability, and this is the focus for Xconn Technology. In this episode of Utilizing CXL, Gerry Fan of Xconn joins Stephen Foskett and Craig Rodgers to discuss the ways that CXL can improve machine learning processing. The CXL Consortium is working with nearly every company in the IT industry to bring this promise to life, but we need hardware and software to enable memory pooling, device sharing, and more. The initial CXL products enable right-sizing memory, regardless of the specific architectural details of the CPU chosen. The next addition will be disaggregated and pooled memory using CXL switches, and this is coming to market in the next year or so. This will enable massive pools of memory on-demand for intensive applications. Xconn promises to make memory pooling available to CXL 1.1 hosts as well, and is working on a fabric manager to enable this.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest:
Gerry Fan, Cofounder CEO, Xconn Technology.
Connect on LinkedIn: https://www.linkedin.com/in/gerry-fan-5769608/
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Experts call CXL the lifeblood of composable datacenter infrastructure, and for good reason. It has unlocked tremendous possibilities and is reshaping the server architecture for good. Over 190 companies including some of the biggest names in the industry are involved in it, and as new versions of CXL are rolling out, the technology is clearly taking steps towards maturity. But the market needs more CXL-based technologies to kick-start its evolution.
In this episode of Utilizing CXL, hosts Stephen Foskett and Craig Rodgers join Founder and CEO of Elastics.cloud, George Apostol to talk about this transition and the need for CXL solutions, and what Elastics.cloud is bringing to market.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest:
George Apostol, Cofounder and CEO, Elastics.cloud. Connect on LinkedIn: https://www.linkedin.com/in/geapostol/
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Although x86 servers have some configuration and expansion options, they are increasingly monolithic, putting customers at the whim of the system vendor. CXL promises to change this, allowing customers to configure, and reconfigure, servers according to their needs. On this episode of Utilizing CXL, Ryan Baxter of Micron Technology joins Stephen Foskett and Nathan Bennett to talk about the options that CXL brings to the table. One of the first benefits of CXL technology is memory expansion, allowing servers to be created with exactly the right amount of memory instead of over-populating memory to fill channels and meet needs. But it will soon allow truly modular servers with exactly the right combination of CPU, memory, storage, and I/O.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Nathan Bennett: https://www.twitter.com/vNathanBennett
Guest:
Ryan Baxter, Senior Director of Marketing, Micron Technology. Connect with Ryan on LinkedIn: https://www.linkedin.com/in/ryan-baxter-a6a24a3/
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Data throughput has grown in leaps and bounds with the advent of AI. But as COVID-era digital transformation left the existing systems stressed out, CXL arrived at the heels of that. The newest memory solution that has got everybody talking, CXL is full of promises for AI computing. With the release of v3.0, CXL has started to gather more steam. More companies are now dipping their toes in CXL water bringing to the market their own brand of CXL products making the technology reachable for enterprises.
In this episode of Utilizing CXL Stephen Foskett and Craig Rodgers sit down with guest Yue Li, Co-Founder and CTO at MemVerge and hold an illuminating discussion on the current CXL product market and things MemVerge is doing on the software side of things.
Hosts:
Stephen Foskett: https://www.twitter.com/SFoskett
Craig Rodgers: https://www.twitter.com/CraigRodgersms
Guest:
Yue Li, Co-Founder and CTO at MemVerge.
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The first CXL products have emerged, with Samsung delivering memory and storage expanders and MemVerge supporting big memory with their software. Stephen Foskett discusses these products with Julie Choi of Samsung, Steve Scargall of MemVerge, Shalesh Thusoo of Marvell, and George Apostol of Elastics.cloud to discuss current and emerging CXL products. This special episode of Utilizing CXL was recorded live at CXL Forum in New York, with the entire industry watching. Once memory expansion is delivered, where do we go next? Marvell is working to support the new protocol in chipsets, and Elastics Cloud developing CXL fabric switches. Everyone is ready for Intel and AMD to release their next-generation server chips, which natively support CXL, and the CXL Consortium is already working on the next release!
Links
- UtilizingTech.com
- Utilizing Tech on Twitter
- GestaltIT.com
Guest and Hosts
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
- Julie Choi, Head of New Product Business Marketing, Samsung Memory. Connect with Julie on LinkedIn.
- Steve Scargall, Senior Product Manager and Software Architect, MemVerge. Connect with Steve on LinkedIn.
- Shalesh Thusoo, VP of CXL Product Development, Marvell. Connect with Shalesh on LinkedIn.
- George Apostol, CEO and Cofounder, Elastics.cloud. Connect with George on LinkedIn.
Date: 10/24/2022, @SFoskett, @MemVerge, @Elastics_cloud, @Samsung, @MarvellTech
The emerging CXL standard is making waves, promising big memory, new system architecture, and maybe even rack-scale computing. Utilizing Tech is switching focus from AI and ML to CXL for Season 4, and that means new companies, new technology and new hosts! Join Stephen Foskett as he introduces Utilizing CXL along with co-hosts Nathan Bennett and Craig Rodgers. Look for new episodes of Utilizing CXL every Monday!
Links
- UtilizingTech.com
- Utilizing Tech on Twitter
Guest and Hosts
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
- Nathan Bennett, Cloud Architect, vExpert Pro, and Tech Evangelist. Connect with Nathan on LinkedIn and follow him on Twitter at vNathanBennett.
- Craig Rodgers, Solutions Architect at Camlin Group. Connect with Craig on LinkedIn and follow him on Twitter at CraigRodgersms.
Date: 10/17/2022, @SFoskett, @vNathanBennett, @CraigRodgersms
Frederic Van Haren and Stephen Foskett look back on all the subjects covered during Season 3 of Utilizing AI. The podcast covered many topics, from religious and ethical implications of AI to the technology that enables machine learning, but one topic that stands out is data science. If data is the key to AI, then the collection, management, organization, and sharing of data is a critical element of making AI projects possible. We also continue our “three questions” tradition by bringing in open-ended questions from Rich Harang of Duo Security, Sunil Samel of Akridata, Adi Gelvan of Speedb, Bin Fan of Alluxio, Professor Katina Michael, and David Kanter of MLCommons.
Three Questions:
Stephen's Question: Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
Frederic's Question:What market is going to benefit the most from AI technology in the next 12 months
Rich Harang Senior Technical Lead, Duo Security: In an alternate timeline where we didn't develop automatic-differentiation and put it on top of GUPs do this entire deep learning hardware family that we depend on now never got invented. What would the dominat AI/ ML technology be and what would have been different?
Sunil Samel, VP of Pusiness Development, Akriadata: How will new technologies like AI help marginalized members of the communities. Folks like senior citizens, minorities, pepole with disabilities, veterans trying to reenter civilian life?
Adi Gelvan, CEO and Co-Founder of Speedb: What do you think the risks of AI are and what is your recommended solution?
Bin Fan, Founding Member, Alluxio: Im wondering if AI can help with a humanitarian crisis happening in the future?
Katina Michael, Professor, School for the Future of Innovation in Society, Arizona State University: If AI was to self replicate what would be the first thing it would do?
David Kanter, Executive Director of MLCommons: what s a problem in the AI world where you are held back by the lack of good publicly available data?
Hosts:
Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 4/25/2022 Tags: @SFoskett, @FredericVHaren,
How fast is your machine learning infrastructure, and how do you measure it? That's the topic of this episode, featuring David Kanter of MLCommons, Frederic Van Haren, and Stephen Foskett. MLCommons is focused on making machine learning better for everyone through metrics, datasets, and enablement. The goal for MLPerf is to come up with a fair and representative benchmark to allow the makers of ML systems to demonstrate the performance of their solutions. They focus on real data from a reference ML model that defines correctness, review the performance of a solution, and post the results. MLPerf started with training then added inferencing, which is the focus for users of ML. We must also consider factors like cost and power use when evaluating a system, and a reliable bench
Links:
- MLCommons.org
- Connect-Converge.com
Three Questions:
- Frederic: Is it possible to create a truly unbiased AI?
- Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
- Andy Hock, Cerebras: What AI application would you build or what AI research would you conduct if you were not constrained by compute?
Gests and Hosts
- David Kanter is the Executive Director of MLCommons. You can connect with David on Twitter at @TheKanter and on LinkedIn. You can also send David an email at david@mlcommons.org.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 4/12/2022 Tags: @SFoskett, @FredericVHaren,
The quality of an AI application depends on the quality of the data that feeds it. Sunil Samel joins Frederic Van Haren and Stephen Foskett to discuss DataOps and the importance of data quality. When we consider data-centric AI, we must consider all aspects of the data pipeline, from storing, transporting, and understanding to controlling access and cost. We must look at the data needed to train our models, think about the desired outcomes, and consider the sources and pipeline needed to get that result. We must also decide how to define quality: Do we need a variety of data sources? Should we reject some data? How does the modality of the data type change this definition? Is there bias in what is included and excluded? Data pipelines are usually simple, ingesting and storing data from the source, slicing and preparing it, and presenting it for processing. But DataOps recognizes that the data pipeline can get very complicated and requires understanding of all these steps as well as adaptation from development to production.
Three Questions:
- Frederic: Do you think we should expect another AI winter?
- Stephen: When will we see a full self-driving car that can drive anywhere, any time?
- Mike O'Malley, Seneca Global: Can you give an example where an AI algorithm went terribly wrong and gave a result that clearly wasn’t correct?
Gests and Hosts
- Sunil Samel, VP of Products at Akridata. Connect with Sunil on LinkedIn or email him at sunil.samel@akridata.com.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/29/2022 Tags: @SFoskett, @FredericVHaren
Machine learning is unlike any other enterprise application, demanding massive datasets from distributed sources. In this episode, Bin Fan of Alluxio discusses the unique challenges of distributed heterogeneous data to support ML workloads with Frederic Van Haren and Stephen Foskett. The systems supporting AI training are unique, with GPUs and other AI accelerators distributed across multiple machines, each accessing the same massive set of small files. Conventional storage solutions are not equipped to serve parallel access to such a large number of small files, and they often become a bottleneck to performance in machine learning training. Another issue is moving data across silos, storage systems and protocols, which is impossible with most solutions.
Three Questions:
- Frederic: What areas are blocking us today to further improve and accelerate AI?
- Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
- Sara E. Berger: With all of the AI that we have in our day-to-day, where should be the limitations? Where should we have it, where shouldn't we have it, where should be the boundaries?
Gests and Hosts
- Bin Fan, Founding Member of Alluxio Inc. Connect with Bin on LinkedIn and on Twitter @BinFan.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/15/2022 Tags: @SFoskett, @FredericVHaren, @BinFan, @Alluxio
With so many AI tools available, it can be a challenge to integrate everything into a productive platform. Orly Amsalem of cnvrg.io joins Frederic Van Haren and Stephen Foskett to discuss the challenges of managing data and resources for AI training, development, management, and deployment. Orly discusses her journey from software development to AI and the challenges people face. Many in the AI community are following the same path, and are looking for tools like cnvrg to help them bring AI to their day to day work. AL blueprints, provided by cnvrg and the community, can help developers and data scientists get started with AI projects. In a recent survey, only 10% of developers said training was their main challenge; nearly every one said that deploying a model to production was the biggest. Orly then discusses the main bottlenecks to MLOps in production and how to break through and normalize AI in the enterprise.
Links:
- "Five Ways to Shift to AI-First"
Three Questions:
- Frederic: When do you think AI will diagnose a patient as accurately as (or better than) a human doctor?
- Stephen: Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Eitan Medina, Habana Labs: If you should choose something for AI to do for you in your day-today life, what would it be?
Gests and Hosts
- Orly Amsalem, VP of AI Innovation & Business Development at cnvrg.io. Read "Five Ways to Shift to AI-First" here.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/01/2022 Tags: @SFoskett, @FredericVHaren, @cnvrg_io
As machine learning is used to market and sell, we must consider how biases in models and data can impact society. Arizona State University Professor Katina Michael joins Frederic Van Haren and Stephen Foskett to discuss the many ways in which algorithms are skewed. Even a perfect model will produce biased answers when fed input data with inherent biases. How can we test and correct this? Awareness is important, but companies and governments should take active interest in detecting bias in models and data.
Links:
- "Algorithmic bias in machine learning-based marketing models"
Three Questions:
- Frederic: When will AI be able to reliably detect when a person is lying?
- Stephen: Is it possible to create a truly unbiased AI?
- Tom Hollingsworth of Gestalt IT: Can AI ever recognize that it is biased and learn how to overcome it?
Gests and Hosts
- Katina Michael, Professor in the School for the Future of Innovation in Society and School of Computing and Augmented Intelligence at Arizona State University. Read here paper here in the Journal of Business Research. You can find more about her at KatinaMichael.com.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 2/21/2022 Tags: @SFoskett, @FredericVHaren
With AI technology changing so quickly, we often need to step back and take a big picture look at the market. In this episode, Manoj Suvarna of Deloitte joins Frederic Van Haren and Stephen Foskett to discuss the many products and applications of AI in the enterprise. It is important for business executives to survey the many ways that AI and data science are being applied across the enterprise and try to find ways to leverage this work in other areas. Deloitte recently conducted a survey of companies around the world to get a sense of the many ways AI is being adopted. 92% of those surveyed said that AI is a competitive area these days, and 83% realize the value of multiple ecosystems. A majority of businesses see AI as a strategic differentiator that they want to invest in, while most of the rest are trying to determine the value of AI to the business.
Links:
- "State of AI and the Enterprise, Fourth Edition"
Three Questions:
- Frederic: Do you believe AI products and models should be regulated by an independent organization?
- Stephen: Are there any jobs that will be completely eliminated by AI in the next five years?
- Amanda Kelly of Streamlit: What is a tool that you were personally using a few years ago but you find you are not using anymore?
Gests and Hosts
- Manoj Suvarna, Managing Director, AI Ecosystems, Deloitte. Connect with Manoj on LinkedIn or on Twitter at @MSuvarna
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 2/15/2022 Tags: @SFoskett, @FredericVHaren, @MSuvarna
Data is the most important element of artificial intelligence, but how is that data managed and stored? In this episode of Utilizing AI, Adi Gelvan of Speedb goes deep under the hood to take a look at the data engine along with Frederic Van Haren and Stephen Foskett. Facebook's RocksDB provides the basic storage for many webscale projects, managing metadata in a massive scale. Because of the inherent limits of RocksDB, most cloud applications shard data across many data engines. But Speedb takes a different approach, bringing more advanced storage technology to build a compatible data engine. A good data engine can massively improve overall performance, and data scientists and AI engineers would be wise to consider the storage engine, not just the processing components and models.
Three Questions:
- Frederic Van Haren: In what areas will AI have little to no impact?
- Stephen: Is AI just a new aspect of data science or is it truly a unique field?
- Rob Telson of BrainChip: Where do you see AI having the most beneficial impact on our society?
Gests and Hosts
- Adi Gelvan, Co-Founder and CEO of Speedb. Find out more at www.speedb.io or reach out to Adi at adi@speedb.io.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 2/08/2022 Tags: @SFoskett, @FredericVHaren, @speedb_io
AI is everywhere, and so are AI accelerators, from CPU to GPU to special-purpose hardware. Eitan Medina, Chief Operating Officer of Habana Labs, an Intel Company, joins Frederic Van Haren and Stephen Foskett to discuss the various specialized AI processors being developed today. Habana Labs has created a special-purpose AI training and inferencing processor with many unique features. Since deep learning is done at scale today, it makes sense to integrate enterprise networking with an accelerator like Habana Gaudi to increase overall system performance thanks to rDMA over Ethernet (RoCE) technology. Habana Gaudi is optimized for matrix math and also includes a fully-programmable vector core for Tensor processing. In October 2021, Amazon AWS launched the new DL1 instance based on Habana Gaudi, offering more performance than many GPU-based instances for a much lower total cost. Habana is very developer-focused as well, working with partners, data scientists, and end users to expand the accessibility of the platform in channels like GitHub and their own developer forum. Habana will soon introduce a 7 nm Gaudi 2 processor with much-improved performance and power efficiency. Habana Labs is also making their hardware more accessible thanks to their SynapseAPI and recently acquired cnvrg.io to bring a higher-level MLOps pipeline to AI.
Three Questions:
- Frederic: At what point in time do you believe AI will be able to show compassion (like humans) if ever?
- Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
- Edward Cui Founder of Graviti: Which will be more important in the future: Bigger and bigger ML or smaller ML?
Gests and Hosts
- Eitan Medina, Chief Operating Officer of Habana Labs, an Intel Company. Visit habana.ai to learn more.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 2/01/2022 Tags: @SFoskett, @FredericVHaren, @HabanaLabs
Among many surprising applications, AI can be used for pain management and medical care. Neuroscientist Sara E. Berger of IBM joins Chris Grundemann and Stephen Foskett to discuss applications of machine learning in medical care. Pain management is a deeply personal field, but there are so many different data points that it can be difficult to see patterns that lead to positive outcomes. Machine learning can assist in sorting and selecting treatments, bringing in different sensors and data types to help patients. The more we see pain in a multi-disciplinary lens, and the more understanding we bring, the better the outcome for patients.
Three Questions:
- Chris: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
- Stephen: Is it possible to create a truly unbiased AI?
- Sriram Chandrasekaran, Professor at the University of Michigan: What do you think is the biggest AI technology that you can think of that will transform medicine in the future?
Gests and Hosts
- Sara E. Berger, Research Staff Member at IBM and Neuroscientist. Connect with Sara on LinkedIn.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 1/25/2022 Tags: @IBM, @SFoskett, @ChrisGrundemann
BrainChip's neuromorphic AI technology has long been the talk of the industry, and now the Akida processor is available for purchase. We invited Rob Telson, VP of Worldwide Sales for BrainChip, to return to the Utilizing AI podcast to give Chris Grundemann and Stephen Foskett an update on the Akida processor. As of today, Akida is available for use by developers and hobbyists to explore neuromorphic compute at the edge. BrainChip enables five sensor modalities: Vision, hearing, touch, olfactory, and taste. BrainChip's architecture allows incremental on-chip learning at extremely low power, potentially bringing this capability to some surprising places, from home appliances to the factory floor. Another differentiator of the BrainChip solution is its event-based architecture, which can trigger based on events rather than sending a continual stream of data. As of today, the BrainChip Akida AKD1000 PCIe development board is available for purchase so everyone can try out the technology.
Three Questions:
- Chris: When will we see a full self-driving car that can drive anywhere, any time?
- Stephen: Are there any jobs that will be completely eliminated by AI in the next five years?
- Girard Kavelines: What is it that scares you about AI in today's industry?
Links:
- shop.brainchipinc.com
Gests and Hosts
- Rob Telson, Vice President, Worldwide Sales, BrainChip. You can find out more information at shop.brainchip.com. For questions you can email sales@brainchip.com.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 1/18/2022 Tags: @BrainChip_inc, @SFoskett, @ChrisGrundemann
Although it’s a powerful tool, deep learning is perhaps over-used in modern applications. In this episode of the Utilizing AI podcast, Rich Harang joins Chris Grundemann and Stephen Foskett to discuss the various reasons people use AI, both good and bad. In a November Twitter thread, Rich posited that the following conditions were required to use AI for real: The cost of errors must be extremely low, the decision needs to be possible but expensive, there needs to be the same kind of decision frequently, there needs to be a benefit and be better than a simple rule, you have to not care how it got the answer, the base rate must be close to even, you need a steady stream of data for training, and you must match the size and cost of the model to the application. On the other hand, these same considerations can point us to problem sets that make a great match for DL, and we should focus on using the right tool for the job.
Three Questions:
- Chris Grundemann: Are there any jobs that will be completely eliminated by AI in the next five years?
- Stephen Foskett: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
- Adam Probst of ZenML: What percentage of companies will be using ML in five years?
Links:
- Rich’s Twitter Thread: https://twitter.com/rharang/status/1465340190919217153
- Sara Hooker’s Paper, “The Hardware Lottery”: https://hardwarelottery.github.io
Gests and Hosts
- Rich Harang, Senior Technical Lead at Duo Security. Connect with Rich on Twitter at @RHarang.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 1/11/2022 Tags: @RHarang, @SFoskett, @ChrisGrundemann
AI is now widespread, and companies are starting to look at the real-world impact of machine learning. In this special episode of the Utilizing AI podcast, the three hosts look forward to AI in 2022 and revisit some of our guest questions from season three. First, we turn to the specific markets and verticals served by AI applications. We feel that datasets and models will increasingly be focused on specific business uses instead of being general-purpose tools. Next, we consider how the AI industry is increasingly concerned about ethics, bias, and privacy of data. Industry leaders like Timnit Gebru and Cynthia Rudin are showing how important social responsibility is to artificial intelligence. Finally we turn to the continuing progress seen in AI technology. New methodologies, larger models, and increasingly critical real-time applications are transforming the technology, and ML hardware and instructions are everywhere from mobile devices to the datacenter and the cloud.
"Three" Questions:
- Chris from Tony Pikeday: Can AI ever teach us to be more human?
- Frederic from Sriam Chandrasakren: What do you think is the biggest AI technology that will transform medicine in the future?
- Chris from Leon Adato: What responsibility do you think IT folks have to insure the things that we build are etihical?
- Frederic from Amanda Kelly: What is a tool that you were personally using a few years ago but you find you are not using anymore?
- Chris from Ben Taylor: Are you living up to your potential legacy?
- Frederic from Girard Kavelines: What scares you the most about AI?
- Stephen from Chris: What do you think will be the most surprising thing to come out of AI in the next three years?
- Stephen from Frederic: When do you think AI will be able to automatically identify if an AI model is ethical or not?
- Stephen from Chris: What do you think is the most over hyped application of AI people are talking about today?
Hosts
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 1/4/2022 Tags: @ChrisGrundemann, @SFoskett, @FredericVHaren
Shadow IT is as old as our profession, so it's no surprise that shadow AI is becoming a major issue. In this episode of Utilizing AI, Ronen Dar and Gijsbert Janssen van Doorn join Frederic Van Haren and Stephen Foskett to discuss resource utilization and shadow AI. One of the biggest issues with shadow IT is the low utilization of these resources that can come when they are purchased and used by a single corporate group or application. This is true both on-premises and in the cloud. But even if enterprise IT operations and infrastructure groups can come to understand AI, they must offer a compelling AI solution if they will be able to get control. The easiest way to do this is to deploy a much larger centralized solution than any group could procure on their own and deliver it with a flexible cloud-like access method. Another issue with shadow AI is that it often relies on a single individual and is difficult to reproduce, put into production, or scale.
Three Questions:
- Frederic: Is it possible to create a truly unbiased AI?
- Stephen: Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Tony Paikeday, Nvidia: Can AI ever teach us to be more human?
Guests and Hosts
- Ronen Dar, CTO and Co-Founder of Run: AI. Connect with Ronen on LinkedIn.
- Gijsbert Janssen van Doorn, Director of Technical Product Marketing at Run: AI. Connect with Gijsbert on LinkedIn.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 12/21/2021 Tags: @runailabs, @SFoskett, @FredericVHaren
AI is coming fast to the information security world, both in terms of tools and threats. In this episode, InfoSec professional Girard Kavelines discusses the reality of AI in security with Chris Grundemann and Stephen Foskett. With AI assistance on both sides of the security divide, will we see an escalation of attack and defense? On the defense side, threats have evolved to advanced attacks that look like system processes and legitimate connections, and machine learning can help process more data than ever before. ML-based systems can also judge unknown threats that a rules-based system would never catch. On the other hand, we are already seeing AI used to generate more effective attacks, from phishing to fuzzing APIs.
Three Questions:
- Chris Grundemann: Are there any jobs that will be completely eliminated by AI in the next five years?
- Stephen Foskett: Can you think of any fields that have not yet been touched by AI?
- Amanda Kelly, Streamlit: What is a tool that you were personally using a few years ago but you are not using anymore?
Links:
TechHouse570- Cisco Champion Highlights
Gests and Hosts
- Girard Kavelines, Founder of TechHouse570. Connect with Girard on LinkedIn and on Twitter at @GKavelines.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 12/14/2021 Tags: @GKavelines, @SFoskett, @ChrisGrundemann
Many of the tasks we perform on a daily basis are beneath our abilities, and these are the ideal targets for AI. In this episode, Ben Taylor of DataRobot joins Frederic Van Haren and Stephen Foskett to talk about AI as a creativity maximizer. Business people too often get stuck in a process rather than innovating, from office work to manufacturing to R&D, and all of these can be augmented by AI-based tools. The most successful companies have hundreds or thousands of AI initiatives across the entire business to help identify opportunities for the technology to help employees be more successful. There is an inherent push and pull between small tactical projects and big strategic ones, and we have to consider the level of effort and the impact of AI projects.
Three Questions
- Frederic: When will we see a full self-driving car that can drive anywhere, any time?
- Stephen: Is AI just a new aspect of data science or is it truly a unique field?
- Mike O'Malle, SVP, Seneca Global: Can you give an example where an AI algorithm went terribly wrong and gave a result that clearly wasn’t correct?
Guests and Hosts
- Ben Taylor, Chief AI Evangelist, DataRobot and host of the More Intelligent Tomorrow podcast. Connect with Ben on LinkedIn or on Twitter at @BenTaylorData
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 12/07/2021 Tags: @DataRobot ,@BenTaylorData, @FredericVHaren, @SFoskett
Machine learning applications require massive datasets, but it can be challenging to build and store large amounts of unstructured data. In this episode of Utilizing AI, Edward Cui of Graviti discusses his creation of an open repository for unstructured data with Frederic Van Haren and Stephen Foskett. Coming from Uber's self-driving organization, Cui realized the value of data and the challenge of storing massive amounts of unstructured data, so he created the Graviti platform and made it available for free to open datasets. These datasets enable development of a variety of applications, from agriculture and environmental science to gaming and robotics. To address the challenge of data sharing and quality, Graviti is working with the Linux Foundation on the OpenBytes project.
Three Questions
- Frederic: Are there any jobs that will be completely eliminated by AI in the next five years?
- Stephen: When will we see a full self-driving car that can drive anywhere, any time?
- Tom Hollingsworth, Gestalt IT: Can AI ever recognize that it's biased and learn how to overcome it?
Guests and Hosts
- Edward Cui, Founder of Graviti. You can follow Graviti on LinkedIn or twitter at @graviti_ai. You can also email Graviti at contact@graviti.com.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 11/30/2021 Tags: @graviti_ai, @FredericVHaren, @SFoskett
Data science and machine learning developments can't have an impact if they don't get into everyone's hands. In this episode, Amanda Kelly of Streamlit joins Chris Grundemann and Stephen Foskett to talk about the challenges and opportunities in bringing data science to everyone's hands. How can we enable marketing, sales, marketing, and other elements of the business to access data and make informed decisions themselves? Data science teams have to meet business people where they are to better answer their questions rather than trying to create a perfect model in a vacuum. Streamlit helps to productize python scripts with a complete and flexible front-end and easy deployment, making it easy to share and iterate. These micro apps foster collaboration and interaction between data science and the business.
Three Questions
- Stephen: Is AI just a new aspect of data science or is it truly a unique field?
- Chris: Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- Leon Adato: What responsibility do you think IT folks have to insure the things that we build are ethical?
Gests and Hosts
- Amanda Kelly, Co-Founder of Streamlit. Follow her thoughts on the Streamlit Blog.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 11/16/2021
Tags: @streamlit, @SFoskett, @ChrisGrundemann
Data is the most important component of AI implementation, but most companies neglect data infrastructure and focus too much on the ML models. In this episode of the Utilizing AI podcast, Melisa Tokmak of Scale AI joins Frederic Van Haren and Stephen Foskett to discuss the democratization of data infrastructure to support machine learning projects. Enterprises often don't have a good understanding of their data, and this can undermine the success of an AI project, and this must be addressed before the project can proceed. Companies also must consider the quality of their data, beginning with a definition of the metrics that will properly assess the data foundation for their ML models.
Three Questions
- Frederic: Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
- Alexandrine Royer: What do you think is one of the biggest ethical challenges that comes with AI that often goes under-discussed and should be more present in conversations surrounding the deployment of AI models?
Guests and Hosts
- Melisa Tokmak, GM at Scale AI. Connect with Melisa on LinkedIn or on Twitter @MelisaTokmak.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 11/09/2021 Tags: @MelisaTokmak, @scale_AI, @SFoskett, @FredericVHaren
Many data scientists and ML engineers have faced the challenge of putting AI models into production, and this is the core of MLOps. In this episode, Adam Probst, Co-Founder of ZenML, joins Frederic Van Haren and Stephen Foskett to discuss the challenges of putting ML models into production. Machine learning pipelines are inherently complex and fragile and require feedback and tuning, and this requires a new approach with continuous improvement and tight integration. Although reminiscent of DevOps, MLOps demands even more collaboration between IT operations, developers and data scientists, and lines of business. ZenML prepares ready-to-use MLOps infrastructure to these groups so they can focus on the model rather than the platform.
Three Questions
- Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
- Frederic: Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Zach DeMeyer: What's the most innovative use of AI you've seen in the real world?
Guests and Hosts
- Adam Probst, Co-Founder, ZenML. Connect with ZenML on GitHub, LinkedIn and on Twitter @zenml_io.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic onHighfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 11/02/2021 Tags: @zenml_io, @SFoskett, @FredericVHaren
AI is spreading around the world, both in terms of technology and workforces. Many tasks that support artificial intelligence are being outsourced globally, with many workers exploited or mistreated as they take up the opportunities offered by the information economy. In this episode, Alexandrine Royer, Student Fellow at the Leverhulme Center for the Future of Intelligence, joins Chris Grundemann and Stephen Foskett to discuss the prospects for global AI workers. The situation is akin to globalization of manufacturing or shipping, with powerful corporations exploiting differing regulations and approaches around the world. Given this situation, collective action and advocacy might be the only way for workers to improve their situation.
Three Questions
- Stephen: How long will it take for a conversational AI to pass the Turing test and fool an average person?
- Chris: Are there any jobs that will be completely eliminated by AI in the next five years?
- Tony Paikeday, Senior Director of AI Systems at NVIDIA: Can AI ever teach us to be more human?
LinksThe urgent need for regulating global ghost work (brookings.edu)
The wellness industry’s risky embrace of AI-driven mental health care (brookings.edu)
Guests and Hosts
- Alexandrine Royer, Student Fellow at the Leverhulme Center for the Future of Intelligence. Connect with Alexandrine on LinkedIn.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 10/26/2021 Tags: @SFoskett, @ChrisGrundemann
Demand for AI compute is growing faster than conventional systems architecture can match, so companies like Cerebras Systems are building massive special-purpose processing units. In this episode, Andy Hock, VP of Product for Cerebras Systems, joins Frederic Van Haren and Stephen Foskett to discuss this new class of hardware. The Cerebras Wafer-Scale Engine (WSE-2) has 850,000 processors on a single chip the size of a dinner plate, along with 40 GB of SRAM and supporting interconnects. But Cerebras also has a software stack that integrates with standard ML frameworks like PyTorch and TensorFlow. Although the trillion-parameter model is a real need for certain applications, platforms need to be flexible to support both massive-scale and more mainstream workloads, and this is a focus for Cerebras as well.
Three Questions
- Frederic's Question: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
- Stephen's Question: Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- Leon Adato, host of the Technically Religious Podcast: I'm curious, what responsibility do you think IT folks have to insure the things that we build are ethical?
Guests and Hosts
- Andy Hock, VP of Product at Cerebras Systems. Connect with Andy on LinkedIn. Follow Cerebras Systems on Twitter at @CerebrasSystems.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 10/19/2021 Tags: @CerebrasSystems, @SFoskett, @FredericVHaren
It is sometimes hard to see how AI technology benefits society, but applications like drug discovery really bring the power home. Sriram Chandrasekaran, Assistant Professor of Biochemical Engineering at the University of Michigan, is using machine learning to assess the properties of drug candidates to fight antibiotic-resistant bacteria. Presented with millions of different potential drugs, machine learning can identify the few most useful to be tested clinically. Because it tries everything and anything without preconceived biases, ML can uncover novel combinations that researchers might never notice. We also discuss specifics of the AI environment, including the preference for random forests to deep learning, privacy concerns, bias in datasets, and the interplay between domain expertise and data science.
Three Questions
- Stephen's Question: Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- Chris's Question: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
- Zach DeMeyer, Gestalt IT: What's the most innovative use of AI you've seen in the real world
Guests and Hosts
- Sriram Chandrasekaran, Assistant Professor of Biomedical Engineering at University of Michigan. Connect with Sriram on LinkedIn or on Twitter at @sriram_lab. You can also email Sriram at csriram@umich.edu.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 10/12/2021 Tags: @sriram_lab , @SFoskett, @ChrisGrundemann
In this episode, we consider the moral and ethical dimensions of artificial intelligence. Leon Adato, host of the Technically Religious podcast, joins Frederic Van Haren and Stephen Foskett to consider the boundaries of technology and the choices we make. Leon suggests that the unintentional, unconscious, and undetectable impact of AI is the key consideration, not the science fiction questions of AI and religion. Many religions seek to apply the lessons of the past to new technologies and situations, and these can provide a unique insight into the question of the way we as a society should proceed. We must also re-evaluate the systems we put in place to ask if the machine is doing what we wanted it to do and what the side effects are.
Three Questions
- Can you think of any fields that have not yet been touched by AI?
- Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- Tom Hollingsworth: Can AI ever recognize that it's biased and learn how to overcome it?
Guests and Hosts
- Leon Adato, Head Geek at SolarWinds and host of the Technically Religious podcast. Follow Leon at adatosystems.com. You can also connect with Leon on LinkedIn or on Twitter @LeonAdato.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 10/05/2021 Tags: @LeonAdato, @SFoskett, @FredericVHaren
Local and wide-area networks can get complex very quickly, so it's no surprise that AI-powered network management is making a huge impact in the enterprise. In this episode, Tom Hollingsworth, who runs Networking Field Day for Gestalt IT, joins Chris Grundemann and Stephen Foskett to discuss applications of AI in network monitoring and management. Solutions like Mist from Juniper Networks give network administrators the ability to ask questions get insight using the power of machine learning. This proactive observability stance allows network administrators to answer difficult questions rather than just keeping things running. AI truly has become a co-pilot for network engineers, helping transform their career once they embrace it. Another use of AI in networking is exemplified by Forward Networks, which can model and test networking concepts before they are pushed to a live environment. Another company, HPE's Aruba, is leveraging AI in edge computing while their Net Insight suggesting best practices. SD-WAN companies are also using AI to accelerate applications, and AI is finding applications in wireless networks. Finally we take on "AI washing" and the need to be skeptical when companies say their solutions use AI.
Three Questions
- Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Are there any jobs that will be completely eliminated by AI in the next five years?
- Tony Paikeday of NVIDIA asks, can AI ever teach us to be more human?
Guests and Hosts
- Tom Hollingsworth, Event Lead for Networking Field Day. Follow Tom's thoughts at networkingnerd.net and Gestaltit.com. You can also connect with Tom on Twitter at @NetworkingNerd.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 9/28/2021 Tags: @GestaltIT, @TechFieldDay, @SFoskett, @ChrisGrundemann, @NetworkingNerd
Enterprises are working to simplify the process of deploying and managing systems to support AI applications. That's what NVIDIA's DGX architecture is designed to do, and what we'll talk about on this episode. Frederic Van Haren and Stephen Foskett are joined by Tony Paikeday, Senior Director, AI Systems at NVIDIA, to discuss the tools needed to operationalize AI at scale. Although many NVIDIA DGX systems have been purchased by data scientists or directly by lines of business, it is also a solution that CIOs have embraced. The system includes NVIDIA GPUs of course but also CPU, storage, and connectivity and all of this is held together with software that makes it easy to use as a unified solution. AI is a unique enterprise workload in that it requires high storage IOPS and low storage and network latency. Another issue is balancing these needs to scale performance in a linear manner as more GPUs are used, and this is why NVIDIA relies on NVLink and NVSwitch as well as DPU and InfiniBand to connect the largest systems
Three Questions
- How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
- Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- Can you give an example where an AI algorithm went terribly wrong and gave a result that clearly wasn’t correct?
*Question asked by Mike O'Malley of SenecaGlobal.
Guests and Hosts
- Tony Paikeday, Senior Director Senior Director, AI systems at NVIDIA. Connect with Tony on LinkedIn or on Twitter at @TonyPaikeday.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 9/21/2021 Tags: @TonyPaikeday, @nvidia, @SFoskett, @FredericVHaren
Machine learning excels at finding needles in haystacks, even unexpected ones, and this helps organizations to assess risks. In this first episode of season 3, Utilizing AI hosts Stephen Foskett and Chris Grundemann discuss risk analysis with Mike O'Malley of SenecaGlobal. ML is extremely good at detecting outliers and adapting to changing patterns, and this can yield excellent results in applications like financial pattern recognition. But AI lacks real understanding, and this can limit the use cases for ML.
Three Questions
- Are there any jobs that will be completely eliminated by AI in the next five years?
- What’s the strangest or most amusing application for ML that you have encountered?
- Can you think of any fields that have not yet been touched by AI?
Guests and Hosts
- Mike O'Malley, SVP at SenecaGlobal. Connect with Mike on LinkedIn.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 9/14/2021 Tags: @senecaglobal, @SFoskett, @ChrisGrundemann
Welcome back to another season of Utilizing AI! In this first episode of season 3, we are taking a look at some of the most memorable moments of season 2. We started season 2 by talking about AI as a co-pilot in the first few episodes and this theme continued throughout the season. AI making our jobs easier was a common discussion we had through the course of the season. Another common discussion had throughout the season was how to make implementing AI easier through tools and platforms. We also discussed the duality of working in AI vs. working on AI. Having AI be more accessible and easier to use was yet another common theme we saw throughout season 2. Some of the most memorable guests that have stuck with our host and co-hosts include Saiph Savage, Sofia Trejo, Ayodele Odubela, and Anti Raman. Speaking of guests, Frederic Van Haren, who is one of our show’s co-hosts, was an early season 2 guest. Our most listened to episode was the discussion we had with BrainChip.
Three Questions
This season, we are continuing with our three questions tradition but we’re throwing in a twist! We are offering the opportunity for our guests and our listeners to pose questions that we may use in a future episode. Each guest will be asked to record a question that may be used to ask a future guest. We also want to offer our listeners the opportunity to become a part of the podcast. If you would like your question asked, send us an email at Host@Utilizing-AI.com and let us know you would like to participate!
Hosts
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 9/7/2021 Tags: @SFoskett, @ChrisGrundemann, @FredericVHaren
Most people think AI in vehicles means autonomous driving, but there are a lot of other applications for the technology. Ever since Mercedes-Benz introduced Linguatronic voice response in the 1990s, vehicles have included verbal control and feedback mechanisms. In this episode, Christophe Couvreur discusses the lessons of bringing AI to vehicles based on his experience at Nuance spin-off, Cerence. As these systems have improved, they have reached the so-called uncanny valley, where people become frustrated by their limitations despite tremendous advancement over the last decade or so. Looking beyond voice response, we can see many driver assistance technologies added to vehicles in the future, and many of these will be ML powered as well.
Three Questions:
- When will we see a full self-driving car that can drive anywhere, any time?
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- Christophe Couvreur, VP of Product at Cerence Inc. Connect with Christophe on LinkedIn here and the company page Cerence.com.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 8/3/2021 Tags: @CerenceInc, @SFoskett, @FredericVHaren
Businesses have long tried to use data to drive decisions, but over the last few years new big data and AI capabilities have appeared. In this episode, Josh Epstein of AtScale discusses the opportunities that enterprise AI brings to drive business decisions. Although executives might not know the details of AI models, they can certainly benefit from the forecasts and recommendations these tools deliver. One benefit of these systems is that they can bring in more diverse data to uncover real value from areas typically outside the sight of executives.
Three Questions
- Are there any jobs that will be completely eliminated by AI in the next five years?
- Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
Guests and Hosts
- Josh Epstein, Chief Marketing Officer at AtScale. Connect with Josh on LinkedIn.
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 7/27/2021 Tags: @AtScale, @SFoskett, @FredericVHaren
Although we usually focus on the ways AI can displace workers, this technology can also create new jobs and help them. In this episode, Saiph Savage joins Chris Grundemann and Stephen Foskett to discuss the many ways AI can help displaced workers. One new type of job created by AI is in the area of model training, and this can help develop digital skills and improve the lives of workers. Digital labor platforms tend to be opaque, however, and we must audit them to understand the wages paid, exposure to negative content, and invisible labor workers do to continue to use these tools. Yet despite these shortcomings, many workers report positive experiences, in terms of life/work balance, opportunity, and flexibility. Researchers like Savage are monitoring these opportunities and developing tools to help workers and policymakers fairly judge the costs and benefits of participating. Ultimately, these jobs can become a stepping stone to digital careers and further opportunities.
References
- Saiph Savage’s Super Turker paper
- Flexible Work and Personal Digital Infrastructures
- Turker Tales: Integrating Tangential Play into Crowd Work
- “Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass” by Mary L. Gray and Siddharth Suri (book)
Three Questions
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- Are there any jobs that will be completely eliminated by AI in the next five years?
- Can you think of any fields that have not yet been touched by AI?
Guests and Hosts
- Saiph Savage, Assistant Professor at Northeastern University. Connect with Saiph on LinkedIn or on Twitter at @Saiphcita.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 7/20/2021 Tags: @Saiphcita, @SFoskett, @ChrisGrundemann
Today’s storage devices (disks and SSDs) have processors and memory already, and this is the concept of computational storage. If drives can process data locally, they can relieve the burden of communication and processing and help reduce the amount of data that gets to the CPU or GPU. In this episode, Vladimir Alves and Scott Shadley join Chris Grundemann and Stephen Foskett to discuss the AI implications of computational storage. Modern SSDs already process data, including encryption and compression, and they are increasingly taking on applications like machine learning. Just as industrial IoT and edge computing is taking on ML processing, so too are storage devices. Current applications for ML on computational storage include local processing of images and video for recognition and language processing, but these devices might even be able to execute ML training locally as in the case of federated learning.
Three Questions
- Are there any jobs that will be completely eliminated by AI in the next five years?
- Can you think of any fields that have not yet been touched by AI?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- Vladimir Alves, CTO and Co-Founder at NGD Systems. Connect with Vladimir on LinkedIn.
- Scott Shadley, VP of Marketing at NGD System. Connect with Scott on LinkedIn or on Twitter @SMShadley.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 7/13/2021 Tags: @SFoskett, @ChrisGrundemann, @SMShadley, @NGDSystems
The MLOps community has grown dramatically recently, with security, a data-centric approach, ethical implications, and a growing and diverse community rising in 2021. In this episode, MLOps Community managers Demetrios Brinkmann and David Aponte join Steph Locke and Stephen Foskett to discuss what has changed over the last year. It seems that a new ML company is launching every week, and the MLOps Community provides a great way to learn about these. We are also seeing a push and pull between open source and cloud platforms, and concern about lock-in and technical debt. Data science and machine learning are merging, with greater focus on data quality and quantity when training models.
Three Questions
- Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?
Companies Mentioned
- Microsoft, Tecton, Scale AI Sara Williams Talk
- D. Sculley interview
Guests and Hosts
- Demetrios Brinkmann, Community Coordinator at MLOps Community. Connect with Demetrios on LinkedIn, on Twitter at @DPBrinkm or at mlops.community.
- David Aponte, Community Coordinator at MLOps Community. Connect with David on LinkedIn.
- Steph Locke, Data Scientist and CEO of Nightingale HQ. Connect with Steph on LinkedIn or on Twitter @TheStephLocke.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 7/6/2021 Tags: @SFoskett, @TheStephLocke, @DPBrinkm, @MLOpsCommunity
Look at a list of the top companies in the world, and most are focused in the United States, China, and Europe, and this causes an imbalance of investment in AI. With most companies building AI infrastructure and applications located in Silicon Valley and similar areas, how will the rest of the world catch up? Sofia Trejo joins Chris Grundemann and Stephen Foskett to discuss the implications of this imbalance, which causes an AI divide. Companies like Facebook, Google, and Amazon increasingly centralize global data through their internet access initiatives, and all are also deeply involved in developing cloud and AI applications. This poses issues for developing countries, which are increasingly dependent on these companies and susceptible to disinformation and misinformation campaigns. Most discussions of bias focus on a first-world context and do not take into account the challenges faced by developing countries, and the same is true of AI development. We must stop thinking that the solution is technological and focus instead on education and digital literacy before AI gets out of control.
Three Questions
- Is it possible to create a truly unbiased AI?
- Can you think of any fields that have not yet been touched by AI?
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
Guests and Hosts
- Sofia Trejo, PhD in Mathematics, Specialist in AI Ethics. Connect with Sofia on LinkedIn.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 6/29/2021 Tags: @SFoskett, @ChrisGrundemann
AI is everywhere these days, powering applications from the enterprise to industrial, medical, education, and mobility. In this episode, David Klee joins Chris Grundemann and Stephen Foskett to discuss the ubiquity of AI technology today. Although not all applications of machine learning have been compelling, we are starting to see novel uses that allow us to do things we could never do before. One exciting application is in root cause analysis across the entire application stack, which has never before been possible.
Three Questions
- Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- When will we see a full self-driving car that can drive anywhere, any time?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- David Klee, Founder of SQLibrium & Heraflux Technologies. Connect with David at LinkedIn and on Twitter at @KleeGeek
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 6/22/2021 Tags: @SFoskett, @ChrisGrundemann, @KleeGeek, @SQLibrium, @HeraFlux
MLOps is similar to DevOps but focused on ML, and focuses on improving quality of delivery for artificial intelligence applications. In this episode, Stephen Foskett discusses MLOps with Steph Locke, CEO of Nightingale HQ. DevOps is very much a cultural shift for software development, while MLOps in practice tends to be more of a team sport, with software developers, data scientists, machine learning experts, and IT infrastructure and operations. Another benefit of MLOps is the improvement of efficiency that results from having all these diverse groups collaborate on application development and deployment.
Three Questions
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- Are there any jobs that will be completely eliminated by AI in the next five years?
- How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?
Guests and Hosts
- Steph Locke, Data Scientist and CEO of Nightingale HQ. Connect with Steph on LinkedIn or on Twitter @TheStephLocke.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 6/15/2021 Tags: @SFoskett, @TheStephLocke, @NightingaleHQAI
Developers of AI applications face many obstacles, but the chief challenge is simply that these are different from traditional software development projects. 85% of businesses say they are looking to adopt AI but a similar percentage of data science projects never reach production. Too many organizations approach AI application development similarly to other software projects. Another issue is focusing on the machine learning model rather than the data set that will be used. Devang Sachdev of Snorkel AI suggests being data-focused instead, and reducing and optimizing models instead of continually expanding the number of parameters. Another issue is the manual process of developing training data, which is time-consuming and error-prone. Finally, we must consider a process of iteration over models and training data to ensure quality. Machine learning is an excellent tool but it requires a re-think in how a company approaches software development.
Three Questions
- Is it possible to create a truly unbiased AI?
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- How big can ML models get? Will today’s hundred-billion parameter model look small tomorrow or have we reached the limit?
Guests and Hosts
- Devang Sachdev, VP of Marketing at Snorkel AI. Connect with Devang on LinkedIn or on Twitter at @DevangSachdev.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 6/08/2021 Tags: @SFoskett, @ChrisGrundemann, @SnorkelAI, @DevangSachdev
Microsoft plays a large role in enterprise IT applications, from the desktop to the datacenter to the Azure cloud, and the company is active in the world of AI as well. But most of Microsoft’s work has gone unnoticed, with high-profile cloud AI and ML applications at companies like Google and Uber getting all the press. In this episode, Steph Locke joins Chris Grundemann and Stephen Foskett to discuss the place of AI inside the Microsoft ecosystem. Microsoft has built AI into search and Cortana and has also produced an AI Builder and ML workspace in Azure that allows developers to produce their own chatbots, recognize images, and more. Steph also discusses the AI-related announcements at Microsoft Build last week. We finish up with a deep discussion of accessibility and diversity and potential solutions from hiring to training to awareness.
Three Questions
- Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- Is it possible to create a truly unbiased AI?
- Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
Guests and Hosts
- Steph Locke, Data Scientist and CEO of Nightingale HQ. Connect with Steph on LinkedIn or on Twitter @TheStephLocke.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 6/1/2021 Tags: @SFoskett, @ChrisGrundemann, @TheStephLocke, @NightingaleHQAI
You might think that 5G and AI are completely unrelated, but these new technologies support each other. Both are expressions of information theory, and both use similar mathematics under the hood. Both 5G and AI are also disruptive to existing business models and enable new applications. EdgeQ develops processors that leverage machine learning to improve customer experience in 5G and enable customers to develop their own AI solutions on-chip. 5G is bringing the edge closer to the cloud and it enables seamless deployment of AI across the network.
Three Questions
- When will we see a full self-driving car that can drive anywhere, any time?
- When will we have video-focused ML in the home that operates like the audio-based AI assistants like Siri or Alexa?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?Guest Information
Guests and Hosts
- Vinay Ravuri, CEO / Founder at EdgeQ Inc. Connect with Vinay on LinkedIn or Twitter at @EdgeQ_Inc
- Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 5/2/2021 Tags: @SFoskett, @EdgeQ_Inc, @FredericVHaren
Machine learning models have grown tremendously in recent years, with some having hundreds of billions of data points, and we wonder how big they can get. How do we deploy even bigger models, whether it’s in the cloud or using captive infrastructure? Models are getting bigger and bigger, then are distilled and annealed, and then grow bigger still. In this episode, Dennis Abts of Groq discusses the scalability of ML models with Stephen Foskett and Chris Grundemann. HPC architecture and concepts are coming to the enterprise, enabling us to work with unthinkable amounts of data. But we are also reducing precision and complexity of models to reduce their size. The result is that businesses will be able to work with ever-larger data sets in the future.
Three Questions
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- Will we ever see a Hollywood-style “artificial mind” like Mr. Data or other characters?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- Dennis Abts, Chief Architect at Groq. Connect with Dennis on LinkedIn or on Twitter @DennisAbts
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 5/18/2021 Tags: @SFoskett, @ChrisGrundemann, @DennisAbts, @GroqInc
AI processing is appearing everywhere, running on just about any kind of infrastructure, from the cloud to the edge to end-user devices. Although we might think AI processing requires massive centralized resources, this is not necessarily the case. Deep learning training might need centralized resources, but the topic goes way beyond this, and it is likely that most production applications will use CPUs to process data in-place. Simpler machine learning applications don’t need specialized accelerators and Intel has been building specialized hardware support into their processors for a decade. DL Boost on Xeon is competitive with discrete GPUs thanks to specialized instructions and optimized software libraries.
Three Questions
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- Is it possible to create a truly unbiased AI?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- Eric Gardner, Director of AI Marketing at Intel. Connect with Eric on LinkedIn or on Twitter @DataEric.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 5/11/2021 Tags: @SFoskett, @ChrisGrundemann, @DataEric, @IntelBusiness
Development of autonomous vehicles is an excellent example of machine learning applied to industrial IoT. In this episode, Alexander Noack of b-plus and Frank Kräemer of IBM Germany join Chris Grundemann and Stephen Foskett to discuss data collection on the road, central processing, and AI model training. Machine learning is part of the development of autonomous vehicle development and is also used in production in vehicles. It is also used to filter data and enhance processing, and this is the same concept found in many edge and industrial use cases. Edge computing is relevant beyond AI, and these technologies are complementary, with the edge moving right into vehicles, factories, retail outlets, medical facilities, and more.
Three Questions
- When will we see a full self-driving car that can drive anywhere, any time?
- Are there any jobs that will be completely eliminated by AI in the next five years?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- Frank Kräemer, Systems Architect at IBM Germany. Connect with Frank on LinkedIn or on Twitter @IBM.
- Alexander Noack, Managing Director at b-plus. Connect with Alexander on LinkedIn.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 5/4/2021 Tags: @SFoskett, @ChrisGrundemann, @IBM
We are on the cusp of a totally new architecture for enterprise IT, and this change toward composability is being driven by applications like AI. Instead of designing around fixed-configuration servers, disaggregation allows the use of pools of resources, and composability allows dynamic allocation of these resources as needed for different applications. When it comes to AI workloads, organizations can deploy a set of expensive GPUs and then allocate these as needed to various tasks, and redeploy these when that task (ML training, for example) is done. That’s what Liqid is delivering for their customers, and why we invited CEO and Co-Founder Sumit Puri and Chief AI Architect Josiah Clark to join Stephen Foskett and Chris Grundemann for this episode of Utilizing AI.
Three Questions
- Are there any jobs that will be completely eliminated by AI in the next five years?
- Can you think of any fields that have not yet been touched by AI?
- How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
Guests and Hosts
- Sumit Puri, CEO and Co-Founder at Liqid. Connect with Sumit on LinkedIn or on Twitter at @WeAreLiqid.
- Josiah Clark, Chief AI Architect at Liqid. Connect with Josiah on LinkedIn.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 4/27/2021 Tags: @SFoskett, @ChrisGrundemann, @WeAreLiqid
Industrial cameras and sensors are generating more data than ever, and companies are increasingly moving machine learning to the edge to meet it. This is the market for FogHorn, so we invited Co-Founder Sastry Malladi to join Chris Grundemann and Stephen Foskett to discuss the implications of this challenge. Industrial IoT, also called operational technology, is the use of distributed connected sensors and devices in industrial environments, from factories to oil rigs to retail. Any solution to this problem must be oriented towards the staff and skills found in these environments and must reflect the data inputs and outputs found there. Another concern is cyber security, since these environments are increasingly being targeted by attackers. Machine learning can be brought in to control industrial processes and monitor sensors locally, with low latency and high accuracy, reducing risk and increasing profitability. These environments also benefit from transfer learning, periodic re-training, and closed-loop machine learning to keep them optimized and functional
Three Questions
- Is machine learning a product or a feature?
- When will we have video-focused ML in the home that operates like the audio-based AI assistants like Siri or Alexa?
- Are there any jobs that will be completely eliminated by AI in the next five years?
Guests and Hosts
- Sastry Malladi, CTO and Co-Founder at FogHorn. Connect with Sastry on LinkedIn or on Twitter at @M_Sastry.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 4/20/2021 Tags: @SFoskett, @ChrisGrundemann, @M_Sastry, @FogHorn_IoT
AI applications typically require massive volumes of data and multiple devices within the datacenter. Nvidia acquired Mellanox to bring them industry-leading networking products to enable next-generation applications, including artificial intelligence. Kevin Deierling joins Chris Grundemann and Stephen Foskett to discuss the Nvidia vision for a datacenter-wide compute unit with integrated networking to bring all of these components together. This represents a continuous evolution of computing, from supercomputers to HPC to big data to AI, all of which have required more compute, memory, and storage resources than any one device and require the connectivity to bring it all together.
Three Questions
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- When will we have video-focused ML in the home that operates like the audio-based AI assistants like Siri or Alexa?
- Are there any jobs that will be completely eliminated by AI in the next five years?
Guests and Hosts
- Kevin Deierling, SVP Nvidia Networking. Connect with Kevin on LinkedIn or find him on Twitter at @TechseerKD.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 4/13/2021 Tags: @SFoskett, @ChrisGrundemann, @TechseerKD, @Nvidia
Training and optimizing a machine learning model takes a lot of compute resources, but what if we used ML to optimize ML? Luis Ceze created Apache Tensor Virtual Machine (TVM) to optimize ML models and has now founded a company, OctoML, to leverage this technology. Fundamentally, machine learning relies on linear algebra, but how should we pick the fastest approach for each model? Today this is done with human intuition, but TVM builds machine learning models to predict the best approaches to try. It also creates an executable so the model can run best on various target hardware platforms. It can also help select the right target platform for a given model.
Three Questions
- How long will it take for a conversational AI to pass the Turing test and fool an average person?
- Is machine learning a product or a feature?
- Can you think of any fields that have not yet been touched by AI?
Guests and Hosts
- Luis Ceze, CEO of OctoML. Find Luis on Twitter at @LuisCeze.
- Chris Grundemann, Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 4/6/2021 Tags: @SFoskett, @ChrisGrundemann, @LuisCeze, @OctoML
AI applications have large data volumes with lots of clients and conventional storage systems aren’t a good fit. In this episode, James Coomer from DDN talks about the lessons they have learned building storage systems to support AI applications. Inferencing requires terabytes or petabytes of data, often large files and streaming data. For example, autonomous driving applications generate hundreds of terabytes of data per vehicle drive, resulting in petabytes of data to ingest and process. DDN’s parallel filesystem goes a step further than NFS with an intelligent client that directs I/O to leverage all network links and storage endpoints available. Deep learning loves data, and a smart client can make the whole application faster. Because data is the biggest AI challenge today, an advanced storage solution can really help deliver AI solutions in the enterprise. Although most companies realize that finding expertise (data scientists, etc) is a major challenge, building infrastructure to support them is just as critical.
Guests and Hosts
- James Coomer is Senior Vice President for Products at DDN. Connect with James on LinkedIn or learn more on Twitter at @DDN_Limitless.
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/30/2021 Tags: @SFoskett, @AndyThurai, @DDN_Limitless
AI and analytics needs access to massive volumes of data, but we are constantly reminded of the importance of securing data. How can data be protected at rest and in flight while still enabling access? That’s what Titaniam is enabling, and this episode of Utilizing AI features CEO Arti Raman, who tells us how they are able to provide access to data without leaving it wide open. They provide granular access according to the needs of the application, enabling access for processing on demand. This approach also protects data in use by researchers and developers, since they can not access the clear text data even while their system is processing it. This has practical applications for medical applications or when dealing with personally identifiable information (PII) in the face of GDPR and CCPA.
Guests and Hosts:
- Arti Raman is CEO and Founder of Titaniam. Connect with Arthi on LinkedIn and learn more on Twitter at @TitaniamLabs.
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/23/2021 Tags: @SFoskett, @ChrisGrundemann, @TitaniamLabs
Most organizations have a vast amount of so-called unstructured data, and this poses a major risk for operations. But what if there was an AI-powered application that could sift through all this data, categorize it, and determine the risk profile for everything? That’s the promise of Concentric IO, and the premise for this episode of Utilizing AI with their CEO, Karthik Krishnan. The company uses a deep learning model trained on a vast pool of data from the Internet to create “Concentric Mind” which can identify documents across many business verticals, and this is continually tuned based on the results at each new customer environment. It also includes a language model to identify clusters of documents thematically.
Guests and Hosts:
- Karthik Krishnan is CEO of Concentric. Connect with Karthik on Twitter at @KK_Karthik.
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/16/2021 Tags: @SFoskett, @ChrisGrundemann, @KK_Karthik, @IncConcentric
Big data really wasn't all that big until modern analytics and machine learning applications appeared, but now storage solutions have to scale capacity and performance like never before. In this episode, Brad King, Co-Founder of Scality, joins Chris Grundemann and Stephen Foskett to discuss this new demand for scalable storage by AI applications. Applications like autonomous driving, log analysis, and travel booking are driving massive need for storage as AI applications detect anomalies and support business intelligence. Scality had to tune their system to handle the massive scale of data supporting these applications, with up to a petabyte of log data being added and deleted in a single day. AI-driven tools are enabling customers to do what they never could do, and it requires a balanced infrastructure stack to make it possible. Brad suggests that companies implementing AI applications need to find a system that scales with their needs and has API-driven data access, preferably with an object-based storage model.
Guests and Hosts:
- Brad King is Co-Founder and CTO of Scality. Connect with Brad on Twitter at @Baslking.
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/9/2021 Tags: @SFoskett, @ChrisGrundemann, @Scality, @Baslking
When it comes to AI, it's garbage in, garbage out: A model is only as good as the data used. In this episode of Utilizing AI, Ayodele Odubela joins Chris Grundemann and Stephen Foskett to discuss practical ways companies can eliminate bias in AI. Data scientists have to focus on building statistical parity to ensure that their data sets are representative of the data to be used in applications. We consider the sociological implications for data modeling, using lending and policing as examples for biased data sets that can lead to errors in modeling. Rather than just believing the answers, we must consider whether the data and the model are unbiased.
Guests and Hosts:
- Ayodele Odubela of @CometML is an ML instructor, founder, and author. Connect with Ayodele on Twitter at @DataSciBae.
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 3/2/2021 Tags: @SFoskett, @ChrisGrundemann, @DataSciBae
Biases can creep into any data set, and these can cause trouble when this data is used to train an AI model. Alf Rehn, Professor of Innovation, Design, and Management at the University of Southern Denmark, joins Andy Thurai and Stephen Foskett to discuss the lessons he has learned about algorithmic bias based on his work with the Velux Foundations Algorithms, Data and Democracy project. Society is directing artificial intelligence to solving some problems and ignoring others, and this can create biases as surely as data selection in model training. Can we ever truly eliminate bias? If not how do we work against it? Can we keep the genie in the bottle even if we want to? And can machines ever make sound, ethical subjective decisions?
Guests and Hosts
- Alf Rehn, Professor of Innovation, Design and Management at the University of Southern Denmark. Find Alf on Twitter as @AlfRehn or at AlfRehn.com
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 2/23/2021 Tags: @SFoskett, @AndyThurai, @AlfRehn
Productive use of AI requires the application of existing models to new applications through a process called transfer learning. In this episode, High-Performance Computing and AI Expert Frederic van Haren joins Stephen Foskett to discuss the topic of transfer learning and what it means, from voice recognition to autonomous driving and enterprise applications. Transfer learning is analogous to the way teachers impart knowledge and experience to their students, and represents a feedback loop that improves the model over time. This is a valuable concept for applications like language processing but requires a feedback mechanism or it is something of a dead end. One challenge for machine learning is that models do not truly understand the world the way people do, but they can fool us into thinking that they do because of their uncanny ability to match patterns the way we would. Over time, we all must develop a better understanding of this technology even as it is being widely deployed around us.
Guests and Hosts
- Frederic Van Haren, CTO and Founder of HighFens. Find Frederic on Twitter as FredericVHaren.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 2/16/2021 Tags: @SFoskett, @FredericVHaren
BrainChip is developing a novel ultra low power “neuromorphic” AI processor that can be embedded in literally any electronic device, rather than centralizing learning in high performance processors. Today’s edge devices are applying exiting models to process inputs but can’t actually learn in the field, but on-chip learning and inference could radically alter the capabilities of devices in automotive, home, medical, and other remote locations. BrainChip is able to reduce power thanks to the neuromorphic self-learning approach and also because they reduce precision down to 4 bits or less. This loses some accuracy, but only a little. The company also creates a mesh of cores that have access to local memory, enabling flexibility of processing.
Guests and Hosts
- Lou DiNardo, President and CEO of BrainChip. Connect with Lou on LinkedIn
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 2/9/2021 Tags: @SFoskett, @AndyThurai, @BrainChip_Inc
AI is impacting IT operations more quickly than expected, and companies like Splunk are leveraging it to augment staff capabilities. Josh Atwell joins Andy Thurai and Stephen Foskett to discuss practical application of AI to help keep IT operations from drowning in data as applications are distributed in containers and the cloud. The key to using AI for operations is to leverage it to assist staff to process the volume and velocity of data, not replace them.
Guests and Hosts
- Josh Atwell is Senior Technology Advocate at Splunk. Find Josh on Twitter as @Josh_Atwell and learn more about Splunk at Splunk.com
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 2/2/2021 Tags: @SFoskett, @AndyThurai, @Josh_Atwell, @Splunk
Ken Grohe of Weka discusses various business use cases for AI-enabled applications with Chris Grundemann and Stephen Foskett. AI is coming into practical use right now in applications like autonomous vehicles, drug development and healthcare, and retail. High-performance scalable storage is necessary for many ML training applications, and can be key to advanced applications in life sciences and others with massive data sets. The Chief Data Officer, and data scientists in general, are the future of the business, and AI is enabling the growth of this field.
Guests and Hosts
- Ken Grohe, President and Chief Revenue Officer of Weka. Find Ken on LinkedIn and Twitter as @LeverageGTM and learn more about Weka at Weka.IO
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 1/26/2021 Tags: @SFoskett, @ChrisGrundemann, @LeverageGTM, @WekaIO
Per Nyberg of Stradigi AI discusses "blue collar" AI applications with Stephen Foskett. What problems can businesses solve with AI technology? Machine learning can find anomalies and outliers in manufacturing and finance, look for relationships in data, and cutting through the complexity of multi-disciplinary data. Consider customer churn: Machine learning can discover features in profiles that might not be visible even to an expert. Data scientists and AI experts must learn to present AI technology to average business people in terms they can understand, and this has lead to a "haves/have nots" situation where some companies or business units don't have access to this technology. We also need to reduce the science fiction appeal of AI and express what it can't do.
Guests and Hosts
- Per Nyberg is Chief Commercial Officer for Stradigi AI. Find Per on LinkedIn to continue the conversation and connect on Twitter as @_PerNyberg or @StradigiAI
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 1/19/2021 Tags: @SFoskett, @_PerNyberg, @StradigiAI
There are many “last mile” items on the enterprise checklist, and companies are struggling to connect everything together. In this episode, Monte Zweben, CEO of Splice Machine, discusses feature stores with Andy Thurai and Stephen Foskett. Data engineers maintain data pipelines, data scientists maintain the data store, and machine learning engineers are trying to create models and package them so they will be useful. One idea is to store a model in a relational database, store records in a feature table, and enable the database to trigger a model based on this data. That’s what Splice Machines is implementing - in-database ML deployment. SQL is making a comeback in ML, with scale-out solutions providing a more familiar and usable environment than leading noSQL databases. Monte believes that SQL will be the dominant data paradigm for machine learning, modeling, experimentation, and deployment. After all, SQL is the dominant language of enterprise data scientists.
Guests and Hosts
- Monte Zweben is CEO of Splice Machine. Find Monte on Twitter as @MZweben and Splice Machine as @SpliceMachine.
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 1/12/2021 Tags: @SFoskett, @AndyThurai, @MZweben, @SpliceMachine
AI will be part of everything we do in the future, not replacing us but augmenting our work, and this is especially true in information security. In this, the first episode of Season 2 of Utilizing AI, Steve Salinas joins Chris Grundemann and Stephen Foskett to discuss AI as a “co-pilot.” Enterprise security saw an explosion of threats in the last decade, outstripping the ability of information security professionals to identify and prevent intrusions. The goal of enterprise AI in security is to help identify threats both known and unknown through deep learning as well as simpler pattern-matching machine learning. Of course, if AI is a co-pilot inside the company it will also be used by intruders, and adversarial machine learning is rising. The industry needs to be ready for anything! We finish the episode with a new feature: Three questions about the future of AI!
Guests and Hosts
- Steve Salinas is an Information Security Product Marketing Professional. You can find Steve Twitter as @So_Cal_Aggie or on LinkedIn as StevetheMarketingGuy
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 1/5/2021 Tags: @SFoskett, @ChrisGrundemann, @So_Cal_Aggie
Just as data analytics transformed business intelligence so is artificial intelligence transforming data science. In this episode, Mel Greer of Intel joins Chris Grundemann and Stephen Foskett to discuss this transformation, which is impacting business of all sorts including Intel itself. Intel's strategy has evolved, and their hardware platforms are following, with the company developing hardware and software to serve AI-driven data analytics. The conversation then turns to the challenges of implementing unbiased AI, from explainable AI to diversity of data and thought within businesses.
Hosts and Guests
Mel Greer, Chief Data Scientist, Americas, at Intel. Connect with Mel on LinkedIn.
Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann
Date: 12/22/2020 Tags: @SFoskett, @ChrisGrundemann, @IntelAI, @Intel
In this episode, we ask Red Hat about the platform requirements for AI applications in production. What makes AI applications special and how does this change the infrastructure required to support these? The demand for flexibility, scalability, and distribution seems to match the capabilities of a hybrid cloud, and this is emerging as the preferred model for AI infrastructure. Red Hat is supporting the container-centric hybrid cloud with OpenShift, and containers are also critical to AI workloads. Red Hat has production customers in healthcare, manufacturing, and financial industries deploying ML workloads in production right now.
Episode Hosts and Guests
- Abhinav Joshi, Senior Manager, Product Marketing, OpenShift Business Unit, Red Hat. Find Abhinav on Twitter at @Abhinav_Joshi.
- Tushar Katarki, Senior Manager, Product Management, OpenShift Business Unit, Red Hat. Find Tushar on Twitter at @TKatarki.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann
Date: 12/15/2020 Tags: @SFoskett, @ChrisGrundemann, @Abhinav_Joshi, @TKatarki, @RedHat, @OpenShift
In this episode, Stephen Foskett and Chris Grundemann discuss the impact of AI on the future of work. How will our everyday lives be transformed by the widespread application of AI? What about the datacenter? Do AI-enabled network management tools mean we lose jobs? We already rely on AI-enabled tools, from Siri to Marvis, and maybe this is the template for the future of work with AI. Not everyone needs to be a data scientist or programmer, we just need to see AI as a co-worker.
Episode Hosts and Guests
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.
Date: 12/8/2020 Tags: @SFoskett, @ChrisGrundemann
We begin by taking a look at the world of data and analytics in the enterprise. Data architects like Yves have been involved in enterprise IT applications for decades, but the world really took off with the advent of data warehouses and the field of data science. Now AI and ML are impacting the field in many ways, and we discuss how this world has changed. Data scientists come from a statistics background, while modelers come from software engineering. How do the tools interact and intersect? What is Yves excited about and what frightens him? How does the infrastructure support all this? We finish with a look at what the future looks like: We will see a lot of evolution in science and medicine for data and ML, and this technology will be found everywhere in the datacenter and the cloud.
Key questions covered include the following:
- How did the world of data and analytics evolve in the enterprise?
- How has AI/ML impacted the job of the data scientist?
- How can the data modelers, software developers, and IT Ops work together?
Episode Hosts and Guests
- Yves Mulkers, Data & Analytics Strategist. Find Yves on Twitter at @YvesMulkers.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
Date: 12/1/2020 Tags: @SFoskett, @YvesMulkers
AI, machine learning, and neural networks are not new ideas. So what changed now? Over the last 5-6 years, advances in software, hardware, and scale have brought machine learning to the forefront, enabling new products and technologies. In this episode, Bob Friday, CTO of Mist, a Juniper Company, discusses the changes that have enabled his company and others to bring AI to the enterprise. We focus on four key questions:
- When did you start to see that AI was going to be a major force in the enterprise networking world?
- How have Google and other companies enabled AI through advances in open source software?
- How about hardware? There have been many advances over the last decade, from GP-GPUs to faster and lower-latency networking in storage. How has this enabled AI?
- How are large neural networks enabling AI in the enterprise?
Episode Hosts and Guests
- Bob Friday, CTO of Mist, a Juniper Company. Find bob on Twitter at @WirelessBob.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai.
Date: 11/24/2020 Tags: @SFoskett, @AndyThurai, @WirelessBob, @JuniperNetworks
Matt Bryson of Wedbush securities joins Stephen Foskett for a discussion of AI hardware companies, focusing on the biggest player, Nvidia. Stephen and Matt start with a look at Nvidia: Just how big is Nvidia in the enterprise AI market? Then we turn to other major player in this space, Intel, which is strong in the inference market with their Xeon processors but obviously wants a bigger piece of the special-purpose processor market. AMD has had success in the cloud but doesn’t seem focused on the AI space. Then we look at the world of AI hardware startups. How will they compete with Nvidia, Intel, and AMD, when they just don’t have the same resources? Companies like BrainChip and Cerebras are trying to be more efficient and go after the gaps in the market rather than compete directly with Nvidia. Then there’s the crossover between AI and HPC, which is an opportunity for AMD, Tachyum, and others. We also see an opportunity for AI at the edge, which brings to mind companies like Apple and Huawei who are adding AI processing to chips used in client systems. We also need to consider companies like Amazon and Google that are creating their own AI solutions and Microsoft using GraphCore. But does AI live in the cloud or will next-generation hardware platforms like Liqid be more compelling? Finally we turn to the pending acquisition of Arm by Nvidia, and what that means if it goes through and if it doesn’t.
Episode Hosts and Guests
- Matt Bryson, Senior Vice President at Wedbush Securities, can be found on LinkedIn at LinkedIn.com/in/Matt-Bryson-3105071/
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 11/17/2020 Tags: @SFoskett, @Nvidia, @Intel, @AMD, @BrainChip_Inc, @CerebrasSystems, @Tachyum
Most enterprise IT projects fail, and it has been this way for decades, and this discussion with Roey Mechrez of BeyondMinds considers why this is the case. One of the primary reasons is the trade-off between building custom solutions and buying off the shelf products. This is doubly different with AI since the success of a model depends on the data and training, not to mention the maintenance and updates needed as issues arise. Data science teams need to invest significant time and model into infrastructure, rather than just jumping in to train the model. This is a similar challenge to DevOps, but the added dimension of models and data makes MLOps even more challenging.
Episode Hosts and Guests
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Andy Thurai, technology influencer and thought leader. Find Andy's content at theFieldCTO.com and on Twitter at @AndyThurai
- Roey Mechrez, CTO at BeyondMinds. Find Roey on Twitter at @BeyondMindsAI
Date: 11/10/2020 Tags: @SFoskett, @AndyThurai, @BeyondMindsAI
David Aponte and Demetrios Brinkmann discuss the future of Kubeflow with Stephen Foskett and Andy Thurai. Kubeflow is getting a lot of attention, but contributions and community seem to be lagging. Is this really the future of machine learning or just another dead-end open source project? Will product vendors pile on top, redirect the project, or kill development? The open source community tends to gravitate to quality projects, and it will develop Kubeflow (or not) based on the usefulness of the solution.
Episode Hosts and Guests
- David Aponte, Machine Learning Engineer. Find David online at LinkedIn.com/in/AponteAnalytics and on Twitter at @AponteAnalytics
- Demetrios Brinkmann, Community Coordinator for @MLOpsCommunity. Find Demetrios online at LinkedIn.com/in/DPBrinkm and on Twitter at @DPBrinkm
- Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
Date: 11/3/2020 Tags: @SFoskett, @AndyThurai, @AponteAnalytics, @DPBrinkm, @MLOpsCommunity
Stephen Foskett and Andy Thurai discuss the parallels between DevOps and MLOps with Gaetan Castelein of Tecton. We are in the middle of a shift in analytics and software engineering, with DevOps and continuous deployment, and this is colliding with the development of data analytics and big data. Machine Learning allows organizations to handle this explosion of data and build new applications and automate new business processes, but MLOps must be converged with big data and DevOps tooling to make this a reality. One key enabler of this transformation is the creation of an ML feature store, which stores curated features for machine learning pipelines. Feature stores typically enable users to build features, have standardized feature definitions, run models using these curated features, and manage MLOps.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Andy Thurai, technology influencer and thought leader. Find Andy's content at theFieldCTO.com and on Twitter at @AndyThurai
- Gaetan Castelein, VP of Marketing at Tecton (@TectonAI). Find Gaetan on Twitter at @GaetCast
Date: 10/27/2020 Tags: @SFoskett, @AndyThurai, @GaetCast, @TectonAI
Stephen Foskett discusses the practicalities involved in packaging, deploying, and operating AI models with Manasi Vartak of Verta. Deploying an AI model in production is a challenge, just like it was in the past with software. Once a company has an AI model to deploy, they must validate its results, create scaffolding code to make it consumable, optimize the data pipelines, instrument it, and assign operators. This is what Manasi and Verta have developed, and the world of AIOps parallels that of DevOps but with some unique twists. The data component of AI models presents a unique challenge not found in some other enterprise applications, and it is important to continually test the model to ensure that it hasn't drifted off target as data changes. Previously, training models was the main challenge for AI, but now it's all about getting things into production. That's why we started this podcast and why we created AI Field Day!
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Manasi Vartak, CEO and Founder of Verta (@VertaAI). Find Manasi on Twitter at @DataCereal
Date: 10/20/2020 Tags: @SFoskett, @DataCereal, @VertaAI
Stephen Foskett and Andy Thurai are joined by Chris Grundemann to discuss how AI is used in enterprise networking, and how it is changing the industry. They begin with a discussion of AI in enterprise networking, connecting it with software-defined networking and other trends. What network management tasks can be improved through the use of AI? Grundemann looks to using the technology in root cause analysis and fault correlation as well as prediction of network events. The discussion then turns to the ways that AI workloads will change the workload or demand on networking. AI systems demand data, throughput, and low latency, and the networking must adapt to support these workloads.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Andy Thurai, technology influencer and thought leader. Find Andy's content at theFieldCTO.com and on Twitter at @AndyThurai
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann
Date: 10/13/2020 Tags: @SFoskett, @AndyThurai, ChrisGrundemann
Stephen Foskett is joined by Josh Fidel, a technologist and futurist who has been inspired by applications for AI. Starting with a discussion of GPT-3, the AI text generation engine, the discussion ranges widely from AI Weirdness to James Yu's Singular to technological determinism to the Melbourne Monolith. Applications of AI are everywhere today, and Fidel's background as an enterprise technologist and futurist gives him a unique perspective. AI is generating compelling content, and we all must be ready to absorb and understand it. How will businesses use machine-generated text? It is likely that we will have to push back on too-broad uses of AI technology in the interest of truth and usefulness, rather than simply applying AI to every task at hand.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Josh Fidel, futurist and technical architect. Find Josh on Twitter at @JCEFidel.
Date: 10/06/2020 Tags: @SFoskett, @JCEFidel
Stephen Foskett is joined by Karen Lopez, an expert and speaker on data management, data quality, and data analysis. Karen focuses on the quality of the data underlying AI systems and the ethics of using this data. She discusses concerns about data reuse, consent for use, and how changes of data cat impact the outcome of models. We also consider the impact of pervasive data collection, and how this flood of data can impact the outcome of AI models. We finish with a discussion of outliers and missing data, and how this can affect the integrity of artificial intelligence applications.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Karen Lopez, Senior Project Manager and Architect at InfoAdvisors. Find Karen's writing at DataModel.com and on Twitter at @Datachick
Date: 09/29/2020 Tags: @SFoskett, @Datachick
Stephen Foskett is joined by Gina Rosenthal, an expert on enterprise IT infrastructure and operations. Gina has made her career in enterprise IT infrastructure and has worked with many of the largest vendors. In this episode, she considers how vendors approach artificial intelligence, what applications they are delivering, and what this means in the enterprise. The conversation turns to ethics and risks of AI applications and how business should approach building AI models. As AI applications are deployed in the line of business, IT infrastructure organizations need to be prepared to handle the demands of these systems with next-generation cloud platforms.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Gina Rosenthal, Founder of Digital Sunshine Solutions. Find Gina on Twitter at @GMinks
Date: 09/22/2020 Tags: @SFoskett, @GMinks
Stephen Foskett is joined by Ray Lucchesi, an expert on enterprise IT infrastructure and operations. Ray has seen many technologies come and go, but he's impressed by AI. Why does he think it's more reality than hype and how does he think it will affect the datacenter going forward? How are product vendors using AI and ML technology today, from storage to security to systems management? What will AI mean to datacenter infrastructure and the future of CPU and GPU hardware?
Stephen Foskett can be found at GestaltIT.com and on Twitter @SFoskett. Ray Lucchesi can be found online at SilvertonConsulting.com and on Twitter @RayLucchesi.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Ray Lucchesi, President of Silverton Consulting. Find Ray on Twitter at @RayLucchesi
Date: 09/15/2020
Tags: @SFoskett, @RayLucchesi
Stephen Foskett discusses practical aspects of enterprise AI with David Aponte and Demetrios Brinkmann. AI offers promise to help IT operations departments deal with the flood of data, since ML is so good at finding needles in haystacks. But is it true or just vendor hype? Are current vendors able to work in this space or do we need a new kind of product or vendor to develop AI models? Does the new data demand a different type of infrastructure? And how are AIOps related to DevOps?
Find Stephen online at GestaltIT.com and on Twitter at @SFoskett. Find David Aponte online at LinkedIn.com/in/AponteAnalytics. Find Demetrios Brinkmann online at LinkedIn.com/in/DPBrinkm and on Twitter at @MLOpsCommunity
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- David Aponte, Machine Learning Engineer. Find David online at LinkedIn.com/in/AponteAnalytics
- Demetrios Brinkmann, Community Coordinator. Find Demetrios online at LinkedIn.com/in/DPBrinkm and on Twitter at @MLOpsCommunity
Date: 09/08/2020
Tags: @SFoskett, @MLOpsCommunity, DavidAponte, DemetriosBrinkmann
Stephen Foskett and Andy Thurai discuss the ethics and morality of AI. Taking a cue from an article written by Andy for AI Trends, we focus on the various biases that can influence AI, and how to prevent these from interfering with the results. Andy recommends modeling knowing that biases would come into the input data, recognizing limitations in technology and data, teaching human values and validating AI models, and setting an ethical tone in the organization creating the AI model. Like people, AI models can only be as unbiased as their environment and training, and it is critical to recognize these limits when deploying them.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Andy Thurai, technology influencer and thought leader. Find Andy's content at theFieldCTO.com and on Twitter at @AndyThurai
Date: 09/01/2020
Tags: @SFoskett, @AndyThurai
In this pilot episode of Utilizing AI, Stephen Foskett and Andy Thurai discuss the reason for the podcast and consider where we go from here.
AI is getting real, moving out of academia and hyperscale and into the enterprise. Businesses are adopting AI in strategically, and IT companies are deploying AI technologies in their products. This trend is quite obvious to Stephen at Gestalt IT, as many Tech Field Day companies present AI-enabled products for network monitoring and management, security, mobility, and much more. Today's infrastructure applications are focused on applying machine learning to large datasets, finding needles in haystacks. But tomorrow will see much more exciting applications.
This episode features:
- Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett
- Andy Thurai, technology influencer and thought leader. Find Andy's content at theFieldCTO.com and on Twitter at @AndyThurai
Date: 08/28/2020
Tags: @SFoskett, @AndyThurai