Productive AI Podcast: Recent Episodes

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In this episode, I speak with Daniel Jeffries. Daniel is a science-fiction author, engineer, futurist, thinker, blogger, systems architect, speaker, crypto nerd, AI evangelist, world traveler, beard-master, and overall renaissance man.

Today we talk about a variety of topics including: why billionaires going to space is good; why how to make better predictions; how COVID will have long-term positive consequences for society; where we are in the long arc of AI; how the model development lifecycle supports and does not replace the software development lifecycle; where we are in terms of understanding MLOps; choosing between end-to-end and best-of-breed ML tools and platforms; what the AI Infrastructure Alliance is and how it’s helping shape the future of ML Platforms; and what to think about when deploying AI/ML in your organization. It’s a long and great conversation. Enjoy the ride!

Timing

00:00 Introduction

02:15 Why billionaires going to space is a good thing
04:13 Dan’s thoughts on the Foundation series
05:55 Predictions - good and bad - that you’ve made
10:19 Thoughts on Kai-Fu Lee’s “2041”
12:40 COVID’s long-term impacts on our society
21:06 Where are we now in the arc of AI?
27:12 This is still the early adopter phase
29:42 Is AI really eating all software?
31:36 The model development lifecycle vs. the software development lifecycle
33:07 MLOps is still evolving as a term and as a practice
36:07 MLOps is not just DevOps brought forward
39:15 ML Platforms: End to end or best of breed components? (Or a blend?)
40:34 The only end to end solution that exists is in the minds of marketers
44:38 There is no LAMP stack for machine learning...yet
47:54 What is the AI Infrastructure Alliance
57:13 Blueprints and design patterns - making sense of the ML platform and tools space
1:05:35 Platform rationalization and maturation is coming but it’s not here yet
1:07:30 How does a customer buy from members of the AIIA?
1:11:45 Education is critical to long-term success
1:17:15 As always, finding the right tool for the job is important
1:21:45 There are two kinds of machine learning: basic and revolutionary
1:24:35 Wrap-up

Links

Get in touch with Dan:

LinkedIn https://www.linkedin.com/in/danjeffries/
Medium https://medium.com/@dan.jeffries
Twitter https://twitter.com/Dan_Jeffries1
Amazon Author Page: https://www.amazon.com/Daniel-Jeffries/e/B00D1HG62U%3Fref=dbs_a_mng_rwt_scns_share
Patreon Page: https://www.patreon.com/danjeffries

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In this episode, I talk with Will Uppington, CEO of TruEra about how trust is a critical output of machine learning and AI systems and AI quality management must be baked into development in order to build trust-worthy systems. We delved into the five core dimensions of model quality, the importance of iterating data and models in parallel, the state of the model development platforms and tools market, the regulatory environment around trusted AI, and ended with some career advice for people entering the field.

-- Links --

https://www.truera.com

https://twimlcon.com

Andrew Ng - From Model-centric to Data-centric AI: https://www.youtube.com/watch?v=06-AZXmwHjo

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In this discussion with Colin Toal, CTO of Chisel.ai, we uncover the challenges being faced by this 5000 year old industry. We walk through the structure and practices of the commercial insurance sector, discuss why it works the way it does, and how AI is now helping to transform it one step at a time. We unpack the differences between basic insurance which is standardized vs. commercial insurance where every policy is a unique snowflake and how that poses challenges for automation and interconnection. We dig into the historical value of automation, and then we touch on the never-ending debate of build vs. buy. Finally, we closed the conversation with some advice to aspiring AI and machine learning engineers looking to build a career.

-- Timing –

00:00 Introduction

01:03 Disruption in the insurance industry

06:22 How AI is modernizing a 5000 year old industry

11:09 When non-standardization and flexibility are features and not bugs

13:27 Automation is an effort-reduction mechanism

16:55 Colin’s winding road to Chisel

19:15 Colin’s time at Amazon learning how to do machine learning at scale

22:11 Bringing it all together - using technology to crack a hard industry problem

28:03 Reduce your customer’s effort now and you’ll get to learn more about their business and help them even more in the future

29:30 Why hasn’t this particular industry problem been solved with EDI or web services?

37:04 Machines trained by humans are basically as good as humans on their best day...but every single day and 24x7

39:35 Once you’ve reduce the effort, you’ve also built a channel and you have more data than anybody else and you can expand your business from there

45:56 The tech stack: Build, buy, borrow, or open source?

53:08 Colin’s advice to aspiring AI and machine learning engineers

-- Links --

https://www.chisel.ai

https://www.chisel.ai/work-at-chisel

https://www.linkedin.com/company/chiselai/

https://twitter.com/chiselai

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Hear how the world’s best companies use Turing.com to hire the world’s best engineers. Listen to Vijay Krishnan, CTO and Co-Founder of Turing discuss how they match the best companies with the best global engineering talent using a combination of deep domain expertise and machine learning. Vijay also shares his lessons to founders and entrepreneurs as well as to aspiring machine learning and AI engineers.

-- TIMING –

00:00 Introduction

01:31 Vijay’s Machine Learning career

05:16 Moving into an Executive in Residence (EIR) role to find the next big idea

11:24 Advice to founders – Three important lessons

15:12 The world was moving to remotely distributed teams before COVID

17:04 If you know your idea is right, ignore the doubters (including the VCs)

19:18 You have to be 10x better than anybody else

19:54 Finding and narrowing in on the biggest, best idea

23:50 What does Turing do and what is the value prop for customers and engineers?

27:36 Never interview for another job again. Oh and work from your beach-house for Silicon valley companies.

28:59 Why is it so hard for companies to hire top quality remote engineers without a platform like Turing?

36:14 Team building is always hard. How do you make these remote teams work for the engineers and the people who hire them?

38:14 Process and practices help build functional remote teams

43:58 Why Turing insists on having good English skills as a baseline (spoiler alert: real-time machine language translation is not here yet.)

46:07 What geographies does Turing serve in terms of customers?

47:02 And what about the engineers and software developers?

49:19 Why focus only on engineers? Why not any of the other surrounding roles?

52:15 Stay hyper-focused so that you can be 20x-30x better than the competition

52:58 Does Turing also work with machine learning and data science engineers?

54:12 What is Turing’s business model?

58:07 Where does Turing USE machine learning?

01:04:00 Raising another round of financing

01:06:30 What is Turing hiring for these days in terms of roles?

01:07:21 Senior engineering talent is in really short supply

01:09:35 Advice to founders: Nail the market – make sure it’s big

01:12:09 Advice to engineers: Learn the foundations but also learn the business

01:17:00 Wrapping up!

01:18:47 Signing off

-- LINKS --

Website: http://turing.com

Hire Top Remote Developers: http://turing.com/hire-engineers

Apply to Top US and Silicon Valley Remote Developer Jobs: http://turing.com/jobs

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How can an AI understand language? Computer-human communication is undergoing a revolution and AI can now listen to, understand, and speak back to us in much more powerful ways than it could before. On this episode, hear Scott Leishman discuss how AI can now write news articles, blog posts, poetry, and novels and how work done in the recent past is making it easier than ever to build incredibly powerful AI applications that can communicate with human beings.

-- TIMING –

00:00 Introduction

00:48 Scott’s background in computer science at FICO, Core Logic, and Nirvana Systems (which exited to Intel for $400M in 2016), and Intel

06:56 What is Natural Language Processing (NLP)?

11:40 What was the significance of GPT-3’s release this year?

16:31 What can GPT-3 do? (explain it to somebody who doesn’t follow the field).

19:15 NLP is having its “ImageNet moment” – what does that mean? (Technical explanation)

25:39 Simplifying NLP for less-technical listeners

28:17 Standing on the shoulders of giants: Pre-trained models are making it easier to build AI applications

30:05 What kinds of new uses cases are possible with the current state of the art NLP?

33:29 Apple Knowledge Navigator – are we there yet?

37:25 Where does NLP live in the AI stack?

41:34 What are you doing with NLP at XOKind?

49:47 What should people be doing to improve their chances of working in this space?

54:05 Summary

-- LINKS --

Books:

Manning & Jurafsky is sort of the best known, comprehensive but is a bit dated at this point. Fortunately they are working on a new draft: https://web.stanford.edu/~jurafsky/slp3/

Conferences: the big ones for NLP are ACL, EMNLP (was just last week), CoNLL, but you’ll also see a lot of new work at ICLR and NeurIPS Papers.

The field moves quick but arXiv is the first place to find new results. I’d highly recommend searching through something like arxiv-sanity instead for a subject/topic of interest.

Mailing lists: I’m a big fan of Sebastian Reuder’s monthly update, you can sign up for at NLP news https://ruder.io/nlp-news/

Sites:

I mentioned https://nlpprogress.com/ to keep tabs on current state of the art for given downstream tasks

For folks that want a good practical introduction I’d recommend Stanford’s undergraduate NLP course (complete with video lectures online): http://web.stanford.edu/class/cs224n/

Getting interested in ML in general, this course is pretty good too if you have some programming experience under your belt: https://course.fast.ai/

Hugging Face are doing a lot of great work in the NLP space, they have easy integrations for various models, a solid python library etc.

Rasa are another open source solution, they now have APIs too for helping build conversation agents

XOKind!

Sign up for our mailing list on the front page here: https://www.xokind.com/ Job openings. List is here: https://www.xokind.com/careers/ (scroll down the page). Growing Frontend and Backend engineering is a current focus for us.

Apple Knowledge Navigator Video: https://www.youtube.com/watch?v=HGYFEI6uLy0

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Listen in to my conversation with Kevin Tu from DFJ Growth while we discuss the next ten years of the AI market, the difference between AI-enabled companies and AI-first companies, characteristics of well-funded startups, the structure of the AI ecosystem, whether AI businesses are really different from a business model perspective, when to build vs. buy AI infrastructure, and finally, some advice to entrepreneurs building AI focused companies.

-- Timing –

00:00 Introduction

01:35 Kevin’s background from engineering to finance and then venture capital

05:38 The next ten years of the A.I. market

09:31 AI-enabled vs. AI-first companies

14:23 What is DFJ Growth looking for and what are a couple of examples of recent funding? (Neocis and DataRobot)

21:06 What are some characteristics of companies that will have a higher chance of success?

23:23 Structure of the ecosystem – layers of the stack

28:20 Are the business models of AI companies really that different?

29:50 In the early days you do the unscalable work, and then optimize and scale later

31:56 Should startups roll their own infrastructure or leverage existing cloud infrastructure?

35:05 Advice to builders and entrepreneurs

38:21 When should a startup reach out to you or your team?

40:28 Contact information

41:09 Wrap-up

-- Links --

https://www.dfjgrowth.com

https://www.linkedin.com/in/kevinbenjamintu/

https://twitter.com/kevbtu

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It is now possible to radically grow your business by improving the hand-off between marketing, sales, customer success, and even finance through the use of advanced intelligent virtual assistants. In this podcast, we’ll talk to Jim Kaskade, CEO of Conversica about how they’re helping their 2000 global customers speed up communications, improve customer satisfaction, get better lead coverage, qualify prospects more effectively, and increase deal close rates (up to 400% improvement!)

-- Timing –

00:00 Introduction

01:21 Sales and marketing have been traditionally misaligned and out of sync

03:00 Unpacking the marketing to sales process

03:20 Marketing often has a low return on investment and a lot of waste

04:19 Lead nurturing might need two follow-ups but probably more likely needs ten or maybe twenty to be successful

05:14 Sales teams are drowning in in-bound “marketing qualified leads”

07:15 There are over 8,000 marketing apps and many sales apps, what’s the gap here?

09:05 Digital transformation and marketing automation has caused a volume problem that the human sales reps can’t handle

10:46 Sales teams now have a filtering and prioritizing problem

11:56 If you haven’t automated marketing, you don’t have these problems yet!

13:55 Automating the back office is about cost optimization, optimizing the front office (sales, marketing, customer experience) is about increasing revenue

14:20 Solving this overload and filtering problem with AI

15:06 From Clippy the paperclip to Intelligent virtual assistants, it has been a long road

17:07 Moving way beyond the initial IVA use cases of technical support and into marketing and sales

18:30 You could have a conversation with an IVA for weeks or months until you’re ready to buy

20:43 Deep learning has significantly changed the game

22:04 Applying and productizing AI to solve a problem…is harder than solving the AI at the core of the product

23:46 Where do Amazon, Microsoft, and Google fit into the picture in terms of providing Natural Language Processing engines?

25:07 How does a customer operationalize something like this? How do they install and use it?

26:01 Platforms are too hard for many customers so we deliver this as an application

27:30 We sell in a way that’s understandable and budgetable – by the “virtual assistant” – assigned to a departmental budget

29:24 You have actual working inside marketing reps, SDRs, and inside sales agents?

30:21 Are these IVAs replacing people? Or augmenting them?

32:04 What if every human member of their team had an assistant who could help move business along?

34:00 Can these assistants replace field sales people or people working high-touch, complex, multi-buyer deals?

35:29 What it’s like when you have virtual team members and how they can hand-off customers to other virtual assistants or to humans

39:30 This all sounds like science fiction

41:00 Use cases – educational course selection, technology conference attendance

42:50 The value of true intelligent virtual assistants – increased close rates (up to 4x improvement); reconnecting with customers who are dropping off usage (CX); 176x pipeline growth; 10x revenue; higher customer retention rates; faster cashflow from receivables and more

45:35 On the internet nobody knows you’re an intelligent virtual assistant – the ethics of disclosure

49:18 98% of the time, people think they’re talking to a real human

50:03 The present and future markets of intelligent automation, robotic process automation, chatbots, conversational AI, and intelligent virtual assistants

53:58 Where is Conversica focused?

57:34 Wrap up and contact info

59:01 Sign-off

-- Links --

https://conversica.com

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It is possible to use AI based speech in many business situations. We’re entering a new era where computers are so good at communicating with us via both spoken word and text conversations that they can perform tasks traditionally handled by call centers. In this conversation with the CEO of Inference Solutions, you’ll learn about the long arc of Natural Language Processing, Conversational AI, and the rise of the Intelligent Virtual Agent Market. We also discuss AI product management and positioning and pricing that will be helpful for anybody building a complex AI-based product or service. Finally we close out with some advice to buyers who are trying to make sense of a noisy marketplace.

NOTE: Since the recording of this podcast, Inference was acquired by Five9 (www.five9.com).

Timing:

00:00 Introduction
01:03 Callan’s career background
02:44 How Inference is leading their segment (and being recognized for it)
03:45 Intelligent Virtual agents vs. business process outsourcing (BPO)
05:16 The importance of channel partners to access the market
06:22 How does the platform work?
07:50 Where did the Conversational AI industry come from?
12:11 What is the current state of the technology?
14:38 Interesting uses for your technology
18:10 The development of technology and maturity curves in NLP, text to speech, speech recognition, etc.
19:06 NLP engines got commoditized
20:34 AI Product Management 101 – don’t get lost in the AI tech, focus on the overall business problem and solve for that
22:39 Summary of the business model and offerings
23:14 The Intelligent Virtual Agent market category has become a real market segment
24:51 How many channels can your system communicate on? (Voice, text, web-chat, message services, WhatsApp, etc.)
27:36 How are you different from other players in this market segment?
31:18 Pricing innovation is still innovation – pricing simplicity helps drive business
35:34 Where can you best apply this kind of technology – to achieve what objectives and to do what jobs?
36:58 What will the next few years bring?
39:30 This market is noisy – there are 2000+ “chatbot companies” – but if you need something multi-channel (voice + text), it drops to 20
42:46 Callan’s advice to buyers in the market
44:53 Connecting with Callan and the team at Inference
46:07 Wrap-up!

Links:

https://www.inferencesolutions.com

callan.schebella@inferencesolutions.com

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Marketers today are drowning in data and have little insight. Too many dashboards from too many channels, all with different user interfaces and data schemas, means that making sense of it all takes too much time, and often doesn’t lead to clear insights such as “this channel doesn’t work and we should stop spending on it”. To make matters worse, each platform’s analytics are siloed and their goal is to increase, not decrease, your spend on that platform. GlanceHQ.ai was formed by a team of marketing agency experts to solve this problem Hear Roy Nallapeta discuss the state of the industry, why marketing software needs to be more like a Tesla, and how leveraging artificial intelligence and machine learning can help marketers make better and more effective allocation decisions in much less time.

To see or hear more episodes:

• Sign up on our site at https://productiveai.com/signup/ to be notified of future episodes.
• Subscribe on Youtube.
• Subscribe to the Productive AI podcast at Apple, Google, Spotify, ListenNotes, or Radio.com.

Timing:

00:00 Introduction
01:29 Roy’s background and career
02:38 About GlanceHQ.ai
03:30 The Marketing Automation and software market
04:42 The Big AHA moment – every marketer has the same questions, too many dashboards, and no answers
06:30 Making channel allocation investment decisions is brutal for marketers
08:39 If your car can drive across town, your marketing app should be able to run itself too
09:15 How does all this magic work?
13:21 What’s it like to live with this kind of toolset compared to how it’s done today?
18:01 What if your intelligent marketing co-pilot could identify risk and predict campaign success?
20:06 What if a marketer could save 30% of their time and avoid cost misallocation of funds to the wrong channels?
25:55 Who’s the competition? Doing nothing and using too many dashboards
27:25 What’s your business model for Glance and who are your customers?
30:28 Connecting with the GlanceHQ.ai team
31:25 Closing comments

Links:

https://glancehq.ai
https://www.linkedin.com/in/rohitnallapeta/
https://medium.com/@katyasakovich/startups-for-digital-marketing-from-disrupt-sf18-expo-94beee15cf02

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Self-driving cars must get better at understanding people’s intentions by “reading” the body language of the humans around them, so that they can co-exist more safely with us. In this amazing discussion with the Co-founder of Perceptive Automata, we will learn how a branch of cognitive science known as psychophysics is being used to teach cars about the intentions of the humans around them so that they can be better and safer drivers.

To see or hear more episodes:

• Sign up on our site at https://productiveai.com/signup/ to be notified of future episodes.
• Subscribe on Youtube.
• Subscribe to the Productive AI podcast at Apple, Google, Spotify, ListenNotes, or Radio.com.

-- TIMING --

00:00 Introduction

01:12 Sam’s career

05:04 Perceptive Au-TAW-mah-ta, not Au-to-MAH-ta

05:52 How are you teaching cars to understand human intention?

11:25 Structure of the autonomous vehicle market and the 5 Levels of autonomous driving

17:06 The Autonomous Vehicle technology stack

23:25 Use case discussion – how does an intuitive car understand multiple scenarios?

31:25 Cars will have general purpose compute platforms

33:49 Where does Tesla fit in here?

38:14 Typical customers for Perceptive Automata’s tools

41:09 Can intuition extend to other non-vehicle environments such as hospitality, delivery robots, and construction

42:34 What about the military applications?

46:52 Summary of market and global need

48:44 How much of this is Edge AI vs. being processed in the datacenter or cloud?

49:53 What does it take to train AI to understand human body language?

53:00 Career advice for people interested in getting into the autonomous vehicle market

57:48 How to contact Perceptive Automata and Sam

59:14 Close

-- LINKS --

https://www.perceptiveautomata.com https://twitter.com/sam_e_anthony https://www.linkedin.com/in/sam-anthony-19a65917/

If you found this podcast episode helpful, don’t forget to subscribe at https://productiveai.com/signup/

DISCLOSURE: To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase, we may receive a small commission or other compensation.

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Neural networks can be made faster, cheaper, and smaller. This can result in higher performing and lower cost operation of complex AI applications at the edge of the network –where ever that edge might be such as a factory, vehicle, ship, or other remote location. In this episode, hear Jags Kandasamy explain how Latent AI’s development platform helps customers and suppliers in every industry compress and adapt neural networks to run “at the edge” and how this ultimately speeds up application development and delivery, as well as improves the performance of the AI application itself. Also we’ll touch on the relationship between Edge AI, and 5G.

Subscribe to get notified of future blog posts and podcast episodes: https://productiveai.com/signup/

-- TIMING --

• 00:00 Introduction
• 00:47 Genesis story
• 03:30 What is Edge computing? And Edge AI?
• 07:02 Wearables and smart watches as edge devices
• 08:04 Video cameras as edge devices
• 11:38 Edge AI can assist with maintaining privacy
• 12:43 Deep learning is too compute heavy for the edge
• 15:07 Automotive production example: predictive maintenance
• 20:59 LEIP Compress compresses the model to 1/10th the size while only reducing predictive accuracy a few percent
• 24:08 LEIP Compile targets various end hardware devices so that the developers don’t have to keep track of it all
• 27:40 AI accelerator chips and hardware are exploding
• 29:49 The telco use case: AI at the edge of the telco network and Content delivery network
• 34:04 Where to focus when there are so many opportunities in so many sectors?
• 38:19 Partners and system integrators are required in order to scale
• 40:26 What types of customers are a fit for Latent AI?
• 43:40 Wrap-up!

-- LINKS --

https://latentai.com

https://www.linkedin.com/in/jagsk/

https://en.wikipedia.org/wiki/Edge_computing

If you found this podcast episode helpful, don’t forget to subscribe at https://productiveai.com/signup/

DISCLOSURE: To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase, we may receive a small commission or other compensation.

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Hear Seth Clark explain how the largest Military, Civil government, and Enterprises design, build, and deploy large-scale AI projects from the lab into production in a way that is quick, safe, and secure. Topics include: the AI Pipeline; the differences between model management, Model Ops, and MLOps; what all the members of an AI team should do (and more importantly, NOT do); how the answer is not build-or-buy, but build-AND-buy; what new threats exist in an AI application and how to defend against them; as well as how to build AI systems that can explain their decisions to humans.

Timing:

• 00:00 Introduction
• 00:49 The genesis story of Modzy – born from Booz Allen consulting engagements
• 03:24 Working with Military, civil government, finance, oil & gas, energy and utilities
• 05:28 Why do customers need a platform? Why not just DIY?
• 08:30 Description of Modzy – the platform and model marketplace
• 15:00 The AI pipeline – from big idea to data collection, model development, training, deployment, assessment, retraining, explainability
• 21:00 What is the difference between model management, Model Ops, and MLOps?
• 22:42 Deploy to where? Tactical edge, public cloud, private datacenter, air-gapped datacenter
• 25:30 AI is a team sport – data scientists, software developers, machine learning engineers, business analysts, executives
• 30:48 Model Marketplace – an app store for AI models
• 33:42 AI requires adversarial defense mechanisms to protect against new and different attacks
• 37:03 What is explainable AI, why do we need it, and how do you achieve it?
• 41:45 Ideal customer for a platform like this
• 44:49 Wrap-up

Links:

https://www.modzy.com

https://www.linkedin.com/in/seth-clark-0820b0b/

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Hear Bharath Gaddam explain how your company can save or reallocate 20% of its marketing spend by establishing a clear cause between online marketing, offline marketing, and current and future sales using the power of AI, in particular, deep learning. He also digs into how 98% of the marketing automation industry is failing its customers by using the wrong tools for the job and what his team is doing about it.

Timing:

• 00:00 Introduction
• 00:46 Bharath’s career – the genesis of DataPOEM
• 05:32 The DataPOEM hypothesis – connected intelligent decision support for Marketers
• 06:55 The Martech 5000 (which is really 8000+)
• 09:04 The language and semantics of “Marketing ROI” is all wrong – how marketing decision makers are being misled by the vendors
• 12:05 Market Mix Modelling, Multi-touch attribution, and hybrid solutions and what they’re missing
• 15:13 Our mission – No FUQs (Frequently UNanswered questions!)
• 17:43 All existing systems only solve operational issues, not leaders issues
• 20:00 Marketing leaders don’t have the information they need so they fall back to Excel and mental models.
• 22:54 Current vendors are using the wrong tool for the job – they’re attempting (and failing) to solve complex multi-variate problems with simple single-variable tools
• 26:55 The solution big idea – a holistic approach to solving the problems
• 30:28 Using syndicated data sources to rely less on the customer bringing the data
• 31:00 Integrations with 200+ data sources
• 34:34 How deep learning solved our problem
• 42:19 Summary of the product
• 44:32 Who are the best fit customers for a solution like this?

Links:

https://datapoem.com/

https://www.linkedin.com/in/bharath-gaddam-3355a821/

https://chiefmartec.com/2020/04/marketing-technology-landscape-2020-martech-5000/

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Description:

Hear Mark Cramer explain his view on Product Management, his definitions of AI and related fields, why he thinks AI is cool and life-long learning is important, how the job of the AI Product manager is challenging (and more fun!) than a regular Product Management job, and what to do if you’re considering becoming an AI product manager.

Timing:

00:00 Introduction
01:28 Mark’s career
04:57 Why is AI Product management more than just product management?
05:25 What is Product Management (without AI)?
14:52 What is AI? (And Machine learning and deep learning)
26:44 AI Product management requires more than just product management skills
40:58 The always critical MVP (Minimum Viable Product) applied to AI products
52:07 Resources for people wanting to be an AI product manager
54:00 Wrap-up!

Links:

Mark Cramer: https://www.linkedin.com/in/mcramer/

Mark’s Writing:

Magic Dust for Artificial Intelligence Product Managers: https://www.linkedin.com/pulse/magic-dust-artificial-intelligence-product-managers-mark-cramer/

Learnin’ Good All this AI Stuff for Product Management: https://www.linkedin.com/pulse/learin-good-all-ai-stuff-product-management-mark-cramer/

Communicating a Red-Hot AI Value Proposition to Your Stakeholders: https://www.linkedin.com/pulse/communicating-red-hot-ai-value-proposition-your-mark-cramer/

AI PM WFH SIP TBR, IMHO; YOLO: https://medium.com/@markdcramer/ai-pm-wfh-tbr-imho-yolo-ddb1497716b

OTHER PEOPLE

Eric Ries / The Lean Startup: http://theleanstartup.com
Steve Blank: https://steveblank.com
Randy Komisar: https://www.kleinerperkins.com/people/randy-komisar/

OTHER RELATED BOOKS:

The Four Steps to the Epiphany by Steve Blank: https://amzn.to/36e8rkZ
The Startup Owner’s Manual: https://amzn.to/3cDHLeJ
The Lean Startup by Eric Ries: https://amzn.to/3cIGB1t
The Startup Way by Eric Ries. https://amzn.to/2S6JM9R
The Monk and the Riddle by Randy Komisar: https://amzn.to/2S7YEVC
Getting to Plan B by John Mullins and Randy Komisar: https://amzn.to/2Hzk1Nf

Affiliate Links used where possible!

DISCLOSURE: We often review or link to products & services we regularly use or have tested or evaluated and think you might find helpful. To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase we may receive a small commission or other compensation.

We're big fans of Amazon, and many of our links to products/gear are links to those products on Amazon. We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and related sites.

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Timing:

• 00:00 Welcome
• 01:02 Definitions of AI, Expert systems, Machine Learning, Deep Learning, and Neural Networks
• 05:00 Generative Adversarial Networks and the need for more data
• 07:26 Strong vs. Weak AI
• 09:52 Artificial General Intelligence: ignore it or immiment threat?
• 11:41 The next 10 years of AI
• 15:15 Adoption of AI in the enterprise
• 22:38 On the difficulty of building an AI product company and how it impacts your margins
• 25:17 What is the state of the art in AI?
• 27:55 Open AI’s GPT-3
• 30:55 The impact of AI on the work force – will it take our jobs?
• 32:23 Robotic Process Automation (RPA) is already taking jobs
• 34:27 Kai-Fu Lee’s grid on the impact of AI on the work force
• 37:32 What is the relationship between AI and 5G, edge computing, and quantum computing?
• 44:44 Advice to enterprises who want to adopt AI
• 48:43 Contact information for Tom

Links

Tom Taulli: http://www.tomtaulli.com

Artificial Intelligence Basics: A Non-Technical Introduction (Kindle/Amazon): https://amzn.to/2ZVapD6

The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems:https://amzn.to/2FS4tU5

Tom Taulli on Forbes: https://www.forbes.com/sites/tomtaulli/

Twitter: https://twitter.com/ttaulli

Other books mentioned:

AI Superpowers: China, Silicon Valley, and the New World Order by Dr. Kai-Fu Lee: https://amzn.to/3hNePlj

Affiliate Links used where possible!

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In this episode, we introduce the Productive AI podcast. 

In this podcast, we will focus on three distinct audiences and conversations: 

We will speak with product teams selling AI-related products and services about why they're building their product, who it's for, what problems it solves, and value it delivers, and what they're ultimately achieving for their customers. 

We will speak to buyers, whether they are in the C-suite or the field, to help them better understand how this market is developing, so that they know what's possible, now vs. later. We'll also discuss strategy, vendor selection, and enterprise integration. 

We will also speak with project teams who are in the trenches, executing AI projects to uncover lessons learned and obstacles to success and potential ways to overcome those obstacles. The goal is to share the lessons learned so that others may follow in their footsteps and have a smoother path.