Tech Friday: Recent Episodes
Zfort Group
Tech Friday by Zfort Group is a weekly podcast with exclusive tech insights, stories, and news. It focuses on major technologies with the strongest potential to change the world, like Artificial Intelligence, Big Data, AR/VR, and Blockchain. All positive and expert reviews for your inspired everyday!
Can Artificial Intelligence understand us better than we understand ourselves?
AI is now the main power of public healthcare systems transformation. As a result of AI-related technology, self-care became easier and more accurate than ever before.
More and more of us start using a variety of gadgets and apps that allow tracking health status.
Some companies take it to the next level. This week we chatted with a nice guy from South Korea, who is working on a contactless health analysis gadget. The app's name is CardiVu, and their main tech is called Automatic Vital Sign Extraction Algorithm, promising over 90% of the accuracy of vital signs.
It appears that regular cameras from your smartphone or laptop can reveal your stress level just by analyzing your eye. Particularly, by the way, your iris moves. You don't even have to interrupt your work for the analysis - just keep doing what you’re doing, and the app will tell you how stressed you are. Oh, and something about your health as well.
So, how does it really work?
As usual, it starts with collecting data. Lots of data. And finding out what’s the correlation between the vital signs and human eye iris micromovements. The more data we gather, the more accurate the results are. The data monitors include heartbeat rate, fluctuations, low-frequency to high-frequency band ratio, and others.
Turns out, when the iris is too calm, you’re in trouble.
And sometime later these measurements are applied to you, and you can learn a lot about your current self. If you’ve been using the app for a while, you can also see the history of your past measurements.
**Not bad for contactless technology, right?
Heart Rate Variability, or HRV, remains one of the core indicators. The Smart Diagnosis researchers also believe that “HRV has been proven to be a very powerful biomarker”. Also, it’s an accurate, non-invasive measure of the Autonomous Nervous System.
Healthy people are supposed to experience large and complex heart rate changes, but complexity is significantly reduced if they have a disease or stress.**
**Having got the approval of KFDA, Smart Diagnosis went further and applied the algorithm to the education industry.
The rise of online educational courses & platforms leads to a question on how to measure the involvement and attentiveness of students.
Even if students’ cameras are turned on, who knows what’s going on in their head, do they really try to grasp the material? Or maybe there’s a game open in a separate browser window?
Of course, it’s hard to achieve involvement similar to personal lessons and discussions, but we could at least start by tracking it.**
**In this case, the camera is more focused on gaze tracking, as opposed to iris contractions. The app tracks time spent with focus, as well as time spent without focus on the screen. The app can also show us a heat map of the student's attention, highlighting the main areas of the screen where the student was focused on most during a certain time.
Some other tracked factors are blinking frequency, pupil reaction, and head position.**
What do you guys think about technology? Is it useful? Dangerous? Ethical enough? Would you use it if you could? These guys are currently in the alpha stage, so we still have some time to think about the options before it hits the market.
While people are used to paying attention to the tone and meaning of the words that have been heard, developed AI-based algorithms are being taught to detect connections between words themselves and to see the irony and intentional lies.
Now intelligence officers/ agencies are ready to prove the ability of artificial intelligence to detect the human art of sarcasm. The tool, funded in part by the U.S. military, lets them analyze the social media content (including comments, posts, forum talks) while ignoring posts that shouldn’t be taken seriously.
A couple of scientists from the University of Central Florida conclude the following: “Сertain words in specific combinations can be a predictable indicator of sarcasm in a social media post, even if there isn’t much context”.
To simplify, there are some words - “triggers”, that get more attention from the system and help it to identify sarcasm. Thus, words as ‘just’, ‘again’, ‘totally’, or exclamation mark make it easier to recognize the suggestion of sarcasm in text. The algorithm learning base datasets include posts from social networks (Twitter, Reddit), dialogues and headlines from The Onion and other news sources.
So, we have here one of the most difficult forms of communication. Moreover, we`re dealing with the text format, which eliminates the voice cues.
How can an algorithm handle this?
Obviously, only neural networks can handle this.
In a nutshell, AI is trained to give more weight to some words than to others, depending on what other words appear nearby. Practical significance is in helping the military to understand what’s happening in key areas where they might be operating.
The work was supported by DARPA (the Defense Advanced Research Projects Agency). They initiated and launched the program to seek a “deeper and more quantitative understanding of adversaries’ use of the global information environment”.
Scientists are now trying to take into account all the results of previous attempts to recognize human emotions. In previous releases of the podcast, we have been discussing the brightest of them.
This attempt was preceded by a more in-depth study, which improved on the previous ones. The main difference lies in the approach to the search for trigger words.
Earlier algorithms were developed to seek words suggesting specific emotions or even emojis. As a result, they skipped expressions that did not contain them. Consequently, most sarcastic comments were skipped.
Neural networks were also used before and tend to perform better. However, it was still impossible to define for sure how the neural network reached the conclusion that it reached.
Finally, research has gone a little further and AI has come to understand us a little better. What "challenge" will be next in this direction? Is it possible to teach algorithms how to analyze slang and spoken words soon? Can it also predetermine the mentality of the commentator depending on his remarks?
And, on a more personal level, do you think the AI can detect your sarcasm?
**Most Americans are unsure of how consciously companies behave while using and protecting personal info. Nearly 81% of them report being insecure about potential risks of data collection, and 66% claim to feel the same about government data collection.
It`s really difficult to weigh the level of potential risks and understand the anticipated harm that irresponsible behavior with personal data can cause. How can this affect our further actions, what restrictions and changes will it bring?
Let`s analyze the main points of what happens when disclosing personal data and how to properly protect yourself using the example of the recent situation in South Korea.**
**Korean company ScatterLab launched a “scientific and data-driven” app, which was supposed to predict the degree of attachment in relationships.
In December 2020, the company introduced an A.I. chatbot Lee-Luda.**
The bot was positioned as a well-trained AI consultant, taught on more than 10 billion conversation logs from the app. “20-year-old female” Lee-Luda is ready to set a true friendship with everybody.
As the company`s CEO mentioned, CEO the purpose of Lee-Luda was to become “an A.I. chatbot that people prefer as a conversation partner over a person.”
Just after a couple of weeks of the bot launch users could not help but pay attention to the harsh treatment and statements from the bot towards certain social groups and minorities (LGBTQ+, people with disabilities, feminists, etc.).
The developer company, ScatterLab, explained this phenomenon by the fact that the bot took information from the basic dataset for training, not from personal user discussions.
Thus, it is clear that the company did not properly filter out the set of phrases and profanity before starting the bot training.
Lee-Luda could not have learned how to include such personal information in its responses unless they existed in the training dataset.
But there some “good news” as well: it is possible to recover the training dataset from the AI chatbot. So, if personal information existed in the training dataset, it can be extracted by querying the chatbot.
Still going not so bad, huh?
To make things worse, ScatterLab had uploaded a training set of 1,700 sentences, which was a part of the larger dataset is collected, on Github.
It exposed names of more than 20 people, along with the locations they have been to, their relationship status, and some of their medical information.
Despite the fact that this situation has become a high-profile event in Korea, it has not received attention on a global scale (and we think quite unfairly).
It's not about the negligence and dishonesty of the creators, this incident reflects the general trend in the development of the AI industry. Users of software based on technology have little control over the collection and use of personal data.
Situations like this should make you think about more careful and conscientious data management.
The pace of technology development is significantly ahead of the adoption of regulatory standards for their use. It is hard to foresee where the technology will lead us in a couple of years.
So, the glocal question is “Are AI and tech companies able to independently control the ethical component of the used and developed innovations?”.
Is it worth going back to the concept of "corporate social responsibility"? And where is this golden mean (Innovation VS Humanity)?
Any software is traditionally based on the programmer`s designed algorithm and is aimed to perform a specific function. But in the case of Artificial Intelligence software, we face taking decisions that can be unpredictable.
A Head of global software standards at Philips, Pat Baird, said: “For these machine learning systems, the programmer doesn't tell the software how to solve the problem”.
In the particular case of AI programming, the result of development is the software being able to find patterns in the data. The reality is the programmer has no idea of the reasons software made a certain decision, he just threw together an engine that calculates a ton of stuff.
The guide of potential problems with AI software avoidance should start with something like “make sure to get good data before all else”. As Pat Baird says about the traditional software,
“It's garbage in, garbage out. But what's going to happen is you have garbage in your data, and since you don't know how the software works, it's going to be a problem.”
So, what are the common problems we face using AI apps? Pad gave a couple of real-life examples of how bad-collected data can cause untrue results delivery.
Example one - “Bad data to start with”
It demonstrates the inability of wearable devices to recognize the mode of travel while the tracker showed over 20,000 steps after an off-road adventure in a Jeep. “This was because of all the potholes and how much I was thrown around in the Jeep,” Baird said.
Example two - “Overfitting”
Here we are talking about the excessive data sets that also lead to inaccuracies. From a huge mass of able data and values, the program selects patterns for analysis and training that are not targeted ones for us. One of the great examples is the image recognition software, the goal of which was to determine the difference between an Alaskan Husky dog and a wolf. Baird commented it in the following way: “The data performed well, but it was picking up on background cues, rather than the ones the programmer intended. What actually happened, was that most of the photos that people had of their dog were taken sometime during the summer or fall in their backyard, whereas the photos of the wolves were taken during the winter out in the wild. The software that looked great to detect the difference between the Alaskan Husky and the wolf was actually picking up whether or not there was snow on the ground.”
Example three - “ Underfitting”
Another issue is appearing as a result of the collected data shortage. Such a simple reason, right..? As a rule, it ends up with making a decision based on noise, not on something real.
Autonomy level - questions to think aboutOne more question appears on share responsibility we are ready to give the technology. What autonomy level should it have? Will it be enough just to give you the right driving direction or we need a self-driving car that actually does the driving for us?
The good news is that most of the companies that worked with data collection have already found better ways to do it in terms of quality control. But still, can we remove errors in developing an excellent AI system and how should we deal with it?
So, how do we avoid failures in AI development?
Here are the very basic principles:
- Collect relevant and verified data
- Collect enough data
- Make sure the data is diverse
- Carefully consider if the data justifies the power we are giving to the AI
Fortunately, the development and implementation of technologies by leading companies provide an opportunity to learn from the mistakes of others and create a "cleaner" technology now.
Well, that’s all for now. Thanks for listening! Be sure to subscribe now to stay on top of the world community news with Zfort Group!
Scientists have recently published an application that will lead to conclusions about the use of AI by completely different categories of consumers:
- ordinary users - showing that they can be deceived by facial expressions,
- critics - to see once again the violation of human rights.
What did the app study let us uncover, why is it appeared so interesting to scientists and cause a public outcry?
While most users are passionate about testing the application and trying to prove it invalid, scientists try to draw public attention to the ethical side of the issue and provoke public debate. Thus the researchers hope to unmask the reality of emotion recognition systems. Critics, in turn, argue that technology violates confidentiality rules, and is even racist.
To raise awareness of the technology and promote conversations about its use, researchers developed an emojify.info website - a program to try an emotion recognition system using PC or mobile camera.
Dr Alexa Hagerty, a scientist at Cambridge University who is also a project lead of Leverhulme Centre for the Future of Intelligence, commented on it in the following way: "These developments are based on one form of facial recognition. But technology has gone beyond that, not just by identifying people, but by asserting that it is capable of accurately counting internal experiences and emotions from our faces".
Let’s go directly to emojify.info website and see what functionality the application provides. In one of the ing, users are asked to make multiple expressions of faces on the camera and see if they have been deceived by the technology.
"The developers of this technology claim to read emotions," said Hagerty. In fact, the system can only count the movements of the face and combine with the assumption of what emotions may be behind it (for example, a smile = happiness).
Facial expressions do not always reflect true emotions, nor do people’s habits of accepting a particular expression. For example, smirks, sarcasm and other ambiguous states cannot be recognized by a robot (that is, not all people and not all people are able to recognize them.).
There is already some scientifically sound evidence that the expression of internal state is not quite as simple as the creators of such developments would like.
Which one of us didn’t fake a smile trying to look cheerful at one of the holidays, huh?
Some scientists have already expressed their view that it is now time to pause the development of emotion recognition systems in the growing market.
What do you think - should AI specialists stop playing with fire now?
Our opinion here in Zfort Group is, there could be potential dangers in almost any technology. If we as humanity were to stop exploring the world and science out of fear of something going wrong, we would be stuck in the Middle Ages forever. We are where we are only because we tame the technologies to serve us.
Let us know if you disagree - open to being wrong, as always.
Hello, this is TechFriday again. It’s great to have you with us!
Today we’ll talk about the challenge of teaching AI relationships or at least generating some pickup lines. A recent scientific experience showed us how AI works on the GPT-3 model in terms of relationship building.
It’s been almost 10 years since we were having fun asking Alexa and Siri. Smart assistants are already "used" not only to perform their core functions but also to being a full-fledged conversation buddy. Now, with the help of AI, scientists decided to go further and teach it the art of flirting.
Let’s see what came out of it and see if the scientists were satisfied.
The main idea belongs to Janelle Shane, a research scientist from Colorado, who implemented it into reality with the help of GPT-3.
How did Shane handle that?
Firstly, the article template was fed to 4 different GPT-3 program variants, which were supposed to enhance the starter file with generated predictions.
**Some of the example pickup lines were from Tinder DM screenshots and sound like
“I'm losing my voice from all the screaming your hotness is causing me to do”.
Another one says: “I will briefly summarize the plot of Back to the Future II for you”.
Let's start with the 1st AI, DaVinci, and its variants. Shane herself characterized it as “the largest and most competent” of the 4 AIs. DaVinci’s pickup lines bordered on cute ridicule, ice-humping sentences, and a few intricate phrases, issuing such gems:**
- I love you. I don't care if you're a doggo in a trenchcoat.
- You have a lovely face. Can I put it on an air freshener? I want to keep your smell close to me always.
- Wait, this beanie hat, is it fashionable?
- You look like Jesus if he were a butler in a Russian mansion.
Curie, the 2nd candidate, which is not so powerful a piece of software, created some poetic, mysterious game of words. The “best” variants included:
- Your eyes are like two rainbows and a rainbow of eyes. I can't help but stare.
- I like the ice cream… You can keep me in the freezer for a while but then I melt!
- Hey, my name is John Smith. Will you sit on my breadbox while I cook or is there some kind of speed limit on that thing?
Babbage, our next pipeline maker, appeared more sophisticated in comparison with previous AIs. This program produced such an interesting “piece of arts” in the pickup world :
- You're looking good today. Want snacks?
- It is urgent that you become a professional athlete.
- (In your best Albert Einstein voice) "I wouldn't change a thing."
The last one, Ada, gave the smallest number of options, which looked more like email headings. Here are the examples:
- Body Softening Pads
- 2017 Rugboat 2-tone Neck Tie Shirt, and
- Future Pop-Tarts by Tracey Thorn
The most memorable line of all the pipelines spouted is still the one that was voiced by a more primitive neural network, which had a trend back in 2017: “You look like a thing and I love you.”
What can we take from the experiment results and trends in the use of AI in the industry of “digital love” and dating?
There are areas of human activity that should still be left to people. The use of artificial intelligence in these areas not only suppresses lively communication, eliminating all manifestations of social groups but also seems at least bizarre and ridiculous.
We’re going to be much more productive and happier using these technologies in business, freeing up time for our loved ones. But not the other way around.
Can a robot become more human than a human being?
Today we’ll go through another great battle of the e-commerce industry in recent years - the confrontation “ Magento vs. Shopify”. Both have a lot going for them, but which one to choose when building an online store? Let’s take a little look at each of them.
Talking about the Design and Themes, there are many ready-to-use themes for both Shopify and Magento, but Magento also allows you to have custom designs of your own.
Next comparison step is Cost and Fees Magento Open Source is available for a free download. Still, your business may need to splash out a little extra cash if you want to use premium plugins and themes or if you need to hire a developer to build out some custom functionality.
Shopify is more of a monthly thing, in that you pay a monthly fee and they give you all the functionality you’ll need, at least for a smaller business. If you’re a more significant business, you might be better off going for Magento if you want to customize your build heavily.
The next major battleground “Ease of Use” and it’s one where there’s a clear winner.
Shopify is undoubtedly one of the most intuitive ecommerce platforms that we’ve come across. It sets something of a benchmark and is only a little trickier to pick up than WordPress. Shopify wins the battle of Shopify vs Magento, but that win comes at a cost.
Now we came to the “Third-Party Integrations” factor This one’s pretty simple, and indeed there’s almost no competition in terms of Magento vs Shopify. With Shopify, what you see is what you get. Third-party integrations are few and far between, and so if it’s not included in the core installation, then you might be out of luck.
With Magento 2, however, there’s a whole community around the core product, with literally thousands of third-party Magento developers working on extra functionality, from new themes to plugins.
What about Merchandising features? Magento 2 comes with the Visual Merchandiser, a suite of tools to add and modify products and categories as smoothly as possible. There is a range of other ecommerce essentials, from product tags and order tracking to their dedicated visual mode, which allows you to view all of your products in a grid.
Shopify has a visual merchandising plugin of its own, and both platforms offer pretty similar features for creating a library of products.
Okay, almost all comparison parts are done. The one we've missed is Market Scalability Both Magento and Shopify have pretty decent scalability, and we’d be hard-pressed to pick a winner here. If we had to pick just one, we’d go for Magento – but only because again, it’s so customizable that even if some aspect or another doesn’t scale, you can probably tweak the code to change that.
The last but not less important is SEO inbuilt tools battleground Shopify is arguably easier to just pick up and run with, mainly because an essential part of SEO is creating and deploying high-quality content.
Magento 2 is more robust and feature heavy, and while it can take a little longer to wrap your head around it, there’s no reason why it can’t be a powerful centerpiece for a solid SEO strategy.
Summary
By now, you know everything you need to know about the pros and cons of Magento Open Source and Shopify, and so you should also be ready to make the call one way or the other.
Well, that's all for now! Hope this piece was helpful for you.Hear you in a couple of weeks, another Friday.
Today we’ll talk about the challenge of ecommerce platform selection and will look deeply into the Magento VS WooCommerce battle as a distillation of the dedicated article.
Batman or Superman? Iron Man or Captain America? Blair or Serena? I bet you have some favs among these long-time competitors.
But what about Magento vs. WooCommerce? If you own an ecommerce business or are about to build it, you must have heard of them. Both ecommerce platforms are popular workable solutions, but how can one choose rightly between the two?
We have been working with both ecommerce technologies long enough and can help you out. If you are equipped with all the relevant, up-to-date info, you'll know for sure which ecommerce solution is the one for your business. Let's start with diving into Magento features!
Both ecommerce platforms work well for numerous domains. Here is a comparison of some basic aspects to bear in mind.
We`ll start with Themes factor:
Magento and WooCommerce offer free and paid themes for various purposes: customer support, accounting, site optimization, etc.
With WooCommerce, there is a chance you'll be able to install the extension yourself. As to Magento, you'll need Magento development services.
However, despite the extra time and money, Magento website additions are more cutting edge, so your ecommerce store will be more powerful. Here we definitely give credit to Magento.
More or less clear, right? And what about their support service?
In this match, Magento vs. WooCommerce, we'll call it a draw. Being open-source, the platforms have huge active communities ready to look into any problem you may face.
Now, here we came to the Pricing factor
As we already discussed, Magento's and WooCommerce's code is out there available to be adjusted according to your needs. WooCommerce is easier for beginners, but you may end up paying a fair amount for any extra feature you'd like.
Magento is more suited for ecommerce businesses, which means that its basic functionality can be sufficient for you. But there is a hosting plan you'll have to purchase. Depending on your ecommerce website specifics, you'll have a monthly hosting package to maintain.
Summing up the Magento vs. WooCommerce comparison, it's safe to say that each has its strengths and weaknesses. WooCommerce is claimed to be more user-friendly and more comfortable to start with, while Magento is more powerful and well-known for its extensions. All of us know that selecting the appropriate tech stack is crucial. But it's not the thing we recommend as a starting point. First, you should elaborate on your business goals and requirements, realize your company needs and define your target audience. The more specific you are, the better we'll know which ecommerce solution will fit best.That’s all from us for now. For a detailed version, you are welcome to check the dedicated article.
Until next time! Bye!
We've been discussing how to choose a domain name for your future e-commerce store, analyze competitors` strategies during the previous episode. We've also revealed the main pros and cons of using website builders (remember, yeah?).
You can think about marketing your ecommerce site in terms of three main audiences:
- People who have shopped with you before or at least know about your store
- People who are looking for the kind of product that you offer
- People who would want the product that you offer if they knew about it.
For your first audience, you should have a direct line to them — email communications, content marketing, or social media. You can communicate for “free” (except the budget spent on tools).
- You can use smart remarketing tactics from CRMs like HubSpot to send highly customized emails to shoppers that have abandoned carts. If a customer has logged in, send them customized emails with pictures of the items they were interested in and customize the emails.
- You can also get them back with remarketing. Facebook, Google, and a few others give you the opportunity to track the actions of your sites’ visitors in order to optimize the way you market to them.
- Connect via social media. Grabbing a user's attention begins with something they're interested in, not something you want them to do. By demonstrating your topical expertise, interested users then have a reason to visit your website and see what you have to offer.
Your second audience should be able to discover the products you have that meet their needs. This can involve search SEO, content creation (like blogging), and search engine marketing.
The main idea is to bring traffic to your site providing helpful info that positions your brand as an authoritative voice in your vertical:
- Create power posts (massive articles that are full of useful information on a particular topic that relates to your industry).
- Publish these power posts as unique pages on your website and they’ll start driving traffic as Google recognizes their value and sends searchers your way.
- Create guides that show off how your products are used in real-world applications. Make sure to keep it visually interesting and focus on creating highly valuable content.
- Try out Google Ads. Using Google Ads for search engine marketing gives you an opportunity to bid on keywords so that you can show up first in search engine results pages.
To reach out to the third audience (people who would want the product that you offer if they knew about it), you’ll need to think in terms of brand awareness. Are you offering a fix for a problem they don’t know about yet?
- You can use influencer marketing as one of the ways of being promoted to the new audience. Influencer campaigns help present your products in front of a potential audience by a trusted person (advisor, blogger, vlogger, etc). But make sure to work with influencers whose brand and audience are in line with your products!
- Participate in online events and discussions. As online communication and events surge, there may be opportunities to increase brand awareness with target communities.
So, we've covered the major online store promotion options. Apart from them, you can certainly sell your product through Instagram, Facebook, or Amazon, but it's a bit of a different story and is better studied separately.
Well, you've chosen a name, went through the hosting process and analyzed your competitors` activities. The point is you are on your way. Let's move on to Design and Development tricky moments discussion.
Here we'll review what ecommerce solutions the market has to offer today.
You must have heard of website builders like Wix or Shopify. Their significant advantages are affordable pricing, usability, and simplicity. You can build an ecommerce shop literally in a day. What is also attractive is the perk of having a hosting plan included in the package.
The other useful factor is buying a ready-made design theme if you don't want to invest much in design. These two benefits indeed make such an ecommerce solution look like a perfect choice; however, it's not always the case.
Your choice is limited by what the website builder foresees if you want to add any additional functionality. Moreover, if it's not included in the package you've purchased, it'll cost extra.
Secondly, such websites can't be scaled. You'll have to migrate to another ecommerce platform.
Thirdly, SEO promotion is complicated for these websites, so if you have any long-term plans to promote your ecommerce store, they won't happen with website builders like Wix. The only option available in this regard is paid advertisement.
Finally, your online store doesn't quite belong to you. The reality is that you are just renting it.
The second option on our list is creating an ecommerce website using a framework. Here you'll need coding knowledge or an expert in the area. It's not a cheap alternative and is a sound choice for large businesses. They normally have a dedicated development team or reach out to software development agencies to implement their project. Such ecommerce projects imply proper long-term planning with an already existing business process that serves as a basis for software.
As you see, there can be a whole team working on ecommerce project design and development.
The third alternative is opening an online store on open-source platforms. It doesn't require that much time and effort compared to the previous solution described. It all depends on your requests, nowadays it's increasingly popular and works for many cases. Such platform examples are Magento, WordPress, etc. Having a convenient ready-made CMS is a huge benefit for any ecommerce project. The other is the variety of plug-ins and extensions, and each meant best for a particular e-commerce need: documentation audit, SEO, payment methods, etc.
Like with website builders, you can go with a theme or implement a custom design to stand out. The pros list also includes scalability and multi-functionality.
Creating an online store using an open-source platform may take a few weeks or months; everything depends on customer traffic. Such ecommerce projects are well-optimized for search engines, which allows setting your marketing strategy way ahead.
That's all for now. During the following episode, we will discuss the promotion and Marketing of an Online Store in 2021.
First and foremost, you must know the formula: set specific goals and step-by-step think through the journey. You can surely do it yourself or follow our recommendations to make it a bit easier and avoid common mistakes.
We assume you already have a clear picture of what you are going to sell, who the target audience is, and what's more important, you've already decided that it will be an online store. So, what's next?
We've been dealing with ecommerce for a while now. That's why we have come up with an essentials kit to launch an online store. In short, it includes:
- domain name & hosting, competitors analysis;
- design and development;
- promotion and marketing.
Looks plain and simple, right? Now let's dive a bit deeper.
We'll start from the beginning and share some tips on the domain name and hosting.
This item comes as the first on the list and for a good reason. Hosting is the place where your online store will dwell, and the domain name will be its address. Which one to choose depends heavily on your budget and plans. If your online store is relatively small, then a $3 per month fee may be sufficient. If the ecommerce business grows, it will be necessary to reconsider the hosting plan.
As to the domain name, it's usually the actual name of the online store. Yet, if it's already occupied, you'll need to think of something else. Our experience shows that in ecommerce, this problem can be possibly solved in three ways:
- You can try and buy out the online store domain name. As the practice shows, it's actually an option and not that expensive.
- Play with the name by adding an extra word or hyphen.
- Finally, you can always think of a different name.
Going further to competitor analysis, research the main competitors' activities taking these actions:
- Find companies with similar business models and products with the help of Google and Amazon.
- Poke around social networks to find more information about their brand and communication style.
- Create your competitor list, including basic info like their shop name, website, strengths, weaknesses, social following etc. In a perfect world, you should select 5-10 competitors.
- You should include data on their: market share, main differentiators / unique value-add, price points, shipping approach, etc.
To reveal some specific elements of your competitors’ approaches, you might consider adding more sections to your competitive analysis:
- Features on competitors' websites (search tools, product images, design/layout, etc.)
- Customer experience elements (cart abandonment strategy, customer support, mobile UX, etc.)
- Social media approach (channels, frequency of posting, engagement, etc.)
- Content marketing tactics (blog topics, content types)
- Marketing tactics (types of promotions, frequency of discounts, etc.)
- Customer reviews (language used around products, recurring complaints, etc.)
If you managed to do all mentioned things - well done, the preparatory stage is finished (as well as the first part of our “How to start an online store” podcast).
Hello everyone! Anytime you work with technology, you need to learn to harness the benefits and minimize the downsides. Today we keep on speaking about AI advantages and disadvantages.
NO SLOWDOWN
AI computing power bypasses humans in speed and quality of work. It is stronger in modeling and forecasting, combining large data sets. AI reduces the time of reviewing insurance claims. It takes days for an insurer to come up with a decision. An artificial intelligence mechanism can cope within minutes.
FEWER CHORES
For households and basic tasks, AI assistants can understand and synchronize data between all the devices like phones, cars, tv, and even fridge. Thus, it is possible to control them from any point.
AI-powered smart homes not only make life simpler and more dynamic but help to save money and reduce water and energy consumption.
BETTER FORECASTS
Meteorologists can trace potential severe storms faster by analyzing clouds movements with the help of AI.
Researchers use computer vision and machine learning to detect coming cyclones in real-time. This functionality allows us to prepare for natural disasters. Besides, the weather determines the best time to plant and harvest crops.
BETTER RESOURCES DISTRIBUTION
AI can reduce the power consumed by houses and machines. Whereas, machinery lifetime and efficiency are likely to increase. AI can run the most profitablе appliance algorithm.
For instance, indoor air control improvement relates to AI sensors usage. They adjust and maintain the desired humidity and temperature in the room.
LESS DISEASE
An AI wearable device can monitor a person 24/7 and ensure fast diagnostics and disease prevention. Image-based artificial intelligence can help doctors reduce the time they spend studying data of a patient.
AI algorithms can help doctors assess risks for health. They will be able to know the side effects that various medicines can have beforehand.
Now let's switch to the Disadvantages of AI
FEWER WORKPLACES
AI will impact more than 95% of jobs either by displacing or complementing them. Moreover, the effect will be uneven for various industries. AI will hit harder laborious areas such as manufacturing, agriculture, and logistics and cause a decline in manual work.
NO ABSTRACT THINKING
What comes instinctively and without any specific preparation to humans is hard for robots. Artificial intelligence is mindless.
LACK OF ETHICS
Morality or fairness measurable for a machine is hard to design and convey. AI can hardly be taught what is right unless the engineers provided this concept.
To prevent disastrous outcomes, we need to realize the relevance of ethical principles.
MORE SLOTH
People let AI think on their behalf since there is no need to learn something or memorize. One can always ask a machine.
Dependency on a machine is a matter of individual choice. This technology needs to be used wisely to avoid abuse and unwanted consequences. At Zfort Group we know how to apply AI.
How can your company take advantage of Big Data? Reach out to Zfort Group to get expert consultation.
That’s all from us for now. For a detailed version, you are welcome to check the dedicated article.
Until next time! Bye!
We are Zfort Group, thanks for joining. Today on Tech Friday we’ll go through the pros and cons of AI as a distillation of the dedicated article.
Let’s start with the main Advantages
LESS ROUTINE
Artificial intelligence automated our day-to-day work and surpasses humanity in meticulous tasks. The robots can assume work related to analyses, subtle judgments, and problem-solving. AI at work decreases the workload, empowers humans to upgrade their skills. Free from monotonous work, employees can focus on the creative aspects of their jobs.
LESS RISK
AI capabilities for dangerous tasks reduce the risk to human well-being and safety. Artificial intelligence technologies help to overcome limitations. It can help to predict wildfire threats and fight it with the help of drones. Modern AI-driven robotics and autonomous underwater vehicles access to the seabed. Marine ecosystem data sheds light on the variety of species. AI-powered robots handle radiation. They can deal with a catastrophe and remove pieces of debris, especially after disasters.
NO BRAKES
People need to have refreshment from time to time. AI-powered machines reinforce humans. They won’t get tired, bored, or distracted. Artificial intelligence-based robots will not need a break. Once programmed for long hours, they will work 24x7.
NO HUMAN ERROR
Physiology, fatigue, stress, emotions, aging hinder decision-making. An overly-stressed doctor or pilot of a falling plane might make a fatal mistake. AI computers are error-free (if adequately programmed, of course).
Eliminating costly errors leads businesses to higher output. Customized AI can improve the decision-making process. Its set of algorithms uses nothing but already collected information. Our AI Development Company can help you to unlock the power of artificial intelligence for your business.
We wrap up here, be sure to tune in to the next part of this podcast.
Bye!
Hello, this is TechFriday. It’s great to have you with us again!
Today we’ll talk about what’s possible with AI in business, learn about innovative use cases and products.
Artificial intelligence is not an imaginary future. It is here and now. AI is more than auto-complete in our search engines, more than self-driving cars. Artificial intelligence enables machines to sense, comprehend, learn, and act.
First off, AI can be divided into two categories - weak (narrow) AI and strong (wide) AI.
The one existing in our world today is weak AI. It means teaching a computer to perform one task, no matter an easy one or complex. A chatbot answers questions, personal assistants organize and maintain the information; Google map routes calculate different options for the most effective way, recommendation engines usher users to choose specific products and services. Weak AI can outwit humanity only at a particular task, but nothing else. Even much simpler things are beyond its powers.
At the opposite extreme, a more extensive simulation of humanlike intelligence is strong AI. Capable of behaving wisely, from driving a car to answering abstract questions. It can replicate human cognitive skills, apply data from one field to another. Strong AI is supposed to be aware of itself as a person and experience consciousness. Strong AI is still a theory, like science fiction, but already controversial in its moral values.
Artificial intelligence technology can help you to build up agility, decrease costs, promote productivity, lessen setbacks, and cut failures. AI innovations will bring more value with less input, driving sustainable growth to the company.
Still, AI initiatives planning should be viewed through the lens of particular business powers rather than technologies. A thorough review of business goals and internal processes is crucial in getting the most of AI. With the toughest business problems uncovered, it will become clear which component needs an AI to deploy - scale, decision-making, personalization, or energizing some legacy systems. This strategy will lead your business to the sort of future you want.
At Zfort Group, we’ve developed extensive expertise in the artificial intelligence area. For more than 20 years, we’ve been helping various companies to build intelligence into their products and reinvent their businesses. If you want to gear up for the future with AI, hit “contact us” on our website.
This is all for now. AI pros and cons coming up soon! Catch us next time!
Hello, today, on TechFriday, we are talking about Big Data analytics.
In itself, Big Data is just a massive chunk of meaningless data. Until it is processed and crunched into structured sets.
Recent stats show that companies using Big Data have grown from 17% to 50% since 2015. With the boom of big data, more and more industries adopt Big Data technologies. How?
Let's begin with Healthcare. How does this sector stand to benefit from the implementation of big data analytics?
The fundamental concept lies within the development of electronic health records. EHR is a system that collects and stores data related to an individual's health. It contains the patient's disease and ailments' history, all the hospitalization records and doctor visits, laboratory tests, vaccinations, examinations, allergies, or any other related facts. Many experts claim Blockchain to be a good technology choice for EHR: it fulfills completeness of data, its protection, and verification.
Combining Blockchain with Big Data and AI makes it possible to use millions of EHR's for detecting hidden dependencies between a disease along with other side-factors as social, territorial, age-related, demographic, etc. The synergy of these three rising technologies allows creating a diagnostic system of a brand new quality level.
Now let's switch to Retail.
Big Data contributes to retail business automatization. On average, managers spend half of their work time on goods reevaluation, which still isn't accurate enough due to human factors. In addition, who can guarantee that the process operates seasonality, stock available, supply and demand structure, competitors' activities, etc.? That is why large retail chains switch to automatized price reevaluation using Big Data technologies.
Using this method, Amazon is adjusting its prices every two minutes. Big Data also plays a big role in making strategic decisions when developing a new entity. By analyzing competitors' locations nearby, transport access, potential customers' income rate, their habits, and preferences, it gives a comprehensive idea of the territory's commercial potential for future business.
What about Law Enforcement? Big Data algorithms are applied in cybersecurity. They detect suspicious activities before they occur and alert companies about potential fraud. Nowadays it’s a must for many businesses to take care of enterprise data security.
Big Data analytics also helps the Police to combat criminal activities. Predictive policing presupposes software with statistical data. It can predict in what geographic areas there is an increased chance of criminal activity and guides the Police in their decision-making.
How can your company take advantage of Big Data? Reach out to Zfort Group to get expert consultation.
That’s all from us for now. For a detailed version, you are welcome to check the dedicated article.
Until next time! Bye!
Hello everybody!
In our previous TechFriday episode, we’ve discussed what a typical PoC is and how to build it. The Proof of concept in software development is not the same. The thing is that they serve different purposes. What does it mean?
Chances are you will face unpredictable challenges when developing a product for an existing enterprise or enhancing it.
Proof of concept in software development is needed to resolve technical issues, find a particular answer to a question, or test new features.
Like, how a particular integration can be achieved. As a result, you understand if your software development idea can be performed and which technologies can help you do that most effectively.
In software development, PoC is a must because:
- You save time and money if the concept won’t work or have guarantees that the idea can be put into life.
- You choose the right technology from the very beginning.
Imagine you need to introduce new technology to cut costs in your company. Everyone knows it is a good motive, yet, there is a question if software development can solve it.
Or: you work at an insurance company, and it would be great to save time and effort spent on every car insurance case examination. Integrating a solution powered by Artificial Intelligence will ensure this optimization. Proof of concept in software development can project and plan this.
If the proof of concept shows that the idea is doable, you can proceed with a prototype, then MVP, or even start developing a product.
Zfort Group has been a software development leader for decades now and can consult for proof of concept or produce one. We can develop a proof of concept for a number of domains, such as fintech, insurtech, logistics, Internet of Things, healthcare, etc.
That's all for today. Thank you and stay tuned for the next episodes of TechFriday.
Hi all,
In this Tech Friday’s episode, we’ve talked to Marvin Liao, an Early Stage VC Investor, former Partner at 500 Startups. He’s now in the frontline of the startup ecosystem helping teams to survive.
You can read the full interview on our blog. And here, we have some key ideas highlighted for you.
The first, and most positive news is that VCs continue investing their own money, unlike many startup accelerators which have put their funding on hold. So, there’s no need to panic, as you’re still able to raise money.
Another important idea Marvin has shared was that nobody actually knows how long the crisis is going to last. Assuming we get a handle on Coronavirus, and we get a better perspective on what's happening and if there's a recovery in summer, then the market will come back in September. If we don't have a handle on this, it might be not for a good while.
The market is gonna shut down for a long time. A lot of folks are just not gonna be spending money. There will be deals being done, but the valuations will come down a lot. The market from the fundraising perspective will come back in September, assuming the overall economy will go back to normal. And if it doesn't, it'll slowly come back in 2021.
A vital thing every startup founder has to concentrate on, is looking at the cost structure. Putting the hiring process on hold, accounts receivables, etc. Any sales that you can try to push to close, if you're fundraising, close whatever you can, close right now. So it's a combination of bringing new money in from either present investors or new customers.
Well, all advice comes regarding managing the money you have right now.
Process optimization won’t include any new partnerships and opportunities. A crisis is more about managing what you have right now. Don’t extend and attract any new obligations and collaborations. Focus narrowly.
Along with struggling economic sectors like restaurants, travel, offline entertainment etc., there are industries experiencing growth: anything related to remote work, remote education, remote companies in general.
If you look at Zoom, they've done incredibly well! Then, Slack has become so critical for so many companies. Whether you're publicly traded or a private company, anything that helps remote work or infrastructure is good. Even gaming as well, a tool for entertainment. Being locked in a house for 2 to 3 weeks or longer, you can see core work functional products, software products, like Loom, video products, Figma, etc. Products have become much more mission-critical today. So looking at these sectors, from an investor’s perspective, might be a good idea now.
Telemedicine is undoubtedly a growing trend and we'll start to see the real use case of telemedicine. For a lot of other companies, it's not as clear they'll make it. Pretty much bullish about that space, even there are a lot of competitors. It's a very good thing from both investors and customer's perspectives.
Another positive impact is that more and more companies are going to realise that good software can be created by distributed teams. There are cases already; there are companies already successful with their fully remote and distributed teams like Close.io, GitLab, GitHub, Buffer, Zapier, InVision. There's a lot of very good software companies. And the reality is that a lot of Silicon Valley companies in the last one or two years have become 100% remote. That will happen to many other companies in the post-Coronavirus world. Most startups are remote by default now, and it's a growing trend.
Hi All,
Today on TechFriday, we are going to talk about the steps to make your PoC effective.
In our previous episode, we’ve rounded up the tools to introduce your idea, proof of concept, minimum viable product, and prototypes.
Decided to start with a PoC? We’ve prepared some tips to ease the flow:
1. Define the goal of your project.
At the early stages, you should have a clear picture - what needs your product solves. Then analyze the market, identify competitors, and ensure your offer is better in a certain way.
2. Organize a team.
Have the right people assist you during all the production stages, from planning to analyzing potential clients' feedback to improve the proof of concept. At Zfort Group, we have expertise.
3. Don't neglect the testing.
Have your potential clients test the product. You'll discover some possible mistakes as well as it'll help you realize if you are moving in the right direction. Also, dealing with your target audience will help you set success criteria to measure its productivity.
4. Document your PoC.
We recommend having everything you do documented. It helps analyze the feedback and decide on the next steps. It's essential for keeping the team well-informed of the entire process.
5. Make a presentation of your PoC.
Having your idea communicated can hardly be overestimated. It would help not to lay out the facts and figures but also elaborate on the proof of concept goals, the big idea behind it, and the audience's needs.
This is it for now! Thanks for listening. Soon, we’ll talk about the peculiarities of proof of concept in software development.
Hello, and welcome to the series of podcasts dedicated to Proof of concept!
More and more businesses start not with an MVP or prototype. They present ideas with a PoC. Let’s see how it works.
Behind every business, there is an idea. How can you tell it will work out? How to attract investments, resources, and expertise to a project.
Any investor wants to see a comprehensive concept, financials, and sound reasoning to invest in your product.
That is why Chief Innovation Officers (CINO), business owners, CEO's, Project Owners, and start-up owners turn to the proof of concept.
PoC presentation contains the product's core goals, functionality, design, and unique features. It shows how to put the idea in place and for what kind of target audience.
Proof of concept is applied in many industries. In software development, POC is used to test new technology or functionality. Here at Zfort Group, we can assist you in developing one.
A prototype is the project's next step. It implies a working POC model that can be tested. The prototype has the full functionality that investors or potential clients can try. It serves the two primary goals. To win more investment and to detect and fix bugs.
After that goes MVP, its main goal is to launch a business and collect real-time data from the competitor's data. MVP entails minimal functionality aimed at the product's end-users. MVP can identify the project's financial success.
That’s all for now! If you want to find out how to keep your PoC effective, listen to our next episode.
Today on TechFriday we are going to talk about big opportunities in insurance. You’ll learn all you need to know about Insurtech and discover inspiring success stories of the top 10 Insurtech companies in 2020.
So, what does insurtech mean?
As you've probably guessed, the word comes as a combination of "insurance" and "technology". It means, Insurtech extracts value from old school insurance but has a hands-on approach to create convenient and unbiased insurance products.
AI, Big Data, Internet of things catalyze InsurTech growth. As an Insurtech AI Development Company, here at Zfort Group we see a huge leap our clients are making toward a cutting edge insurtech that goes hand in hand with the customers’ reality.
Why is this happening? Living in the technological world, Insurance consumers see no point in navigating piles of paperwork anymore. They are not willing to deal with sales agents driven by commissions instead of the customer’s best interests.
As a result, insurers all over the world evolve and reinvent themselves. Now the whole industry cannot afford to stand still. So, how does instrutech work?
Well, Insurtech works from the customer’s pocket: its platform is a gadget; its connection is the Internet; its priority is customer convenience. Clients research, compare policies and buy online personalized products based on their individual interests without any approximate client groups.
Social networks, wearable gadgets (from GPS tracking of cars to the activity trackers on our wrists) help Insurtech companies to receive relevant information in real-time. Then insurtech AI refines data sources, makes it possible to choose the perfect program for the specific customer. Insurance tech space gives customers better conditions based on their own traits.
This way, Insurance tailored to the specific customer's needs and behaviors put them in charge of their own fees. As a result, customers enjoy lower insurance premiums. What's more, insurance technology reduces insurance claim processing time. It ensures immediate communication with the insurer at any time. Here are some examples of insurtech companies breathing new life into the insurance business.
- The first one is Trov, one of today’s digital insurance leaders. Headquartered in San Francisco, Trov operates in 5 countries and has raised $114m of funding. AI tech powers the company to offer its white-label insurtech platform for mobility, retail, rental, and finance. For example, one of Trov’s recent product launches was a personal auto & mobility insurance app designed to align with auto-owners’ needs.
- Another example is ZhongAn, China’s first online-only insurance company, based in Shanghai. It has become the country’s largest insurtech business with a total market capitalization of HK$38.5 billion. The company aims to reshape insurance by applying Big Data analytics.
- Next comes Lemonade, an insurtech company providing policies for homeowners and renters. The total funding amount counts at the moment $480m. Lemonade uses chatbots to process claims and underwrite policies. It strives to move away from paperwork and bureaucracy. For instance, a few years ago, Lemonade handled a claim in three seconds, setting a world record in the insurtech world. The business positions itself as insurtech driven by social good. A user can select a non-profit organization to receive funds from the unclaimed premiums.
Surprised that insurtech is so diverse? That’s not all. For more information, you are welcome to check our articles WHAT IS INSURTECH and TOP 15 INSURTECH COMPANIES IN 2020.
In this Tech Friday’s episode we’ll discuss the reasons Why 2020 Is the Best Time to Go Digital and the key difference between Digitization, digitalization, and digital transformation.
Learn more about here --> Digital Transformation.
In a narrow sense, digital transformation can be mentioned as a paperless office.
In the broadest sense of the term, it is a new approach to doing business, applying digital capabilities to improve organization efficiencies, increase customer value, and create new monetization opportunities. Digital transformation strategy is changing the way business moves; in some cases, creating entirely new classes of businesses.
When businesses are driven right by experts in the digital transformation journey, they become aligned with customers’ demands and flexible in the fast-moving digital future.
Digitization, digitalization, and digital transformation. How not to be confused?
The question is more crucial than a linguistic exercise.
In some respects, digitization of manufacturing industries has enabled new production processes, the Internet of things, and machine vision. Digitization provoked such technologies as smartphones, video games, web applications, cloud services, electronic identification, and even blockchain.
Digitalization refers not only to the IT sphere. Today 94% of all information is digital. Digital data has become a fundamental part of our everyday lives. A wave of digitalization is fueling innovation in business. Organizations change the way they operate to be digitally compatible.
Why Digital Transformation Matters for Your Business?
According to IDC’s research, global spending on the technologies and services that bring the digital transformation process is going to reach $2.3 trillion in 2023, with the period from 2019 to 2023 seeing a steady expansion of digital transformations to reach this figure. This shows that ever-increasing digital transformation is being considered as a long-term investment, with initiatives set to seize a 50% share of worldwide technology investment by 2023.
Here are the 5 main upcoming trends in digital transformation:
- Number 1: More attention to the use of AI and machine learning. Businesses should cope with their wrangled data and then scale this data conveniently. Machine learning and AI will be used in terms of data architecture models — to automate and mitigate the data governance problem.
- Number 2: Public cloud adoption expansion. IT leaders will strive to leverage public cloud capabilities to accelerate value rather than investing time and money developing them internally.
- Number 3: Low-Code Development. This is a faster and simpler way to create an application. Low-code platforms are the most suitable tool for accelerating app delivery. A minimum of hand-coding, and fast setup and deployment enable business users with zero programming experience to build applications using drag-and-drop segments through a clear user interface.
- Number 4: Personalizing customer experience. More and more customers expect from their service provider a seamless omnichannel experience accessible anytime from anywhere. Google, Facebook, Amazon and other giants drive transformation in consumer experiences.
- Number 5: Support Vector Machines. An algorithm that analyzes complex data transformations and figures out how to separate data based on the labels. This helps businesses in their decision-making process. Some typical applications of SVM are face detection, text and hypertext categorization, classification of images, and handwriting recognition.
You can read more on What is Digital Transformation.