Introduction to Guest

Dr. Abraham Heifets is CEO and Co-Founder of Atomwise, where he and Co-Founder Dr. Izhar Wallach invented the use of deep convolutional neural networks for drug discovery. Dr. Heifets was a Massey Fellow at the University of Toronto—a center for AI innovation—and a Fellow of the Ontario Brain Institute. His doctoral work applied machine learning and classical AI techniques to organic synthesis planning, which is a long-standing challenge in chemistry. His vision of AI bringing better, safer and more potent drugs to patients was recently featured in the July 2019 Moonshot issue of Newsweek. Dr. Heifets is a thought-leader on the use of AI for drug discovery and is an author on 24 papers, patents and patent applications. Dr. Heifets has presented his work to the U.S. GAO, the National Institutes of Health, the American Chemical Society, and the Association for the Advancement of Artificial Intelligence.

Abe’s degrees are all in computer science – think good old fashion Artificial Intelligence. Abe wound up working for IBM Research in Boston for about five years working on big data problems. Up until this point, he hadn’t looked into chemistry or biology. What happened was, a good friend of his decided to become a doctor. After Abe’s friend began his pre-med courses, he would complain to Abe about organic chemistry (in particular). To Abe, however, organic chemistry sounded fun, since the algorithms in organic chemistry are very similar to algorithms that computers use. For example – IBM at the time created computers that started to beat humans as chess, which was a huge breakthrough, and one that had been open for 80 years. When it came to the problem of organic chemistry, Abe realized it was deeply linked to the problem of playing chess. Abe then began taking organic chemistry courses at Harvard to see if he could actually do the work. This new venture (or hobby) was how Abe got into the life sciences.

Abe soon decided to take his education further. He had the good fortune to complete his PhD at the University of Toronto where modern machine learning was being discovered. Abe began to shift from old fashioned AI techniques to machine learning techniques, while learning about neural networks and convolutional neural networks. There Abe met his cofounder – where together they combined protein analysis algorithms, big data approaches to chemistry, and machine learning to create Atomwise.

Coming Up with The Idea

Atomwise really began as a research question. People have been trying to use computers for chemistry for decades. The use of modern machine learning of convolutional neural networks of deep learning was new, essentially what Atomwise brought in, but the idea of computers and chemistry was not new. Every major industry uses computers to do its designs – with most of the prototypes made tested in the computer first. Pharma is really the last industry where you build every single prototype manually. So, the idea that we could use computers was not working because the accuracies were never good enough. This in itself sparked an idea in Abe. With computational chemistry literature (datasets), people began looking at the weaknesses of these open problems. Luckily, Abe and his co-founder had access to these new datasets while earning their PhD’s.

Abe and his cofounder started discussing the fact that data sets enabling the use of machine learning algorithms are just being curated and formalized. And maybe, together, those datasets could solve these long-standing problems. Atomwise really began with that open research question, which, essentially, kept working. Even when it was tested, it kept working. Abe knew they were onto something big, and it was worth taking the risk to implement it beyond paper. Creating a product with the drive and notion to have a deep and everlasting impact on the world.

Raising Capital

The company was originally founded in Toronto where Abe’s day to day duties entailed raising funding for Atomwise. This was a discouraging task, as he kept receiving feedback that the product was not going to work. So, in 2015, Abe and his co-founder relocated to the Bay area for Y Combinator. It was there where they met Tim Draper of Draper Fisher Jurvetson, who funded two companies working on a similar idea 20 years ago but decided to take a shot at it again with Atomwise

At this time, Abe wound up giving a very similar presentation to what he had been pitching a couple months earlier in Toronto. However, there was a huge difference in perspective and mentality between Toronto and the Bay Area. People in both places looked at the same data and came to the exact opposite conclusions about whether Atomwise was fundable. At this moment, Abe and his co-founder knew they found their home to grow the company.

Artificial Intelligence for Drug Discovery

Atomwise uses AI to help design drugs. For a drug to work, it has to stick to a disease protein to shut down the functioning of that protein to reset the disease process. That same drug simultaneously has to bounce off proteins in your liver, your kidneys, your heart, or your brain that you want to keep functioning – organs that are important to your health and your happiness. Fundamentally, you want it to “stick to the thing you want it to stick to”, to bounce off and not stick to the proteins you do not want it to stick to. By setting up a physical environment you can test whether the molecule sticks to a given protein or not.

As you can imagine, in our physical world that is laborious, expensive, tricky, and time consuming. Atomwise is able to frame this as a classification problem: where you have a binary classification, does it stick? Yes or No? Atomwise then puts in the structure of the protein and the structure of the molecule to see if they fit together. Having built that predictive system they can screen billions of compounds that are commercially available today and pull down a shortlist of molecules that are worth testing physically.

All in all, Atomwise is predicting if the molecule is going to bind to the protein or not. Through simulation using deep learning AI and machine learning algorithms, they’re saving the actual experiment exercise while offering predictability if the molecule is going to work or not.

Proving the Technology Works to the Pharma Industry

To start with, the life science industry is very skeptical. In some cases, to be a good scientist you have to be skeptical, to look at the data and ask: “how can this data be an artifact?” Atomwise pitch is one that is familiar, in fact has been around for decades, so people are skeptical of their business approach. This creates a burden on Atomwise. In proving their new technology is able to do something never done before, that it actually works. Atomwise must show their product works. They must show this 100 different times on 100 different proteins on different diseases on different parts of biology, in many people’s hands, that it works over and over again without fail.

With that being the goal, Atomwise spun up a program called AIMS – the Artificial Intelligence Molecular Screening Program. Through this program Atomwise is able to solve the problem that has been left open for decades. For example, if you’re a professor at a university and you have been studying a protein, you might have great evidence that if you could block protein XYZ and would have great impact for people with Alzheimer’s, Cancer, or COVID-19. As a professor, you have insights into biology and know how to test whether you’re blocking the protein, but what you need is the drug or the molecule. You need a molecule that can safely and effectively block up protein XYZ. This is where you come to Atomwise and tell them you’re interested in protein XYZ. Atomwise will run the AI and screen billions of molecules to then compile a short list. From there, Atomwise will ship you compounds for you to test, and to share the data back with Atomwise. This procedure has been extremely successful. Over the last year, Atomwise has had over 1,000 applications to come work on this, and an additional 750 projects. Atomwise has the results for over 100 different such projects, so they can actually demonstrate with statistical significance where and how it works. They can look at the success rates and demonstrate over and over again that they can unlock these proteins that otherwise are impossible. And Atomwise can do it faster than setting up a physical experiment. Their partners can do experiments they couldn’t do otherwise, with the ability to ask questions they couldn’t have asked before. The process would be way too hard for them to set up on their own. A huge reason why Atomwise is so valuable.

Advice for Aspiring Entrepreneurs

Do not wait for permission from others to do something, do not wait for the moment from others to say “go”. Always think about what you need to prove, while making sure you’re providing value to other people. You must be providing value to customers, your partners, and collaborators. That’s how proof works. It’s not about doing better on a benchmark, which AI often thinks about, for the benchmarks have to link into people’s lives or business outcomes.

Know you are not going to have all of the right answers. You must iterate your way to success. You must take as many steps and get the gradient updates as quickly as possible, from here you can find progress. If you wait, you do not get back a signal as for what is not working. It’s critically important to keep trying, and to not delay. You learn so much more this way. Take small steps and take many gradient updates.

Future Endeavors

The future is extremely bright for Atomwise. Atomwise recently raised $123 million, Series B, enabling the company to grow and scale in a number of important ways. First, the range of molecules that you can work with today is massive, and it has been becoming more massive in the last 15 years. This is a new world that we’re living in, we’re living through a transition where synthesis on demand lets us do chemistry in a different way, to access chemistry we’ve never been able to access before. The chemical vendors are adding about a billion molecules a month to the catalogs. For context, that’s like taking the entire corporate collection of all of the top 20 pharma companies and putting them together, multiplying it by 10, and adding every month to what you can order out of a catalog today. There’s really no way to test that physically. You have to have incredibly accurate computational approaches to test those molecules to even keep up. Because if you have even a small error rate, the false positives swamp you entirely. Atomwise really has to scale their technology to keep up with the massive amount of chemistry that is out there. They have to scale their technology to work on broader classes of proteins, and to answer questions as they take the molecules deeper into drug discovery.

On the business side, Atomwise is continuing to grow their partnerships, their portfolio to work with Big pharma and Small Pharma. Atomwise has had an increased focus on doing their own discovery. With this kind of scaling, Atomwise gets to grow the team, which is another main focus of the company.

https://www.atomwise.com/

Introduction

Archie Cheisvili is in his 20s and taking over the world of AI. He has recently been listed as Forbes 30 under 30, is a Harvard graduate with a degree in economics, and is frequently published by media outlets such as the New York Post and Yahoo Finance. Archie traded in a career at the largest fund in the world to be an AI entrepreneur. He now is the founder and CEO of GenesisAI, an Artificial Intelligence marketplace that allows companies to buy or sell AI products and services.

GenesisAI

GenesisAI is an AI Marketplace, think Amazon for Artificial Intelligence.. People can go to the Genesis platform, discover, tests and integrate different AI APIs. Users are also able to combine different AI tools for better performance and functionality. GenesisAI’s mission is to provide a very accessible AI marketplace that also combines different AI tools for better accuracy rates.

For example, if you want to do a deep fake analysis, you can go to the GenesisAI platform first and compare different services. You can upload any video, for example, and it will tell you if it contains a deep fake. If you want to systemize it, this is also possible. All you have to do is copy and paste a few lines of code, put it into your own development environment and then you’re able to make API calls to the AI model.

Building the Marketplace

First, you must focus on making sure you have the products that the customers of your marketplace need. And at the same time, you must build a customer base. They both need each other. This is always a tough problem to solve, which GenesisAI solved by seeding the supply side of the marketplace themselves. GenesisAI has about 40 AI tools deployed by their own engineering team; this way, when buyers come to the site they can already use tools and designs. There are over 2000 users on the platform.

Learnings & Challenges Encountered

The first bit of advice from GenesisAI is to first gather a core team through your own personal relations, database, and connections. Once you look at your immediate circle, you can then use recruiters to assist you with the hiring process. Working with 10-20 recruiters was key for GenesisAI. Many people think that hiring data scientists is a challenge, but it is not that hard when done correctly.

Next bit of advice is on raising capital. You do not need to be very creative or good at pitching, it is really all about storytelling, you must get your story and narrative right.

Fundraising

So far, GenesisAI has raised about 5 million in total. The company has two VCs as investors. They also did equity crowdfunding raising with about 5000 investors. Archie explains that it’s important to not just focus on VCs but equity crowdfunding as well. Raising capital through crowdfunding allowed GenesisAI to get the capital and the and users at the same time. GenesisAI used WeFunder for their crowdfunding campaign.

Advice For Aspiring Entrepreneurs

Advice for future AI entrepreneurs is to choose between the commercial use of AI – where AI can be applicable and where people might pay for you, or AI that is solving hardcore AI challenges and deeply rooted AI tech problems. For example, how do you combine AI models for better performance? It is very important to decide between one of the two when starting your own AI company.

AI in Marketing Conference:

GenesisAI is hosting AI in Marketing conference in Miami this May, where they will have long list of reputable industry experts presenting the commercial use of AI in marketing now and where they see it heading 5 years from now.

The conference will be held on May 24th and 25th

The link to get tickets to the conference: https://www.miamiai.io/

Company URL: https://genesisai.io/

Intro

Rob Carpenter is the CEO and Founder of Valyant AI, an enterprise grade conversational AI platform for the quick serve restaurant industry. Valyant has developed a proprietary software application that integrates within a restaurant’s existing hardware infrastructure and allows the AI software to take the vast majority of customer orders and insert them directly into the POS for payment.

Prior to founding Valyant AI, Rob was the CEO of AppIt Ventures, a custom software development company with offices in Denver, Hyderabad and London. Over the first six years in business, AppIt built and launched over 350 custom software applications, including mobile apps on all major platforms, backend web applications and sophisticated databases.

Rob has a master’s degree in Business Administration with a specialization in Enterprise Technology Management. He spent two years on the board for the Rutt Bridges Venture Capital Fund. In 2013 Rob was named one of the top 25 most influential young professionals in Colorado by ColoradoBiz Magazine and in 2016 he received the Denver Trailblazer award.

Rob’s entrepreneurship journey began when he was young, after being introduced to Robert Kiyosaki’s book “Rich Dad, Poor Dad” which opened up new ways of thinking for him. He realized that there are so many possibilities in life beyond earning a degree, getting a job, clocking in, clocking out, getting a pension and retiring. When in fact, we all can take control of our lives and embark on projects and work that we are passionate about. Although a chaotic journey at times for Rob, this realization paved the way for an exciting and rewarding journey into entrepreneurship.

Initially Rob started a custom software development company where he built mobile applications for other people. Whenever he met and talked to other entrepreneurs, especially people that wanted to build companies, he highly recommends they start service based businesses. Service based businesses are more easy to get off the ground and are labor intensive. And if you work really hard, you can beat out your competition. Rob explains that through this path you can carve out a nice lifestyle business for yourself.

However, what a lot of people discover in about three to six years starting a service-based business is that even though it is quicker to start, they’re on a treadmill, constantly running. There is a constant turnover process of your customers. And at the end of the day, it’s exhausting. Rob soon decided he wanted to transition into a product-based company. While harder to get off the ground, once it’s up and running they tend to be more lucrative. A product-based business became easier to run for Rob because the product was earning revenue for him. This revenue stream is constant and comes through when Rob sleeps and when he is on vacation. He does not have to be constantly selling. This new way of doing business is what ultimately led Rob to wanting to get into some sort of forward-facing future technology. Artificial Intelligence is what became his perfect middle ground.

Rob strongly believes that a lot of entrepreneurs get too focused on their first company needing to be the next Google, Uber, or Facebook. When in fact, that’s not true. A bunch of studies have shown that the number one predictor of being able to start a billion-dollar company is having previously started another company and having had some sort of an exit. It’s important for people not to feel like they must shoot to the moon right out of the gate. Rob started a nice business, built his network and connections, and they launched Valyant after.

Developing the Technology and Finding the Product-Market Fit

Rob’s initial idea was to build AI employees for physical retail locations that have a physical presence. Basically, what we refer to as holograms. Rob and his team used a transparent OLED display to render the first version of this AI employee, integrating with this cutting edge and cool visual interaction with a holographic employee. However, the actual voice interaction was terrible. This was in 2017, and Valyant realized that the off the shelf technology worked for something simple, such as Alexa to turn off your lights. But it didn’t work for carrying on a fluid conversation like you might have with an employee. From here, Valyant had to pivot the business and focus on building an enterprise grade conversational AI platform. Rob realized that if they are going to make this holographic employee ever work, voice AI would have to work first.

The first customer that Valyant onboarded to their technology was called “Good Times”, it was a burger restaurant out in Denver, Colorado. The restaurant is a double drive through, with lanes on either side of the building. After a year and half, this was the first time a restaurant was willing to pilot with Valyant’s technology. At that time, the labor shortage in hospitality was already up to about 800,000 vacant positions. The product officially launched in October 2018, and Valyant was the first company to get this technology into market. Since then, Rob and his team have been continuing to grind and refine, iterate, and improve since then. Now the company is at a point where the technology is starting to propagate into the market. These are very exciting times for Valyant.

There have been hundreds, if not thousands of challenges related to trying to launch this type of technology, starting with the physical on-premises sort of hardware challenges. For example, there is no way to get audio off premise. So Valyant had to build their own hardware. They also faced AI challenges such as speech to text. There were massive amounts of work that must be done to get this technology to function properly. The NLP systems that were tested were not fast or responsive enough or able to handle multiple intents. Valyant had to build one from scratch.

Along with a plethora of additional challenges, Valyant had to learn how to package both the physical and digital human psychology all into a package that can respond back to the customer in a drive through in two seconds. This has been a tough problem for the company to solve.

Valyant has 22 people, with eight positions currently available. Valyant is looking to have about 30 employees by next month. Valyant is looking for talented engineers, or people that are really excited about the world of AI and want to get into the Artificial Intelligence space. By the end of the year, Valyant is expected to grow to about 50 employees.

In about five years, Valyant is looking to have about 10,000 restaurants running their voice AI technology. They are hoping to have digital holographic employees live in a couple of different industries, acquiring a few additional voice AI companies along the way. Valyant will also be looking for opportunities in new markets. As of right now, Rob and his team are hyper focused on delivering a fantastic product to the quick serve restaurant industry.

Rob’s Advice on Finding your First Customer

Make sure you always start with a problem first before you raise money and before you hire engineers. Gain an understanding of AI’s capabilities. Go out into the market and segment the market and talk to people in each of the different segments. Really sit down and brainstorm on where you think your technology can work. Once you identified a market, and you believe you identified a problem that your AI could solve, then work hard – either through networking, or through cold calls to meet people in that industry. Run your idea past them.

When you’re at the idea phase, ideas are a dime a dozen. There is nothing wrong with throwing your ideas against the real world and seeing what sticks and what doesn’t stick. Once you identify who the potential market is, that is when you can start to build the product and pre-sell to companies. If the problem you’re solving is big enough, companies should be willing to invest some money upfront to be able to get access to your solution. This would be the best-case scenario for selling your product and finding a market opportunity fit.

https://valyant.ai/

Introduction To Guest

Dr. Lawrence Ngo is a practicing radiologist with PhD in neurobiology. He has received his MD PhD from Duke University School of Medicine and is now the cofounder and CEO of CoRead AI, a start-up that has developed machine learning algorithms that analyzes the images and reports from radiology exams, and flags studies with high probability of error for peer review. They are essentially looking for blind spots and things that could have been missed by a radiologist and flagging them.

Dr. Ngo has always been fascinated by how to bring in advanced technologies to improve the human condition. Throughout his life, he has always jumped back and forth between highly technical fields and humanities. For example, he majored in philosophy and then went to medical school. Dr. Ngo soon went on to receive his PhD in neurobiology, creating a project that used MRI scanners to scan people’s brains while they make moral decisions, and really understanding how their brains work for that. Dr. Ngo has always had an interest in combining the two.

The latest thread he’s been working on is from several years ago, when IBM created an algorithm called Watson that was able to play Jeopardy against reigning Jeopardy champions. By doing so and playing those games, and being able to beat the top champions, Watson was able to prove that it was able to do something that people thought was uniquely human. However, what really caught Dr. Ngo’s attention was when the headlines claimed that Watson was going to medical school. Watson’s algorithm was being trained to take the same test and try to beat human’s doing so. This together was how Dr. Ngo initially became interested in really trying to figure out how AI could be applied to the field.

Fast forward to when he became a resident. After medical school, Dr. Ngo got more into clinical practices. He got connected to a large teleradiology company that had access to very large amounts of data. Dr. Ngo had developed some technical skills during his PhD in data science using some machine learning and decided to join their incubator program. This was tough since he was in the middle of his residency.

Coming Up with The Idea

For Dr. Ngo, it was the confluence of two different threads radiology resident in the daytime and training machine learning algorithms radiology at night. On one hand, he was a radiology resident. The time it takes getting your residency also comes with a lot of mistakes. He had to become familiar with his limitations. On the other hand, Dr. Ngo oversaw doing diagnosis and interpretation of image imaging. And at nights, for example, when no supervising physician was around, Dr. Ngo would learn of the things he could have done better the morning after. He was trying to train algorithms using AI. Dr. Ngo also had an advantage since he was a radiologist himself. He spent a lot of time reviewing the outputs of the AI and trying to label the data where the algorithm had made mistakes. In Dr. Ngo’s findings, he saw that at times algorithm was correct – and the initial radiologist who was making the diagnosis was making the mistake.

To better understand how this works, Dr. Ngo had an algorithm that looks at images like an X ray or CT images. Then another algorithm looking at the report that radiologists have rendered during regular clinical practices interpreting the images.

As Dr. Ngo and his team continued to develop more and their algorithms got better, they started finding more mistakes from radiologists. During a residency you have a safety net, a supervising radiologist checking your work. But in regular practice, you don’t have anyone going behind you to check your work. By having an algorithm that can process 10s of 1000s of images in a very small amount of time, you’re able to do a rough review of a very large number of studies, finding weaknesses in people’s practice patterns.

Production and Partnerships

CoRead AI has been very fortunate to get connected with a lot of other companies in the field. Those companies have done a lot of work in trying to build platforms at hospital systems and radiology practices to deploy AI algorithms. The field of Radiology and AI is very hot with a lot of investments. Dr. Ngo believes that it’s important to establish platform at a network of hospitals around the world where any other company can plug in their AI algorithm for whatever use case and then be able to serve on the platform.

CoRead AI’s focus is narrower. They have the medical expertise and the AI development expertise. They want to develop some high-quality models that both look at the images and the reports and are happy to partner with as many people as possible to get that delivered to different hospitals and radiology practices. CoRead.AI is open to as much collaboration as possible.

Challenges Encountered

There are always challenges, as Dr. Ngo explains. The first thing is with AI development, the quality of the data is super important. They deal with very large datasets. Even though Dr. Ngo is highly motivated to make sure that annotations are as high quality as possible, mistakes happen along the way in terms of labeling data. Many people focus on the out-algorithm development and fine tuning everything, but it comes down to really understanding the data and making sure that it’s labeled in a high-quality fashion has been a challenge.

The other challenge in the field of algorithm development is not as rigorous regarding controlling for biases. It’s difficult to run studies showing that algorithms can impact how the patient outcomes and those studies are starting to come out now. There’s a struggle in trying to make sure that ultimately, you’re able to prove that the algorithms are doing something important, rather than just being part of the AI hype and having an algorithm like everyone else.

Future Plans

There are two sides of the coin for radiology: improving the quality of the care that’s being provided and improving the efficiency of radiologists. The progress of CoRead AI is that they continue to improve their algorithms to a degree of higher accuracy, such that the peer review system can be more streamlined. Perhaps get to a point where the algorithms can augment the workflow. Right now, CoRead AI is helping the quality standpoint, but potentially help radiologists become more efficient as well. To augment the work that radiologists already do and help to improve higher quality care while keeping the radiologists more balanced and less partout.

Challenges Encountered

Dr. Ngo’s advice is if you’re already in the healthcare space, it’s a huge opportunity to learn machine learning and AI, because you have this advantage of knowing something about the healthcare space already. But if you’re coming from the outside, really take some time to dig deep into understanding what the flow is, how healthcare interacts with patients for that condition, how they improve outcomes for that condition, and then how economics works around that.

Also, make sure you have a strong support network around you. This has greatly helped Dr. Ngo.

Introduction To Guest

Jared Ficklin is Co-Founder & Chief Creative Technologist at Argodesign, with two decades of experience creating products and visions for major companies. For his previous work integrating technology into the design process at Frog Design, Jared was named one of 4 Frog fellows. He has contributed to the visions, strategy, intellectual property, and products of clients including HP, Microsoft, AT&T, LG, SanDisk, Motorola, CognitiveScale, and Magic Leap.

Jared’s path to design was an interesting one, as he originally thought he was going to be a professional musician. Jared dedicated years of schooling studying music, which led him to confront the harsh reality that he was not going to be a professional musician. Jared later began studying philosophy, psychology, and marketing, which led to an epiphany. When Jared was eight years old, his dad purchased him an Atari 800 and a subscription to a basic magazine intersected with the world of creativity applied to business. This was a lightbulb moment for Jared. His years of computer skills were now very applicable during the boom of the world wide web, and he discovered a design niche that was in the world of tech. It was in this moment where Jared’s new life began. Jared soon landed a job at a shop called Interactive Services Incorporated.

Shortly after Jared moved to Austin, Texas where he picked up a job at a design firm called Frog Design, where he worked for 14 years. Jared describes his time at Frog like having four different careers. It was with Frog where Jared received his education in product design. Jared was then able to join a startup where he had the opportunity to take his design work even further. Jared credits his enthusiasm for technology that found him a ‘home’ in the design world at the right moment, right when his curiosity and pursuit of creativity landed him in a world of designing products.

Shifting World of Experience Design

Jared was right on track with his timing and was working in the perfect space.

Product design forms from a state of emotions. This design movement started in the 60s and still greatly applies to the tech world today. It’s not enough to just have a website. The website needs to have a client experience behind it. The perfect example of a company that demonstrates this is Apple.

From around 2000 to 2020 most products needed to be redesigned and remodeled for web and mobile. Jared also reiterates that machine learning and AI provides enough context to not need as much user interface to understand what the user is attempting to do with a device. This is allowing individual features to show up as single function devices. Also, the costs of parts are coming down, as a wide deployment of single function computers are enabled by machine learning.

On the other side we’re seeing the same technologies enabling new interfaces, voice, for example. The industry is transitioning into the next pattern of computing, which can be far more ubiquitous. Everything will be getting a new embodiment. Jared feels like the next 20 years are going to be just as exciting as the first 20 years.

Design Principles

There are processes and certain design philosophies that help Jared with designing. However, there is a unique space within the design world – when technology is sufficiently new enough and the deployment is unique, but the user lacks familiarity. There becomes this little place on the map that Jared calls the unique place. In that place, it is really hard to do design research or use convention. For example, we can ask people “How would you use AI in the car?” but no one would really have an answer to this question. They’re not familiar enough with what AI is capable of. There’s no patterns or conventions for them to draw from in the past. In western culture, something will usually just get made up. In order to validate what users, want, a prototype must be built and quickly put in front of users. This is what user simulation does. This is how design practices use cognitive skills.

Many times, Jared and his team must understand the problem of the client as deeply as possible and conduct the design research needed. From there, Jared could build things to have them tested out by the users. The orchestration of AI is not a linear process. Much productivity is done through the user experience in design but many times it is presumptive and then rapidly tested. There are no conventional or best practices to draw from. ArgoDesign has to use his own method and technique when designing.

Designing For Responsible AI

Responsible AI is a very hot topic now. This is a consortium that is building essentially what’s like a LEED certification for AI solutions. It’s a series of best practices, but also a consulting way to look at anything from as small as a model to the way the model is used in code. It is then graded for how responsibly it’s being deployed. There have been some big hiccups in the deployment of AI around this issue where either unethical, illegal or immoral behavior has taken place. It’s very important to have transparency. There is a huge motivation to build a layer on top of AI right now that can watch the AI, in turn making sure that it’s hitting business goals, regulatory goals, and ethical human goals.

There is now a reporting interface called poles where business owners can, in a low code environment, build up monitors. Flags and alerts will go off if needed to make sure AI does not get out of hand. This is working well for Argo Design as they design products that both serve humanity and fall in line of high-quality standards.

Advice For Industry Leaders

Jared’s advice for industry leaders is to first understand that we’re still at a system level. Think as a system and not an individual feature. Design can lead you down a primrose path easily because we tend to imagine way far out ahead of what is capable and what will generate value.

You must be aware of system level design and start with the capabilities of an organization. Get a clear understanding of how data is going to thread into their existing workflow processes. From there, the features will start to manifest themselves. You must work with people who can know these things, otherwise, you will get glass type design. Design products that make sense and add value.

Advice For Aspiring AI Designers

Jared feels a career in design is similar to a career as a jazz musician. A jazz musician knows their instruments that they’ve chosen. They do not need to think about the technique when playing, it becomes automatic and practiced. With design, it’s about mastery of a tool set. You will be applying your taste, your talent, your emotional creative expression. However, it’s all synergistic. It’s not just playing what you want. A jazz musician is playing at a club in front of an audience, giving them what they want to hear. There’s a resonance with who’s listening. This all needs to be practiced. As a designer, and as a musician, you must put yourself out there to get the gig. Your design portfolio is everything. Just like there is no better practice for a jazz musician to play than a live show, there is no better practice for a designer than to work with real users on real products. Practice working on a project before going to the customer.

After this practice, both the musician and designer will find a moment where they flip over to the other side, a side where they are able to project the future. They can now understand how to attract and impact the right clients. During this phase, you can showcase the work that you personally would like to produce. However, make sure to make it meaningful. Make sure your work makes a difference, that it holds a certain level of quality. And make sure to connect with the user.

There are three kinds of “smart”. One is you know the right answer. The second is that you can communicate the answer in a way that someone understands the right answer. Third, you can communicate it to someone else in a way that will accept your answer. You must have all three forms of intelligence to truly step into the role of a successful designer.

https://www.argodesign.com/

Introduction To Guest

Patrick Lucey is the Chief Scientist at Stats Perform, maximizing the value of the company’s 35+ years of sports data. Previously, he was at Disney Research, where he researched automatic sports broadcasting using large amounts of spatiotemporal tracking data. Prior to that, Patrick was a Postdoctoral Researcher at the Robotics Institute at Carnegie Mellon University/Department of Psychology at University of Pittsburgh conducting research on automatic facial expression recognition. He was a co-author of the best paper at the 2016 MIT Sloan Sports Analytics Conference and in 2017 and 2018 was co-author of best-paper runner-up at the same conference. Additionally, he’s won best paper awards at INTERSPEECH and WACV international conferences.

Patrick is a sports lover who was born in Australia. Growing up, he played almost every sport you could participate in – soccer, cricket, tennis, golf, rugby, you name it. Patrick has always loved numbers and following statistics in sports. During his time growing up in Australia, Patrick played semi-pro soccer while completing his electrical engineering undergraduate, and later his PhD. After he finished his PhD, which was in audio speech recognition, he began analyzing visual and audio data to help improve speech intelligibility and speech prediction. However, Patrick’s passion was in sports.

Patrick was soon lucky enough to obtain a position at CMU, working on analyzing facial expressions, which then led him to work at Disney research Pittsburgh. At Disney, owned by ESPN, the team was working on producing an automatic sports podcast. At this time, sports started to be consumed via streaming, and Patrick was able to track this data, publish papers, which then led him to start his own AI group. With 40 years worth of data at his hands, Patrick and his team became the pioneers in computer vision tracking.

Stats Perform

Stats Perform is a B2B company. The markets they serve are team performance and media/tech. Stats Perform works with the biggest companies around the world. For example, when you check a soccer score in Google, that data comes from Stats Perform. If you ask any sports question to Siri (Apple), the sports data is from Stats Perform. The same with Alexa (Amazon). Even all sports books use the data from Stats Perform. The company has their live Moneyball feed where they have the fastest and best data for in play betting, especially in soccer. Also, the opt out is also part of Stats Perform. Whenever you check any media or follow NBC coverage, data is provided by Stats Perform. Billions of people touch this data every single day. Stats Perform is truly the DNA of sports.

Patrick’s path to Stats Perform

Through Disney research, Patrick was able to work on a lot of great papers. He iterates that to do anything in Artificial Intelligence you must have big sources of data. And now, most of the problems in the industry stem from having to go to places which have the data. Stats Perform has been around since 1981 – which got started through Bill James, James wanted more sports data back in the 80s, which soon spun off stats while providing data across all different sports for a long period of time. However, even though James’ company inherited all this data, they were not utilizing it. James and his team needed someone to set up a data science group or an AI group. Based off of Patrick’s work, he was asked by Stats Perform to join the team and start an AI group. Since starting the AI group at Stats Perform, the company has grown from just Patrick to now 50 people in the AI innovation team. This includes data scientists, computer vision engineers, as well as machine learning engineers. While Patrick was not one of the founders of the company, he did start the AI group on his own in 2015.

Using AI to Harness the Power of Sports Data

There are a couple of ways that Stats Perform uses AI to tell stories. The first is by using computer vision to generate more data. Through having cameras in venues, Stats Perform has been the pioneers in player tracking data over the past ten years. Through the cameras they were able to track players at a very high frame rate. So, a lot of the next generation statistics that you’ve seen in terms of player tracking, basketball, and soccer, initially come from Stats Perform. The company has also embarked on creating more tracking data from broadcasting. With a partnership with Orlando Magic, Stats Perform can collect tracking data from broadcasts, which enables them to go back in time and collect historical footage. By tracking games ten years ago, they can enable teams to come up with recruitment models to predict future performances of college players in the NBA. Stats Perform can create and generate more granular data to tell better stories and make better predictions.

Another aspect of using AI is Stats Perform is they can do a Smart Lookup to generate insights. Just based on the structured data that they have; the team can give natural language insights into what’s happened in sports. This can also be converted into many languages. Stats Perform also has smart ratings where they can tell you when something interesting occurs, or when a moment is very important. Stats Perform has live probability models and season simulation models which can give you the impact of a certain event.

Challenges Encountered on the Path

Patrick and his team have experienced quite a few challenges, starting with getting the data in a form, which is utilized for AI capabilities – building the whole infrastructure and pipeline and getting the team set up. It is one thing to possess the data, it’s another thing to get it in the form where you can put it in for modeling purposes. It’s about leaning into being data centric instead of being model centric. Patrick states if you do not have differentiated data when building a new product, you shouldn’t proceed. This was one of Patrick’s key learnings.

Dealing with building AI models or building AI products is tricky. You need to be always ready for anything. Sometimes Stats Perform has to simplify or make the system more robust before focusing on the prediction accuracy, but you also need reliability. And again, adhering to and addressing all of the edge cases.

Future Endeavors

Stats Pefrom is on a journey to scale out data collection. Their goal is to digitize every video, and to get tracking data from every video that has ever been played, starting with basketball and soccer. Or going back and getting tracking data to get more detailed information. Even though the data exists in terms of videos, it needs to be in a digitized form. The first hurdle is scaling this out.

The next endeavor is being able to create live consumable AI, to make live predictions. This opens the optionality of doing live assisted coaching, providing live insights to people at home, or gamification.

And third, Stats Perform’s additional goal is to lean into reinforcement learning. This includes being able to forecast how a player is going to play long term and how to integrate in private information, such as injury. Stats Perform would like to merge public and private data together to focus on long term forecasting.

Advice For Aspiring Sports Data Scientists

Start by having a background in data literacy and a deep interest in sports. Sports is a great vehicle to start learning AI. Being able to explain human behavior and being able to measure human behavior by numbers can be taught through sports. Start doing your own analysis. Show what you can do in this space, show your knowledge, and show how you approach it. This is how you can get a leg up in the industry.

https://www.statsperform.com/

Introduction To Guest

Slater Victoroff is the Founder and CTO of Indico, an enterprise AI solution for unstructured content that emphasizes document understanding. Slater has been building machine learning solutions for startups, governments, and Fortune 100 companies for the past seven years and is a frequent speaker at AI conferences. Indico’s framework requires 1000x less data than traditional machine learning techniques, and they regularly beat the likes of AWS, Google, Microsoft, and IBM in head-to-head bake-offs. Indico recently announced a $22 million Series B raise.

Coming Up with The Idea

Slater and his friend began doing Kaggle competitions for about six months in 2012. Slater was involved in more traditional techniques, while his friend was into deep learning. Together they soon realized the impact that machine/deep learning had in solving problems was huge. Slater and his friend began shipping their deep learning techniques into production, which was something that very few people knew how to do in 2012. More and more people started to reach out asking to do projects with Slater, which was essentially the birthing of Indico. Slater realized there was a massive gap in what was possible in academia with what was possible in the industry. Slater and his friend took this incredible machine learning technology to see how they could make it practical, tractable, and useful to people in the industry. After a lot of research, they realized you can get the accuracy of deep learning models with 1/1000 the amount of data that you would previously require. Slater then realized that they could build a whole company around the idea of transfer learning, while making this technology more accessible.

Indico first decided to bring their idea to developers to build developer-facing products, a series of API’s that were usable. Back in 2014-2015 there were very few offerings like this sort of alchemy API. This was the first time that these models were radically accessible. Since then, Indico made some significant advances in the usability of that particular API that provided user traction.

While the users were into it and Indico got very good at designing API’s, they found that ultimately it was a very bad business model for the kind of technology that they were taking to market. The principal reason was that unstructured data isn’t something that people have been able to work with effectively before. Unstructured data has been too difficult to deal with in traditional software. When you add deep learning into the mix, you change the game in a fundamental way. You can get after a lot of unstructured reasoning tasks that you couldn’t have tackled before. What Slater found was the technical side and the business side have to come together to create a solution that makes both parties successful. This was the gap that needed to be bridged.

The Product

The most updated version of the Indico product came three years ago. It is a platform that allows people to create their own models and gives them full transparency into the model that they are building. Indico 2.0 still has the APIs of the last generation, but Slater and his team added more to the product. But first and foremost, this product faces the non-technical subject: document extraction API’s, email classification models, object detection, etc. with the goal of giving that non-technical user the very clear intuitive interface to train the model, control it, and understand exactly what it’s doing. For the non-technical users, it’s a no-code experience. However, even though there’s not a code used to build, you still must plug two API’s in on the backend. There’s still that rich API which means it will usually be a low code integration downstream.

Business Model

Indico has two main modes that they’re licensing. One is a managed service. They have a private cluster for the customer that Indico manages on AWS that scales up and down, where the client pays for the hardware that it’s hosted on. There is also a rate card for the capacity that they’re pumping through the application. Indico also supports a fully on-prem version. For some customers, they want to manage the platform themselves. About 80% of customers have a managed service while 20% run on premise.

Working with Fortune 100 Clients

Indico credits its clients from grit, and determination. What people don’t realize is that once you go into an enterprise sales cycle you must recognize that 12 months is a good case scenario in the cycle. You cannot put all your eggs in one basket, you must recognize that it may take two or three years to go from a prospect to a signed deal. MetLife, for instance, is one of Indico’s top clients. It took Indico two and half years from their first meeting with MetLife to their final deal. You must possess a mature product and know how to document things. It’s imperative that you understand the complicated engine that is the enterprise. There could be eight different stakeholders that have to sign off on what you’re doing. Any single one of the stakeholders can say no. It’s tough and it’s a grind to acquire Fortune 100 clients. You must get the right conversation started and go from there.

Future Endeavors

Indico has recently evolved past this document centric positioning into the unstructured data platform. This is a big piece of the company’s three-to-five-year vision. Indico has built explainability and transparency into their foundation and products. This will become more important over time. Slater believes that people are going to think about models in terms of lineages. We’re already starting to see a lot more of these techniques that fuse image and text information, and audio and image information. This is where we start to talk about the future of unstructured data. Indico hopes to create more impactful applications in three to five years.

Advice For Aspiring Entrepreneurs

Something more entrepreneurs in the AI space need to hear is do not be afraid to chase the frontier of technology. It can be very intimidating to see all these massive organizations going after the field of AI and making these incredible advances. Many entrepreneurs become intimidated and do not even try to start their business. Go out there and give it a shot. Do not leave a big opportunity on the table out of fear. Stand up for what you believe and go after your dreams.

https://indico.io/

Introduction To Guest

Daniel DeMillard is the Chief Technology Officer of Foodspace, an AI company that helps eCommerce teams increase online sales and brand loyalty by building underlying product data foundations. Foodspace uses vision AI to derive scanned product data into shopper-centric attributes needed for online grocery discovery.

Daniel uses his background in math, along with his spirit of learning, to explore the depths of possibilities of computer vision, natural language processing, and machine learning. Together with his team at Foodspace, they are focused on creating products to improve people’s lives. Previously, Daniel worked as a Data Scientist at IBM Watson and an AI Architect at Zvelo. He graduated from the University of Wyoming with a degree in MS Economics and Finance.

About the Company

Many people began purchasing groceries online to be delivered, especially during the pandemic. Daniel explains that you really need that information digitized on the e-commerce website. What Foodspace found is a lot of product companies do not have that completed or it’s out of date, or even inaccurate. So, the solution that Foodspace provides is they take images from brands or retailers, upload them to their portal or use Dropbox, and then they take those images and extract the structured information such as: nutrition ingredients, net weight, brand name, product name, certification claims, whether it’s non-GMO or gluten free, certified, or vegan certified, etc. Foodspace will then use the date to build some enrichments on it, listing what allergens are present in the ingredients, the dietary information, whether it is vegan, vegetarian, pescatarian, etc. and then all the information is sent back to the brand so they can become more discoverable on platforms that support those filters. Foodspace is also improving the user experience. For example, if you are looking for low sodium or low sugar, you can easily find the products you are looking for online.

Testing the Product

Initially, Foodspace thought that their customers would be grocery stores. But since they’re slower to innovate, this was not the case. Before the pandemic, around 95% of sales still happened in a store, and only 5% were online. After the pandemic, that has jumped to 15-25% of online sales. However, this jump was not enough. There was still a very large portion of purchase service being made in the store. To some extent, the retailers must be very forward thinking with this technology and the value of getting everything digitized and accurate.
Whereas the brands specifically easily understand the value of providing high quality information to their customers.
With that being discovered, Foodspace’s pivoted to have brands as their primary customers. Foodspace digitizes all their information for them. Foodspace realized that many of these brands’ product labeling is all managed through a third-party agency. Brands are also updating recipes and product information on the package per year, which can take non-AI solutions two to six months to get the data labeled. Foodspace can complete the same exact task with a turnaround time of a day or two.
Another big add is that Foodspace has their own internal data model and can map their model to each one of the retailers. Foodspace will extract all the information from the images, digitize it, and then map it to whatever format a retailer wants. Foodspace will even put it all into an Excel spreadsheet. All the brands must do is copy and paste the information that Foodspace cannot extract from the images (such as price) that’s usually not included in the product image.

Validating The Market & Learnings

Foodspace started looking at retailers first. They really struggled because they were getting a lot of positive feedback, and there was a lot of interest, but little sales. Foodspace discovered that on walmart.com between 40-60% of products had at least one product, or had at least one data error in the ingredients, or the nutrition. There is a massive data accuracy problem. When Daniel and his team talked to large retailers, the digital market was only a small percentage of sales. Foodspace had large contracts with big retailers, but it was difficult to close the deal. This challenge pivoted the company to speaking with brands.

The brands were very excited to move forward with the Foodspace pilot because they did not have any data yet. The contracts with brands are smaller, so it’s a lot easier to onboard new clients for Foodspace.

Working with brands was a big aha moment for Foodspace, especially when they began gaining positive feedback from the brands. Foodspace realized that this was the right direction to go. They also started to build relationships with grocery delivery companies like Instacart and Ship.
With online grocery companies being digital first, the only interaction with the food products is through the app or website. These companies care a lot about the digital assets and making sure the products are searchable. They want to make sure the customer experience is great.

The idea of targeting brands to start with was not obvious to Foodspace. There was a lot of trial and error and networking.

Business Model

When a brand comes to Foodspace, they have an upfront digitization fee. So, they can then download it in a CSV or JSON. Then Foodspace will charge each retailer for data mappings. An additional service Foodspace provides is monitoring. They will specifically scrape those websites and monitor a brand’s product to make sure that it is up to date and accurate on the website. There is no change for each retailer that they’re syndicating to or sending information over to, but there is a monitoring fee if the brand would like the additional peace of mind.

Challenges Encountered

Daniel and his team are obsessed with accuracy. One major problem that they have faced with their OCR models is adding every single comma that is present. This is tedious because they can be small and a little harder to notice on scar models. But these commas are super important to separate what the individual ingredients are because they can span multiple words. This can take a lot of time.

At a high level, there are multiple small challenges. For example, lighting changes in the angle of the pictures, changes in the surface material, changes in background color gradient, changes in container tape, etc. Every single problem encountered requires a different technological solution. They must be solved one by one. This takes a lot of work and time.

Another challenge is adding in allergies, or even vegan food. “Vegan” can have a different meaning for each person.

Future Endeavors

Foodspace believes that the trend for buying your groceries online is going to increase. More people will opt for the experience of either having their groceries delivered to their doorstep or driving to the store to have their groceries loaded into the car.

Foodspace is looking to expand into new markets like Europe and Asia, as well as adding new product categories – pets, alcohol, health, and beauty. Their goal is to really expand the product offering to include more retail brands. They would also like to see more of their data integrated into a smart fridge where you can order directly from your smart fridge. Another cool idea that Foodspace would like to accomplish is automatically setting your oven to your microwave to the correct amount just by using your smartphone to scan your product.

Advice For Aspiring Entrepreneurs

Start with something solvable, tractable, and concrete. Use AI to target problems with a focus of solving one thing. Daniel also recommends having an expert who can help you. There will be a lot of nuances where you won’t know where things are breaking, or why they’re breaking, if you do not have the expertise.

Daniel’s final piece of advice is to build out MVPs. Build out the smallest thing possible and then make it better. Oftentimes the MVP is asking a question, not developing a product or a demo. Talk to the people in your industry. Some people are better networkers than others. Hopefully you can either develop that skill set yourself or find a partner that can do that for you. Talk to as many people in your industry as possible before you build a fancy technological solution.

https://seekartech.com/

Introduction

Kordel France is the founder and CEO of Seekar Technologies, a technology startup that builds Artificial Intelligence products for a variety of industries.

Seekar, while still a startup, has put three different medical AI products through four board-reviewed clinical trials. In response to the COVID-19 pandemic, Seekar contributed two products free of charge to physicians to help ease demand on medical staffing and screening for the virus faster. They are currently building the first clinical AI tool used to advise neuro-psychologists in diagnosing mental disorders.

Kordel graduated from Utah State University with a Bachelor of Science degree, he received a Master of Science degree in Artificial Intelligence from John Hopkins University, and a graduate certificate in Machine Learning & AI from MIT.

Kordel’s interest began as a kid. Growing up on a farm, Kordel’s father had autonomous driving software for large tractor machinery. He remembers seeing these big machines steer themselves autonomously, which triggered a keen interest for Kordel in robotics and AI. The seeds were planted at a young age. Kordel has also always excelled in math, which played a heavy role in his future. He received his undergraduate degree in AI and computer science, which led him to a few jobs in the defense industry working as an engineer (mechanical, systems, and software engineering). Kordel had the opportunity to see many different and unique aspects of product development, just given those different engineering disciplines alone.

Fast forward to two years ago, Kordel and his colleague and co-founder, Zach, decided to fully start Seekar Technologies. Six months later, the two entrepreneurs wound up leaving their full-time jobs to pursue customers and seek revenue for Seekar. Since then, Kordel has never looked back. It’s been an exciting ride.

Startup Journey

It was a very tough decision for Kordel to leave his full-time position. He had a great job that he really enjoyed, not to mention was learning a lot under knowledgeable mentors. However, Kordel’s true passion and interest lay in AI, and the defense sector which he was working in was incredibly difficult to incorporate Artificial Intelligence into products. This is when Kordel and his co-founder decided to start their own AI company that benefited many different industries. This new vision opened opportunities to become more amenable to accepting AI into products that can augment people’s capabilities instead of necessarily replacing them.

Seekar Technologies started solely in image recognition, building image recognition systems for optics systems in hunting, shooting sports and animal conservation. . Conservation was a heavy first sector – spotting scopes for rifles and spotting in different optics such as binoculars. For example, if someone looked through binoculars or through a rifle scope and saw an endangered animal, Seekar Technologies had an active recognition on the system that said: “Do not take action because this is an endangered species, you cannot actually go after this animal.”

Seekar was able to easily bridge their technologies into other industries, such as medicine. Yet they did not have medical expertise to begin with. Kordel and his co-founder had to go out and seek advice on how to build different things for healthcare using AI. Interestingly enough, the pandemic positioned the company in a beneficial way. Seekar was able to take their image recognition technology and partner with medical experts who knew a lot more about the medical field, radiology, than Kordel and Zach did. Seekar took their image recognition platform and built a mobile application to screen for different radiology conditions in an X-ray or CT scan. Seekar’s goal was to make screening for COVID-19 easier and ease the new burden on physicians, particularly for radiologists and radiologic technicians. For example, the app allows you to either import an X-ray image or to capture an X-ray image just by taking a picture of the screen. The AI will then go through and filter out all the article glares, texts, and images and then provide feedback. The AI classifies whether the image contains signs of pneumonia, Covid-19, emphysema, or other conditions to divert people to the appropriate medical attention needed. It was a great opportunity for Seekar to bridge their image recognition technology into something highly beneficial and in demand to the world at a concerning time. It passed through a legitimate board-reviewed clinical trial and was one of the first AI products to do so in regards to the pandemic. The technology happened to be wildly successful, proving to be 95.5% as accurate as a physician in detecting Covid-19 in chest X-rays of patients. Many of Seekar’s medical prospects who were apprehensive about using “blackbox” AI before the pandemic became much more amenable to incorporating AI into medicine due to the success of this clinical trial.

Keys to Success

The COVID product made by Seekar Technologies was fully donated entirely for free to help the cause. It was also to try and make the medical field more amenable to AI solutions. The funding Seekar received up to this point was more for recreation, security, and the defense sector. This is where the money started. However, through the donated COVID app, Seekar started to see more monetary revenue from the healthcare and medicine and began to branch out into audio and different signal recognition. Transferring this technology from one sector to another was not really that difficult, especially once they had the necessary advisors to guide them on what to look for in certain images.

Seekar was founded on four principles: to make AI mobile, ethical, explainable, and dynamic.
1) A focus on mobility allows Seekar AI to remain decentralized from the cloud.
2) Ethical focus ensures its intelligence doesn’t experience the bias we see with facial recognition systems.
3) Explainability helps remove the “blackbox” state of AI so that it explains its answers just as a human can, which provides a sense of security to some users.
4) Dynamic models are able to adapt to their environment just as the human brain does.
These four principles are embedded into every product Seekar creates, regardless of the industry. These principles have garnered attention and interest in their products thus far.

Right now, Seekar Technologies is software as a service. They started out building under contracts for certain companies and building out different products. Seekar builds out software under the four principles mentioned above to be licensed under different companies and different individuals in general. As they matured and began acquiring more customers, they transitioned to the more scalable service model. A custom API that allows users to train their own models on their own data has allowed them to begin to scale much faster. Being able to make AI explainable is a big topic right now in AI. If AI is to be fully trusted in sensitive applications such as medicine and facial recognition, AI models need to be able to explain their decisions to the degree that a human can. This is what Seekar is building.Seekar can print out reports to explain every decision a neural network makes with its technology. Seekar has a huge focus on mobility – without tying anything to a cloud. Seekar’s technology can compress everything small enough to fit onto a device, to make everything so small that you protect patient’s and/or people’s data because there is nothing transmitted back and forth. The technology can run faster in real time.

There is also quite a bit of emphasis on ethics right now in the Artificial Intelligence space. Data can be super biased in the AI world. Seekar can take a particular interest in a particular sensitivity and go through the data, making sure that everything is addressed from multiple angles. It reads as much bias from the data as possible. This is particularly advantageous in a realm such as medicine, where misclassification can result in dire consequences.

Seekar’s products update themselves over time while also maintaining accuracy and consistency, while also pruning away things that do not need to be classified anymore. This allows a Seekar AI model to self-learn to identify new patterns without human intervention over time. Most deployed AI models today do not facilitate this type of feature. Once they are deployed, they can only identify the categories of objects they were programmed for until they are retrained.

Seekar originally started by just meeting with clients to try and tailor an exact solution to what they wanted. Originally, Seekar was feeling out the market in general to see where things will go. Original investors felt that their investment was best protected by Seekar trying to attract revenue from multiple industries in case one industry proved intractable. Now, Seekar has a scalable API with different marketing strategies which can be used for different industries and customers. They’ve particularly narrowed focus on the medical and autonomous vehilce industries. However, Seekar’s platform has been built to be on several different processors in different industries with different use cases to prove that its technology is capable of exceptional performance in diverse environments.

Challenges Encountered

Seekar is targeting industries, such as autonomous vehicles in agriculture and some aspects of medicine. They are specifically focused on industries that do not gain significant AI investment, do not receive adequae AI research, and do not quite understand AI or how it can be used in their business practices. This makes it more challenging to do business in general. However, once you explain the use of AI to these industries, they become enthusiastic. Some companies even contribute to Seekar’s funding. This has been a unique learning lesson that Seekar has encountered along the way.

Another challenge is marketing and sales. AI can sometimes sell itself in some regard since it’s a hot topic now but being able to paint a picture for your clients, users, and customers that enables them to purchase the product can be very challenging. Kordel sees the potential of AI in so many ways and how it can affect so many different lives. He sees how certain forks in the road can be because a company is not using AI. Relaying this message to companies is something Kordel has personally struggled with when marketing Seekar.

Future Endeavors

Seekar is working towards setting a standard for AI, in being explainable and mobile, trying to really branch out to industries that aren’t getting a lot of attention with AI. Seekar’s goal is to democratize AI and facilitate adequate attention and investment in the industries such as agriculture, security, medicine, recreation, and energy. Seekar hopes to be a huge contributor in these spaces, all while creating unbiased data sets.

Seekar is striving to explain their decisions so that they can adopt more trust with Artificial Intelligence, earning more of people’s trust in different industries. Seekar’s ultimate goal is to simulate human consciousness. Its computer vision platform is designed specifically to follow the architecture of the human visual cortex and they hope that further development allows it to fully replicate and simulate certain aspects of human cognition in the near future.

Advice For Aspiring Entrepreneurs

The first piece of advice is to seek negative feedback. This is challenging, but negative feedback allows you to stay on path with your products, making sure you do not steer off course. Going through a very tough process of being extremely critical of your idea and your technology, and then seeking feedback is incredibly important.

On the other hand, there is no one that’s going to realize your vision like you are. If something is important enough to you, you must take that negative feedback into consideration for your customers, but also be willing to sift through what is relevant and explainable.

It’s also important to question the status quo. AI can be an incredibly transformative, untapped technology. It’s synonymous now to what the internet was in the 1990s. We’re largely still experimenting with a lot of things AI can do – and its capabilities are greatly untouched. This allows smaller players to enter the market and really make a massive impact.

Gain the negative feedback but remain confident in your vision.

https://seekartech.com/