The AI in Business Podcast

Today's episode is about continual learning, a focus of Cogitai, a company dedicated to building AI's that interact and learn from the real world. Cogitai's Cofound and CEO Mark Ring talks about the differences between supervised and reinforcement, and how Cogitai intends to take reinforcement learning in the direction of continual learning. Ring also touches on where he sees an opportunity for applying continual learning in domains like vehicles, consumer apps, etc., and improving abstract levels of understanding by machines.

There's not that many serial tech entrepreneurs in the legal space, but Gary Sangha is one of them. Sangha is CEO and founder of Lit IQ, which is applying machine learning and computational linguistics to legal documents to help lawyers avoid making drafting mistakes. In this episode, Sangha talks about where this type of software is most useful and legitimate, what the legal landscape in relationship to machine learning may look like in the next few years, and how this technology may apply across industries.

Some organizations are leveraging artificial intelligence (AI) to help the world with research, some to help companies with marketing, and some are intent on ensuring that the future of AI doesn't result in the end of humanity. Theres'a good likelihood that if you're reading this interview, that you're already familiar with OpenAI, an organization with the sole purpose of ensuring that the future of man and machines is a friendly one, and that the concentration of power and intelligence isn't centralized in a way that would make AI a dangerous tool. In this episode, we speak with Ilya Sutskever, research director for Open AI. This was a fun but frustrating interview; Sutskever held his cards close to his chest, but we gain some perspective on what he considers to be areas of importance regarding the future of AI and considerations for safely furthering advances in the field.

Right now, you can take a picture of a flower in your garden and post it on social media to see if anyone knows its proper name. Wouldn't it be nice, though, if a machine could identify the correct name and species in the picture you just took? Solving this problem in applications of machine vision is something that CEO Igal Raichelgauz and his team are working on at Cortica, a machine learning company that is not focused on deep learning, but is instead taking a more "shallow" approach. In this episode, Raichelgauz articulates Cortica's approach, which is based on neurology and goes against some of the current approaches in getting machines to learn. We discuss some of these primary differences and dive into Cortica's goals for applying machine vision in consumer products.

This week's guest is Kimberly Powell, senior director of business development at NVIDIA. In an interview conducted at the 2016 AI Summit in San Francisco, Powell spoke with TechEmergence about GPUs and the factors that are making them easier to use, how Nvidia and others are working to make this technology more accessible to small businesses and startups, and about some of Nvidia's and other similar players' innovations in the deep learning field.

Accenture is a pretty large company in the tech space, providing services to many of the Fortune 500 and global equivalents. They recently conducted a study of their own, combined with expertise from economists and AI researchers, about the longer-term economic impact of artificial intelligence on economies around the world. In this episode, I speak with Chief Technology Officer Paul Daughtery, who has been with Accenture since 1986, who was joined by Global Technology R&D Lead Marc Carrel-Billiard. We met up at a coffee shop after an AI Summit in San Francisco, and I asked Paul and Marc about what they had learned from this newly-published study and what they consider to be the significant impacts of AI and automation on the future job market.

Crowdsourcing is a relatively common term in technical vernacular today. Even if you're not a self-identified "techie", you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn't really taken off is in finance and hedge funds. In this episode, we speak with Richard Craib, founder of Numerai, about the company's model for pooling data science talent, using "anonymous" models to train financial data, and competing against one another, in which winners are rewarded in bitcoin to exchange through virtual markets. Craib speaks about his overarching vision for the company, and also delves into the past, present, and future of AI applications in finance.

What does the world look like when we can replicate human expertise in an assistant? Are we close to developing human-level chatbots that we can ask about law or medical conditions? We dive into this topic with Founder and CEO of exClone Dr. Riza Berkan, whose personal assistant and chat-bot company is leveraging day-to-day human conversational templates in machine learning technology in order to better approach the tough task of replicating human expertise through a machine. Berkan talks about the edge layer of his company's "secret sauce", and touches on the future applications of what might manifest in this field in 5 to 10 years in medical and other consumer applications.

In one of our most recent consensus, we took a close look at future trends in artificial intelligence consumer applications, but it's also interesting to see what's happening now in businesses. Chris Nicholson is the CEO of Skymind.io, which offers deep learning applications that integrate with Hadoop and Spark. In this episode, Nicholson sheds light on current trends that he sees across industries and best practices for implementing AI solutions to gain consistent return on investment.

We've interviewed a number of guests on TechEmergence, but very few who have had a serious part of their career in selling automobiles. But Michael Perry did just that for 5 years before founding Kit, his third startup - an AI application that works in marketing for small businesses and was acquired by Shopify in April 2016. In this episode, Perry speaks about how Kit and Shopify leverage AI on a daily basis, and how a "non-tech" person with no formal background in AI or data science can build a team for an AI project.

Martin Ford started off as a software entrepreneur in Silicon Valley, but became better known for his speaking and writing on robotics' and automation's influence on the job market after writing his best-selling book, Rise of the Robots: Technology and the Threat of a Jobless Future. In this episode, Martin talks about why he believes 'white collar' jobs (as opposed to blue) are at a higher risk for automation, and gives his predictions on how automation and robotics will impact the job market over the next 5 to 10 years.

As Senior Director and World Wide Head of the Cognitive Innovation Group at Nuance Communications, Mark Hanson works on bringing Nuance lab innovations to business applications, with the guiding goals of improving customer experience and business efficiency. In this episode, Hanson speaks about natural language processing (NLP), where he believes this technology is headed in the future and where it's driving value now, and how companies are applying NLP in Silicon Valley and elsewhere.

How do you know if you've made the right decision for a hire? Often, employers go off gut instinct and make a decision retrospectively, but it turns out AI might be able to help out in human resource management through shedding light on best hiring decisions. In this episode, Pasha Roberts, chief scientist at Talent Analytics, tells us about how his company is working on helping companies make better decisions before they hire by applying machine learning and artificial intelligence to various data points on a given applicant, including information from aptitude tests that may help predict not only performance but retention.

Big data is often a buzzword, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, Chief Analytics Officer and Economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.

When Google's DeepMind won against one of the best modern Go champions, is used multiple AI approaches and exposed gaps in some individual strategies. This even has shed more light on AI, but also on the utility in combining approaches to AI for individual problems. Data security is one of these problem areas where multiple AI approaches is being used to make our information safer. Dr. Sal Stolfo has been a professor at Columbia in Computer Science since 1972 and is now also the CEO of Allure Security, with a focus on engineering network intrusion detection solutions using AI applications. In this episode, Stolfo talks about the various styles of AI and statical methods that have been and are being used to detect malicious activity, as well as how he believes the future of security is going to have to adapt as increasing amounts of data become available.

This episode's guest is Uri Sarid, CTO at Mulesoft. Sarid speaks about where he believes the future of machine learning (ML) applications in industry might go - he thinks applications might stay small and niche-based, and will develop based on how well they each serve their individual purposes. He also speaks on his belief that companies will get used to dealing with disparate ML technologies and that finding ways to connect these technologies will be an important path for future trends in technology development.

It isn't by chance that birds fly in flocks and fish swim in schools - they're actually smarter when they act in a group. Could it be possible to extend that collective intelligence to human beings, and even AI? Louis Rosenberg is a PhD from Stanford, previously founder of Immersion and who now runs Unanimous AI, a company focusing on harnessing swarm intelligence with human beings. In this episode, Rosenberg speaks about how this collective-intelligence approach has been applied to human beings in terms of garnering improvements in a range of predictions, and he also touches on what this type of swarm intelligence might mean when we talk about multiple AI's in the future.

Predictive analytics and machine learning are all the rage in Silicon Valley, but how do companies actually derive value by leveraging these technologies? We asked this question to Dr. Ronen Meiri, CTO and Founder of DMWay, a predictive analytics and machine learning platform company based in Israel. In this episode, Ronen speaks about what his company does and how smart executives are starting to make decisions how to choose and decide on the a smart, user-friendly platform that fits their business' needs.

Our guest in this episode has spent a large part of his life on figuring out how to make machines more intelligent. LoopAI Chief Scientist Patrick Ehlen has worked on a number of important projects, from DARPA projects to big-company AI solutions at places like AT&T. LoopAI works on getting AI to make sense and meaning of unstructured text, and Ehlen talks about the potential business applications for this technology and where it's making way its way into industry. Ehlen also touches on the implications for developers in the nascent AI field - like LoopAI - that are vying to implement its technology as an industry standard, and how such organizations will have to market themselves and deliver services to develop a thriving AI ecosystem.

This week's guest is Senior Vice President of SPARK, an economic development organization dedicated to getting startups and other early-stage companies off the ground in Ann Arbor. Skip Simms speaks on how to convey complex technologies to investors who don't necessarily have your technical expertise, and still close the deal and get the investment. Simms talks about companies he's seen do this well (and not so well), and how aspiring companies can do a better job of convincing investors to get in on new or unfamiliar technologies, something many AI company founders will have to deal with in some shape or form in launching a new entity.

For some companies, big data remains an abstraction; for others, it's an integral part of the lifeblood of a business. Mat Harris is vice president at Sojern, a travel marketing platform that has leveraged big data to grow $3 billion in bookings and 1/3 of a billion traveler profiles across its platform. In this episode, Harris speaks about how Sojern and other businesses are using a combination of their data and other sources of data (what he calls third and "second" data sources) in order to make informed marketing decisions and better market their services to buyers. Harris sheds light on the direct ROI for big data in different businesses, and it's an interesting episode from the perspective of an executive who is using big data to make decisions on business directions.

Companies looking to raise money are often asking what investors think of their company, their industry, and how they're making investment decisions in related companies. In this episode, I ask these questions of Robert Seidel, who is managing partner of Motus Ventures, an investment firm focusing on autonomous Vehicles and the IoT. Seidl talks about various data sources and the people and networks from which investors draw information when they don't have what they need on-hand and need to make important investment decisions. He also shares his perspective on the high-energy and competitive investment world of AI, including his thoughts on the most exciting (and confusing) areas in the industry.

This week's interview was recorded live at Nuance's Silicon Valley office with guest Charlie Ortiz, director of the AI and Natural Language (NL) Processing Lab for Nuance Communications in Silicon Valley. In this episode, Ortiz speaks about what he sees as the most important developments in natural language processing (NLP) over the last few years, what advancements brought us to where we are today, and where progress might take NLP in the coming years ahead (both at Nuance and beyond).

Fifteen years ago, investing in AI may have seemed a bit far-fetched, but today it's not at all a rare occurrence; however, it's more rare to find entire firms dedicated to investing entirely in AI. In today's episode, we're joined by Saman Farid, co-founder of Comet Labs, an investment firm focused on investment in AI companies across industries. He speaks about his investment hypothesis in the future of AI, why he's decided to hone his funds in this domain, and the different domains where he believes AI is ripe to disrupt on a global level in the coming few years.

One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today's guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.

In this episode, we speak with Dr. Matteo Berlucchi, the founder of Your.MD, which uses artificial intelligence to create one of the first personal health assistant platforms in 70+ countries. Berlucchi talks about the challenges in making an AI do what you want, specifically helping people self diagnose and seek proper treatment. He discusses the multiple approaches to AI that are blended together in order to yield optimal results, and touches on the sometimes stark differences between what AI can do in the lab versus the functional application for tens of thousands of people. If you're interested in the diverse applications of AI and the challenges in running a startup, Dr. Berlucci's makes for an interesting episode.

CTO and Co-founder of Sumo Logic Christian Beedgen gives his take on how to glean return on investment from applying machine learning to companies. There are no easy answers, but Beedgen boils down simple concepts for thinking about humans thinking through causation, machines working out correlations, and how the combination of the two can glean us better ideas and get to answers faster than humans could do alone.

Artificial intelligence (AI) and virtual reality (VR) are often seen as different trends, but there is a lot of overlap in these areas, where you might not expect. Lucid VR's CTO Adam Rowell speaks today about how AI plays a role in making VR work, augmenting the accuracy of images and making a more immersive and convincing experience for users. Rowell also touches on non-gaming VR apps that he and his company are excited about launching in the future.

At Facebook headquarters, I learned there are 1 billion active users every month. In a more recent interview at eBay headquarters in San Jose, l learned that the well-known digital store has over 1 billion products for sale. eBay is, without a doubt, the world's largest marketplace, and there's enough incoming data to keep a large team of data scientists busy for years. I speak with Zoher Karu, eBay's Chief Data Officer, about how eBay leverages data and machine learning to create a better experience for its customers and also their sellers, shedding light on important lessons for anyone looking to sell a product online.

In this week's episode, I speak with Igor Baikalov, Chief Scientist at cybersecurity company Securonix, about the trends in data security and where security itself has had to take a step up in the last five years. Igor touches on major meta-trends that have forced data security to advance, as well as what has made AI and machine learning a 'requirement' of modern data security strategy, something that has changed significantly in the last decade. Igor sheds light on these issues and likely future trends in cybersecurity over the next five to 10 years.

In this week's episode, we feature an in-person interview from Facebook's headquarters with Hussein Mehanna, director of engineering of the Core Machine Learning group. Mehanna and I talk in-depth about the topic of personalization, touching on the pros and cons, how it works at Facebook, and how his team is working to overcome technological barriers to implement personalization in a way that improves the customer experience.

When one thinks through important industry apps of AI, law or legal apps are not usually the first to jump to mind, but there's certainly a need. Richard Downe, Ph.D. is Vice President of Data Science at Casetext, a startup working on improving search and natural language processing and democratizing legal information. In this episode, he speaks about the current bottlenecks for people trying to get more out of legal case documents, as well as some of the apps on which the Casetext team is working, to make these processes easier and to gain a strategic advantage in this industry.

Learning about the research behind machine learning is always fun, but so is learning about the real-world applications. In today's episode, we're joined by the CEO and founder of Wrike, Andrew Filev. Filev speak about where Wrike is currently applying machine learning and AI in their fast-growing, data-driven company. He shares his insights as to why he thinks marketing might be the most ripe for disruption by AI, and also discusses how most companies can prepare to take advantage of machine learning in any industry.

Today we have a guest who has interviewed more futurists than anyone else I know. While at TechEmergence a lot of our interviews focus on executives in AI, Nikola Danaylov has had the pleasure of interviewing some of the finest futurists and forward-thinking minds in the world, including Ray Kurzweil, Verner Vinge, Marvin Minsky, and many others. We speak today about the trends he's seen aggregated (if any) amongst futurists, and about how technology may be dragging us farther into a transhuman future, whether that be closer to a utopia or a dystopia.

A lot of companies in the San Francisco Bay make the claim that they can do something great with data; many fewer are at a degree of scale to make this vision possible. Today we speak with Nicholas Clark, CEO of DoubleDutch, a company now powering thousands of events nationally and implementing machine learning into their operations, including predicting business results from actual attendees. DoubleDutch is at the beginning of its journey with predictive analytics, having to make hard choices around what sort of information and thought processes they need in order to use machine learning and remain profitable. Nicholas gives his perspective on these decisions, as well as how he thinks DoubleDutch's efforts will impact the conference/event industry at scale.

Natural language processing (NLP) sounds cool in theory. We're familiar with Siri and Echo of course, but where does it play a role in other companies? In today's episode, we speak with Samiur Rahman from Mattermark, whose entire business model is predicated on organizing and making findable information about companies, and generating a platform to search by unique criterion. Doing so involves some conceptual work with NLP to make things findable. Samiur talks about what Mattermark is doing with this technology now and where he thinks the future may take the field, and interesting topic for investors and founders alike.

We've spoken in the past about computer vision on the TechEmergence show, but we haven't covered much about it in industry apps. Few businesses have better mastered this technology in the form of an app better than Shutterstock. In today's episode, we speak with Nathan Hurst, currently a distinguished engineer with Shutterstock and previously with Google, Amazon, and Adobe. Nathan delves into the topic of business apps that can "see", and touches on what that means for the industry, some of the exciting developments that he's seen over last the 10 years, and what he sees coming up in the next few years.

In addition to focusing on industry applications of artificial intelligence and emerging technology, we also focus on ethical and societal impacts of emerging technology. In this episode, we get back to ethics with Wendell Wallach, a scholar at Yale's Interdisciplinary Center for Bioethics and author of "A Dangerous Master", which addresses tech governance and other emerging technology issues. In this week's episode, Wendell talks about the problems of governing technologies that are developing faster than we can possibly assess all the risks, a topic that Wendell has thought about in-depth through both his extensive consulting, speaking and writing.

The artificial intelligence field is normally seen as burgeoning and new, populated with lots of small, scrappy companies aiming to become the next de-facto solution, with maybe one exception - "Big Blue". IBM has been involved since the 'beginning' and is perhaps best known for Watson, which has from Jeopardy to a range of applications in small and big businesses, as well as the public sector. Swami Chandrasekaran is Chief Technologist of Industry Apps and Solutions for IBM, and he speaks in this episode about what he sees as some of the low-hanging fruit for applying predictive models to business data. Swami has seen this technology applied in a variety of contexts, from automotive and shipping to telcos and more, providing an informed perspective for industry executives, data scientists, and anyone else interested in the intersection of predictive analytics and business.

"Artificial intelligence (AI) can be seen as a progression in our scalability of labor." This quote comes from this week's guest, Naveen Rao, who received his PhD in Neuroscience from Brown before becoming CEO at Nervanasys, which works on full stack solutions to help companies solve machine learning (ML) problems at scale. In this week's episode, Rao speaks about certain domains in industry where he feels optimistic about machine learning (ML) making a difference in the next five to 10 years, providing interesting perspectives that include advances in the areas of agriculture and oil & gas.

Many employers (and employees) are familiar with the 'painful' learning curves of using multiple software products or platforms at once, but these may not be gripes you want to share with Amotz Maimon. This week, we feature an interview recorded at Yahoo headquarters with its Chief Architect, Amotz Maimon. He speaks about technology governance and how companies small and large can make faster and better decisions around what technologies to use, how to integrate and streamline the processes, and how to integrate machine learning into the mix (which Yahoo has been using for the past decade). This episode provides important insights for those looking to scale such technologies within their own businesses.

If you're in the San Francisco Bay area, it's not all that novel to be trained in or working on some form of AI; however, to be doing so in the 1980s and 1990s was a more rare occurrence. Dr. Lorien Pratt has been working with neural nets and AI applications for many decades, and she does lots of consulting work in implementing these technologies with companies in the Bay area. In this episode, Lorien provides her unique perspective on decades of development and adoption in AI as we ask, where is the traction today in places where it wasn't 5 or 10 years ago? We also discuss where Lorien thinks machine learning applications in business and government seem to be headed in the near term.

When we think about applying AI and data science to different areas of business, we often think about those domains that offer a wide swath of quantitative metrics that we can feed a machine, like marketing or finance. Human resources (HR) normally doesn't fit the bill. How we hired someone, how we felt about them when we hired them, how they perform qualitatively, these are things that are often difficult to discern in team dynamics. That being said, big teams like Google are applying machine learning (ML) to some of their HR choices, and our guest today believes more companies will be doing the same in future. CEO of Humanyze Ben Waber applies ML to HR decision-making, helping people get better employees and better performance by measuring and improving using data science in new ways.

There are hedge funds and financial institutions that already use real-time data and sentiment analysis from social media, articles and videos in real-time to potentially make better trading decisions - but what does it mean when those same companies can use real-time satellite information to detect company activities and make trades based on that data? In this episode, Research Director of Capital Markets at Celent Securities discusses the focus on emerging technologies in trading and finance. He talks about the way that analytics and machine learning have affected the ways banks operate, the kinds of data that hedge funds and individual investors now have at their fingertips, and what that means for the future implications of AI-related technology in the finance world.

Ten years ago, it would have been difficult to talk into your phone and have anything meaningful happen. AI and natural language processing (NLP) have made large leaps in the last decade, and in this episode Dr. Catherine Havasi articulates why and how. Havasi talks about how NLP used to work, and how a focus on deep learning has helped transform the prevalence and capabilities of NLP in the industry. For the last 17 years, Havasi has been working on a project through the MIT Media Lab called ConceptNet, a common sense lexicon for machines. She is also Founder of Luminoso, which helps businesses make sense of text data and improve their business processes.

When it comes to data science and machine learning, what are the related skills that are getting people jobs and what are the industries that are supplying those in-demand jobs? These are two important questions that we discuss in this week's episode with CrowdFlower's CEO Lukas Biewald, whose company is providing a pragmatic perspective of the industry by focusing on assessing job listings and related information in the field of data science. If you're a company that is interested in finding someone with in-demand data science and related skills, or if you're in the market to find a position in this field, this episode will likely be very useful!

When you go to Harvard Business School and then to McKinsey company to work in private equity, there's really only one thing left to do - go to Silicon Valley and start an AI startup. At least, this is exactly what CEO Praful Krishna did when he moved to San Francisco to start Coseer, an AI company focused on understanding natural language and unstructured data. In this week's episode, we speak about where unstructured data lives in a business, and how a business can be changed if the right data is unlocked. Krishna also discusses his experience in how executives are making decisions around how or how not to leverage AI in their companies.

While we've featured quite a few companies that use and implement AI systems, we've more rarely gone behind the scenes with companies or consultants providing AI-related services to companies. In this week's episode, we talk with Machine Learning Consultant Charles Martin, a data scientist and machine learning expert who has done freelance consulting on machine learning systems at companies including eBay, GoDaddy, and Aardvark. In this interview, Charles talks about the areas in AI that he believes are ripe for implementation in a business context, and where he sees businesses getting AI 'wrong' before getting to the hard work of implementing systems that work for them.

Tad Slaff is the founder of Inovance, the creator of TRAIDE - a strategy creation platform that use machine learning algorithms to help traders uncover patterns in assets and indicators and build more reliable trading strategies. In this episode, Tad speaks about the state of machine learning in finance today, and touches on how future applications of machine learning and trends may alter what gives an edge to one hedge fund or institutional investor over another.

It's more common to ask what AI can to do to win at games, but it's less common to ask what games can do to help develop AI. This is a particularly fitting topic after Google's DeepMind's defeat of Go, and in this episode we talk with New York University's Julian Togelius about his research in how games can help us develop AI. We discuss how simple AI has been used in more common video games; the 'smoke and mirrors' effect that is more often used to mimic AI; and the more innovative ways that AI are being used in gaming at present, setting precedents for the future role of AI in gaming.