In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning. This week, we’re joined by CEO and Co-founder of Kavout Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services.  

Market research and trends is important when discussing AI and business, but it's also worthwhile to contemplate the ethical and social implications further down the line. How will countries deal with potential unemployment problems? How might countries collaborate to hedge against the risks that AI poses to the future of work and other economic facets? A relatively small group is helping people do just that i.e. getting organizations and countries to think through how they could hedge against the grander risks inherent in a world powered by AI. In this episode, we speak with Jerome Glenn, head of the Millennium Project, an initiative that focuses on research implementing the organizational means, operational priorities, and financing structures necessary to achieve the Millennium Development Goals or (MDGs). Glenn talks about how he gets principalities of the world to bring their big industrial players and the public to talk through possible scenarios that are 30, 40, even 50 years in the future, and about ways we might potentially hedge against risks and make the most of the upsides of AI in a global economy.

A medium-size business with a $20M marketing budget can run into issues when aiming to track an attribute, what marketing dollars brought in customers, etc. But when you're managing $90B for customers all over the world and working in every conceivable channel, things get all the more complicated. Josh Sutton, global head of Data and AI at Publicis.Sapient, speaks in this episode about the future of advertising attribution with machine learning. Specifically, Sutton discusses how his team of publicists is working on managing, tracking, and determining cohorts and attribution across more channels and numerous clients, and touches on ways that the company is applying ML to make sense of marketing data and spend marketing dollars more effectively.

I remember reading an article in Scientific American years ago about a poster of a person looking in the direction people sitting in a school dining room, and that this poster would make people sitting in the dining room less likely to litter. This seems like an absurd example of holding people accountable for their actions, but as it turns out, there are a lot more serious consequences to ensuring behavior change through observation, and one area where this matters is medicine. Today, there’s a major issue with people who don't adhere to their medical regimens, only to relapse or experience more serious symptoms later on. This week's guest, Cory Kidd, CEO of Catalia Health and known for his work at MIT on human-robotic interaction, is working to help solve this problem by developing a robot that adds some of that physical presence and accountability. This is likely one of many novel medical AI applications that we're likely to see roll out in healthcare over the next decade.

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.