Investing In AI

Hear Benjamin Cohen, CEO of Proton.ai, talk about how to apply artificial intelligence to the distribution business - a large sector of the economy currently underserved by AI.  Benj talks about how he stumbled on the idea, and how to recruit AI talent for companies in sectors that are less commonly tech-enabled.

In this episode, I talk to Michelle Zhou about chatbots, natural language processing, and why teaching AI to listen is as important as teaching AI to speak.

In this episode I interview Amy Webb from the Future Today Institute, and we discuss her book on technology - The Big Nine, and all the issues AI/ML create for the world that we need to address.

In this episode, I talk to Rob Aitken from ARM about AI hardware and the future of edge AI.  Rob explains the differences between CPUs and GPUs, what we mean when we say AI accelerators, and the future of AI hardware.  

In this episode, Ash Fontana discusses his book "The AI-First Company:  How to Compete and Win With Artificial Intelligence".  We discuss the role of data network effects, how data learning effects are bigger and more important, and many other AI related topics.

In this episode, Karim Lakhani, a professor at Harvard Business School,  discusses his book "Competing in the Age of AI."  We talk about how vectors of competition change in an AI world, and how traditional business strategy has to adapt.

Upstart is a publicly traded company (NASDAQ:  UPST) that provides AI powered loan approvals for banks.  Listen as Dave talks about why there is a huge gap in approvals, and thus an opportunity for Upstart, and how to build competitive advantage in AI related spaces.

In this episode, Matt Jones from Sourse discusses a common challenge among AI companies - how much to focus on a horizontal platform vs a vertical integration play.  It comes up a lot in the AI companies I see, so listen in to hear Matt's take on how they worked through the process and found a solution.

Rana is computer science PhD,  an expert in AI for emotions, and CEO of Affectiva.  Listen in to hear her discuss emotional computing, using AI to detect emotions,  the MLOps tech stack, the role of synthetic data in training models, and more.

Anno.ai is a machine learning platform that started in the government sector but expanded into commercial use for other customers needing security and privacy around data labeling and model management.  Ashley Antonides, VP of Data Science at Anno, joins me here to discuss data labeling, government requirements around machine learning models, and many other topics.

Edge AI is one of the most explosive areas of growth for AI, and Deeplite is a market leader in compressing AI models so that they work on the edge.  Hear Davis talk about the technology and how it will impact edge devices as that market approaches the size of the cloud computing market.

Kevin Roose recently released a book called Futureproof - about how to stay relevant in a world of increasingly automated work.  In this episode Kevin and I discuss AI and automation and relevant topics from the book.

In this episode, Yashar and Sergey from Synthesis.ai discuss the uses of synthetic data.  This topic is becoming increasingly important for AI, and we discuss how it enables companies to expand smaller data sets, run AI simulations, and more.

In this episode I interview James Cham from Bloomberg Beta.  We discuss the book "The Man Who Lied to His Laptop" and the book "Prediction Machines" about the economics of AI.  We discuss how startups compete with big companies when it comes to AI and the data sets needed, and we have a spirited debate about whether or not AI is the new electricity.

Heath Terry, Managing Director at Goldman Sachs, discusses AI in public markets.  We talk about why Goldman started paying attention to AI, how it is changing how public companies compete with each other, and how AI is touching some surprising areas of business.  Note that Heath has since left GS for Third Point Capital.

In this episode I talk to Rob Toews, from Highland Capital  about his 2021 predictions for AI.  We cover federated learning, synthetic media and deepfakes, and the ramifications of political content made with these tools.  We also touch on MLOps and why Rob and I both expect some consolidation in the space, and the intersection of AI and biology.