Welcome to the podcast AI Keyhole: Evolution, Applications & Policy.

What is artificial intelligence (AI)? Where did it come from? What are some of its innovative applications today? And how does the law deal with regulating this technological genie (or can they?)? During this series, we’ll be discussing these issues with experts in their respective AI fields.

In this blog, we’ll go over the basics of the podcast, including:

Meet the host: Ryan E. Long

Ryan is a non-residential fellow of Stanford Law School’s Center for Internet and Society. He has written about and presented on AI evolution, applications, and policy for the Stanford Center for Legal Informatics, AI magazine Cognitive Times, and the London School of Economics Business Review, respectively.

Ryan has over 21 years of experience solving tricky intellectual property-related litigation and licensing issues, including circumvention (also known as “hacking”) claims, for tech and media companies.

Clients include former Google privacy researcher Mr. Martin Shelton, iTunes app Kanvas Labs (sold to AOL), Taiwan-based hardware company HDFury, and New York City-based digital marketing company Situation Marketing.

Why 'AI Keyhole'?

“As with other technologies, there is often a scientific and non-scientific divide concerning AI. Oftentimes, I’ve found these to be two different worlds on the same coin – viewing the same technology in entirely different ways: completely bullish or entirely bearish. By doing this podcast, my intention is to create a forum where these sides can, to some extent, interweave.” – Ryan E. Long, host

AI Keyhole endeavors to provide you with a holistic understanding of AI. Whether it's the evolution of AI, innovative applications for cybersecurity, or emerging legal issues, AI Keyhole will provide you with sufficient depth to understand the technology whilst also giving an overview of emerging regulatory issues regarding implementation.

Who is 'AI Keyhole' for?

For non-AI practitioners and AI enthusiasts alike

This podcast is primarily intended for policymakers interested in regulating AI, financial professionals who are keen on investing in the AI marketplace, and other non-AI professionals, whether in cybersecurity, media, or medicine, interested in learning more about AI.

Our journey begins with a bird’s eye view of where AI came from. We then go into the workings of AI to better understand some of tomorrow’s innovative applications.

The podcast lineup

We have an exciting lineup for the first few episodes, with a diverse set of topics and speakers.

Origination & Development of AI, with Professor Wooldridge

Professor Michael Wooldridge, of the Department of Computer Science at the University of Oxford, answers these questions and more. Among other things, he provides some insights into AI’s shortcomings compared to naturally occurring intelligence, and why they exist.

Key talking points include:

  • Who developed AI and when?
  • What‘s programmatic language and its role in AI?
  • Whether AI is lacking common sense and why.
  • Potential future dangers and dilemmas of AI applications.

Mathematics of AI: Applications, with Professor Christian Igel

Professor Christian Igel, of the Department of Computer Science at the University of Copenhagen, provides insight into these issues. He also discusses types of learning in machine learning and an explanation of pattern finding.

Key talking points include:

  • What types of math are used in AI?
  • What’s the relationship between math and algorithms?
  • Are mathematical errors the cause of functionality problems in, say, facial recognition technology?
  • How important is training data for building mathematical models?

AI, Finance, and Fintech, with Daniel Wu

Mr. Daniel Wu of JP Morgan & Chase is the guest for this session. Among other things, he provides some insights into types of fintech, commercial banking applications of fintech, and AI applications in finance.

Key talking points include:

  • What is commercial banking?
  • The role of AI in creating efficiencies in commercial banking.
  • Examples of fintech in action – including payment solutions.
  • How important is training data for building mathematical models?
  • The relevance of data quality to the use of AI in banking.

And more!

Keep watching this space for more episodes. 👀

Tune in and enjoy

We want your feedback!

We’d love to hear feedback and ideas from all of you in our Community. Tells us what topics and guests you’d like to hear from!

Or, maybe you’d like to take part? Let us know by dropping us an email at marisa@aiacceleratorinstitute.com.