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Emergent Intelligence: Sanjay Krishnan On AI’s Rapid Evolution

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Manage episode 498290693 series 1538640
Content provided by Richard Jacobs. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Richard Jacobs or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

What’s driving the rapid evolution of AI? Where do emergent behaviors come from? In today’s episode, Sanjay Krishnan joins us to unpack how modern machine learning models are developing specialized skill sets, solving increasingly complex problems, and adapting in surprising ways to real-world data.

Sanjay is an Assistant Professor of Computer Science at the University of Chicago, where his research group focuses on making data systems more reliable – even when the information they handle is messy or incomplete. In the real world, data is often missing, corrupted, or uncertain, and Sanjay’s work aims to help databases and information systems still function accurately under those conditions.

Currently, Sanjay and his team are exploring several key challenges. One area of focus is building systems that can still provide accurate answers even when the underlying data is only partially complete. They’re also developing ways to measure the trustworthiness of a result when the data has been corrupted. Another project involves automatically detecting when sensitive data may have leaked, helping improve both privacy and security in data systems.

This conversation outlines:

  • The natural progression of AI over the past 15 years.

  • Three factors that make AI powerful.

  • The role that human-provided data plays in machine learning.

  • What AI poisoning is, and how it can be identified.

You can learn more about Sanjay and his research by clicking here!

🎧 Listen now on Apple Podcasts: http://apple.co/30PvU9C

🛍️ Recommended Books from This Episode:

📖 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Learn how to build intelligent systems and make the most of machine learning techniques. 👉 Get it here

📘 Artificial Intelligence: A Guide for Thinking Humans – Explore the world of AI with a thoughtful and engaging guide to understanding its impact on society. 👉 Try it here

📙 AI Superpowers: China, Silicon Valley, and the New World Order – Understand the global AI race and the economic and geopolitical implications of artificial intelligence. 👉 Shop now

Note: These are affiliate links. If you make a purchase through them, we may earn a small commission at no extra cost to you. It helps support the podcast. Thank you!

  continue reading

4187 episodes

Artwork
iconShare
 
Manage episode 498290693 series 1538640
Content provided by Richard Jacobs. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Richard Jacobs or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

What’s driving the rapid evolution of AI? Where do emergent behaviors come from? In today’s episode, Sanjay Krishnan joins us to unpack how modern machine learning models are developing specialized skill sets, solving increasingly complex problems, and adapting in surprising ways to real-world data.

Sanjay is an Assistant Professor of Computer Science at the University of Chicago, where his research group focuses on making data systems more reliable – even when the information they handle is messy or incomplete. In the real world, data is often missing, corrupted, or uncertain, and Sanjay’s work aims to help databases and information systems still function accurately under those conditions.

Currently, Sanjay and his team are exploring several key challenges. One area of focus is building systems that can still provide accurate answers even when the underlying data is only partially complete. They’re also developing ways to measure the trustworthiness of a result when the data has been corrupted. Another project involves automatically detecting when sensitive data may have leaked, helping improve both privacy and security in data systems.

This conversation outlines:

  • The natural progression of AI over the past 15 years.

  • Three factors that make AI powerful.

  • The role that human-provided data plays in machine learning.

  • What AI poisoning is, and how it can be identified.

You can learn more about Sanjay and his research by clicking here!

🎧 Listen now on Apple Podcasts: http://apple.co/30PvU9C

🛍️ Recommended Books from This Episode:

📖 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Learn how to build intelligent systems and make the most of machine learning techniques. 👉 Get it here

📘 Artificial Intelligence: A Guide for Thinking Humans – Explore the world of AI with a thoughtful and engaging guide to understanding its impact on society. 👉 Try it here

📙 AI Superpowers: China, Silicon Valley, and the New World Order – Understand the global AI race and the economic and geopolitical implications of artificial intelligence. 👉 Shop now

Note: These are affiliate links. If you make a purchase through them, we may earn a small commission at no extra cost to you. It helps support the podcast. Thank you!

  continue reading

4187 episodes

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