I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!

I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.

Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!

https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO

#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning

Babbage: The science that built the AI revolution—part three

Listen to this episode from Babbage from The Economist on Spotify. What made AI take off? A decade ago many computer scientists were focused on building algorithms that would allow machines to see and recognise objects. In doing so they hit upon two innovations—big datasets and specialised computer chips—that quickly transformed the potential of artificial intelligence. How did the growth of the world wide web and the design of 3D arcade games create a turning point for AI?This is the third episode in a four-part series on the evolution of modern generative AI. What were the scientific and technological developments that took the very first, clunky artificial neurons and ended up with the astonishingly powerful large language models that power apps such as ChatGPT?Host: Alok Jha, The Economist’s science and technology editor. Contributors: Fei-Fei Li of Stanford University; Robert Ajemian and Karthik Srinivasan of MIT; Kelly Clancy, author of “Playing with Reality”; Pietro Perona of the California Institute of Technology; Tom Standage, The Economist’s deputy editor.On Thursday April 4th, we’re hosting a live event where we’ll answer as many of your questions on AI as possible, following this Babbage series. If you’re a subscriber, you can submit your question and find out more at economist.com/aievent. Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.

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I was an invited speaker at the Neurips conference in New Orleans in Dec 2023 for the NeuroAI social.

I was more than surprised to be invited to what is now primarily an AI/ML conference (despite "Neural" being the first word, and the conference's origins in comp neuroscience). To say that the successful AI systems currently deployed and neuroscience/study of biological intelligence have diverged would be an understatement, it was a somewhat odd choice for the organizers to invite a neurophysiologist like me.

So, I took the invite as an opportunity to talk about attention in biological vision and how whatever they now call as attention in AI/ML/CNN/transformers
is almost orthogonal to what many others and I study within visual neuroscience or psychology or cognitive science.

While the talk was a partial critique of current AI models, it was more a call for them to take seriously the one instance of intelligence (i.e., the biological world) seriously and how it still has much to offer towards designing better AI systems.

If attention is not one of the cognitive ingredients that makes up the intelligence recipe towards autonomous systems, I don't know what is.

The talk slides can be found here: https://www.dropbox.com/scl/fi/927f50bfvqpwtserizgl5/NeuroAI_Neurips_KS2023.pdf?rlkey=r3pgvsyoudwczapjijx80pj7l

#Neurips2023 #NeuroAI #Attention #Vision #BiologicalVision #ActiveVision #SpaceVariance #NonlinearCompression #EyeMovements #Neurodynamics #AutonomousSystems #AI #ML

NeuroAI_Neurips_KS2023.pdf

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