Eye On A.I.

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Ben Sorscher: Data Pruning for Efficient Machine Learning Mar 02, 2023

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In this episode, Ben Sorscher, a PhD student at Stanford, sheds light on the challenges posed by the ever-increasing size of data sets used to train machine learning models, specifically large language models. The sheer size of these data sets has been pushing the limits of scaling, as the cost of training and the environmental impact of the electricity they consume becomes increasingly enormous. As a solution, Ben discusses the concept of "data pruning" - a method of reducing the size of data sets without sacrificing model performance. Data pruning involves selecting the most important or representative data points and removing the rest, resulting in a smaller, more efficient data set that still produces accurate results. Throughout the podcast, Ben delves into the intricacies of data pruning, including the benefits and drawbacks of the technique, the practical considerations for implementing it in machine learning models, and the potential impact it could have on the field of artificial intelligence. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

Yann LeCun: Filling the Gap in Large Language Models Feb 16, 2023

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In this episode, Yann LeCun, a renowned computer scientist and AI researcher, shares his insights on the limitations of large language models and how his new joint embedding predictive architecture could help bridge the gap. While large language models have made remarkable strides in natural language processing and understanding, they are still far from perfect. Yann LeCun points out that these models often cannot capture the nuances and complexities of language, leading to inaccuracies and errors. To address this gap, Yann LeCun introduces his new joint embedding predictive architecture - a novel approach to language modelling that combines techniques from computer vision and natural language processing. This approach involves jointly embedding text and images, allowing for more accurate predictions and a better understanding of the relationships between original concepts and objects. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

Terry Sejnowski: NeurIPS and the Future of AI Feb 01, 2023

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In this episode, Terry Sejnowski, an AI pioneer, chairman of the NeurIPS Foundation, and co-creator of Boltzmann Machines, delves into the latest developments in deep learning and their potential impact on our understanding of the human brain. Terry Sejnowski begins by discussing the NeurIPS conference - one of the most significant events in the field of artificial intelligence - and its role in advancing research and innovation in deep learning. He shares insights into the latest breakthroughs in the field, including the repurposing of the sleep-wake cycle of Boltzmann Machines in Geoff Hinton's new Forward-Forward algorithm. Throughout the episode, Terry Sejnowski shares his expertise on the intersection of artificial intelligence and neuroscience, exploring how advances in deep learning may help us better understand the complexities of the human brain. He discusses how researchers are using AI techniques to study brain activity and the potential implications for fields such as medicine and psychology. Overall, this episode will be of particular interest to those interested in the latest developments in artificial intelligence and their potential applications in neuroscience and related fields. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

Geoffrey Hinton: Unpacking The Forward-Forward Algorithm Jan 19, 2023

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In this episode, Geoffrey Hinton, a renowned computer scientist and a leading expert in deep learning, provides an in-depth exploration of his groundbreaking new learning algorithm - the forward-forward algorithm. Hinton argues this algorithm provides a more plausible model for how the cerebral cortex might learn, and could be the key to unlocking new possibilities in artificial intelligence. Throughout the episode, Hinton discusses the mechanics of the forward-forward algorithm, including how it differs from traditional deep learning models and what makes it more effective. He also provides insights into the potential applications of this new algorithm, such as enabling machines to perform tasks that were previously thought to be exclusive to human cognition. Hinton shares his thoughts on the current state of deep learning and its future prospects, particularly in neuroscience. He explores how advances in deep learning may help us gain a better understanding of our own brains and how we can use this knowledge to create more intelligent machines. Overall, this podcast provides a fascinating glimpse into the latest developments in artificial intelligence and the cutting-edge research being conducted by one of its leading pioneers. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

Setting the stage for 2023 Jan 02, 2023

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To set the stage for some terrific conversations I have coming to you in the new year, in this episode we go back to some earlier conversations that talk about how we got to where we are in deep learning and how those early threads continue to lead innovation.

AI Supply Chain Optimization Nov 09, 2022

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This week I talk to Bob Rogers, a Harvard trained astrophysicist who once built digital twins of black holes to better understand them, and now builds digital twins of supply chains to help make them more efficient and resilient.

NO-CODE WITH AKKIO Oct 20, 2022

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Jonathon Reilly, co-founder of Akkio, a no-code AI platform, talks about how users with a web browser and an idea have the power to bring AI to life themselves without having to write code.

MLOps with ClearML Oct 05, 2022

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Moses Guttmann, founded ClearML, talks about the evolution of the MLOps industry over the past few years and ClearML's contribution to it.

Amazon's Sagemaker Sep 21, 2022

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Bratin Saha, head of Amazon's machine learning services, talks about Amazon's growing dominance in model building and deploying AI, about the company's SageMaker platform, and whether anyone can compete with the behemoth.

AUTOMATED CODE GENERATION Sep 08, 2022

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Peter Schrammel, one of the founders of Diffblue, an automated unit-test writing software company, speaks about the increasing automatic generation of code and how he sees such automation increasing the productivity of developers.

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