Francois Chollet, an AI researcher renowned for creating Keras, dives deep into defining intelligence in both humans and machines. He critiques traditional AI models for their reliance on mere skill rather than true intelligence and proposes a new framework emphasizing generalization. The discussion also touches on the integration of human-like priors into AI, the evolution of intelligence over a century, and the complexities of evaluating AI systems. Chollet's insights challenge listeners to rethink what it means to measure and understand intelligence in a rapidly advancing technological landscape.
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insights INSIGHT
Intelligence as Skill Acquisition Efficiency
Intelligence is the efficiency of converting experience into generalizable programs.
It's about turning sampled experiences into broader processing abilities.
insights INSIGHT
Generalization vs. Skill
Skill can be bought with unlimited priors or training data, masking true generalization.
Chollet argues for prioritizing generalization over skill in AI.
insights INSIGHT
Deep Learning's Limits
Deep learning excels at pattern recognition, crucial for machine perception.
However, simply scaling up deep learning won't achieve Artificial General Intelligence (AGI).
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This book by Douglas Hofstadter is a comprehensive and interdisciplinary work that explores the interrelated ideas of Kurt Gödel, M.C. Escher, and Johann Sebastian Bach. It delves into concepts such as self-reference, recursion, and the limits of formal systems, particularly through Gödel's Incompleteness Theorem. The book uses dialogues between fictional characters, including Achilles and the Tortoise, to intuitively present complex ideas before they are formally explained. It covers a wide range of topics including cognitive science, artificial intelligence, number theory, and the philosophy of mind, aiming to understand how consciousness and intelligence emerge from formal systems[2][4][5].
We cover Francois Chollet's recent paper.
Abstract; To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.
Machine Learning Street Talk (MLST) 