Summary of François Chollet: Measures of Intelligence | Lex Fridman Podcast #120

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00:00:00 - 01:00:00

In this podcast, François Chollet discusses his paper "Measures of Intelligence," which explores the definition and measurement of intelligence. He argues that humans are generally intelligent and that this gives us a strong signal that the human is intelligent. He also discusses the concept of deep learning and its ability to model the mind.

00:00:00 François Chollet discusses how we might measure general intelligence in computing machinery. He argues that the mainstream machine learning community works on very narrow AI with very narrow benchmarks, while the outside the mainstream renegade community works on approaches that verge on the philosophical and even the literary. Chole talks about his paper Measures of Intelligence, which discusses how we might define and measure general intelligence in our computing machinery. Finally, he talks about his sponsors Babel, Masterclass, and Cash App.
00:05:00 François Chollet discusses the ideas that had a big impact on him when he was growing up, including Jean-Pierre Chevreau's work on intelligence development, Jeff Hawkins's book "Intelligence" and Chomsky's theory of language acquisition. He notes that language is more than just a means of storing memories, it is also a way to access memories in a deliberate manner.
00:10:00 François Chollet discusses how he sees language as a fundamental aspect of cognition, and how it is important for babies to develop a sense of movement and space before they start thinking in words.
00:15:00 François Chollet discusses the different ways in which he organizes his thoughts, including using mind maps. He points out that mind maps impose a syntactic structure over ideas, and that once a mind map is drawn, it can be turned into a paragraph-long summary.
00:20:00 François Chollet's paper "Measure of Intelligence" explores the definition and measurement of intelligence as well as the potential for artificial intelligence to eventually be referred to as "intelligence."
00:25:00 According to François Chollet, the definition of intelligence is the efficiency with which you acquire new skills at tasks that you did not previously know about. This definition is different from that of Einstein, who said the measure of intelligence is the ability to change.
00:30:00 The goal of the paper is to measure intelligence, and the paper argues that humans are generally intelligent and that this gives us a strong signal that the human is intelligent.
00:35:00 The two views of intelligence are the evolutionary psychology view, which sees the mind as a collection of static programs, and the brain as a blank slate, which is the more popular view. The article discusses how the two views have shaped the history of cognitive sciences and how recent advances in machine learning have led to the recognition of learning as an important part of intelligence.
00:40:00 François Chollet discusses the concept of deep learning and its ability to model the mind. He feels that many researchers are conceptualizing the mind via a deep learning metaphor, and that this is a lazy way to think about intelligence. He also discusses the possibility that neural networks may be able to model general artificial intelligence, and that this is an exciting new development.
00:45:00 François Chollet discusses the measures of intelligence, Lex Fridman podcast #120. He believes that gptn will improve on the strength of gpt2 and 3, which will be able to generate more plausible text in context. If you train bigger and more on more data, your text will be increasingly more context aware and plausible. However, gpt3 is susceptible to adversarial attacks, and it is difficult to program it explicitly.
00:50:00 François Chollet explains that the bottleneck for deep learning models is the amount of data that is trained on, not the amount of computation that is required. He argues that the self-supervised learning of models is a more promising path forward for making the knowledge on the web available to machines.
00:55:00 François Chollet discusses how much reasoning is necessary for tasks like driving, and how deep learning is not a good medium for explicit reasoning.

01:00:00 - 02:00:00

In the video, François Chollet discusses the concept of intelligence and how it can be measured. He explains that intelligence is a difficult thing to define and that there are different types of intelligence. He also discusses the importance of coming up with new, original ideas in order to keep the test of machine intelligence interesting and challenging.

01:00:00 François Chollet discusses measures of intelligence, including how difficult it is to build an intelligent system, and how much intelligence is required to solve certain tasks. He explains that, even with infinite data, it would be difficult to build a system that could drive without any real amount of intelligence. Chollet says that, instead of trying to achieve alpha in ai by demonstrating general intelligence, a more efficient approach would be to use deep learning modules in combination with engineering an explicit model of the surrounding environment.
01:05:00 The paper discusses the concept of intelligence, and discusses how it can be measured. It also discusses how tests of intelligence should be different for humans and machines.
01:10:00 François Chollet discusses the different constraints that apply to artificial intelligence and to human intelligence, and how the test for human intelligence should account for these differences. Psychometrics is the field of psychology that tries to measure and quantify aspects of the human mind.
01:15:00 The goal of psychometric testing is to create reliable, valid, and unbiased measurements of intelligence. Psychometric tests are most useful as a tool at scale and when correlating test results across a large number of individuals. The g factor is a statistical construct that explains the correlations among test results.
01:20:00 psychologists have found that human beings share common cognitive abilities which can be explained by a hierarchy of three levels. The most mainstream theory of this structure is called the "chc theory" and it describes cognitive abilities as a cattle horn carol - a hierarchy with a "g" factor at the top. This does not mean that all human beings are capable of achieving the same goals, as different people have different physical abilities. One interesting analogy for understanding human cognitive abilities is the sports analogy, which describes them as being analogous to physical abilities.
01:25:00 François Chollet discusses the degree of generalization that is seen in human cognitive abilities. He says that while our cognitive abilities are general, they are still very specialized in the human condition. There are no questions that are intrinsically difficult, they must be difficult to the things that the individual knows and can already do. In terms of an IQ test question, the input must be surprising and unexpected and the output must be recognized or produced. A difficult question is one that requires some amount of test time adaptation and improvisation.
01:30:00 The video discusses the difficulty of designing difficult IQ tests, and François Chollet provides examples of how this can be done.
01:35:00 François Chollet discusses the knowledge priors that are innate to humans and how they can be used to solve novel tasks.
01:40:00 The video discusses the principles that constitute a good test of machine intelligence, and François Chollet discusses one example of a test that tries to embody these principles. The arc challenge is an attempt to embody as many of these principles as possible.
01:45:00 François Chollet's Arc software is designed to help people understand the nature of abstraction. The software includes a set of questions designed to probe people's ability to generate abstraction in machines.
01:50:00 François Chollet discusses the importance of coming up with new, original ideas in order to keep the test of machine intelligence interesting and challenging. He also mentions the popularity of Ark as a game among humans, and how it forces people to introspect on their cognitive processes.
01:55:00 François Chollet discusses the future of artificial intelligence and how arc will help advance machine learning. Chollet notes that arc is a difficult task that requires progress but also doesn't present an impossible change. He predicts that by the time humans achieve parity with artificial intelligence, general flu intelligence will be near human level.

02:00:00 - 02:30:00

In the video, François Chollet discusses different measures of intelligence, including developer generalization and robustness, and argues that computers are already a large part of human intelligence. He also talks about the Tour de France test, which is an attempt to measure human cognitive abilities, and the drawbacks of this test. Chollet prefers the extra price, which is more practical and incentivizes developers to create something useful.

02:00:00 François Chollet discusses the measures of intelligence, which include developer generalization and robustness, and how they can be used to describe different levels of intelligence. He also talks about neuralink, a project aimed at creating artificial intelligence that is as intelligent as humans.
02:05:00 François Chollet discusses the idea that there are fundamental bottlenecks to human intelligence, and that computers are already a large part of this. He also discusses the Tour de France test, which is an attempt to measure human cognitive abilities.
02:10:00 François Chollet discusses the drawbacks of a test of human intelligence that relies on human judges to determine whether a machine has achieved the same level of intelligence as a human. He argues that the test is unreliable and violates the standardization and freedom from bias requirements.
02:15:00 François Chollet discusses thedifficulties of trying to convince people of the merits of the alexa prize's formulation of intelligence, which focuses on the difficulty of creating tests that are unfair but still measure intelligence. Chollet prefers the extra price, which is more practical and incentivizes developers to create something useful. Chollet also has reservations about the touring test, which he believes is not conducive to progress due to its reliance on human judges and its susceptibility to subjective interpretation.
02:20:00 The video discusses measures of intelligence, including the arc challenge and the idea that cognition is compression. It notes that compression is a tool used in many ways, but is not cognition itself. The video compares compression to investing, noting that compression is used to simplify models in order to make them more efficient, but that they are not perfectly compressed because they need to account for future possibilities.
02:25:00 François Chollet discusses the difference between the goals of a machine and the goals of a human, and how humans are essentially cultural beings. He argues that everything we do reflects the past and that our contribution to the collective will be remembered for eternity.
02:30:00 François Chollet discusses the idea that intelligence can be measured in terms of the number and diversity of reports that are created as a result of an individual's actions. He says that the path to immortality is to contribute things to culture, and that as humans continue to influence others thousands of years from now, their actions today will create reports that will propagate into the future.