year: 2019
paper: https://arxiv.org/pdf/1911.01547
website:
code:
connections: francois chollet, intelligence, AI trends, history and predictions
Status: Stopped at page 20 or sth.
YK:
Previous notions of intelligence
The evolutionary psychology view of human nature is that much of the human cognitive function is the result of special-purpose adaptations that arose to solve specific problems encountered by humans throughout their evolution – an idea which originated with Darwin and that coalesced in the 1960s and 1970s. Around the same time that these ideas were gaining prominence in cognitive psychology, early AI researchers, perhaps seeing in electronic computers an analogue of the mind, mainly gravitated towards a view of intelligence as a set of static program-like routines, heavily relying on logical operators, and storing learned knowledge in a database-like memory. This vision of the mind as a wide collection of vertical, relatively static programs that collectively implement “intelligence”, was most prominently endorsed by influential AI pioneer Marvin Minsky (see e.g. The Society of Mind, 1986). This view gave rise to definitions of intelligence and evaluation protocols for intelligence that are focused on task-specific performance.
Optimizing for a single metric or set of metrics often leads to tradeoffs and shortcuts when it comes to everything that isn’t being measured and optimized for (a well-known effect on Kaggle, …
… where winning models are often overly specialized for the specific benchmark they won and cannot be deployed on real-world versions of the underlying problem).
In the case of AI, the focus on achieving task-specific performance while placing no conditions on how the system arrives at this performance has led to systems that, despite performing the target tasks well, largely do not feature the sort of human intelligence that the field of AI set out to build.
Goalpoast moving “AI-Effect” fallacy due to anthropomorphisation of artificial intelligence.
Goalposts move every time progress in AI is made: “every time somebody figured out how to make a computer do somethingplay good checkers, solve simple but relatively informal problemsthere was a chorus of critics to say, ‘that’s not thinking’ ”. Similarly, Reed notes: “When we know how a machine does something ‘intelligent’, it ceases to be regarded as intelligent. If I beat the world’s chess champion, I’d be regarded as highly bright.”. This interpretation arises from overly anthropocentric assumptions. As humans, we can only display high skill at a specific task if we have the ability to efficiently acquire skills in general, which corresponds to intelligence as characterized in II. No one is born knowing chess, or predisposed specifically for playing chess. Thus, if a human plays chess at a high level, we can safely assume that this person is intelligent, because we implicitly know that they had to use their general intelligence to acquire this specific skill over their lifetime, which reflects their general ability to acquire many other possible skills in the same way. But the same assumption does not apply to a non-human system that does not arrive at competence the way humans do.
Intelligence is skill-acquisition efficiency.
The size of the skill-space you can navigate within a given time / budget is the generality of the intelligence.
Joscha Bach calls this the ability to make models, which is the same thing. Being good at a single task is a skill. Having a model that allows you to pick up different skills is intelligence.
Humans use their general abilities to play chess. Playing chess does not require general abilities.
It may be obvious from a modern perspective that a static chess-playing program based on minimax and tree search would not be informative about human intelligence, nor competitive with humans in anything other than chess. But it was not obvious in the 1970s, when chess-playing was thought by many to capture, and require, the entire scope of rational human thought. Perhaps less obvious in 2019 is that efforts to “solve” complex video games using modern machine learning methods still follow the same pattern. Newell wrote: “we know already from existing work [psychological studies on humans] that the task chess involves forms of reasoning and search and complex perceptual and memorial processes. For more general considerations we know that it also involves planning, evaluation, means-ends analysis and redefinition of the situation, as well as several varieties of learning – short-term, post-hoc analysis, preparatory analysis, study from books, etc.”.
⠀ general abilities. Chess does indeed involve these abilities – in humans. But while possessing these general abilities makes it possible to solve chess (and many more problems), by going from the general to the specific, inversely, there is no clear path from the specific to the general. Chess does not require any of these abilities, and can be solved by taking radical shortcuts that run orthogonal to human cognition.
The assumption was that solving chess would require implementing these
They failed to go from the particular to the general:
Transclude of trotzki#^86b1ba
An anthropocentric frame of reference is not only legitimate, it is necessary.
We propose that research on developing broad intelligence in AI systems (up to “general” AI, i.e. AI with a degree of generality comparable to human intelligence) should focus on defining, measuring, and developing a specifically human-like form of intelligence, and should benchmark progress specifically against human intelligence (which is itself highly specialized). This isn’t because we believe that intelligence that greatly differs from our own couldn’t exist or wouldn’t have value; rather, we recognize that characterizing and measuring intelligence is a process that must be tied to a well-defined scope of application, and at this time, the space of human-relevant tasks is the only scope that we can meaningfully approach and assess. We thus disagree with the perspective of Universal Psychometrics or Legg and Hutter’s Universal Intelligence, which reject anthropocentrism altogether and seek to measure all intelligence against a single absolute scale.
How to measure intelligence
- Measure the skill-acquisition efficiency, not skills. “Abilities” (broad range of potentially prev. unknown tasks)
- …