You might ask, if competence can be achieved either way (by exhaustive preparation, or by having higher intelligence), why would we even care about creating actual intelligence? Isn't collecting dense enough training data good enough to achieve the goal? Intelligence is a
@fchollet
-
Fluid Intelligence vs Memorized Templates in AI Systems
By
–
People struggle to differentiate fluid intelligence from knowledge because, given enough preparation, memorized templates become a solid substitute for on-the-fly adaptation
-
Possibility Space Exceeds Conceivable Action in AI Systems
By
–
The space of things you can do is much greater than the space of things you can conceive of doing.
-
ARC-AGI-3 Launch Announced for Next Week
By
–
The ARC-AGI-3 launch is next week. Incredible work by the team over the past year.
-
AI Capabilities: The Moving Goalposts of Human Comparison
By
–
When the latest AI systems can't do something, there's a category of people who will immediately say, "well humans can't do it either!" – Then they stop saying it when AI improves a bit. Been hearing it for 4+ years, "humans can't reason either", "humans can't adapt to a task
-
Reasoning versus Pattern Matching: Causal and Correlative Models
By
–
To make it very short: reasoning generates causal models of the data, pattern matching uses associative/correlative models of the data.
-
Model Limitations: Why AGI Needs True Metalearning Capabilities
By
–
The fact that you need to provide a specialized harness clearly shows the model *does not* encode the kind of metalearning knowledge and problem-solving strategies that humans use. Humans solve novel problems without being told how to proceed step by step. AGI would *not* need a
-
Defining Reasoning and Pattern Matching in AI Systems
By
–
"all reasoning is pattern matching" is a useless statement if you don't define "reasoning" and "pattern matching" first. You might as well say "all information processing is information processing." With grounded definitions of both, reasoning and pattern matching are *very*
-
Learning New Programming Languages Zero-Shot: Achievable Challenge
By
–
You won't convince me that approaching a new programming language and working with it zero-shot is insurmountable. At my first job I had to work with a stack I had zero experience in (aside from Python) and I was shipping PRs in my first week. I had <1000 hours of programming
-
Encoding Changes Degrade Frontier Model Performance on ARC
By
–
This is similar to how applying basic changes to how ARC tasks are encoded considerably degrades frontier model performance. If you're looking at the test for the first time, it really shouldn't matter what the encoding is. Unless you've studied specifically for the test, using a