There is still a lingering belief from classical machine learning that bigger models overfit and thus don't generalize well. This is described by the bias-variance trade-off, but this no longer holds in the new age of machine learning. This is empi…
Older AI models showed some capacity for generalization, but pre-O1 models weren't directly incentivized to reason. This fundamentally differs from humans: our limbic system can choose its reward function and reward us for making correct reasoning …
The relationship between human and artificial reasoning reveals an interesting tension in reward function design. While the human brain features a remarkably flexible reward system through its limbic system, current AI architectures rely on more rigid …
For the video of this, click here. Time and time again it's been proven that in the long run, scale beats any kind of performance gain we get from implementing smart heuristics; This idea is known as "The bitter lesson". The idea that we …
The Library of Babel greatly improved my intuitive understanding of how neural networks can learn. The Library of Babel is a library with every book imaginable in it. There lay the books with all possible combinations of words and thus all possible com…
For a video on this click here. I recently came across an intriguing paper (https://arxiv.org/html/2406.06489v1) that tested various machine learning models, including a transformer-based language model, on out-of-distribution (OOD) prediction tasks. T…
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