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If we can analyze these machines deeply and understand how they are doing this, is it possible that we can extract some kind of general purpose pattern recognit
by api 3y ago
If we can analyze these machines deeply and understand how they are doing this, is it possible that we can extract some kind of general purpose pattern recognition and manipulation algorithm or set of algorithms?
Are we just inductively generating algorithms that could in fact run more powerfully and efficiently if they were “liberated” from the sea of matrix math in which they are embedded?
Or is the sea of matrix math fundamental to how these things work?
- golol 3y agoI don't think there is a magic alrogithm at play. I think it's all about studying the data - the geometry of the data manifold. In the case of a transformer more precisely learning the geometry of the stochastic process of tokens. Common "patterns", such as repetition or symmetry, appear there and can be learned. I think like learning eigenvectors in PCA you just learn more and more patterns and combinations of patterns. Then at some point that starts looking like pattern recognition.
- jncfhnb 3y agoThe sea of matrix math is the fundamental way these things work. You could grossly simplify individual patterns to small explicit algorithms but this is the way for general patterns. Compress your information, and then synthesize its new information with your understanding of the world into insights.
- cubefox 3y agoIt seems at least likely that a model with only has this general pattern prediction ability could be much smaller than current models. After all, these models don't just predict abstract patterns, they also "predict" a lot of declarative knowledge about history, science, human languages, pop culture, programming languages, and so on. They memorize a lot. But the vast majority of these memories are not necessary for a general pattern inductor. Such a model (or "algorithm") wouldn't need or have the ability to answer factual questions about the capital of France, or even the ability to understand English. So I think it should be theoretically possible to separate the "inductive inference" abilities of a model from its "knowledge". Currently both faculties are mixed together, but this doesn't mean that they can't be separated somehow. We already know than knowledge and "inductive inference ability" (~intelligence) are not in fact the same in humans, even though both appear to be implemented in a fairly unified way in the brain. Though I still think the pattern inductor algorithm wouldn't be something you could implement in C++. Programming languages are far too discrete, where all conditions are either met or not met, instead of being met more or less. Neural networks don't have this problem.
- api 3y agoThis makes me think a bit about how digital technology is changing the way humans use their intelligence. Memorization is de-emphasized since I can look up anything at any time. Instead, it's more valuable to use space in my brain to learn concepts and "meta" reasoning tasks. We're probably becoming smarter in terms of general intelligence but dumber in terms of remembered knowledge. There's only so much space up there.
- cubefox 3y agoUnfortunately as a species we are in fact getting dumber because of dysgenics. Intelligence (which is strongly heritable) and fertility are anti-correlated, especially in women. See e.g. https://georgefrancis.substack.com/p/dysgenics-by-the-numbers https://georgefrancis.substack.com/p/dysgenics-by-the-number...
- optimalsolver 3y agoHow useful would a model be if humans can't query it?
- cubefox 3y agoWe can't ask it questions, but we can enter a data sequence and let the model extrapolate it. Similar to how foundation language models (without instruction tuning) used to work, but not just for human readable text.
- sifar 3y agoThe query language doesn't have to English or a complete language.