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Well, part of the reason that this writeup doesn't mention the brain is that the author is ignorant of precise details of how the brain works, and chose not to

Well, part of the reason that this writeup doesn't mention the brain is that the author is ignorant of precise details of how the brain works, and chose not to speculate :) Which is exactly why these papers are such a useful reading list. Cheers! It's fascinating that we have had so many attempts to add memories to NNs, but it was the end-to-end-learned one based on textual context that eventually drove us to models that have emergent reasoning abilities. RNNs basically have a memory, in the form of their hidden state, but it's been hard to train them efficiently in parallel. There may be a better way though, if we can.

nickpsecurity 2y ago
I forgot one tech that’s actually commercialized by lamini.ai: https://arxiv.org/abs/2406.17642 https://arxiv.org/abs/2406.17642 https://medium.com/pythons-gurus/mixture-of-memory-experts-lamini-memory-tuning-9f81f3f2765a https://medium.com/pythons-gurus/mixture-of-memory-experts-l... Re your reply I’ll add two things to “drive us to models that have emergent, reasoning abilities:” 1. It didn’t have that until they spent around $30 million pre-training a specific architecture (GPT3-176B) on TB’s of data with portions having built-in, reasoning steps it could extrapolate. Doing the same with the other architectures might have led to similar, better, or worse results. GPT3’s training investment, not Transformer architecture itself, led to seeing them as reasoning engines. 2. The memory-less architectures also had lots of hallucinations. Another submission said they’re like professional liars who also don’t know when they’re lying. It used to hurt me a lot with even GPT4-generated code. What reduces that, especially simply using huge models, drives pre-training cost back up to exorbitant levels. So, if you have tens of millions, you can pretrain models for reasoning that also confidently lie to us. The open question is whether that’s built into the prevailing architectures. I think it is given brains’ performance. Also, how specific components of brains being damaged causes hallucinations. Brain architecture has layers of mitigations for it. If integrating memory, we might see architectures with few to no hallucinations. That can be combined with other techniques already in use. They might also be cheaper to train, too, if they build on what they remember. That’s a bigger maybe.

nickpsecurity 2y ago

I forgot one tech that’s actually commercialized by lamini.ai: https://arxiv.org/abs/2406.17642 https://arxiv.org/abs/2406.17642 https://medium.com/pythons-gurus/mixture-of-memory-experts-lamini-memory-tuning-9f81f3f2765a https://medium.com/pythons-gurus/mixture-of-memory-experts-l... Re your reply I’ll add two things to “drive us to models that have emergent, reasoning abilities:” 1. It didn’t have that until they spent around $30 million pre-training a specific architecture (GPT3-176B) on TB’s of data with portions having built-in, reasoning steps it could extrapolate. Doing the same with the other architectures might have led to similar, better, or worse results. GPT3’s training investment, not Transformer architecture itself, led to seeing them as reasoning engines. 2. The memory-less architectures also had lots of hallucinations. Another submission said they’re like professional liars who also don’t know when they’re lying. It used to hurt me a lot with even GPT4-generated code. What reduces that, especially simply using huge models, drives pre-training cost back up to exorbitant levels. So, if you have tens of millions, you can pretrain models for reasoning that also confidently lie to us. The open question is whether that’s built into the prevailing architectures. I think it is given brains’ performance. Also, how specific components of brains being damaged causes hallucinations. Brain architecture has layers of mitigations for it. If integrating memory, we might see architectures with few to no hallucinations. That can be combined with other techniques already in use. They might also be cheaper to train, too, if they build on what they remember. That’s a bigger maybe.