samshap

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To the second point, because humans are already general intelligences.

But more seriously, I think the monolithic AI approach will ultimately be uncompetitive with modular AI for real life applications. Modular AI dramatically reduces the search space. And I would contend that prediction over complex real life systems over long-term timescales will always be data-starved. Therefore being able to reduce your search space will be a critical competitive advantage, and worth the hit from having suboptimal interfaces.

Why is this relevant for alignment? Because you can train and evaluate the AI modules independently, individually they are much less intelligent and less likely to be deceptive, you can monitor their communications, etc.

I take issue with the initial supposition:

  • How could the AI gain practical understanding of long-term planning if it's only trained on short time scales?
  • Writing code, how servers work, and how users behave seen like very different types of knowledge, operating with very different feedback mechanisms and learning rules. Why would you use a single, monolithic 'AI' to do all three?

My weak prediction is that adding low levels of noise would change the polysemantic activations, but not the monosemantic ones.

Adding L1 to the loss allows the network to converge on solutions that are more monosemantic than otherwise, at the cost of some estimation error. Basically, the network is less likely to lean on polysemantic neurons to make up small errors. I think your best bet is to apply the L1 loss on the hidden layer and the output later activations.

I've been thinking along very similar lines, and would probably generalize even further:

Hypothesis: All DNNs thus far developed are basically limited to system-1 like reasoning.

Great stuff!

Do you have results with noisy inputs?

The negative bias lines up well with previous sparse coding implementations: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=JHuo2D0AAAAJ&citation_for_view=JHuo2D0AAAAJ:u-x6o8ySG0sC

Note that in that research, the negative bias has a couple of meanings/implications:

  • It should correspond to the noise level in your input channel.
  • Higher negative biases directly contribute to the sparsity/monosemanticty of the network.

Along those lines, you might be able to further improve monosemanticity by using the lasso loss function.

Yes, but that was decades ago, when Yeltsin was president! The 'union state' has been moribund since the early aughts.

I have some technical background in neuromorphic AI.

There are certainly things that the current deep learning paradigm is bad at which are critical to animal intelligence: e.g. power efficiency, highly recurrent networks, and complex internal dynamics.

It's unclear to me whether any of these are necessary for AGI. Something, something executive function and global workspace theory?

I once would have said that feedback circuits used in the sensory cortex for predictive coding were a vital component, but apparently transformers can do similar tasks using purely feedforward methods.

My guess is that the scale and technology lead of DL is sufficient that it will hit AGI first, even if a more neuro way might be orders of magnitude more computationally efficient.

Where neuro AI is most useful in the near future is for embodied sensing and control, especially with limited compute or power. However, those constraints would seem to drastically curtail the potential for AGI.

If the world's governments decided tomorrow that RL was top-secret military technology (similar to nuclear weapons tech, for example), how much time would that buy us, if any? (Feel free to pick a different gateway technology for AGI, RL just seems like the most salient descriptor).

In my model, Chevron and the US military are probably open to AI governance, because: 1 - they are institutions traditionally enmeshed in larger cooperative/rule-of-law systems, AND 2 - their leadership is unlikely to believe they can do AI 'better' than the larger AI community.

My worry is instead about criminal organizations and 'anti-social' states (e.g. North korea) because of #1, and big tech because of #2.

Because of location, EA can (and should) make decent connective with US big tech. I think the bigger challenge will be tech companies in other countries , especially China.

I published an article on induction https://www.lesswrong.com/posts/7x4eGxXL5DMwRwzDQ/commensurable-scientific-paradigms-or-computable-induction of decent length/complexity that send to have gotten no visibility at all, which I found very discouraging for my desire to ever do so again. I could only find it by checking my user profile!

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