Steelmaning AI risk critiques

Stuart_Armstrong

At some point soon, I'm going to attempt to steelman the position of those who reject the AI risk thesis, to see if it can be made solid. Here, I'm just asking if people can link to the most convincing arguments they've found against AI risk.

EDIT: Thanks for all the contribution! Keep them coming...

The deepmind's team's solution to this is the neural Turing machine model, which is a hybrid system between a neural network and a database. It's not a pure ANN.

Yes it is a pure ANN - according to my use of the term ANN (arguing over definitions is a waste of time). ANNs are fully general circuit models, which obviously can re-implement any module from any computer - memory, database, whatever. The defining characteristics of an ANN are - simulated network circuit structure based on analog/real valued nodes, and some universal learning algorithm over the weights - such as SGD.

Your position is not as mainstream as you like to present it.

You don't understand my position. I don't believe DL as it exists today is somehow the grail of AI. And yes I'm familiar with Hinton's 'Capsule' proposals. And yes I agree there is still substantial room for improvement in ANN microarchitecture, and especially for learning invariances - and unsupervised especially.

This is far from the mainstream linguistic perspective.

For any theory of anything the brain does - if it isn't grounded in computational neuroscience data, it is probably wrong - mainstream or not.

No, it really isn't. I don't update based on forum posts on topics I don't understand, because I have no way to distinguish experts from crackpots.

You don't update on forum posts? Really? You seem pretty familiar with MIRI and LW positions. So are you saying that you arrived at those positions all on your own somehow? Then you just showed up here, thankfully finding other people who just happened to have arrived at all the same ideas?

Yes it is a pure ANN - according to my use of the term ANN (arguing over definitions is a waste of time). ANNs are fully general circuit models, which obviously can re-implement any module from any computer - memory, database, whatever. The defining characteristics of an ANN are - simulated network circuit structure based on analog/real valued nodes, and some universal learning algorithm over the weights - such as SGD.

You could say that any machine learning system is an ANN, under a sufficiently vague definition. That's not particularly useful in a discussion, however.

-1AndreInfante11y

I think you misunderstood me. The current DeepMind AI that they've shown the public is a pure ANN. However, it has serious limitations because it's not easy to implement long-term memory as a naive ANN. So they're working on a successor called the "neural Turing machine" which marries an ANN to a database retrieval system - a specialized module. [...] The thing is, many of those improvements are dependent on the task at hand. It's really, really hard for an off-the-shelf convnet neural network to learn the rules of three dimensional geometry, so we have to build it into the network. Our own visual processing shows signs of having the same structure imbedded in it. The same structure would not, for example, benefit an NLP system, so we'd give it a different specialized structure, tuned to the hierarchical nature of language. The future, past a certain point, isn't making 'neural networks' better. It's making 'machine vision' networks better, or 'natural language' networks better. To make a long story short, specialized modules are an obvious place to go when you run into problem too complex to teach a naive convnet to do efficiently. Both for human engineers over the next 5-10, and for evolution over the last couple of billion. [...] I have a CS and machine learning background, and am well-read on the subject outside LW. My math is extremely spotty, and my physics is non-existent. I update on things I read that I understand, or things from people I believe to be reputable. I don't know you well enough to judge whether you usually say things that make sense, and I don't have the physics to understand the argument you made or judge its validity. Therefore, I'm not inclined to update much on your conclusion. EDIT: Oh, and you still haven't responded to the cat thing. Which, seriously, seems like a pretty big hole in the universal learner hypothesis.

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Steelmaning AI risk critiques

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