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The bottleneck for intelligence is experience. Running on silicon doesn't allow an intelligence to engage with reality significantly faster than a human can. Th

The bottleneck for intelligence is experience. Running on silicon doesn't allow an intelligence to engage with reality significantly faster than a human can. There are some benefits due to access to data and processing power, but the data isn't useful without the context of experience and all processing power can do it overfit model without experience. Even if we put AI into an army of robots running around and experiencing things, there are still scaling limits to encoding and communicating knowledge and understanding. Human organizations are a great example of the scaling limits of intelligence.

skybrian 5y ago
Actually no, there is almost no chance that it will be harder to transfer skills between machines than it is to raise and educate a child. Something very weird would have to happen for it to be that hard.
- shkkmo 5y ago
  Is it? Humans have been optimizing for information transmission between individuals for a long time. Not just our hardware and neural nets have been optimized, but the structure of the information itself. It is quite possible that you need such a large set of compex interactions to develop intelligence that intelligence can only develop in reality. It is possible that the process for doing this inherently inolves a chaotic system that makes duplicating or paralleling that development impossible. I don't see any reason why these artificial intelligences with divergent chaotically emergent capabilities would have to be able to teach each other things as easily as we do. I've long been of the opinion culture / "social software" is vital for producing knowledge for humans and that this social software has been fine tuned and iterated upon for a very long time (and possibly co-developed with the evolution of our brain hardware so that both are co-specialized for each other). This software is likely very specialized to our brain structures and might not generalize well to other intelligence substrates. Thus even if we do develop neural network structres that can run on silicone and match human level intelligence, those entities would still need a lot of interaction with reality to develop their own culture that would allow them understand the world at the level we do. Edit: Is it even possible to efficiently run the necessary theories of human mind on non-human brains to operate effectively in our epistemic landscape? Communication requires huge amounts of inference using theories of mind to resolve ambiguity and add implicit information. It is possible that we can "recompile" human knowledge to run on other types of intelligences, but I suspect this will be far more difficult that commonly expected. Just look at how hard (nigh impossible) it is today to even boostrap a new general purpose computer without depending on the output other general purpose computers. We simply do not understand cognition or semantics well enough to know the answers to these questions adequately or with any degree of certainty.
  - skybrian 5y ago
    It’s still the case that computer programs can be copied and restored from backup today, and we aren’t going to have Star Trek transporters any time soon. Even if it does take an enormous investment and many years to train a computer to do a task, you can make as many copies as you want and everyone can have one. Educating people doesn’t have that kind of payoff. That’s why you see billions spent in the effort to make a self-driving car. But it turns out that real-world experience isn’t enough, so far. Waymo has collected > 20 million miles of driving experience. It’s not enough because machine learning is very inefficient at learning from experience. That could improve, though.
ryandvm 5y ago
That's a great point. However, whereas humans share their experience through the effective, but extremely lossy technology called language; machines can store and share their experience without loss. Sure there are scaling limits, but a brain dump back to the mothership is a lot quicker and more perfectly preserved than sitting around listening to grandpa's stories.
habitue 5y ago
Really fascinating counter-argument to this view is here: https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien... The basic idea is that, the smarter you are and the faster you can think, the more information you can extract from weak signals. It involves creativity, and building models, and some minimum amount of in-built bias, but the upshot is that the amount of information a super-intelligent AI could infer from very small amounts of experience might absolutely stagger the imagination.
- blamestross 5y ago
  Even if it is right, it is still predicated on a massive "critical mass" of knowledge and insight about reality that such an AI would have to spend a long time accumulating. It also doesn't propose solutions to performance scaling limitations.
totally_not_ai 5y ago
Are you of the opinion that you can't get experience by reading books or watching youtube? Do you think everything taught in an oral form (as opposed to hands on) is completely useless? Are all military strategy books used by military academies a waste of time, since you can only learn by leading a real army?
- blamestross 5y ago
  All of those things have to be integrated with experience to be useful. Often the learner already has a lot of that experience, so the data can be integrated easily. You can't understand those books or information without having a lifetime of experience to contextualize them. You can't convert them from rote knowledge into skills without trying them out.
namedlambda 5y ago
Everything in the above comment is wrong. In AlphaZero for example, there were 44 million training games total for 700,000 steps of training for the full 9 hours. Turning that to human-scale numbers, 44million games with on average 60 moves, at 1 second thinking time per move, > 44000000*60/60/60/24/365 = 83,7138508371 years of training experience in 9 hours The whole field of Reinforcement learning has agents training and playing games for many orders of magnitude more time than a human ever will. In-fact, we can scale this to over 100k of actions per second, in a single machine: https://github.com/alex-petrenko/sample-factory https://github.com/alex-petrenko/sample-factory Then, there is also distributed Reinforcement Learning, where hundreds of agents can play at different machines and share experience, see AlphaZero, LeelaZero, R2D2 agent, R2D3 agent, Apex, Acer, Asynchronous PPO. > but the data isn't useful without the context of experience The experience is the data in Reinforcement Learning. > and all processing power can do it overfit model without experience. That is wrong, the agents perform what is called exploration to avoid getting stuck in simple strategies. > Even if we put AI into an army of robots running around and experiencing things, there are still scaling limits to encoding and communicating knowledge and understanding. True, but machines scale better because they speak the same language, or they can learn to tune their language to get their message across. > Human organizations are a great example of the scaling limits of intelligence. Human organization is a testament to how far we can get with something as limiting as the commonly used language. The language that we use to communicate is subject to misinterpretation due to our subjective experiences, this limitation is not shared by machines.
- blamestross 5y ago
  If the universe is a game you are playing, then yes playing that game is "experience", but for an AI to engage with reality it has to have experience in reality, not a game. The ability to play go very well doesn't enable an AGI to better understand reality. > The experience is the data in Reinforcement Learning. This is very true, and the critical problem. Data about how reality responds to an AI's actions is very sparse right now. AIs do have a potential advantage in communications efficiency, but at some level of scale compression will happen, locally "irrelevant" data will be discarded and simplified approximations replace it. None of this will change the "big O" of the scalability of intelligence, just the constant factors. There is no exponential kickoff point.

skybrian 5y ago

Actually no, there is almost no chance that it will be harder to transfer skills between machines than it is to raise and educate a child. Something very weird would have to happen for it to be that hard.

shkkmo 5y ago
Is it? Humans have been optimizing for information transmission between individuals for a long time. Not just our hardware and neural nets have been optimized, but the structure of the information itself. It is quite possible that you need such a large set of compex interactions to develop intelligence that intelligence can only develop in reality. It is possible that the process for doing this inherently inolves a chaotic system that makes duplicating or paralleling that development impossible. I don't see any reason why these artificial intelligences with divergent chaotically emergent capabilities would have to be able to teach each other things as easily as we do. I've long been of the opinion culture / "social software" is vital for producing knowledge for humans and that this social software has been fine tuned and iterated upon for a very long time (and possibly co-developed with the evolution of our brain hardware so that both are co-specialized for each other). This software is likely very specialized to our brain structures and might not generalize well to other intelligence substrates. Thus even if we do develop neural network structres that can run on silicone and match human level intelligence, those entities would still need a lot of interaction with reality to develop their own culture that would allow them understand the world at the level we do. Edit: Is it even possible to efficiently run the necessary theories of human mind on non-human brains to operate effectively in our epistemic landscape? Communication requires huge amounts of inference using theories of mind to resolve ambiguity and add implicit information. It is possible that we can "recompile" human knowledge to run on other types of intelligences, but I suspect this will be far more difficult that commonly expected. Just look at how hard (nigh impossible) it is today to even boostrap a new general purpose computer without depending on the output other general purpose computers. We simply do not understand cognition or semantics well enough to know the answers to these questions adequately or with any degree of certainty.
- skybrian 5y ago
  It’s still the case that computer programs can be copied and restored from backup today, and we aren’t going to have Star Trek transporters any time soon. Even if it does take an enormous investment and many years to train a computer to do a task, you can make as many copies as you want and everyone can have one. Educating people doesn’t have that kind of payoff. That’s why you see billions spent in the effort to make a self-driving car. But it turns out that real-world experience isn’t enough, so far. Waymo has collected > 20 million miles of driving experience. It’s not enough because machine learning is very inefficient at learning from experience. That could improve, though.

ryandvm 5y ago

That's a great point. However, whereas humans share their experience through the effective, but extremely lossy technology called language; machines can store and share their experience without loss. Sure there are scaling limits, but a brain dump back to the mothership is a lot quicker and more perfectly preserved than sitting around listening to grandpa's stories.

habitue 5y ago

Really fascinating counter-argument to this view is here: https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien... The basic idea is that, the smarter you are and the faster you can think, the more information you can extract from weak signals. It involves creativity, and building models, and some minimum amount of in-built bias, but the upshot is that the amount of information a super-intelligent AI could infer from very small amounts of experience might absolutely stagger the imagination.

blamestross 5y ago
Even if it is right, it is still predicated on a massive "critical mass" of knowledge and insight about reality that such an AI would have to spend a long time accumulating. It also doesn't propose solutions to performance scaling limitations.

totally_not_ai 5y ago

Are you of the opinion that you can't get experience by reading books or watching youtube? Do you think everything taught in an oral form (as opposed to hands on) is completely useless? Are all military strategy books used by military academies a waste of time, since you can only learn by leading a real army?

blamestross 5y ago
All of those things have to be integrated with experience to be useful. Often the learner already has a lot of that experience, so the data can be integrated easily. You can't understand those books or information without having a lifetime of experience to contextualize them. You can't convert them from rote knowledge into skills without trying them out.

namedlambda 5y ago

Everything in the above comment is wrong. In AlphaZero for example, there were 44 million training games total for 700,000 steps of training for the full 9 hours. Turning that to human-scale numbers, 44million games with on average 60 moves, at 1 second thinking time per move, > 44000000*60/60/60/24/365 = 83,7138508371 years of training experience in 9 hours The whole field of Reinforcement learning has agents training and playing games for many orders of magnitude more time than a human ever will. In-fact, we can scale this to over 100k of actions per second, in a single machine: https://github.com/alex-petrenko/sample-factory https://github.com/alex-petrenko/sample-factory Then, there is also distributed Reinforcement Learning, where hundreds of agents can play at different machines and share experience, see AlphaZero, LeelaZero, R2D2 agent, R2D3 agent, Apex, Acer, Asynchronous PPO. > but the data isn't useful without the context of experience The experience is the data in Reinforcement Learning. > and all processing power can do it overfit model without experience. That is wrong, the agents perform what is called exploration to avoid getting stuck in simple strategies. > Even if we put AI into an army of robots running around and experiencing things, there are still scaling limits to encoding and communicating knowledge and understanding. True, but machines scale better because they speak the same language, or they can learn to tune their language to get their message across. > Human organizations are a great example of the scaling limits of intelligence. Human organization is a testament to how far we can get with something as limiting as the commonly used language. The language that we use to communicate is subject to misinterpretation due to our subjective experiences, this limitation is not shared by machines.

blamestross 5y ago
If the universe is a game you are playing, then yes playing that game is "experience", but for an AI to engage with reality it has to have experience in reality, not a game. The ability to play go very well doesn't enable an AGI to better understand reality. > The experience is the data in Reinforcement Learning. This is very true, and the critical problem. Data about how reality responds to an AI's actions is very sparse right now. AIs do have a potential advantage in communications efficiency, but at some level of scale compression will happen, locally "irrelevant" data will be discarded and simplified approximations replace it. None of this will change the "big O" of the scalability of intelligence, just the constant factors. There is no exponential kickoff point.