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> We have good reason to believe our computers are underpowered for the task. What are those reasons? It appeared to me as if we had amassed a terrific reser

> We have good reason to believe our computers are underpowered for the task. What are those reasons? It appeared to me as if we had amassed a terrific reservoir of computational ability - but we wasted nearly all because it was expedient to waste resources when they were ample. If I gave you a computer with 1 billion times the processing ability of current supercomputers, can you convince me that you'd be capable of replicating the functionality of a biological brain?

state_less 8y ago
Do we have a terrific reservoir of computational ability though? The number are important because they seem to be about a million times too inefficient. A human brain might take 36.8e15 computations per second [1] and does all this using about ~20 watts! That's pretty impressive at around 1.84e+15 ops / watt vs our current 6e+9 ops / watt [2] from silicon. We have to make about a million times more efficient processors to match the brains efficiency. My 4lb laptop takes what feels like forever to do a limited deep learning task given how inefficient it is. If you gave just me the sort of computer needed, that alone would very likely not be sufficient. I just don't think we should draw too strong of a negative conclusions before we all have the necessary equipment to match the work of the brain. But if myself and fellow computer users did have access to efficient powerful computers, we'd probably want to write algorithms that allowed us to make many attempts at the solution per second. I'd imagine writing a simulator to project many agents into where they are competing for resources and many of these simulations taking place concurrently. Something of a multiverse of simulation and optimization. [1] https://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude https://en.wikipedia.org/wiki/Computer_performance_by_orders... [2] https://en.wikipedia.org/wiki/Performance_per_watt https://en.wikipedia.org/wiki/Performance_per_watt
- bachbach 8y ago
  > Do we have a terrific reservoir of computational ability though? We do (and I wrote a long post with a list of bullet points proving so before I just deleted it), but I don't think we should waste time debating whether this is true or not true. We're on the same side after all, your objective is my objective. > But if myself and fellow computer users did have access to efficient powerful computers, we'd probably want to write algorithms that allowed us to make many attempts at the solution per second. I'd imagine writing a simulator to project many agents into where they are competing for resources and many of these simulations taking place concurrently. I recommend you look at David Krateneur's ideas, he has a video called "The Stupid Ways That We Have Thought About Intelligence". https://www.youtube.com/watch?v=pi7h6nmkvAM https://www.youtube.com/watch?v=pi7h6nmkvAM > Something of a multiverse of simulation and optimization. An artificial imagination really. I really recommend watching that video.
pharrington 8y ago
My 100% unfounded, total wild guess is that a computer with 1 billion times the processing ability of current supercomputers could replicate the functionality of a biological honey bee brain. From what I can tell though, I agree that there are alot of very subtle, but fundamental mistakes and omissions in both the design and combinations of current generation intelligence algorithms that are impeding a viable path toward general intelligence. Of course, if all we care about is dramatically increasing the efficiency of solving problems humans care about, this totally isn't a problem, because we apparently don't need general intelligence to do that.
- bachbach 8y ago
  I thought I'd scribble on the back of the envelope. Henry Markram, a neuroscience person with a desire to emulate the human brain believes it takes 1 exaflop. It's a start. Existing supercomputers reach 50 petaflops. 50 billion petaflops is 50 million exaflops. Is this 50 million brain EM moments? It's possible it was in the ballpark of a human brain emulation although I strongly suspect a fair comparison isn't easily comprehensible. > I agree that there are alot of very subtle, but fundamental mistakes and omissions in both the design and combinations of current generation intelligence algorithms that are impeding a viable path toward general intelligence I think that is true of specialized intelligence, and that zero progress of any sort has been made on AGI. I don't mind being wrong if you know of primitive examples that qualify. It is unclear how to begin. Maybe rehearsing something like real biological evolution. > , if all we care about is dramatically increasing the efficiency of solving problems humans care about, this totally isn't a problem, because we apparently don't need general intelligence to do that. Daniel Dennett makes this point philosophically in a video I watched yesterday. Other people have made the observation that we're already surrounded by AGIs in the form human society based collective intelligences we know well.

state_less 8y ago

Do we have a terrific reservoir of computational ability though? The number are important because they seem to be about a million times too inefficient. A human brain might take 36.8e15 computations per second [1] and does all this using about ~20 watts! That's pretty impressive at around 1.84e+15 ops / watt vs our current 6e+9 ops / watt [2] from silicon. We have to make about a million times more efficient processors to match the brains efficiency. My 4lb laptop takes what feels like forever to do a limited deep learning task given how inefficient it is. If you gave just me the sort of computer needed, that alone would very likely not be sufficient. I just don't think we should draw too strong of a negative conclusions before we all have the necessary equipment to match the work of the brain. But if myself and fellow computer users did have access to efficient powerful computers, we'd probably want to write algorithms that allowed us to make many attempts at the solution per second. I'd imagine writing a simulator to project many agents into where they are competing for resources and many of these simulations taking place concurrently. Something of a multiverse of simulation and optimization. [1] https://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude https://en.wikipedia.org/wiki/Computer_performance_by_orders... [2] https://en.wikipedia.org/wiki/Performance_per_watt https://en.wikipedia.org/wiki/Performance_per_watt

bachbach 8y ago
> Do we have a terrific reservoir of computational ability though? We do (and I wrote a long post with a list of bullet points proving so before I just deleted it), but I don't think we should waste time debating whether this is true or not true. We're on the same side after all, your objective is my objective. > But if myself and fellow computer users did have access to efficient powerful computers, we'd probably want to write algorithms that allowed us to make many attempts at the solution per second. I'd imagine writing a simulator to project many agents into where they are competing for resources and many of these simulations taking place concurrently. I recommend you look at David Krateneur's ideas, he has a video called "The Stupid Ways That We Have Thought About Intelligence". https://www.youtube.com/watch?v=pi7h6nmkvAM https://www.youtube.com/watch?v=pi7h6nmkvAM > Something of a multiverse of simulation and optimization. An artificial imagination really. I really recommend watching that video.

pharrington 8y ago

My 100% unfounded, total wild guess is that a computer with 1 billion times the processing ability of current supercomputers could replicate the functionality of a biological honey bee brain. From what I can tell though, I agree that there are alot of very subtle, but fundamental mistakes and omissions in both the design and combinations of current generation intelligence algorithms that are impeding a viable path toward general intelligence. Of course, if all we care about is dramatically increasing the efficiency of solving problems humans care about, this totally isn't a problem, because we apparently don't need general intelligence to do that.

bachbach 8y ago
I thought I'd scribble on the back of the envelope. Henry Markram, a neuroscience person with a desire to emulate the human brain believes it takes 1 exaflop. It's a start. Existing supercomputers reach 50 petaflops. 50 billion petaflops is 50 million exaflops. Is this 50 million brain EM moments? It's possible it was in the ballpark of a human brain emulation although I strongly suspect a fair comparison isn't easily comprehensible. > I agree that there are alot of very subtle, but fundamental mistakes and omissions in both the design and combinations of current generation intelligence algorithms that are impeding a viable path toward general intelligence I think that is true of specialized intelligence, and that zero progress of any sort has been made on AGI. I don't mind being wrong if you know of primitive examples that qualify. It is unclear how to begin. Maybe rehearsing something like real biological evolution. > , if all we care about is dramatically increasing the efficiency of solving problems humans care about, this totally isn't a problem, because we apparently don't need general intelligence to do that. Daniel Dennett makes this point philosophically in a video I watched yesterday. Other people have made the observation that we're already surrounded by AGIs in the form human society based collective intelligences we know well.