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What really matters is if we've made enough hardware progress to allow us to develop strong AI. And IMO the answer is a resounding 'yes'.

by fizixer 12y ago

theandrewbailey 12y ago
It has been yes for a very long time. What really matters is understanding how general intelligence works and how to translate that into software.
- AndrewOMartin 12y ago
  I've been studying that very problem for a few years and I currently hold the view about "general intelligence" that it is untranslatable into software, or at least, not an algorithm. I use the term algorithm because it's a pretty fixed definition, and "software" is relatively woolly. This isn't a spooky, defeatist or handwavy argument. To be "generally" intelligent you need to be intimately physically linked to your environment in a way that the "input -> processing -> output" character of an algorithm, even when realised on a microchip, is not.
marcosdumay 12y ago
At the 90's, the MIT published a lower bound estimate of the processing capacity of a person's brain. We are currently entering the era when "all the computers, added" have reached this capacity. I simply have no idea where did you got that "resound yes" from.
- fizixer 12y ago
  You didn't provide the source of your MIT study but that's okay because: - I didn't provide any source either. - But more importantly, I'm well aware that there are studies out there that grossly overestimate the information processing capacity of a human brain. Please see this link: http://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude http://en.wikipedia.org/wiki/Computer_performance_by_orders_... In the petascale section, the top supercomputer as of today Tianhe-2 (~33 petaflops) is listed right next to the computing power of human brain (~36 petaflops). Also this link is relevant: http://cs.stackexchange.com/questions/20016 http://cs.stackexchange.com/questions/20016 But it lists many mutually disagreeing estimates. About half of the estimates indicate we have already achieved the capability (in the form of the top supercomputer, not necessarily in the form of a sub-$1000 computer). I should point out the accepted answer in particular. I have spent some time on the RIKEN simulation story (1 second of activity of 1% of human brain took 40 minutes on a 10 petaflops RIKEN supercomputer) in the past and I'm beginning to believe it's nothing more than a marketing gimmick: - They never released a comprehensive scientific paper, detailing the methods they used. - 1% refers to ~1.73 billion neurons, which is really 2% of the whole CNS (~86 billion neurons). But calling the whole CNS human brain is nonsense. The brain only has about ~19-23 billion neurons. So in reality it was ~10% of human brain. - 'Simulation' could have vastly different definitions, and associated computational complexities. If they were solving differential equations of human brain's biochemistry then I wouldn't be surprised it took 40 minutes to simulate 1 second of activity of 1.73 billion neurons. But that is computationally expensive, and a wasteful way of doing brain simulation, because we're mostly concerned with the information-processing nature of the neurons, not their biochemistry. Furthermore: The top supercomputer as of today, Tianhe-2, is almost two years old. Do you know how much it costs today to buy GPUs worth 33 petaflops? AMD's R9 290X is a 6 teraflop GPU costing $250. You do the math. (Hint: It's less than $1.5 million! Tianhe-2 was a $390 million project, Titan $97 million at ~18 petaflops). Finally: If you plan to run a dedicated piece of software on your hardware, FPGA blows GPU out of the water, and an ASIC blows FPGA out of the water. If someone today implements a deep learning system (just giving an example, not claiming it's the end-all-be-all of strong AI) on an ASIC, I wouldn't be suprised if one such card performs equivalent of between 100 and 1000 teraflops of a GPU's performance. And I'm talking about 28nm process. 22nm would only sweeten the deal. So yes, Moore's law has been a great help in its leg in the "relay race" lasting ~50 years. Now it's time to pass the baton to the next leg, optimize the architecture and put some strong AI software in it.

What really matters is if we've made enough hardware progress to allow us to develop strong AI. And IMO the answer is a resounding 'yes'.

by fizixer 12y ago

What really matters is if we've made enough hardware progress to allow us to develop strong AI. And IMO the answer is a resounding 'yes'.

theandrewbailey 12y ago

It has been yes for a very long time. What really matters is understanding how general intelligence works and how to translate that into software.

AndrewOMartin 12y ago
I've been studying that very problem for a few years and I currently hold the view about "general intelligence" that it is untranslatable into software, or at least, not an algorithm. I use the term algorithm because it's a pretty fixed definition, and "software" is relatively woolly. This isn't a spooky, defeatist or handwavy argument. To be "generally" intelligent you need to be intimately physically linked to your environment in a way that the "input -> processing -> output" character of an algorithm, even when realised on a microchip, is not.

marcosdumay 12y ago

At the 90's, the MIT published a lower bound estimate of the processing capacity of a person's brain. We are currently entering the era when "all the computers, added" have reached this capacity. I simply have no idea where did you got that "resound yes" from.

fizixer 12y ago
You didn't provide the source of your MIT study but that's okay because: - I didn't provide any source either. - But more importantly, I'm well aware that there are studies out there that grossly overestimate the information processing capacity of a human brain. Please see this link: http://en.wikipedia.org/wiki/Computer_performance_by_orders_of_magnitude http://en.wikipedia.org/wiki/Computer_performance_by_orders_... In the petascale section, the top supercomputer as of today Tianhe-2 (~33 petaflops) is listed right next to the computing power of human brain (~36 petaflops). Also this link is relevant: http://cs.stackexchange.com/questions/20016 http://cs.stackexchange.com/questions/20016 But it lists many mutually disagreeing estimates. About half of the estimates indicate we have already achieved the capability (in the form of the top supercomputer, not necessarily in the form of a sub-$1000 computer). I should point out the accepted answer in particular. I have spent some time on the RIKEN simulation story (1 second of activity of 1% of human brain took 40 minutes on a 10 petaflops RIKEN supercomputer) in the past and I'm beginning to believe it's nothing more than a marketing gimmick: - They never released a comprehensive scientific paper, detailing the methods they used. - 1% refers to ~1.73 billion neurons, which is really 2% of the whole CNS (~86 billion neurons). But calling the whole CNS human brain is nonsense. The brain only has about ~19-23 billion neurons. So in reality it was ~10% of human brain. - 'Simulation' could have vastly different definitions, and associated computational complexities. If they were solving differential equations of human brain's biochemistry then I wouldn't be surprised it took 40 minutes to simulate 1 second of activity of 1.73 billion neurons. But that is computationally expensive, and a wasteful way of doing brain simulation, because we're mostly concerned with the information-processing nature of the neurons, not their biochemistry. Furthermore: The top supercomputer as of today, Tianhe-2, is almost two years old. Do you know how much it costs today to buy GPUs worth 33 petaflops? AMD's R9 290X is a 6 teraflop GPU costing $250. You do the math. (Hint: It's less than $1.5 million! Tianhe-2 was a $390 million project, Titan $97 million at ~18 petaflops). Finally: If you plan to run a dedicated piece of software on your hardware, FPGA blows GPU out of the water, and an ASIC blows FPGA out of the water. If someone today implements a deep learning system (just giving an example, not claiming it's the end-all-be-all of strong AI) on an ASIC, I wouldn't be suprised if one such card performs equivalent of between 100 and 1000 teraflops of a GPU's performance. And I'm talking about 28nm process. 22nm would only sweeten the deal. So yes, Moore's law has been a great help in its leg in the "relay race" lasting ~50 years. Now it's time to pass the baton to the next leg, optimize the architecture and put some strong AI software in it.