7 ms·

How Long Before Superintelligence? (1997)

59 points by joaorico 11y ago

Piskvorrr 11y ago
Yup, the XKCD translations still hold: https://xkcd.com/678/ https://xkcd.com/678/
nefitty 11y ago
[Regarding power of artificial intelligence] "...If Moore's law continues to hold then the lower bound will be reached sometime between 2004 and 2008, and the upper bound between 2015 and 2024." I guess his prognostication here depends on super-powerful computing and brain-emulation software. China's Tianhe-2 has already hit 3.3^15 ops, Bostrom was anticipating for 10^14 - 10^17 to be the runway. Now, I am not sure what the state of brain emulation is at the moment but it looks like our biggest snag is there. Researchers are having a hard time bubbling up new paradigms for artificial intelligence software. Anyone have any insight into that?
- marcosdumay 11y ago
  Besides, there's this: > it seems that speed rather than memory would be the bottleneck in brain simulations on the neuronal level Currently, memory access is the bottleneck of most applications that need big amounts of data. Anyway, as we get to his prescribed 100 Tops, we are seeing more and more applications like image identification, and self driving cars.
  - nefitty 11y ago
    Right. I mean, it could be that the bottom of the exponential curve looks nothing like the top. We might just be beginning to see the lift off the x-axis in the form of practical AI applications ("soft takeoff"). Who knows what it will be like when these technologies begin to be stacked.
- zardo 11y ago
  Those estimstes assume neurons are all you need to simulate. In the brain neurons interact with the glia. This is interaction looks pretty important. http://www.scientificamerican.com/article/the-root-of-thought-what/ http://www.scientificamerican.com/article/the-root-of-though...
  - TheOtherHobbes 11y ago
    This very reminiscent of the hype around DNA sequencing. It turns out that genetics is vastly more complicated than the old "gene = expression" model. By the time you've added epigenetics, environmental control of expression, proteome vs genome, and all kinds of other details, you get something we're barely beginning to understand even now. "neurons = intelligence" looks like another version of the same thing. My guess is neural nets will turn out to be useful for certain kinds of preprocessing, just as they are in the brain. But GI is going to have to come from something else entirely.
- dhj 11y ago
  Off by a few orders of magnitude. Tihane-2 hit 33 pflops or 3.3*10^16 flops or approx 1/3 of the upper bound. Brain simulation is a snag, but it isn't our only snag. Like you said, it's a general algorithm issue. We do not remotely understand the brain well enough to simulate it. We have very little idea of what an intelligent algorithm (other than brain sim) would look like. Also, all of these estimates are based on flops and none of them consider bandwidth. We are a few orders of magnitude lower in gigabits/s than we are in flops. I personally think that is where the bottleneck is. 100 billion neurons with a 100 gigabit/second pipe could interact once per second and then only at the level of a toggle switch. Granted not all neurons have to interact with one another, but we are significantly behind in bandwidth and structural organization. Bandwidth is intimately tied to processing capacity. I dont think the bandwidth will be there until 2045-2065 and like you say we have serious software/algorithm/understanding deficiencies to resolve before then. I would be very surprised if we get general AI before 2065 if ever. I do not expect it in my lifetime and would be pleasantly surprised if it happened.
  - nefitty 11y ago
    Oops, excuse my mistaken quote of the Tianhe flops. Regarding the bandwidth bottleneck, it's fascinating to see that as one hardware problem is overcome, the next one looms even greater. The same is happening with the software, as machine learning, etc. is advancing (as contentious as that statement may be to people deep in the industry) the coming hurdles look even more intimidating. The algorithms that need to be developed to reach the milestones of intelligence are incredibly difficult. What excites me is evolutionary algorithms that may be harnessed to reach those milestones. This may be a brute-force method, and researchers would have to know what to tell the algorithms to select for at first, but with increasing computational power, running significant amounts of these algorithms in parallel could be negligible. If you see this comment dhj, have you considered evolutionary computation in your predictions? I'd be interested in what you think, as your clarification of the bandwidth problem was enlightening to me.
    - dhj 11y ago
      I agree that some form of evolutionary algorithm will be our path to intelligent software (or a component of it). However, as genetic algorithms are currently implemented I would say the following analogy holds neural_net:brain::evolutionary_algorithm:evolution ... In other words GAs/EAs are a simplistic and minimal scratching of the surface compared to the complexity we see in nature. The problem is two fold: 1) we guide the evolution with specific artificial goals (get a high score for instance) 2) the ideal "DNA" of a genetic algorithm is undefined. In evolution we know post-hoc that DNA is at least good enough (if not ideal) for the building blocks. However, we have had very little success with identifying the DNA for genetic algorithms. If we make it commands or function sets we end up with divergence (results get worse or stay the same per iteration rather than better). The most successful GAs are where the DNA components have been customized to a specific problem domain. Regarding the target goal selection that is a major field of study itself with reinforcement learning. What is the best way to identify reward? In nature it is simple -- survival. In the computer it is artificial in some way. Survival is an attribute or dynamic interaction selected by the programmer. I believe that multiple algorithmic techniques will come together in a final solution (GA, NN, SVM, MCMC, kmeans, etc). So GA is still part of a large and difficult algorithmic challenge rather than a well defined solution. The algorithmic challenge is definitely non-exponential -- there are breakthroughs that could happen next year or in 100 years. The bandwidth issue is the main reason I would put AGI at 2045-2065 (closer to 2065), but with the algorithmic issue I would put it post 2065 (in other words, far enough out that 50 years from now it could still be 50 years out). Regardless of the timeframe, it is a fascinating subject and I do think we will get there eventually, but I wouldn't put the algorithmic level closer than 50 years out until we get a good dog, mouse or even worm (c.elegans) level of intelligence programmed in software or robots.
- eli_gottlieb 11y ago
  Modern ML is glorified statistics, and our current chips are completely the wrong architecture for doing statistics (since they try to achieve maximum precision and zero stochasticity at all stages).
- tim333 11y ago
  >Researchers are having a hard time bubbling up new paradigms One of the most promising approaches at the moment seems to be Deep Mind trying to reverse engineer the human brain. Demis Hassabis their main guy did a PhD in neuroscience with that in mind and they are currently trying to kind of replicate the hippocampus. ("Deep Learning is about essentially [mimicking the] cortex. But the hippocampus is another critical part of the brain and it’s built very differently, a much older structure. If you knock it out, you don’t have memories. So I was fascinated how this all works together. There’s consolidation [between the cortex and the hippocampus] at times like when you’re sleeping. Memories you’ve recorded during the day get replayed orders of magnitude faster back to the rest of the brain.")
deleted 11y ago
[deleted]
ctl 11y ago
Let's look at the most important section of the paper. He estimates the processing power of the brain: The human brain contains about 10^11 neurons. Each neuron has about 5 • 10^3 synapses, and signals are transmitted along these synapses at an average frequency of about 10^2 Hz. Each signal contains, say, 5 bits. This equals 10^17 ops. The true value cannot be much higher than this, but it might be much lower. In other words, there are 5 * 10^14 synapses in the brain, and each synapse transmits up to 100 signals per second, and we can probably encode each signal with 5 bits. That's ~10^17 bits per second. So, uh... does anybody else notice that that's not an estimate of processing power? That's an estimate of the rate of information flow between neurons, across the whole brain. The level of confused thinking here is off the charts. Does this guy not understand that in order to simulate the brain, you not only have to keep track of information flows between neurons, you also need to simulate the neurons themselves? That's not merely a flaw in his argument. It indicates that he has no idea what he's talking about, at all. Needless to say, this paper and its conclusions are complete nonsense.
- themgt 11y ago
  Yup. I want to again quote a paper recommended to me on HN a while back [1] However, the relevance of Turing model is questioned even in case of present-day computing [33] [34]. Indeed, any computing machine that follows a Turing model would be highly inefficient to simulate the activity of biological neurons and experience an increased slowdown. Since the super-Turing computing power of the brain has its origins in these ‘strong’ interactions that occur inside neurons, current models have missed the most important part. Simply, Nature doesn’t care if the N-body problem has analytical solutions [36] or can be simulated in real time on a Turing machine [37]. ... While previous models have attempted to represent Hamiltonians using Turing machines [35] the paper [1] shows that the Hamiltonian model of interaction can represent itself a far more powerful model of computation. Turing made an important step forward; however, there is no need to limit natural models of computation to Turing models. In this sense, the new framework of computation using interaction is universal in nature and provides a more general description of computation than the formal Turing model. In other words God was unaware of Turing's work and has put forward a better model for physical computation in the brain. http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf
transfire 11y ago
Computers a still far too slow to exhibit any kind of real-time intelligence. I suspect we still need three orders of magnitude improvement.
themgt 11y ago
The human brain contains about 10^11 neurons. Each neuron has about 510^3 synapses, and signals are transmitted along these synapses at an average frequency of about 10^2 Hz. Each signal contains, say, 5 bits. This equals 10^17 ops.* This kind of nonsense is why no one should take Bostrom seriously. We did not then and do not know even begin to know how to write software to "simulate" a human brain, or whether such a task is even possible with modern day tools. Multiplying random neurobiology stats times 0.5 bits pulled out of your ass == AI in 2004? We have "AI" that can drive a car or a copter, play Chess or Go, translate speech to text, do image recognition ... but what we mean by human intelligence is something different. And I see no evidence anyone has made much progress developing a truly biological-like AI even at the level of say, a mouse. Which according to Bostrom's math ought to be doable in a 2U chassis by now, right? If someone does succeed in writing mouse-AI or dog-AI, I'd believe that could be scaled up to human-level intelligence very rapidly. But it's clear to me there's (at least one) fundamental breakthrough missing from the current approach, because my dog can't play chess or drive a car, but he has a degree of sentience and awareness (and self-awareness) that no 2016 AI even approaches.
- davmre 11y ago
  I don't know where in the article you see Bostrom predicting AI in 2004. That's a lower bound on when we might achieve the needed hardware capacity. Bostrom's predictions about hardware advancements have more or less come to pass (closer to the upper than lower bounds, granted). I think it's a common opinion among AI researchers that the level of computing power available today is plausibly sufficient for human-level AI, if only we knew how to build the software. The closest Bostrom gets to a prediction on software is that the "the required neuroscientific knowledge" to do brain simulation "might be obtained" by 2012. Which has turned out to be optimistic, but is very far from the strawman position of "hardware capability implies software capability" that you seem to be painting him with.
- zardo 11y ago
  http://www.openworm.org/ http://www.openworm.org/ We are on the cusp of revolutionary general nematode level intelligence. Soon we'll be able to upload nematode minds to the cloud and they can live forever in the nemamatrix.
NoMoreNicksLeft 11y ago
I'm more concerned with when I can expect to see the regular variety of intelligence.
tensafefrogs 11y ago
There's a more recent article in the New Yorker that follows Mr. Bostrom around a bit and is a good general read: http://www.newyorker.com/magazine/2015/11/23/doomsday-invention-artificial-intelligence-nick-bostrom http://www.newyorker.com/magazine/2015/11/23/doomsday-invent...
- vonnik 11y ago
  Actually, that article is fluff reporting that doesn't convey Bostrom's central arguments, and spends a lot of time on his lifestyle. I walked away from it thinking that neither the reporter, nor Mr. Bostrom, knew what they were talking about.
- T-A 11y ago
  One sentence in that article made me do a double take: "He was learning to code." Past tense, but it's about what Bostrom was doing at the time of the interview (last year, well after the publication of "Superintelligence"). So the most influential AI doomsayer on the planet has been writing about artificial minds for a couple of decades, without even knowing enough to get a computer to say "Hello, world"? OK...
  - theoh 11y ago
    I believe a key factor in AI doomsayers' thinking is the interconnectedness, complexity and automation of an AI-driven world. The actual presence of intelligence is a red herring: we already have problems with complex automatic systems failing catastrophically: https://en.wikipedia.org/wiki/Northeast_blackout_of_2003 https://en.wikipedia.org/wiki/Northeast_blackout_of_2003
vonnik 11y ago
Nick Bostrom is a peddlar of the apocalypse who has made his name by spreading fear about a fairy creature called superintelligence. He's convinced people to go looking for weapons of mass destruction in snippets of math and code. But the WMDs aren't there any more than they were in Iraq. Nice work if you can get it.
atemerev 11y ago
That took some... balls, back in 1997. There were a lot of strong AI sceptics, who repeated on and on: oh, computers can calculate, but can they play chess? Oh, chess was easy, how about understanding what this picture about? Driving cars? Talking like humans? Oh, they can talk now, but do they really _think_? Reality happens faster than anybody imagined. Except a few visionaires like Bostrom.
- igravious 11y ago
  Bostrom is not a visionary. He's just a philosopher. Nietzsche, Ecce Homo "Philosophy, as I have so far understood and lived it, means living voluntarily among ice and high mountains — seeking out everything strange and questionable in existence, everything so far placed under a ban by morality." I would argue that if philosophy isn't producing Bostroms then it's not doing its job right.
  - sawwit 11y ago
    Wait, so Steve Jobs wasn't a visionary. He was just a salesman exploring unexploited markets as any salesman does? Albert Einstein wasn't a visionary. He was just a physicist that explored unconsidered theories as any physicist does? It seems to me that in philosophy there is just as much "groupthink" as in almost any human endeavor—there is a maybe little less in the hard sciences where the systems give you feedback about whether an idea is correct or not.
    - igravious 11y ago
      You're just biased towards the so-called hard sciences. There is nothing about them specifically that prevents groupthink as you call it. Science deals with what is, not with what ought to be or shouldn't be and so on. Sure, for a scientist the universe kicks back but that is nothing to do with how a scientist chooses what to work on in the first place, and what preconceived notions and frameworks that scientist is operating under. I could give countless examples of scientists comfortably working within ideological frameworks or reasoning using incorrect theories. The whole point of philosophy is that it is meant to encourage freedom of thought and free-thinking individuals. That's its job spec. I disagree that “there is just as much "groupthink" as in almost any human endeavor” -- if that really is the case then philosophy is failing at what philosophy _ought_ to be succeeding at. I'm not saying Bostrom isn't an extraordinarily good philsopher, I'm saying that seeing the big picture and going against conventional wisdom and intuition goes with the territory. Don't imagine that I'm running Bostrom down, I very much enjoy reading the guy and listening to his thought processes, I find him to be a very rigourous thinker. Maybe it's a small quibble, of course he can be both.
erikpukinskis 11y ago
The thing people don't understand is that in order to simulate human intelligence, you have to be able to simulate TWO things: 1) A human brain 2) An entire human development up to the age of intelligence you are looking for The first one is not the harder of the two. Now, many AI researchers believe they can cut corners on the whole simulating an entire human lifetime thing, and that they can use a more impoverished simulation and make up for it on volume... say, just flashing a billion images in front of the AI hoping that's enough to form the specific subset of intelligences you are hoping for. Or letting the AI read the entire internet. But at this point it's an open question whether that could even theoretically lead to generalized intelligence.
- whitegrape 11y ago
  In order to simulate human-level intelligence, the machine doesn't necessarily need to be modeled on the human brain. It doesn't even need to use neural nets. In order to simulate a human (which by definition only has human intelligence) then only your point 1 is true. Point 2 is not, because the standard way to know we've got 1 right is uploading/emulation: take existing humans, scan their brains at a sufficient level of detail (which will probably require destructive scanning), and use that as the base information of the simulation.
  - erikpukinskis 11y ago
    The neural nets still need to have experiences that are trained on a realistic simulation of the events which it needs to understand. Historical data does not train the networks the same way that interactive learning does. There was an old study, in an earlier time of ethical strictures, where they took two kittens, paralyzed one, and strapped it onto the other one and let them run around and do kitten stuff. The paralyzed kitten saw everything the intact kitten did, felt the same breeze on its fur, but was completely blind to the universe. Without interaction, you cannot learn. In order to provide that interactive environment you either need a robot body or a really rich virtual environment for your AI to grow up in. At that point, they are developmentally limited to human timescales. No hyper-accelerated intelligence exponential.
- 11y ago
proc0 11y ago
Seems AGI is the rage these days. David Deutch has an article and outlines a good point. We won't have an AGI before a good theory of consciousness. Some philosopher will first need to explain consciousness in detail (more so than Dan Denett, which already did an amazing job), and then neuroscientists might have to prove that theory right, AND THEN AI researchers will be able to take a stab at it. So I don't think it will just pop in to existence by running some neural network training over and over again.
- tim333 11y ago
  More likely the engineers will build AGI first and the philosophers try to explain it after.
jbpetersen 11y ago
AI is the wrong way to go looking for superintelligence. Far more realistic is developing means of organizing humans together effectively enough to achieve superintelligent levels of collaboration. I think before 2025 is quite reasonable given this approach.
- tim333 11y ago
  Humans have been trying collaboration for a while and the results have been patchy.

59 points by joaorico 11y ago

Yup, the XKCD translations still hold: https://xkcd.com/678/ https://xkcd.com/678/

[Regarding power of artificial intelligence] "...If Moore's law continues to hold then the lower bound will be reached sometime between 2004 and 2008, and the upper bound between 2015 and 2024." I guess his prognostication here depends on super-powerful computing and brain-emulation software. China's Tianhe-2 has already hit 3.3^15 ops, Bostrom was anticipating for 10^14 - 10^17 to be the runway. Now, I am not sure what the state of brain emulation is at the moment but it looks like our biggest snag is there. Researchers are having a hard time bubbling up new paradigms for artificial intelligence software. Anyone have any insight into that?

marcosdumay 11y ago
Besides, there's this: > it seems that speed rather than memory would be the bottleneck in brain simulations on the neuronal level Currently, memory access is the bottleneck of most applications that need big amounts of data. Anyway, as we get to his prescribed 100 Tops, we are seeing more and more applications like image identification, and self driving cars.
- nefitty 11y ago
  Right. I mean, it could be that the bottom of the exponential curve looks nothing like the top. We might just be beginning to see the lift off the x-axis in the form of practical AI applications ("soft takeoff"). Who knows what it will be like when these technologies begin to be stacked.
zardo 11y ago
Those estimstes assume neurons are all you need to simulate. In the brain neurons interact with the glia. This is interaction looks pretty important. http://www.scientificamerican.com/article/the-root-of-thought-what/ http://www.scientificamerican.com/article/the-root-of-though...
- TheOtherHobbes 11y ago
  This very reminiscent of the hype around DNA sequencing. It turns out that genetics is vastly more complicated than the old "gene = expression" model. By the time you've added epigenetics, environmental control of expression, proteome vs genome, and all kinds of other details, you get something we're barely beginning to understand even now. "neurons = intelligence" looks like another version of the same thing. My guess is neural nets will turn out to be useful for certain kinds of preprocessing, just as they are in the brain. But GI is going to have to come from something else entirely.
dhj 11y ago
Off by a few orders of magnitude. Tihane-2 hit 33 pflops or 3.3*10^16 flops or approx 1/3 of the upper bound. Brain simulation is a snag, but it isn't our only snag. Like you said, it's a general algorithm issue. We do not remotely understand the brain well enough to simulate it. We have very little idea of what an intelligent algorithm (other than brain sim) would look like. Also, all of these estimates are based on flops and none of them consider bandwidth. We are a few orders of magnitude lower in gigabits/s than we are in flops. I personally think that is where the bottleneck is. 100 billion neurons with a 100 gigabit/second pipe could interact once per second and then only at the level of a toggle switch. Granted not all neurons have to interact with one another, but we are significantly behind in bandwidth and structural organization. Bandwidth is intimately tied to processing capacity. I dont think the bandwidth will be there until 2045-2065 and like you say we have serious software/algorithm/understanding deficiencies to resolve before then. I would be very surprised if we get general AI before 2065 if ever. I do not expect it in my lifetime and would be pleasantly surprised if it happened.
- nefitty 11y ago
  Oops, excuse my mistaken quote of the Tianhe flops. Regarding the bandwidth bottleneck, it's fascinating to see that as one hardware problem is overcome, the next one looms even greater. The same is happening with the software, as machine learning, etc. is advancing (as contentious as that statement may be to people deep in the industry) the coming hurdles look even more intimidating. The algorithms that need to be developed to reach the milestones of intelligence are incredibly difficult. What excites me is evolutionary algorithms that may be harnessed to reach those milestones. This may be a brute-force method, and researchers would have to know what to tell the algorithms to select for at first, but with increasing computational power, running significant amounts of these algorithms in parallel could be negligible. If you see this comment dhj, have you considered evolutionary computation in your predictions? I'd be interested in what you think, as your clarification of the bandwidth problem was enlightening to me.
  - dhj 11y ago
    I agree that some form of evolutionary algorithm will be our path to intelligent software (or a component of it). However, as genetic algorithms are currently implemented I would say the following analogy holds neural_net:brain::evolutionary_algorithm:evolution ... In other words GAs/EAs are a simplistic and minimal scratching of the surface compared to the complexity we see in nature. The problem is two fold: 1) we guide the evolution with specific artificial goals (get a high score for instance) 2) the ideal "DNA" of a genetic algorithm is undefined. In evolution we know post-hoc that DNA is at least good enough (if not ideal) for the building blocks. However, we have had very little success with identifying the DNA for genetic algorithms. If we make it commands or function sets we end up with divergence (results get worse or stay the same per iteration rather than better). The most successful GAs are where the DNA components have been customized to a specific problem domain. Regarding the target goal selection that is a major field of study itself with reinforcement learning. What is the best way to identify reward? In nature it is simple -- survival. In the computer it is artificial in some way. Survival is an attribute or dynamic interaction selected by the programmer. I believe that multiple algorithmic techniques will come together in a final solution (GA, NN, SVM, MCMC, kmeans, etc). So GA is still part of a large and difficult algorithmic challenge rather than a well defined solution. The algorithmic challenge is definitely non-exponential -- there are breakthroughs that could happen next year or in 100 years. The bandwidth issue is the main reason I would put AGI at 2045-2065 (closer to 2065), but with the algorithmic issue I would put it post 2065 (in other words, far enough out that 50 years from now it could still be 50 years out). Regardless of the timeframe, it is a fascinating subject and I do think we will get there eventually, but I wouldn't put the algorithmic level closer than 50 years out until we get a good dog, mouse or even worm (c.elegans) level of intelligence programmed in software or robots.
eli_gottlieb 11y ago
Modern ML is glorified statistics, and our current chips are completely the wrong architecture for doing statistics (since they try to achieve maximum precision and zero stochasticity at all stages).
tim333 11y ago
>Researchers are having a hard time bubbling up new paradigms One of the most promising approaches at the moment seems to be Deep Mind trying to reverse engineer the human brain. Demis Hassabis their main guy did a PhD in neuroscience with that in mind and they are currently trying to kind of replicate the hippocampus. ("Deep Learning is about essentially [mimicking the] cortex. But the hippocampus is another critical part of the brain and it’s built very differently, a much older structure. If you knock it out, you don’t have memories. So I was fascinated how this all works together. There’s consolidation [between the cortex and the hippocampus] at times like when you’re sleeping. Memories you’ve recorded during the day get replayed orders of magnitude faster back to the rest of the brain.")

deleted 11y ago

[deleted]

ctl 11y ago

Let's look at the most important section of the paper. He estimates the processing power of the brain: The human brain contains about 10^11 neurons. Each neuron has about 5 • 10^3 synapses, and signals are transmitted along these synapses at an average frequency of about 10^2 Hz. Each signal contains, say, 5 bits. This equals 10^17 ops. The true value cannot be much higher than this, but it might be much lower. In other words, there are 5 * 10^14 synapses in the brain, and each synapse transmits up to 100 signals per second, and we can probably encode each signal with 5 bits. That's ~10^17 bits per second. So, uh... does anybody else notice that that's not an estimate of processing power? That's an estimate of the rate of information flow between neurons, across the whole brain. The level of confused thinking here is off the charts. Does this guy not understand that in order to simulate the brain, you not only have to keep track of information flows between neurons, you also need to simulate the neurons themselves? That's not merely a flaw in his argument. It indicates that he has no idea what he's talking about, at all. Needless to say, this paper and its conclusions are complete nonsense.

themgt 11y ago
Yup. I want to again quote a paper recommended to me on HN a while back [1] However, the relevance of Turing model is questioned even in case of present-day computing [33] [34]. Indeed, any computing machine that follows a Turing model would be highly inefficient to simulate the activity of biological neurons and experience an increased slowdown. Since the super-Turing computing power of the brain has its origins in these ‘strong’ interactions that occur inside neurons, current models have missed the most important part. Simply, Nature doesn’t care if the N-body problem has analytical solutions [36] or can be simulated in real time on a Turing machine [37]. ... While previous models have attempted to represent Hamiltonians using Turing machines [35] the paper [1] shows that the Hamiltonian model of interaction can represent itself a far more powerful model of computation. Turing made an important step forward; however, there is no need to limit natural models of computation to Turing models. In this sense, the new framework of computation using interaction is universal in nature and provides a more general description of computation than the formal Turing model. In other words God was unaware of Turing's work and has put forward a better model for physical computation in the brain. http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf http://arxiv.org/ftp/arxiv/papers/1210/1210.1983.pdf

transfire 11y ago

Computers a still far too slow to exhibit any kind of real-time intelligence. I suspect we still need three orders of magnitude improvement.

themgt 11y ago

The human brain contains about 10^11 neurons. Each neuron has about 510^3 synapses, and signals are transmitted along these synapses at an average frequency of about 10^2 Hz. Each signal contains, say, 5 bits. This equals 10^17 ops.* This kind of nonsense is why no one should take Bostrom seriously. We did not then and do not know even begin to know how to write software to "simulate" a human brain, or whether such a task is even possible with modern day tools. Multiplying random neurobiology stats times 0.5 bits pulled out of your ass == AI in 2004? We have "AI" that can drive a car or a copter, play Chess or Go, translate speech to text, do image recognition ... but what we mean by human intelligence is something different. And I see no evidence anyone has made much progress developing a truly biological-like AI even at the level of say, a mouse. Which according to Bostrom's math ought to be doable in a 2U chassis by now, right? If someone does succeed in writing mouse-AI or dog-AI, I'd believe that could be scaled up to human-level intelligence very rapidly. But it's clear to me there's (at least one) fundamental breakthrough missing from the current approach, because my dog can't play chess or drive a car, but he has a degree of sentience and awareness (and self-awareness) that no 2016 AI even approaches.

davmre 11y ago
I don't know where in the article you see Bostrom predicting AI in 2004. That's a lower bound on when we might achieve the needed hardware capacity. Bostrom's predictions about hardware advancements have more or less come to pass (closer to the upper than lower bounds, granted). I think it's a common opinion among AI researchers that the level of computing power available today is plausibly sufficient for human-level AI, if only we knew how to build the software. The closest Bostrom gets to a prediction on software is that the "the required neuroscientific knowledge" to do brain simulation "might be obtained" by 2012. Which has turned out to be optimistic, but is very far from the strawman position of "hardware capability implies software capability" that you seem to be painting him with.
zardo 11y ago
http://www.openworm.org/ http://www.openworm.org/ We are on the cusp of revolutionary general nematode level intelligence. Soon we'll be able to upload nematode minds to the cloud and they can live forever in the nemamatrix.

NoMoreNicksLeft 11y ago

I'm more concerned with when I can expect to see the regular variety of intelligence.

tensafefrogs 11y ago

There's a more recent article in the New Yorker that follows Mr. Bostrom around a bit and is a good general read: http://www.newyorker.com/magazine/2015/11/23/doomsday-invention-artificial-intelligence-nick-bostrom http://www.newyorker.com/magazine/2015/11/23/doomsday-invent...

vonnik 11y ago
Actually, that article is fluff reporting that doesn't convey Bostrom's central arguments, and spends a lot of time on his lifestyle. I walked away from it thinking that neither the reporter, nor Mr. Bostrom, knew what they were talking about.
T-A 11y ago
One sentence in that article made me do a double take: "He was learning to code." Past tense, but it's about what Bostrom was doing at the time of the interview (last year, well after the publication of "Superintelligence"). So the most influential AI doomsayer on the planet has been writing about artificial minds for a couple of decades, without even knowing enough to get a computer to say "Hello, world"? OK...
- theoh 11y ago
  I believe a key factor in AI doomsayers' thinking is the interconnectedness, complexity and automation of an AI-driven world. The actual presence of intelligence is a red herring: we already have problems with complex automatic systems failing catastrophically: https://en.wikipedia.org/wiki/Northeast_blackout_of_2003 https://en.wikipedia.org/wiki/Northeast_blackout_of_2003

vonnik 11y ago

Nick Bostrom is a peddlar of the apocalypse who has made his name by spreading fear about a fairy creature called superintelligence. He's convinced people to go looking for weapons of mass destruction in snippets of math and code. But the WMDs aren't there any more than they were in Iraq. Nice work if you can get it.

atemerev 11y ago

That took some... balls, back in 1997. There were a lot of strong AI sceptics, who repeated on and on: oh, computers can calculate, but can they play chess? Oh, chess was easy, how about understanding what this picture about? Driving cars? Talking like humans? Oh, they can talk now, but do they really _think_? Reality happens faster than anybody imagined. Except a few visionaires like Bostrom.

igravious 11y ago
Bostrom is not a visionary. He's just a philosopher. Nietzsche, Ecce Homo "Philosophy, as I have so far understood and lived it, means living voluntarily among ice and high mountains — seeking out everything strange and questionable in existence, everything so far placed under a ban by morality." I would argue that if philosophy isn't producing Bostroms then it's not doing its job right.
- sawwit 11y ago
  Wait, so Steve Jobs wasn't a visionary. He was just a salesman exploring unexploited markets as any salesman does? Albert Einstein wasn't a visionary. He was just a physicist that explored unconsidered theories as any physicist does? It seems to me that in philosophy there is just as much "groupthink" as in almost any human endeavor—there is a maybe little less in the hard sciences where the systems give you feedback about whether an idea is correct or not.
  - igravious 11y ago
    You're just biased towards the so-called hard sciences. There is nothing about them specifically that prevents groupthink as you call it. Science deals with what is, not with what ought to be or shouldn't be and so on. Sure, for a scientist the universe kicks back but that is nothing to do with how a scientist chooses what to work on in the first place, and what preconceived notions and frameworks that scientist is operating under. I could give countless examples of scientists comfortably working within ideological frameworks or reasoning using incorrect theories. The whole point of philosophy is that it is meant to encourage freedom of thought and free-thinking individuals. That's its job spec. I disagree that “there is just as much "groupthink" as in almost any human endeavor” -- if that really is the case then philosophy is failing at what philosophy _ought_ to be succeeding at. I'm not saying Bostrom isn't an extraordinarily good philsopher, I'm saying that seeing the big picture and going against conventional wisdom and intuition goes with the territory. Don't imagine that I'm running Bostrom down, I very much enjoy reading the guy and listening to his thought processes, I find him to be a very rigourous thinker. Maybe it's a small quibble, of course he can be both.

erikpukinskis 11y ago

The thing people don't understand is that in order to simulate human intelligence, you have to be able to simulate TWO things: 1) A human brain 2) An entire human development up to the age of intelligence you are looking for The first one is not the harder of the two. Now, many AI researchers believe they can cut corners on the whole simulating an entire human lifetime thing, and that they can use a more impoverished simulation and make up for it on volume... say, just flashing a billion images in front of the AI hoping that's enough to form the specific subset of intelligences you are hoping for. Or letting the AI read the entire internet. But at this point it's an open question whether that could even theoretically lead to generalized intelligence.

whitegrape 11y ago
In order to simulate human-level intelligence, the machine doesn't necessarily need to be modeled on the human brain. It doesn't even need to use neural nets. In order to simulate a human (which by definition only has human intelligence) then only your point 1 is true. Point 2 is not, because the standard way to know we've got 1 right is uploading/emulation: take existing humans, scan their brains at a sufficient level of detail (which will probably require destructive scanning), and use that as the base information of the simulation.
- erikpukinskis 11y ago
  The neural nets still need to have experiences that are trained on a realistic simulation of the events which it needs to understand. Historical data does not train the networks the same way that interactive learning does. There was an old study, in an earlier time of ethical strictures, where they took two kittens, paralyzed one, and strapped it onto the other one and let them run around and do kitten stuff. The paralyzed kitten saw everything the intact kitten did, felt the same breeze on its fur, but was completely blind to the universe. Without interaction, you cannot learn. In order to provide that interactive environment you either need a robot body or a really rich virtual environment for your AI to grow up in. At that point, they are developmentally limited to human timescales. No hyper-accelerated intelligence exponential.
11y ago

proc0 11y ago

Seems AGI is the rage these days. David Deutch has an article and outlines a good point. We won't have an AGI before a good theory of consciousness. Some philosopher will first need to explain consciousness in detail (more so than Dan Denett, which already did an amazing job), and then neuroscientists might have to prove that theory right, AND THEN AI researchers will be able to take a stab at it. So I don't think it will just pop in to existence by running some neural network training over and over again.

tim333 11y ago
More likely the engineers will build AGI first and the philosophers try to explain it after.

jbpetersen 11y ago

AI is the wrong way to go looking for superintelligence. Far more realistic is developing means of organizing humans together effectively enough to achieve superintelligent levels of collaboration. I think before 2025 is quite reasonable given this approach.

tim333 11y ago
Humans have been trying collaboration for a while and the results have been patchy.