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Note: Below are just my opinions. I don't think downvoting is justified. I'm completely right and downvoters are wrong, but anyway this thread asks for what

Note: Below are just my opinions. I don't think downvoting is justified. I'm completely right and downvoters are wrong, but anyway this thread asks for what we think. ---------- My opinion: (100.000000% guaranteed to be right in this case.) It's obvious that general artificial intelligence will be a reality and surpass human cognition in every way, because our brains are a couple of pounds of meat running on less than a hundred watts, using biochemical pathways: no light-speed switching, about a six orders of magnitude slower. Meanwhile, our datacenters run in the gigahertz domain. (There are, however, about a hundred billion neurons in the human brain.) Some people think a computer will never be able to do general cognition. They're idiots. It is like saying that nothing but a sack of meat will ever fly (i.e. that only a bird can, no human-made engine can) -- at a time when we have engines that are 1,000,000 times more powerful than birds' muscles. (I mean at the time that Lord Kelvin famously stated that heavier-than-air flight is impossible.) Any task of human cognition that the sack of meat in a skull can do, a digital algorithm will be able to do, because the former is just slow, slow analog meat. Granted the emotional part is a point of difficulty but higher mental functions seem pretty unrelated to emotions. AI is like when people were saying heavier than air flight is impossible. Obviously it's possible. And it will rock and change the world. (Anyone who doesn't see it this way is stupid and wrong. They're just on the wrong side of history. They have zero vision for things that are completely obvious. It's not even debatable. It's a sack of meat in a skull doing slow analog operations 24/7 for 3 years before it can even read. The bar is not as high as people think.)

mtmail 9y ago
You're describing a long term view (singularity?) rather than the up-and-coming technology in the question. Claiming to be 100% right and calling people with different opinion idiots might have added a downvote or two (Personally I upvoted).
- logicallee 9y ago
  I don't think it's a long-term view due to the numbers involved. For example, everyday developers might have access to 64-bit laptop computer with 4 cores @ 4 ghz and 16 GB of RAM (literally millions of people use such a machine, zero exaggeration). When working on their own desktops, AI researches might use an 6-core or 8-core desktop with 64 GB of RAM or, rarely, 128 GB of RAM. Access to 2-32 terabytes of RAM are not in the realm of what most AI researchers get to run their algorithms on on a daily basis. Google's AlphaGo ran on[1] 1202 CPU's, 176 GPU's, and I didn't immediately find the amount of memory but on the order of magnitude you would think it is from 1 terabyte (if it's 1 GB of RAM per CPU) up to (reasonably) about 128 terabytes, with my guess being 4 TB-16 TB. However I didn't find this figure. This is hardware that exists today, but AI researchers would have to wait a few years to have normal everyday access to this kind of thing in their normal everyday devices. Right now only datacenters match the processing ability of the human mind. Although they certainly exist in one cluster, they're far from the type of thing that is in everyone's hands yet. On the other hand, once it is set up these clusters can be far faster than the human brain, which runs at really slow analog speeds. (Look up the propagation speed of neural signals.) The human brain is just slightly ahead of the curve right now without any specialized hardware. There are 7 billion extant examples of intelligent human brains. There are probably less than 10,000 clusters AI researchers can play around with for months, which have 16-128 TB of RAM and aren't used for anything more important, just whatever they feel like running on it. (Versus easily more than 15 million computers with just 16 GB of RAM, though that may not be enough.) I don't think this is very far-off. [1] http://uk.businessinsider.com/heres-how-much-computing-power-google-deepmind-needed-to-beat-lee-sedol-2016-3 http://uk.businessinsider.com/heres-how-much-computing-power...
  - rapnie 9y ago
    Agree. I just saw doyoutrustthiscomputer.org (still on YouTube) where it was stated by some guy that Google is building a 100 million server strong datacenter for DeepMind, which has access to all their data. On the example of AlphaGo.. in 1997 a scientist and Go fan stated that it would take at least 100 yrs or longer for a computer to beat humans at Go. 20 yrs later the champion was beaten..
  - crote 9y ago
    I'm very sceptical about it. First of all, the current state of AI and machine learning seems to be abysmal. The major breakthroughs (such as backpropagation) were made decades ago. The only reason that AI seems to be improving, is that a couple of companies are throwing an immense amount of money, data, and computing power into it. Even then, the results are relatively poor at best: systems like Google Now and Siri can only respond to a limited set of hardcoded queries and every kind of "suggestion" system seems to be actually getting worse in the last few years. Secondly, Moore's law is as good as dead. If an algorithm requires a terabyte of ram, it is all but useless for most practical purposes. I'm currently looking for a replacement of a 5-year old laptop and the newest model is at most 20% faster, with the same amount of memory! Heck, last time I got a desktop it had an AMD Phenom II X6 1055T: for the same amount of money, you'd now get an i5 8500 or a Ryzen 5 1600x. The performance gain? Well judging by the Passmark scores, it would be a jump from ~5000 to ~13000. So that's roughly 2.5 times as fast in eight years, with a jump from 45nm to 14nm. Memory progression is about the same. Considering that the progression is slowing down, it'll take quite a few decades before the performance required for current AI is generally available. Thirdly, the stuff which is impersonating AI isn't actually intelligent. Every current solution focuses on what basically boils down to a very complex set of linear regressions and similar methods. There is no adaptability, no sense of context, not even the slightest form of creativity. Everything which we consider to be intelligence is lacking. This isn't really a big surprise, because we simply don't know how it works in living creatures. Sure, we know how the neurons work, but the high-level understanding is lacking, so how could we even begin to imitate it or even improve on it? Sure, we could try simulating an actual brain, but a Blue Gene/L can only simulate half of a mouse's brain at one-tenth of the speed - and that's assuming the simulation is actually correctly modelling the biology. So no, I don't think we're on the brink of an AI revolution. We might have something with the intelligence of a service dog in a decade or three, but even that would probably be a stretch. Unless a few very major breakthroughs occur, AI is going to be a bit of a disappointment.
deleted 9y ago
[deleted]
icebraining 9y ago
You're missing another alternative: what if AI is totally possible, but our sacks of meat in skulls are not able to achieve it? I don't have to disbelieve the possibility of the existence of humans to doubt that flies could create a human.
- deleted 9y ago
  [deleted]
- logicallee 9y ago
  You raise a good and apt point. I would say firstly, we don't have to know how to achieve it exactly, since once we have the hardware we can just experiment. The human source code is about seven hundred megabytes (fairly precisely, DNA is almost precisely 2 bits per base pair) so somewhere in that is the encoding for human intelligence, since humans inherit it. So we know that there is some < 700 MB program. Now 700 MB is a ton of source code, but it's not an unimaginable amount. We know some of the compiled results, as we can see how human brains are built actually. So there's lots of indications that we will be able to get similar results once we have the hardware for it - which we do. Secondly I would point to many of the specific advances reached using machine learning as excellent indications that we are quite near the kind of algorithmic breakthroughs that are necessary. Since the topology of these algorithms is in many cases closely inspired by human neural topology, and many algorithms achieve many similar results, it seems quite likely that we would be able to reproduce some of these effects experimentally. More than "likely", in many domains the computers have done just that.
- fspear 9y ago
  >what if AI is totally possible, but our sacks of meat in skulls are not able to achieve it? Won't be a problem if we use CRISPR to make ourselves smarter first!
babygoat 9y ago
> Any task of human cognition that the sack of meat in a skull can do, a digital algorithm will be able to do, because the former is just slow, slow analog meat. It's not slow at all. And you are obsessed with meat.
- sharemywin 9y ago
  All this talk of meat is making me hungry...stupid auto correct.... :<
  - rapnie 9y ago
    Ah.. brings me to another invention: artificial meat, but as tasty and indiscernable as the real thing, without all the food waste required to raise cattle. Currently still in the lab phase, I think.
- logicallee 9y ago
  It's exceedingly slow. Neurons fire at not 4 gigahertz, 400 megahertz, 40 megahertz, 4 megahertz, 400 kilohertz, 40 kilohertz, 4 kilohertz, or 0.4 kilohertz (400 hertz.) They fire at < 1 hertz to about 200 hertz. [1] That is literally 8 orders of magnitude slower than processers. 8 orders of magnitude is the size of difference between a grain of sand and 1.2 short tons. In the time that your neuron moves 1 grain of sand, a computer can move 1.2 short tons worth of sand 1 grain at a time (talking about just cache here) - because it's switching at 4 GHz instead of 10 hertz. That is enough to make a lot of back and forth trips. Now, you have to make a lot of these back and forth trips because the brain is extremely parallel. But the difference between these extremely slow biochemical firing speeds and gigahertz light speed enables this. The speed of propagation in the brain is about 100 meters/second [2]. The speed of propagation between two fiber optic end-points is about 299 792 458 meters/second. That gives you 7 orders of magnitude to play with. Or, in other words, it can let you take seven orders of magnitude more trips across a data center (or go that much farther) before you miss a real-time deadline for when you need to address a neuron by. In the future it's pretty clear that computers are not going to think as fast as the human brain when doing similar calculations using a similar topology, but, for example, 10,000 times faster than real-time. This really shouldn't surprise anyone growing up in technology. Brains are slow, mechanical devices. They're not digital and they don't do signal processing at the speed of light. It's taken us some time to catch up because a hundred billion neurons is still a lot of neurons, and algorithm development using similar topologies has been slow. [1] https://aiimpacts.org/rate-of-neuron-firing/#Estimates_of_rate_of_firing_in_humanneocortex https://aiimpacts.org/rate-of-neuron-firing/#Estimates_of_ra... [2] https://hypertextbook.com/facts/2002/DavidParizh.shtml https://hypertextbook.com/facts/2002/DavidParizh.shtml
Semirhage 9y ago
Your humility is as inspiring as your many arguments by analogy.
- logicallee 9y ago
  my sibling post lists some of the numbers involved, but you should especially look the number of neurons, their connectedness, firing speed, and above all realize how incredibly slow this analog biochemical signal propagation is. You can look at how many parts make it up. (About a hundred billion.) Finally you can know for sure that the algorithm can be encoded in well under 700 MB, since that is literally the entire blue print of the human genome (bit-perfect, at 2 bits per base pair), which (obviously) encodes human intelligence as just a small part of it. (Since it includes everything else too, such as your entire rest-of-your-body.) Look at the number of "mechanical" (biochemical) parts making up the human brain, their blueprint, the size they're contained in. Finally look at the results that analogous machine learning algorithms are making with (albeit much simpler) neural net topology models. The idea that nobody will ever match it is like looking at a mechanical watch and saying no digital model of it could ever come up with similar emulated results. What. When you look at the size of the datasets involved you will see that it is exactly the sort of thing we can expect to be emulating now.

mtmail 9y ago

You're describing a long term view (singularity?) rather than the up-and-coming technology in the question. Claiming to be 100% right and calling people with different opinion idiots might have added a downvote or two (Personally I upvoted).

logicallee 9y ago
I don't think it's a long-term view due to the numbers involved. For example, everyday developers might have access to 64-bit laptop computer with 4 cores @ 4 ghz and 16 GB of RAM (literally millions of people use such a machine, zero exaggeration). When working on their own desktops, AI researches might use an 6-core or 8-core desktop with 64 GB of RAM or, rarely, 128 GB of RAM. Access to 2-32 terabytes of RAM are not in the realm of what most AI researchers get to run their algorithms on on a daily basis. Google's AlphaGo ran on[1] 1202 CPU's, 176 GPU's, and I didn't immediately find the amount of memory but on the order of magnitude you would think it is from 1 terabyte (if it's 1 GB of RAM per CPU) up to (reasonably) about 128 terabytes, with my guess being 4 TB-16 TB. However I didn't find this figure. This is hardware that exists today, but AI researchers would have to wait a few years to have normal everyday access to this kind of thing in their normal everyday devices. Right now only datacenters match the processing ability of the human mind. Although they certainly exist in one cluster, they're far from the type of thing that is in everyone's hands yet. On the other hand, once it is set up these clusters can be far faster than the human brain, which runs at really slow analog speeds. (Look up the propagation speed of neural signals.) The human brain is just slightly ahead of the curve right now without any specialized hardware. There are 7 billion extant examples of intelligent human brains. There are probably less than 10,000 clusters AI researchers can play around with for months, which have 16-128 TB of RAM and aren't used for anything more important, just whatever they feel like running on it. (Versus easily more than 15 million computers with just 16 GB of RAM, though that may not be enough.) I don't think this is very far-off. [1] http://uk.businessinsider.com/heres-how-much-computing-power-google-deepmind-needed-to-beat-lee-sedol-2016-3 http://uk.businessinsider.com/heres-how-much-computing-power...
- rapnie 9y ago
  Agree. I just saw doyoutrustthiscomputer.org (still on YouTube) where it was stated by some guy that Google is building a 100 million server strong datacenter for DeepMind, which has access to all their data. On the example of AlphaGo.. in 1997 a scientist and Go fan stated that it would take at least 100 yrs or longer for a computer to beat humans at Go. 20 yrs later the champion was beaten..
- crote 9y ago
  I'm very sceptical about it. First of all, the current state of AI and machine learning seems to be abysmal. The major breakthroughs (such as backpropagation) were made decades ago. The only reason that AI seems to be improving, is that a couple of companies are throwing an immense amount of money, data, and computing power into it. Even then, the results are relatively poor at best: systems like Google Now and Siri can only respond to a limited set of hardcoded queries and every kind of "suggestion" system seems to be actually getting worse in the last few years. Secondly, Moore's law is as good as dead. If an algorithm requires a terabyte of ram, it is all but useless for most practical purposes. I'm currently looking for a replacement of a 5-year old laptop and the newest model is at most 20% faster, with the same amount of memory! Heck, last time I got a desktop it had an AMD Phenom II X6 1055T: for the same amount of money, you'd now get an i5 8500 or a Ryzen 5 1600x. The performance gain? Well judging by the Passmark scores, it would be a jump from ~5000 to ~13000. So that's roughly 2.5 times as fast in eight years, with a jump from 45nm to 14nm. Memory progression is about the same. Considering that the progression is slowing down, it'll take quite a few decades before the performance required for current AI is generally available. Thirdly, the stuff which is impersonating AI isn't actually intelligent. Every current solution focuses on what basically boils down to a very complex set of linear regressions and similar methods. There is no adaptability, no sense of context, not even the slightest form of creativity. Everything which we consider to be intelligence is lacking. This isn't really a big surprise, because we simply don't know how it works in living creatures. Sure, we know how the neurons work, but the high-level understanding is lacking, so how could we even begin to imitate it or even improve on it? Sure, we could try simulating an actual brain, but a Blue Gene/L can only simulate half of a mouse's brain at one-tenth of the speed - and that's assuming the simulation is actually correctly modelling the biology. So no, I don't think we're on the brink of an AI revolution. We might have something with the intelligence of a service dog in a decade or three, but even that would probably be a stretch. Unless a few very major breakthroughs occur, AI is going to be a bit of a disappointment.

deleted 9y ago

[deleted]

icebraining 9y ago

You're missing another alternative: what if AI is totally possible, but our sacks of meat in skulls are not able to achieve it? I don't have to disbelieve the possibility of the existence of humans to doubt that flies could create a human.

deleted 9y ago
[deleted]
logicallee 9y ago
You raise a good and apt point. I would say firstly, we don't have to know how to achieve it exactly, since once we have the hardware we can just experiment. The human source code is about seven hundred megabytes (fairly precisely, DNA is almost precisely 2 bits per base pair) so somewhere in that is the encoding for human intelligence, since humans inherit it. So we know that there is some < 700 MB program. Now 700 MB is a ton of source code, but it's not an unimaginable amount. We know some of the compiled results, as we can see how human brains are built actually. So there's lots of indications that we will be able to get similar results once we have the hardware for it - which we do. Secondly I would point to many of the specific advances reached using machine learning as excellent indications that we are quite near the kind of algorithmic breakthroughs that are necessary. Since the topology of these algorithms is in many cases closely inspired by human neural topology, and many algorithms achieve many similar results, it seems quite likely that we would be able to reproduce some of these effects experimentally. More than "likely", in many domains the computers have done just that.
fspear 9y ago
>what if AI is totally possible, but our sacks of meat in skulls are not able to achieve it? Won't be a problem if we use CRISPR to make ourselves smarter first!

babygoat 9y ago

> Any task of human cognition that the sack of meat in a skull can do, a digital algorithm will be able to do, because the former is just slow, slow analog meat. It's not slow at all. And you are obsessed with meat.

sharemywin 9y ago
All this talk of meat is making me hungry...stupid auto correct.... :<
- rapnie 9y ago
  Ah.. brings me to another invention: artificial meat, but as tasty and indiscernable as the real thing, without all the food waste required to raise cattle. Currently still in the lab phase, I think.
logicallee 9y ago
It's exceedingly slow. Neurons fire at not 4 gigahertz, 400 megahertz, 40 megahertz, 4 megahertz, 400 kilohertz, 40 kilohertz, 4 kilohertz, or 0.4 kilohertz (400 hertz.) They fire at < 1 hertz to about 200 hertz. [1] That is literally 8 orders of magnitude slower than processers. 8 orders of magnitude is the size of difference between a grain of sand and 1.2 short tons. In the time that your neuron moves 1 grain of sand, a computer can move 1.2 short tons worth of sand 1 grain at a time (talking about just cache here) - because it's switching at 4 GHz instead of 10 hertz. That is enough to make a lot of back and forth trips. Now, you have to make a lot of these back and forth trips because the brain is extremely parallel. But the difference between these extremely slow biochemical firing speeds and gigahertz light speed enables this. The speed of propagation in the brain is about 100 meters/second [2]. The speed of propagation between two fiber optic end-points is about 299 792 458 meters/second. That gives you 7 orders of magnitude to play with. Or, in other words, it can let you take seven orders of magnitude more trips across a data center (or go that much farther) before you miss a real-time deadline for when you need to address a neuron by. In the future it's pretty clear that computers are not going to think as fast as the human brain when doing similar calculations using a similar topology, but, for example, 10,000 times faster than real-time. This really shouldn't surprise anyone growing up in technology. Brains are slow, mechanical devices. They're not digital and they don't do signal processing at the speed of light. It's taken us some time to catch up because a hundred billion neurons is still a lot of neurons, and algorithm development using similar topologies has been slow. [1] https://aiimpacts.org/rate-of-neuron-firing/#Estimates_of_rate_of_firing_in_humanneocortex https://aiimpacts.org/rate-of-neuron-firing/#Estimates_of_ra... [2] https://hypertextbook.com/facts/2002/DavidParizh.shtml https://hypertextbook.com/facts/2002/DavidParizh.shtml

Semirhage 9y ago

Your humility is as inspiring as your many arguments by analogy.

logicallee 9y ago
my sibling post lists some of the numbers involved, but you should especially look the number of neurons, their connectedness, firing speed, and above all realize how incredibly slow this analog biochemical signal propagation is. You can look at how many parts make it up. (About a hundred billion.) Finally you can know for sure that the algorithm can be encoded in well under 700 MB, since that is literally the entire blue print of the human genome (bit-perfect, at 2 bits per base pair), which (obviously) encodes human intelligence as just a small part of it. (Since it includes everything else too, such as your entire rest-of-your-body.) Look at the number of "mechanical" (biochemical) parts making up the human brain, their blueprint, the size they're contained in. Finally look at the results that analogous machine learning algorithms are making with (albeit much simpler) neural net topology models. The idea that nobody will ever match it is like looking at a mechanical watch and saying no digital model of it could ever come up with similar emulated results. What. When you look at the size of the datasets involved you will see that it is exactly the sort of thing we can expect to be emulating now.