5 ms·

I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way

I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings. It's possible that scaling up does lead to generality and we've seen hints of that. - https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play https://deepmind.com/blog/article/generally-capable-agents-e... Also check out GPT-3’s performance on arithmetic tasks in the original paper (https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165) Pages: 21-23, 63 Which shows some generality, the best way to accurately predict an arithmetic answer is to deduce how the mathematical rules work. That paper shows some evidence of that and that’s just from a relatively dumb predict what comes next model. It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure?

skohan 5y ago
I've heard this airplane argument before, and while I do consider it plausible that AGI might be achievable with some system which is fundamentally much different than the human brain, I still don't think it can be achieved using simple scaling and optimization of the techniques in use today. I think this for a couple reasons: 1. The current gap in complexity is so huge. Nodes in an ANN roughly correspond to neurons, and the brain has somewhere on the order of 100 billion of them. Even if we built an ANN that big, we would only be scratching the surface of the complexity we have in the brain. Each synapse is basically an information processing unit, with behavioral characteristics much more complicated than a simple weight function. 2. The brain is highly specific. The structure and function of the auditory cortex is totally different to that of the motor cortices, to that of the hypothalamus and so on. Some brain regions depend heavily on things like spike timing and ordering to perform their functions. Different brain regions use different mechanisms of plasticity in order to learn. Currently most ANN's we have are vaguely inspired by the visual cortex (which is probably why a lot of the most interesting things to come out of ML so far have been related to image processing) and use something roughly analogous to net firing frequency for signal processing. I would consider it highly likely that our current ANNs are just structurally incapable of performing some of the types of computation we would consider intrinsically linked to what we think of as general intelligence. To make the airplane analogy, I believe we're probably closer to Leonardo da Vinci's early sketches of flying machines than we are to the Right Brothers. We might have the basic idea, but I would wager we're still missing some of the key insights required to get AGI off the ground. edit: it looks like you added some lines while I was typing, so to respond to your last points: > it’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? I totally agree that it's hard to predict, that technology usually advances faster than we expect, and that tremendous progress is being made. But the road to understanding human intelligence has been characterized by a series of periods of premature optimism followed by setbacks. For instance, in the 20th century, when dyes were getting better, and we were starting to understand how different brain regions had different functions, it may have seemed like we were close to just mapping all the different pieces of the brain, and that completing the resulting puzzle would give a clear insight into the workings of the human mind. Of course it turns out we were quite far from that. As far as what we can expect in the years leading up to AGI, I suspect it's going to be something that comes on gradually - I think computers will take on more and more tasks that were once reserved for humans over time, and the way we think about interfacing with technology might change so much that the concept of AGI might not seem relevant at some point. As to whether the current state of things is a failure - I would not characterize it that way. I think we're making real progress, I just also think there is a bit of hubris that we may have "cracked the code" of true machine intelligence. I think we're still a few major revelations away from that.
- deleted 5y ago
  [deleted]
dtech 5y ago
> It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? Few have predicted a reasonably-capable text-writing engine or automatic video face replacement, but many have predicted self-driving cars would have been readily available to consumers by now and semi-intelligent helper-robots being around. Just because unforeseen advancements have been made, does not mean that foreseen advancements come true.
- jltsiren 5y ago
  Automatic video face replacement always seemed an obvious application to me. I'm more surprised that the tools for it are still so rough. I guess we can thank social taboos for that. When I was a kid, I remember wondering how Soviets were obsessed with faking photos. A few years later, I saw Terminator 2 and realized that faking videos was also a thing. The tools for it would clearly get better and better over time. When I studied ML in the early 2000s, it seemed obvious that pattern recognition tasks such as image manipulation would be "easy" for computers, once we found the right approach and made the ML systems big enough. In the end, I decided not to pursue ML, because jobs were still scarce and I found discrete problems more interesting. That was probably the worst career mistake I've ever made.
- exporectomy 5y ago
  People tend to predict simple technological substitutes for human tasks rather than novel things. I suspect we won't get artificial humans because we'll end up not actually wanting that and getting something better instead. Just like we got cars instead of artificial horses.
  - fossuser 5y ago
    AGI isn't really about artificial humans. It's about very good general problem solving software that's way beyond the capabilities of humans while not being aligned with human interests. Not because the software is evil, but because aligning values is an unsolved problem (humans aren't even totally aligned - and values also change). If you have an intelligence that's very good at achieving its goal and you don't have a good way to align its goal with human goals, you can very quickly get into trouble if that intelligence thinks much faster than you do. https://www.lesswrong.com/posts/mMBTPTjRbsrqbSkZE/sorting-pebbles-into-correct-heaps https://www.lesswrong.com/posts/mMBTPTjRbsrqbSkZE/sorting-pe... https://www.lesswrong.com/posts/4ARaTpNX62uaL86j6/the-hidden-complexity-of-wishes https://www.lesswrong.com/posts/4ARaTpNX62uaL86j6/the-hidden... https://www.lesswrong.com/posts/BEtzRE2M5m9YEAQpX/there-s-no-fire-alarm-for-artificial-general-intelligence https://www.lesswrong.com/posts/BEtzRE2M5m9YEAQpX/there-s-no... https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien...
lumost 5y ago
The quirky thing to remember about gpt-3 is that it really is just a giant autocomplete based on the internet. It can do math insofar as it’s memorized some text which did that math with slightly different verbiage etc. If you ask it to compute something that would never have been seen on the internet it’s likely to fail. E.g. add 2 extremely large/rare numbers together
- fossuser 5y ago
  You should read the excerpts in the paper I link to which suggest otherwise (that it’s not memorized and that it’s deducing rules).
- vladTheInhaler 5y ago
  As another poster indicates, that's specifically not the case. Most possible arithmetic problems of reasonable size aren't anywhere in the dataset, but it can solve them with pretty good accuracy.
roenxi 5y ago
A good example of those constraints is there are hard upper limits to heat and energy use by a brain that are simply outdone by, say, a massive supercomputer. A rough calculation, humans can feasibly consume 4-5 TJ/annum of energy, of which a lot is going to go into motion or whatever. And if devoted to mental activity, it has a shelf life of ~70 years before they die. A distributed computer might theoretically burn TJ/hr and once the weights are known they may as well be in a permanent record. The upper limits of what computers can learn and get good at are much higher than what humans can. They won't need as much implementation trickery as biology to get results.
- naasking 5y ago
  The converse is also true: a supercomputer built using current tech that could do everything a human can, could neither fit in the volume of a human skull nor use as little energy as a human brain.
BurningFrog 5y ago
> The way a plane flies is quite different than the way a bird flies in complexity And a plane is a vastly simpler machine than a bird!
thaumasiotes 5y ago
> they share an underlying mechanism, but planes don't need to flap wings. A lot of birds don't need to flap their wings either.
- Aeolun 5y ago
  Not if we mount a jet engine to their backs.
lelanthran 5y ago
> Few would have predicted the results we’re seeing today in 2010. That's hardly accurate - didn't Musk and Co. promise self-driving cars by 2012? We're in 2020, and the SDC's are great for making youtube videos, but not any good at piloting a vehicle without human intervention. Since the 90s it has been clear that the only thing holding back what we have today is limited processing power. While there may be some new insights and directions in AI, they are not "general" and they require 3 orders of magnitude more processing power for a lot smaller improvement in performance. What has been clear since 2010 is that this field has passed the point of diminishing returns already. We throw vastly more computational power at problems that we ever did before, and then call the result an improvement. Deep blue beat the best human at chess using 11.8 GFLOPS of computational power. Alphago beat the best human at go using 720000 GFLOPS of power. The complexity difference between Chess and Go are within a single order of magnitude - 10x to 99x difference in complexity (https://en.wikipedia.org/wiki/Game_complexity https://en.wikipedia.org/wiki/Game_complexity). The difference in AI processing power to beat the best human between Chess and Go is between 4 and 5 orders of magnitude (1000x and 100000x). This does not look like a success to me - it looks like a brute-force approach. If you spend 10000x more resources for a 10x more benefit, you're at the point of diminishing returns. Here's a great paper that should be written (but won't be) - plot the improvements in AI and the usage of computational power for AI on the same chart. From the 90s (https://en.wikipedia.org/wiki/History_of_self-driving_cars#1990s https://en.wikipedia.org/wiki/History_of_self-driving_cars#1...): "The robot achieved speeds exceeding 109 miles per hour (175 km/h) on the German Autobahn, with a mean time between human interventions of 5.6 miles (9.0 km), or 95% autonomous driving." Yup, 95% autonomous. Today we have 95.x% autonomous with roughly 10000x the resource power thrown at the problem. So, yeah, your assertion that "Few would have predicted the results we’re seeing today in 2010." is wildly off mark, we predicted more than what we see today because we did not expect to hit a point of diminishing returns quite so quickly. The people who did the 95% SDC in 1997 would have been disbelieving if anyone told them, in 1997, that even with 10000x more processing power thrown at the problem and new sensor hardware that was not available to them, it won't get much better than what they had.
- amelius 5y ago
  Waymo is doing better, I believe. > plot the improvements in AI and the usage of computational power for AI on the same chart. Would that be meaningful? I mean, I use an infinity times the computational power for writing a letter than people did 100 years ago, still producing more or less the same results.

skohan 5y ago

I've heard this airplane argument before, and while I do consider it plausible that AGI might be achievable with some system which is fundamentally much different than the human brain, I still don't think it can be achieved using simple scaling and optimization of the techniques in use today. I think this for a couple reasons: 1. The current gap in complexity is so huge. Nodes in an ANN roughly correspond to neurons, and the brain has somewhere on the order of 100 billion of them. Even if we built an ANN that big, we would only be scratching the surface of the complexity we have in the brain. Each synapse is basically an information processing unit, with behavioral characteristics much more complicated than a simple weight function. 2. The brain is highly specific. The structure and function of the auditory cortex is totally different to that of the motor cortices, to that of the hypothalamus and so on. Some brain regions depend heavily on things like spike timing and ordering to perform their functions. Different brain regions use different mechanisms of plasticity in order to learn. Currently most ANN's we have are vaguely inspired by the visual cortex (which is probably why a lot of the most interesting things to come out of ML so far have been related to image processing) and use something roughly analogous to net firing frequency for signal processing. I would consider it highly likely that our current ANNs are just structurally incapable of performing some of the types of computation we would consider intrinsically linked to what we think of as general intelligence. To make the airplane analogy, I believe we're probably closer to Leonardo da Vinci's early sketches of flying machines than we are to the Right Brothers. We might have the basic idea, but I would wager we're still missing some of the key insights required to get AGI off the ground. edit: it looks like you added some lines while I was typing, so to respond to your last points: > it’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? I totally agree that it's hard to predict, that technology usually advances faster than we expect, and that tremendous progress is being made. But the road to understanding human intelligence has been characterized by a series of periods of premature optimism followed by setbacks. For instance, in the 20th century, when dyes were getting better, and we were starting to understand how different brain regions had different functions, it may have seemed like we were close to just mapping all the different pieces of the brain, and that completing the resulting puzzle would give a clear insight into the workings of the human mind. Of course it turns out we were quite far from that. As far as what we can expect in the years leading up to AGI, I suspect it's going to be something that comes on gradually - I think computers will take on more and more tasks that were once reserved for humans over time, and the way we think about interfacing with technology might change so much that the concept of AGI might not seem relevant at some point. As to whether the current state of things is a failure - I would not characterize it that way. I think we're making real progress, I just also think there is a bit of hubris that we may have "cracked the code" of true machine intelligence. I think we're still a few major revelations away from that.

deleted 5y ago
[deleted]

dtech 5y ago

> It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? Few have predicted a reasonably-capable text-writing engine or automatic video face replacement, but many have predicted self-driving cars would have been readily available to consumers by now and semi-intelligent helper-robots being around. Just because unforeseen advancements have been made, does not mean that foreseen advancements come true.

jltsiren 5y ago
Automatic video face replacement always seemed an obvious application to me. I'm more surprised that the tools for it are still so rough. I guess we can thank social taboos for that. When I was a kid, I remember wondering how Soviets were obsessed with faking photos. A few years later, I saw Terminator 2 and realized that faking videos was also a thing. The tools for it would clearly get better and better over time. When I studied ML in the early 2000s, it seemed obvious that pattern recognition tasks such as image manipulation would be "easy" for computers, once we found the right approach and made the ML systems big enough. In the end, I decided not to pursue ML, because jobs were still scarce and I found discrete problems more interesting. That was probably the worst career mistake I've ever made.
exporectomy 5y ago
People tend to predict simple technological substitutes for human tasks rather than novel things. I suspect we won't get artificial humans because we'll end up not actually wanting that and getting something better instead. Just like we got cars instead of artificial horses.
- fossuser 5y ago
  AGI isn't really about artificial humans. It's about very good general problem solving software that's way beyond the capabilities of humans while not being aligned with human interests. Not because the software is evil, but because aligning values is an unsolved problem (humans aren't even totally aligned - and values also change). If you have an intelligence that's very good at achieving its goal and you don't have a good way to align its goal with human goals, you can very quickly get into trouble if that intelligence thinks much faster than you do. https://www.lesswrong.com/posts/mMBTPTjRbsrqbSkZE/sorting-pebbles-into-correct-heaps https://www.lesswrong.com/posts/mMBTPTjRbsrqbSkZE/sorting-pe... https://www.lesswrong.com/posts/4ARaTpNX62uaL86j6/the-hidden-complexity-of-wishes https://www.lesswrong.com/posts/4ARaTpNX62uaL86j6/the-hidden... https://www.lesswrong.com/posts/BEtzRE2M5m9YEAQpX/there-s-no-fire-alarm-for-artificial-general-intelligence https://www.lesswrong.com/posts/BEtzRE2M5m9YEAQpX/there-s-no... https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien...

lumost 5y ago

The quirky thing to remember about gpt-3 is that it really is just a giant autocomplete based on the internet. It can do math insofar as it’s memorized some text which did that math with slightly different verbiage etc. If you ask it to compute something that would never have been seen on the internet it’s likely to fail. E.g. add 2 extremely large/rare numbers together

fossuser 5y ago
You should read the excerpts in the paper I link to which suggest otherwise (that it’s not memorized and that it’s deducing rules).
vladTheInhaler 5y ago
As another poster indicates, that's specifically not the case. Most possible arithmetic problems of reasonable size aren't anywhere in the dataset, but it can solve them with pretty good accuracy.

roenxi 5y ago

A good example of those constraints is there are hard upper limits to heat and energy use by a brain that are simply outdone by, say, a massive supercomputer. A rough calculation, humans can feasibly consume 4-5 TJ/annum of energy, of which a lot is going to go into motion or whatever. And if devoted to mental activity, it has a shelf life of ~70 years before they die. A distributed computer might theoretically burn TJ/hr and once the weights are known they may as well be in a permanent record. The upper limits of what computers can learn and get good at are much higher than what humans can. They won't need as much implementation trickery as biology to get results.

naasking 5y ago
The converse is also true: a supercomputer built using current tech that could do everything a human can, could neither fit in the volume of a human skull nor use as little energy as a human brain.

BurningFrog 5y ago

> The way a plane flies is quite different than the way a bird flies in complexity And a plane is a vastly simpler machine than a bird!

thaumasiotes 5y ago

> they share an underlying mechanism, but planes don't need to flap wings. A lot of birds don't need to flap their wings either.

Aeolun 5y ago
Not if we mount a jet engine to their backs.

lelanthran 5y ago

> Few would have predicted the results we’re seeing today in 2010. That's hardly accurate - didn't Musk and Co. promise self-driving cars by 2012? We're in 2020, and the SDC's are great for making youtube videos, but not any good at piloting a vehicle without human intervention. Since the 90s it has been clear that the only thing holding back what we have today is limited processing power. While there may be some new insights and directions in AI, they are not "general" and they require 3 orders of magnitude more processing power for a lot smaller improvement in performance. What has been clear since 2010 is that this field has passed the point of diminishing returns already. We throw vastly more computational power at problems that we ever did before, and then call the result an improvement. Deep blue beat the best human at chess using 11.8 GFLOPS of computational power. Alphago beat the best human at go using 720000 GFLOPS of power. The complexity difference between Chess and Go are within a single order of magnitude - 10x to 99x difference in complexity (https://en.wikipedia.org/wiki/Game_complexity https://en.wikipedia.org/wiki/Game_complexity). The difference in AI processing power to beat the best human between Chess and Go is between 4 and 5 orders of magnitude (1000x and 100000x). This does not look like a success to me - it looks like a brute-force approach. If you spend 10000x more resources for a 10x more benefit, you're at the point of diminishing returns. Here's a great paper that should be written (but won't be) - plot the improvements in AI and the usage of computational power for AI on the same chart. From the 90s (https://en.wikipedia.org/wiki/History_of_self-driving_cars#1990s https://en.wikipedia.org/wiki/History_of_self-driving_cars#1...): "The robot achieved speeds exceeding 109 miles per hour (175 km/h) on the German Autobahn, with a mean time between human interventions of 5.6 miles (9.0 km), or 95% autonomous driving." Yup, 95% autonomous. Today we have 95.x% autonomous with roughly 10000x the resource power thrown at the problem. So, yeah, your assertion that "Few would have predicted the results we’re seeing today in 2010." is wildly off mark, we predicted more than what we see today because we did not expect to hit a point of diminishing returns quite so quickly. The people who did the 95% SDC in 1997 would have been disbelieving if anyone told them, in 1997, that even with 10000x more processing power thrown at the problem and new sensor hardware that was not available to them, it won't get much better than what they had.

amelius 5y ago
Waymo is doing better, I believe. > plot the improvements in AI and the usage of computational power for AI on the same chart. Would that be meaningful? I mean, I use an infinity times the computational power for writing a letter than people did 100 years ago, still producing more or less the same results.