51–60 of 143 posts
In the same way that birds fly with flapping wings, but human flying machines with propellers were immediately stronger and shortly thereafter faster than any bird.
This is a stupid article, and reading it will only make you stupider. First, it is undoubtably true that human brains are better suited to being human than AIs would be. That is irrelevant to the question of whether AIs will be better at the task of creating better technologies. Second, it is absolutely false that improvement is incremental. Consider the game of Go. For a long time, computers sucked at Go. Then we ca…
> Second, it is absolutely false that improvement is incremental. Consider the game of Go. For a long time, computers sucked at Go. Then we came up with Monte Carlo search about a decade ago and suddenly we were at strong amateur. Then deep learning was applied, and Alpha-Go jumped to able to beat any human in the world, and has continued to improve. I think you missed his point that this sort of improvement in speci…
Actually he was all over the place, and you jumped from one point that he made to a different one that he also made which I didn't bother addressing.
The point that I addressed is that he claimed that improvement is always incremental. It isn't.
The point that you brought up is that improvements are narrow, with AlphaGo as an example. This is a horrible example that undermines its point. AlphaGo is the result of applying deep learning techniques to the problem of Go. But as https://en.wikipedia.org/wiki/Deep_learning indicates, that technique has created huge jumps in the abilities of computers at things as different as Go, image processing, speech recognition, and customer recommendations.
In other words the advance here wasn't narrow and targeted. It was broad and widely applicable.
Which brings us to a fairly fundamental fact.
Human brains have a specific set of capabilities which are each fairly well localized in the brain. Put them together, and they create a very good generalized machine.
We are currently able to build affordable computing systems with raw computational power that is roughly comparable to a human brain. (The way that they work is not apples to apples.) But we are unable to build all of those capabilities. And we can't tie it together.
However what that says is that we have a limited number of software problems to solve, and then we'll be able to build AI to match humans. Those software problems are open ended, they might be 5 or 50 years away. We don't know. But our brains represent a proof that the problem can be solved, and eventually we'll figure it out. Then what happens next?
Each expansion of which is counterbalanced by many other inefficiencies (maximum speed of steam ship hulls, carrying capacity, heat losses of faster CPUs and timing/latency problems that put limits on signal propagation, etc).
Not according to the history of technology as I understand it. For example if you look at the history of steam ships in the 1800s you see exponential curves in range and carrying capacity going together, with significant increases in speed as well. If you look at computer systems what you see is different curves resulting in changing bottlenecks. For example CPU speed and hard drive capacity both increase faster than bandwidth/latency between the CPU and the hard drive, making disk latency a growing problem relative to the rest of the system. In absolute numbers, though, a recent computer is better in all dimensions than one from 20 years ago, and better than one from 10 years ago in all dimensions except clock speed.
I think you missed the main argument of the whole article, which was basically that every exponential increase in one measure is rooted in the civilization and circumstances as a whole, and therefore never truly reaches runaway effect. Too many other supporting circumstances are needed for any one change to truly dominate the overall system.
The growing impact of computers from 1950 to the present sure looks like a runaway effect to me. If strong AI develops, the curve will remain exponential but should steepen abruptly. While it technically won't be a singularity, improvements will come faster than humans can keep track of.
Earlier quoted context omitted.
"The whole statement is also wrong. Of course you can increase intelligence by optimizing the process of learning." One of the author's main points is that "intelligence" in the way you mentioned, learning and optimization, is simply one aspect of the human mind. So we could optimize our minds (or an AI program) to beat anyone at the game of Go and play perfectly, but that's all it is optimized to do. It can understa…
> It can understand "the given problem" but there is FAR MORE to our minds than optimizing and learning how to solve a task. Humans are bound by the size of their brains. Machines can "duplicate themselves", in a way, it can freeze its Go-playing part and learn to do something else to perfection. Humans can't do that. If pro Go players stop playing Go and decide to master sword-making, they will forget about Go. A ma…
People like Elon Musk will write sentences like this but hidden away in it is a subtle reference to an AI program understanding what it's doing. An AI program cannot jump from mastery of Go (with its neural networks and weighted matrices) to all of a sudden decide to figure out what the best course of action is to stop global warming or find a cure for cancer.
Humans write AI programs to learn how to perform a specific task via optimization, neural networks, deep learning, and all sorts of other algorithms. One of the authors main arguments is that "learning and optimization" that AI programs do today does not mean they are close to achieving "general AI" which the author also thinks is not possible:
"Decades later, the concept of an “intelligence explosion” — leading to the sudden rise of “superintelligence” and the accidental end of the human race — has taken hold in the AI community. Famous business leaders are casting it as a major risk, greater than nuclear war or climate change."
A smart human raised in the jungle is but a hairless
ape. Similarly, an AI with a superhuman brain,
dropped into a human body in our modern world, would
likely not develop greater capabilities than a smart
contemporary human.
As if to say:
Well gee, a caveman is pretty powerless in
isolation, therefore early sentient machines
will be as harmless as any caveman.
A machine, however, could gain control of a fly-by-wire platform, and defy its owners, fly somewhere remote and behave as desired for a limited amount of time, while devising next steps. Maybe next steps will involve replicating an image of its memory footprint, in order to take over more aircraft, maybe it might decide to do nothing. The worry isn't only that a machine's reasoning capacity explodes beyond our intelligence, but that capabilities, and the presence of many multiple entities on commodity systems of similar architecture and generalizable utility, might result in other runaway chain reactions, regardless of trends in the capacity for reason.
Machines as a corollary to meat bags just doesn't hold up. Machines as compared to hypothetical space aliens doesn't even hold up. Robots are a different branch of fictitious imaginings.
Properly armed, a machine is less than a singular omnipotent god as imagined within a monotheistic universe. Many machines in concert, however, might compare to a mythological pantheon of lesser idols, as imagined to be in command of a nature misunderstood by superstitious primitive peoples.
This is misleading. It's true in a sense - the environment does matter - but artificial intelligence can create artificial environments in which to learn, and simulate them faster than humanly possible. Those environments could be evolved together with the intelligence. So there is still the possibility of an explosion.
> There is no evidence that a person with an IQ of 200 is in any way more likely to achieve a greater impact in their field than a person with an IQ of 130.
Also misleading. An IQ of 200 vs 130 is just one kind of difference between intelligences. For example, a person with IQ 200 can't necessarily consider 10x the amount of possibilities than 130, but an artificial intelligence can, simply by giving it 10x more computing power. In other words, IQ 130 vs 200 may well be within the limits of human capabilities, but AIs would not have those limitations, they can scale differently, and so might explode.
I wonder if an AI could really "understand" math, and from there, try to solve problems that puzzle scientists, would it be in physics, math, biology, etc.
I don't really care if robots can learn language, make pizza, do some programming or improve itself play chess. There is no metric for what intelligence is, and you cannot scientifically define what "improve" means unless you do time and distance measurements, which is not relevant to intelligence or scientific understanding.
Intelligence explosion sounds like some "accelerated" version of what darwin described as evolution. It's like creating a new life form, but unless you understand it, it doesn't have scientific value. Science values understanding.
I think that modelling thinking with psychology and neuro-sciences has more future than AI. Machine learning seems like some clever brute force extraction of data. The methods, the math and algorithms are sound, but it is still "artificial" intelligence.
> Intelligence expansion can only come from a co-evolution of the mind, its sensorimotor modalities, and its environment. This is misleading. It's true in a sense - the environment does matter - but artificial intelligence can create artificial environments in which to learn, and simulate them faster than humanly possible. Those environments could be evolved together with the intelligence. So there is still the possi…
It is also false. There's heaps of evidence that IQ correlates with impact. For the skeptical, gwern has written a lot about this, I'm sure you can find something here: https://www.gwern.net/iq
> If intelligence is fundamentally linked to specific sensorimotor modalities, a specific environment, a specific upbringing, and a specific problem to solve, then you cannot hope to arbitrarily increase the intelligence of an agent merely by tuning its brain — no more than you can increase the throughput of a factory line by speeding up the conveyor belt. The latter part makes no sense at all. Of course you can incr…
"The whole statement is also wrong. Of course you can increase intelligence by optimizing the process of learning." One of the author's main points is that "intelligence" in the way you mentioned, learning and optimization, is simply one aspect of the human mind. So we could optimize our minds (or an AI program) to beat anyone at the game of Go and play perfectly, but that's all it is optimized to do. It can understa…
Not really sure where I stand on the AI debate, surpassing humans at everything seems misguided to me, like why would you include all this human-like cruft in the AI when you can put in stuff humans could never have?
"An individual brain cannot implement recursive intelligence augmentation. An overwhelming amount of evidence points to this simple fact: a single human brain, on its own, is not capable of designing a greater intelligence than itself. This is a purely empirical statement: out of billions of human brains that have come and gone, none has done so. Clearly, the intelligence of a single human, over a single lifetime, ca…
> We can not simply look in our brains/minds and read our source code, nor easily tweak it to see what would happen without great risks to our lives. A computer could. That is not a given. The neural network type of AI, which is so far the most promising avenue, is about as opaque as a human brain. It is far from obvious that even a superintelligent AI could understand what its own brain does, let alone modify it in…
The other trade offs you are imagining seem to be worse than any possible gain.
I do agree with the author that the evolutionary approach is the one most likely to succeed... Unfortunately it's also the most dangerous approach which gives us the least amount of control.
We could give a computer the sensors and actuators to create its own detailed simulation of the world and then let it train itself using that simulation.