I think it’s a good idea not to add unwarranted mystery to the subject. We have good reason to believe our computers are underpowered for the task. This is a challenge, and while I think it’s going very fast, our collective sense of proportion of the timeline seems optimistic. In my view, if it happens this century, humans will have achieved an impressive feat.
If we continue with geometric increases in flops/watt, maybe the rosy predictions will be closer than expected.
I suppose what I’m implying is that nervous systems are doing computation, and not something mysterious that we don’t understand. The tree climbers knew they needed to climb higher and realized they’ll need another tool not a different understanding of spatial geometry.
There's something deeper missing (maybe something related to recursion or self-reflection?) and we don't even know what it is, much less how to add it. Yes maybe we'll make a breakthrough and build a rocket tomorrow but it won't be because of any insight we gained from tree-climbing.
The hard problem is how to wire up adaptivity and the specialized faculties.
Sticking with the OP's analogy, the number of people who think it is impossible to get to the mooon in any way is much, much smaller than the number of people who, misunderstanding how tree-climbing works, believe that is the way, that climbing to the moon is just a manner of climbing trees better than we presently do and that if everyone keeps climbing trees someone will reach the moon any time now.
The problem of course, is that there are no trees that reach all the way to the moon. We can keep climbing all we want and we'll never reach as high as the moon. To get to the moon, we need a radically different approach.
A rocket, perhaps- certainly! But, how long have people been climbing trees for, before the first rocket was made?
And where are the people proposing rocket designs, in modern AI research?
We like people like Geoffrey Hinton as their work produces results - he is the first to admit you can't get there from here.
I think it's the people who are concerned with fundamentals, like the people at the Sante Fe Institute - complex adaptive systems.
In the beginning we believed in programming AI from the axioms of mathematics - not many people believe that now. A bit like configuring an animal from atoms. Difficult, pointless.
The first story is that 'fundamentals' might mean some evolutionary framework being required to plug in results from other areas like computer vision and machine learning, my guess would be something from genetic algorithms.
The other story is there exists a 'God Algorithm' that all sapient beings possess - but I find that difficult to believe.
Another story is that we simply have to start from the start - literally everything we know is nearly worthless because consciousness is too unlikely with computer hardware - that the entire history of computing is a dead end. Great for human technology but just not the route to a synthetic version of biological beings. I suspect we're more biased against this possibility than we should be.
I hope that it is the first story - my rationale is that biological intelligence and consciousness is not as far away as we think it is. If we look to evolution we see these vast periods of time in which nature works on different paths and it's only in the last few moments that an animal begins building cities and civilizations. There are also lots of the neurological version of transition fossils if you look around - lots of animals exist that exhibit human level cognitive abilities and some of them outright outrank us. I'm sure you've seen those birds assessing situations and solving puzzles, heard of monkeys coming up with original innovations like salted potatoes - a lot of animals outperform human children.
My thought is that when/if we break Moravec's Paradox and develop a robot that can walk, really walk, then we're some way towards developing machines that perform comparably to biological brains.
The catch is that people who think a walking robot is trivial are so wrong - it is spectacularly difficult. Boston Dynamics is impressive until you understand that a three or four year old is the Albert Einstein of getting about on arms and legs. Show me a robot that can mimic a human walking down the street and I'll stand up at the other side of this internet connection and start clapping. Most people start to change the definition of walking because that is easier than admitting we can't do something that looks easy. I didn't say give a machine locomotive power by changing the shape of the machine or its environment. I said walk - I meant walk.
1950 - https://www.youtube.com/watch?v=Fj0Mr3mLujE
A toy, but not bad for £16.00
66 years, a team of experts and millions of dollars.
2016 - https://www.youtube.com/watch?v=rVlhMGQgDkY
We could be waiting until 2082 or longer.
What are those reasons?
It appeared to me as if we had amassed a terrific reservoir of computational ability - but we wasted nearly all because it was expedient to waste resources when they were ample.
If I gave you a computer with 1 billion times the processing ability of current supercomputers, can you convince me that you'd be capable of replicating the functionality of a biological brain?
A human brain might take 36.8e15 computations per second [1] and does all this using about ~20 watts! That's pretty impressive at around 1.84e+15 ops / watt vs our current 6e+9 ops / watt [2] from silicon. We have to make about a million times more efficient processors to match the brains efficiency. My 4lb laptop takes what feels like forever to do a limited deep learning task given how inefficient it is.
If you gave just me the sort of computer needed, that alone would very likely not be sufficient. I just don't think we should draw too strong of a negative conclusions before we all have the necessary equipment to match the work of the brain.
But if myself and fellow computer users did have access to efficient powerful computers, we'd probably want to write algorithms that allowed us to make many attempts at the solution per second. I'd imagine writing a simulator to project many agents into where they are competing for resources and many of these simulations taking place concurrently. Something of a multiverse of simulation and optimization.
[1] https://en.wikipedia.org/wiki/Computer_performance_by_orders... [2] https://en.wikipedia.org/wiki/Performance_per_watt
We do (and I wrote a long post with a list of bullet points proving so before I just deleted it), but I don't think we should waste time debating whether this is true or not true. We're on the same side after all, your objective is my objective.
> But if myself and fellow computer users did have access to efficient powerful computers, we'd probably want to write algorithms that allowed us to make many attempts at the solution per second. I'd imagine writing a simulator to project many agents into where they are competing for resources and many of these simulations taking place concurrently.
I recommend you look at David Krateneur's ideas, he has a video called "The Stupid Ways That We Have Thought About Intelligence". https://www.youtube.com/watch?v=pi7h6nmkvAM
> Something of a multiverse of simulation and optimization.
An artificial imagination really.
I really recommend watching that video.
From what I can tell though, I agree that there are alot of very subtle, but fundamental mistakes and omissions in both the design and combinations of current generation intelligence algorithms that are impeding a viable path toward general intelligence. Of course, if all we care about is dramatically increasing the efficiency of solving problems humans care about, this totally isn't a problem, because we apparently don't need general intelligence to do that.
Henry Markram, a neuroscience person with a desire to emulate the human brain believes it takes 1 exaflop. It's a start.
Existing supercomputers reach 50 petaflops. 50 billion petaflops is 50 million exaflops.
Is this 50 million brain EM moments?
It's possible it was in the ballpark of a human brain emulation although I strongly suspect a fair comparison isn't easily comprehensible.
> I agree that there are alot of very subtle, but fundamental mistakes and omissions in both the design and combinations of current generation intelligence algorithms that are impeding a viable path toward general intelligence
I think that is true of specialized intelligence, and that zero progress of any sort has been made on AGI. I don't mind being wrong if you know of primitive examples that qualify.
It is unclear how to begin. Maybe rehearsing something like real biological evolution.
> , if all we care about is dramatically increasing the efficiency of solving problems humans care about, this totally isn't a problem, because we apparently don't need general intelligence to do that.
Daniel Dennett makes this point philosophically in a video I watched yesterday. Other people have made the observation that we're already surrounded by AGIs in the form human society based collective intelligences we know well.
My hunch with regard to neural nets is that we’ve got something that looks like biology, and has a certain level of efficacy, but is missing a lot of the finer details that we’re unaware of in biology. Right now we’re just brute forcing problems with as much computing power and data as possible, biology obviously does better.
a big one is timing — neurons in the real brain can delay firing depending on what signals they receive.
there is research in training artificial neural networks with timing but it is not widely used for AI.
The issue that the real world is mostly not composed of optimisation problems with possible moves and win conditions as neatly and completely described in code and as replayable as Go is probably a thornier one for building AGI (as opposed to useful tools) through learning processes than the adequacy of the hardware...
This is too early to call. NN acquires knowledge in a fundamentally different way than human.
For example, to have a reasonable understanding of a certain language, like producing a sample of grammatically correct utterance, it definitely won't require a human to read the amount of text of Wikipedia, let alone multiple times.
We're really just hosts for our genetic code. It's the star of the show, not us.
Neural nets have fundamental limitations, particularly their inability to generalise outside a narrow band around their training set [1].
For instance- you will not find anything in the neural network bibliography about learning such a fundamental concept as counting, in the sense of finding the number n+1 that follows from a given number n, for arbitrary n. A neural net can certainly be trained on that task for numbers in a range [k,n] - but, given numbers outside this range its performance would "fall off a cliff". That's because neural networks can't learn general rules such as "x > y → y = x + k" etc.
Add to this the fact that neural nets, while very good at specific tasks, must be trained anew for each new task- and you see that there is a big problem in getting anything approaching "general intelligence" (which might involve any number of wildly varying tasks) just with neural nets. For one thing- even if you could train a neural net model for something like a million tasks, there would remain the question of somehow stitching them all together in a coherent whole capable of performing the right task at the right time or combining decisions from multiple models.
Finally, note that nobody is even trying to train a neural net to learn "general intelligence" end-to-end from examples; first of all, because we have no idea what constitutes general intelligence and therefore how to collect its examples.
So it's not a matter of scale. More of a matter that there isn't anyone living today that has any idea how to get to AGI with any existing technique- including neural nets.
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[1] https://blog.keras.io/the-limitations-of-deep-learning.html
As usual, I'll recommend the classic AI textbook, "AI - A modern approach" by Russel and Norvig:
This website's community has got peculiar more and more over the last 3-5 years and not in a positive way. There were the assholes of yesteryear but even they seemed like more sincere assholes than present day assholes.
https://www.youtube.com/playlist?list=PLGtryE0TPZPGReCdQ_F-n...
https://groups.google.com/forum/#!topic/artificial-general-i...
Better still, could you link to a paper where the relevant results are presented?
Sparse Distributed Representations, The data model of the brain and the key to how the brain generalizes.
All the working examples he shares in this talk are exciting, but this one stands out.
Not true.
What do you mean by this?
As such the evolution of human intelligence is largely an enormous chain of favorable mutations having been coupled and survived over billions of years. And each mutation that did occur was owing to nothing more than chance.
Ethical considerations are preventing us or at the very least limiting the speed of development. But mostly our lack of knowledge of the brain was what stopped us. Now that we are advancing on all fronts, I think we are going to be forced to deal with the ethics of neural/brain creation/destruction/experimentation.
If scientists are able to keep disembodied brains "alive", then it will open up avenues for experimentation and ethical issues.
https://news.ycombinator.com/item?id=16924764
There are even ethical issues of hybrid organ growth ( starting from the embryonic level ) where human organs are grown in pigs. The question is whether human cells can travel to pig's brains and cause pigs to be "more conscious".
https://www.newsweek.com/it-ethical-grow-human-organs-pigs-4...
At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI.
Human brains can evolve by chance. Nothing fundamental is stopping us from creating a synthetic one. Natural Intelligence isn't that smart either, realistically.