[epicureanideal]

https://en.wikiquote.org/wiki/Incorrect_predictions

"Hence, if it requires, say, a thousand years to fit for easy flight a bird which started with rudimentary wings, or ten thousand for one which started with no wings at all and had to sprout them ab initio, it might be assumed that the flying machine which will really fly might be evolved by the combined and continuous efforts of mathematicians and mechanicians in from one million to ten million years--provided, of course, we can meanwhile eliminate such little drawbacks and embarrassments as the existing relation between weight and strength in inorganic materials. [Emphasis added.] The New York Times, Oct 9, 1903, p. 6."

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A couple of the leading minds in AGI say it's a long ways away... just because the universe likes to give us the finger, maybe AGI is on the horizon. Maybe we'll look back at this in 10 years and laugh (if we're here).

[nabla9]

The arguments like above are "platitude level arguments".

We really don't learn anything from the problem in had by talking in generic terms. We use these arguments when we want to justify our hopes and feeling, but there is really nothing to learn from it.

Hinton, Hassabis, Bengio and others point out that we can't 'brute force' AI development. There needs to be actual breakthroughs in the field and there may be several decades between them.

AI, brain science and cognitive science are extremely difficult fields with small advances, yet people assume that it's possible to 'brute force' AGI by just adding more computing power and doing more of the same.

Macroeconomics is probably less complex research subject than AI or brain science, but nobody assumes that you can just brute force truly great macroeconomic model in few years if you just spend little more resources.

[edanm]

> AI, brain science and cognitive science are extremely difficult fields with small advances, yet people assume that it's possible to 'brute force' AGI by just adding more computing power and doing more of the same.

Do people assume that? I mean, I'm sure some people do, but I don't think I've encountered many people, at least not in the AI safety movement, that actually think it's a matter of more hardware power. Some people think it's possible that that's all that's necessary, but I don't think most will say that that's the most likely path to AGI (rather than, as you say, actual breakthroughs happening).

[YeGoblynQueenne]

That's pretty much the Singularity conjecture in a nutshell: that exponential advances in computing power will drive an exponential increase in machine intelligence.

It gets more nuanced than that but there are actually very specialised people who argue very forcefully that AGI is a hair's breadth away and we must act now to protect ourselves from it.

Edit: so not "most" people but definitely some very high-profile people. Although granted, they're high-profile exactly because they keep saying those things.

[spot]

nope https://en.wikipedia.org/wiki/Technological_singularity#Algo...

"Carl Shulman and Anders Sandberg suggest that algorithm improvements may be the limiting factor for a singularity because whereas hardware efficiency tends to improve at a steady pace, software innovations are more unpredictable and may be bottlenecked by serial, cumulative research."

[YeGoblynQueenne]

For flight, the components necessary were obvious very early on: you need some kind of structure to hold you aloft and some kind of powered apparatus to propel you forwards. Once those were found, mechanical flight was achieved (and unpowered flight was already possible long before that).

What are the components of intelligence? For example, AlphaZero can solve problems that are hard for humans to solve in the domain of chess, shogi and go- is it intelligent? Is its problem-solving ability, limited as it is to the domain of three board games, a necessary component of general intelligence? Have we even made any tiny baby steps on the road to AGI, with the advances of the last few years, or are we merely chasing our tails in a dead end of statistical approximation that will never sufficiently, well, approximate, true intelligence?

These are very hard questions to answer and the most conservative answers suggest that AGI will not happen in a short time, as a sudden growth spurt that takes us from no-AGI to AGI. With flight, it sufficed to blow up a big balloon with hot air and- tadaaaa! Flight. There really seems to be no such one neat trick for AGI. It will most likely be tiny baby steps all the way up.

[cr0sh]

Interestingly, Hinton is on record as essentially saying that there's a good possibility that what's currently being done is wrong - and that we need to rethink our approach.

Mainly in the idea/concept of back-propagation. It's something that I've thought about myself. For the longest time, I could never understand how it worked, then I went thru Ng's "ML Class" (in 2011, which was based around Octave), and one part was developing a neural network with backprop - and the calcs being done using linear algebra. It suddenly "clicked" for me; I finally understood (maybe not to the detailed level I'd like - but to the general idea) how it all worked.

And while I was excited (and still am) by that revelation, at the same time I thought "this seems really overly complex" and "there's no way this kind of thing is happening in a real brain".

Indeed, as far as we've been able to find (although research continues, and there's been hints and model which may challenge things) - brains (well, neurons) don't do backprop; as far as we know, there's no biological mechanism to allow for backprop to occur.

So how do biological brains learn? Furthermore, how are they able to learn from only a very few examples in most cases (vs the thousands to millions examples needed by deep learning neural networks)?

We've come up with a very well engineering solution to the problem, that works - but it seems overly complex. We've essentially have made an airplane that is part ornithopter, part fixed-wing, part balloon, and part helicopter. Sure it flies - but it's rather overly complex, right?

Humanity cracked the nut when it came to heavier-than-air flight when it finally shed the idea that the wings had to flap. While it was known this was the way forward long before the Wright's or even Langley (and likely even before Lilienthal), a lot of wasted time and effort went into flying machines with flapping wings, because it was thought that "that's the way birds do it, right"?

So - in addition to the idea that backprop may not be all it's cracked up to be - what if we also need to figure out the "fixed wing" solution to artificial intelligence? Instead of trying to emulate and imitate nature so closely, perhaps there's a shortcut that currently we're missing?

I do recall a recent paper that was mentioned here on HN that I don't completely understand - that may be a way forward (the paper was called "Neural Ordinary Differential Equations"). Even so, it too seems way too complex to be a biologically plausible model of what a brain does...

[virgilp]

You're contradicting yourself with your examples. If we didn't manage to fly by imitating birds - why do you care that AI doesn't work the brain does? That should be a _good_ sign, if we trust the analogy - right?

[zaksoup]

I think the best interpretation of their point is that at some point the breakthrough was questioning a fundamental assumption. I think the point about matching real neurons was just to give credence to their hunch that backprop is not quite the right track to be taking.