Several good points made by Linch, Aryeh and steve2512.
As for making my skepticism more precise in terms of probability, it’s less about me having a clear sense of timeline predictions that are radically different from those who believe that AGI will explode upon us in the next few decades, and more about the fact that I find most justifications and arguments made in favor of a timeline of less than 50 years to be rather unconvincing.
For instance, having studied and used state-of-the-art deep learning models, I am simply not able to understand why we are significantly closer to AGI today than we were in 1950s. General intelligence requires something qualitatively different from GPT-3 or Alpha Go, and I have seen literally zero evidence that any AI systems comprehend things even remotely close how humans operate.
Note that the last point is not a requirement (namely that AI should understand objects, events and relations like humans do) as such for AGI but it does make me skeptical of people who cite these examples as evidence of progress we’ve made towards such a general intelligence.
I have looked at Holden’s post and there are several things that are not clear to me. Here is one: there appears to be a lot of focus on the number of computations, especially in comparison to the human brain, and while I have little doubt that artificial systems would surpass those limitations (if it has already not done so), the real question is decoding the nature of wiring and the functional form of the relation between the inputs and outputs. Perhaps there is something I am not getting here but (at least in principle) isn’t there an infinite degree of freedom associated with a continuous function? Even if one argued that we can define equivalence class of similar functions (made rigorous), does that still not leave us with an extremely large number of possibilities?
I find most justifications and arguments made in favor of a timeline of less than 50 years to be rather unconvincing.
If we don’t have convincing evidence in favor of a timeline <50 years, and we also don’t have convincing evidence in favor of a timeline ≥50 years, then we just have to say that this is a question on which we don’t have convincing evidence of anything in particular. But we still have to take whatever evidence we have and make the best decisions we can. ¯\_(ツ)_/¯
(You don’t say this explicitly but your wording kinda implies that ≥50 years is the default, and we need convincing evidence to change our mind away from that default. If so, I would ask why we should take ≥50 years to be the default. Or sorry if I’m putting words in your mouth.)
I am simply not able to understand why we are significantly closer to AGI today than we were in 1950s
Lots of ingredients go into AGI, including (1) algorithms, (2) lots of inexpensive chips that can do lots of calculations per second, (3) technology for fast communication between these chips, (4) infrastructure for managing large jobs on compute clusters, (5) frameworks and expertise in parallelizing algorithms, (6) general willingness to spend millions of dollars and roll custom ASICs to run a learning algorithm, (7) coding and debugging tools and optimizing compilers, etc. Even if you believe that you’ve made no progress whatsoever on algorithms since the 1950s, we’ve made massive progress in the other categories. I think that alone puts us “significantly closer to AGI today than we were in the 1950s”: once we get the algorithms, at least everything else will be ready to go, and that wasn’t true in the 1950s, right?
But I would also strongly disagree with the idea that we’ve made no progress whatsoever on algorithms since the 1950s. Even if you think that GPT-3 and AlphaGo have absolutely nothing whatsoever to do with AGI algorithms (which strikes me as an implausibly strong statement, although I would endorse much weaker versions of that statement), that’s far from the only strand of research in AI, let alone neuroscience. For example, there’s a (IMO plausible) argument that PGMs and causal diagrams will be more important to AGI than deep neural networks are. But that would still imply that we’ve learned AGI-relevant things about algorithms since the 1950s. Or as another example, there’s a (IMO misleading) argument that the brain is horrifically complicated and we still have centuries of work ahead of us in understanding how it works. But even people who strongly endorse that claim wouldn’t also say that we’ve made “no progress whatsoever” in understanding brain algorithms since the 1950s.
Sorry if I’m misunderstanding.
isn’t there an infinite degree of freedom associated with a continuous function?
I’m a bit confused by this; are you saying that the only possible AGI algorithm is “the exact algorithm that the human brain runs”? The brain is wired up by a finite number of genes, right?
Great points again!I have only cursorily examined the links you’ve shared (bookmarked them for later) but I hope the central thrust of what I am saying does not depend too strongly on being closely familiar with the contents of those.
A few clarifications are in order. I am really not sure about AGI timelines and that’s why I am reluctant to attach any probability to it. For instance, the only reason I believe that there is less than 50% chance that we will have AGI in the next 50 years is because we have not seen it yet and IMO it seems rather unlikely to me that the current directions will lead us there. But that is a very weak justification. What I do know is that there has to be some radical qualitative change for artificial agents to go from excelling in narrow tasks to developing general intelligence.
That said, it may seem like nit-picking but I do want to draw the distinction between “not significant progress” and “no progress at all” towards AGI. Not only am I stating the former, I have no doubt that we have made incredible progress with algorithms in general. I am less convinced about how much those algorithms help us get closer towards an AGI. (In hindsight, it may turn out that our current deep learning approaches such as GANs contain path-breaking proto-AGI ideas /principles, but I am unable to see it that way).
If we consider a scale of 0-100 where 100 represents AGI attainment and 0 is some starting point in the 1950s, I have no clear idea whether the progress we’ve made thus far is close to 5 or 0.5 or even 0.05. I have no strong arguments to justify one or the other because I am way too uncertain about how far the final stage is.
There can also be no question with respect to the other categories of progress that you have highlighted such as compute power and infrastructure and large datasets -indeed I see these as central to the remarkable performance we have come to witness with deep learning models.
The perspective I have is that while acknowledging plenty of progress in understanding several processes in the brain such as signal propagation, mapping of specific sensory stimuli to neuronal activity, theories of how brain wiring at birth may have encoded several learning algorithms, they constitute piece-meal knowledge and they still seem quite a few strides removed the bigger question—how do we attain high level cognition, develop abstract thinking, be able to reason and solve complex mathematical problems ?
“isn’t there an infinite degree of freedom associated with a continuous function?”
I agree that we don’t necessarily have to reproduce the exact wiring or the functional relation in order to create a general intelligence (which is why I mentioned the equivalence classes).
Finite number of genes implies finite steps/information/computation (and that is not disputable of course) but the number of potential wiring options in the brain and functional forms between input and output is exponentially large. (It is in principle, infinite, if we want to reproduce the exact function, but we both agree that that may not be necessary). Pure exploratory search may not be feasible and one may make the case that with appropriate priors and assuming some modular structure of the brain, the search space will reduce considerably, but still how much of a quantitative grip do we have on this? And how much rests on speculation?