All of this AI stuff is a misguided sideshow. The disconnect between what Machine Learning represents and the desired or “hyped” abilities is very real. The flashy, headline grabbing results of the past decade are certainly a sideshow, but instead of hiding how far these systems are from actual cognition...
Fighting a decade of Machine Learning momentum. It seems the well received posts follow many of the same arguments, not about viability or milestones, but compute hardware trajectories or the pace of arXiv submissions reaching a point where even those in the field can't keep up anymore. I'm not suggesting...
I created a post for the Future Fund AI World Prize (AGI xRisk Competition) only days after seeing it mentioned on twitter, but I don’t really follow people on the alignment-side of the isle, so of course that post fell flat. As I’ve finally decided to make the formal post...
Errors from transposing or misreading numbers, placing the decimal in the wrong location, etc. The machine mind has a "perfect cache" to hold numbers, concepts, and steps involved. Math is just the simple example of their ability. Such machine minds will be able to hold every state and federal law in their mind, and could re-draft legislation that is "clean" while porting case law from the old to the new legal references.
*for an example of current tech getting simple math wrong: https://twitter.com/KordingLab/status/1588625510804119553?s=20&t=lIJcvTaFTLK8ZlgEfT-NfA
*You can't align something that doesn't really work (it not really working is the current danger). A better question is, can you take a working AI and brainwash it? The unhackable (machine) mind? A good system, in the wrong hands, and all that.
**Again, free yourself from the monolithic view of current architectures.
The disconnect between what Machine Learning represents and the desired or “hyped” abilities is very real. The flashy, headline grabbing results of the past decade are certainly a sideshow, but instead of hiding how far these systems are from actual cognition (and especially from being sentient), they obscure the simple nature of what the xNN models represent. They are a vast probability table with a search function. The impressive outputs are based on the impressive inputs that went into the training process. There is no active mind in the middle. There is no form of cognition taking place at the time of training.
I don't see how mechanistic and modular are different, and it's only clear (upon reading your last point) that your analogy might be flipped. If a car breaks-down, you need to troubleshoot, which means checking that different parts are working correctly. Interpretability (which I think fails as a term because it's very specific to math/xNN's, and not high-level behaviors) happens at that level. Specific to the argument, though, is that these are not two different concepts. Going back to the car analogy, a spark plug and fuel injector are roughly the same size, and we can assume represent an equivalent number of neurons to function. ML fails by being focused on the... (read more)
First, we can all ignore LeCun, because despite his born-again claims, the guy wants to solve all the DL problems he has been pushing (and winning awards for) with more DL (absolutely not neuro-symbolic, despite borrowing related terms).
Second, I made the case that amazing progress in ML is only good for more ML, and that AGI will come from a different direction. Wanting to know that seems to have been the claim of this contest, but the distribution of posts indicates strong confirmation bias toward more, faster, sooner, danger!
Third, I think most people understand your position, but you have to understand the audience. Even if there is no AGI by 2029, on... (read more)
Advances in ML over the next few years as being no different than advances (over the next few years) of any other technology VS the hard leap into something that is right out of science fiction. There is a gap, and a very large one at that. What I have posted for this "prize" (and personally as a regular course of action in calling out the ability gap) is about looking for milestones of development of that sci-fi stuff, while giving less weight to flashy demo's that don't reflect core methods (only incremental advancement of existing methods).
*under current group think, risk from ML is going to happen faster than can be planned for, while AGI risk sneaks-up on you because you were looking in the wrong direction. At least, mitigation policies for AGI risk will target ML methods, and won't even apply to AGI fundamentals.
True enough that n AGI won't have the same "emotional loop" as humans, and that could be grounds for risk, of some kind. Not clear if such "feelings" at that level are actually needed, and no one seems to have concerns about loss of such an ability from mind uploading (so perhaps it's just a bias against machines?).
Also true that current levels of compute are enough for an AGI, and you at least hint at a change in architecture.
However, for the rest of the post, your descriptions are strictly talking about machine learning. It's my continued contention that we don't reach AGI under current paradigms, making such arguments about AGI risk moot.
Computer vision is just scanning for high probability matches between an area of the image and a set of tokenized segments that have an assigned label. No conceptual understanding of objects or actions in an image. No internal representation, and no expectations for what should "be there" a moment later. And no form of attention to drive focus (area of interest).
Canned performances and human control just off camera give the false impression of animal behaviors in what we see today, but there has been little progress since the mid-1980's into behavior-driven research. *learning to play a video game with only 20 hours of real-time play would be a better measure than trying to understand (and match) animal minds (though good research in the direction of human-level will absolutely include that).
This is the boiled-down version of your argument? You clearly state that GOFAI failed and that classic symbolic AI won't help. You also seem to be in favor of DL methods, but in a confusing way, say they don't go far enough (and then refer to others that have said as much). But I don't understand the leap to AGI, where you only seem to say we can't get there, not really connecting the two ideas. Neurosymbolic AI is not enough?
I think you're saying DL captures how the brain works without being a model for cognitive functions, but you don't expand on the link between human cognition and AGI. And while I've... (read more)
Saying "a substantial amount of expertise is required" goes past what general actually implies (I use the example of 8th grade level knowledge, at which point, the system can branch in thousands of directions on it's own).
Saying that a good test of an AGI is for it to build a better AGI, while also saying that such an effort represents an immense risk?
It seems the well received posts follow many of the same arguments, not about viability or milestones, but compute hardware trajectories or the pace of arXiv submissions reaching a point where even those in the field can't keep up anymore. I'm not suggesting people haven't made well reasoned arguments, but will say they don't break new ground. If everyone is just reading thru the posts, nodding their head, and saying "yep," then where is the challenge to existing views?
ML is moving in one direction, and it has built its own cult following. Miles Brundage posted a thread on Twitter earlier today about the importance of having... (read 204 more words →)
I created a post for the Future Fund AI World Prize (AGI xRisk Competition) only days after seeing it mentioned on twitter, but I don’t really follow people on the alignment-side of the isle, so of course that post fell flat. As I’ve finally decided to make the formal post here according to the challenge rules, it was yet another knock against me seeing that I may have already poked the bear when I was participating in the MIRI/Visible Thoughts Project. It’s the reason I already had an account on LessWrong, but my post covering the experience called into question the reasoning. Short version: it was aimed at collecting data that explicitly... (read 220 more words →)
With the collapse of FTX Future Fund, I'm guessing this prize/contest is no longer funded.