101–110 of 388 posts
So far some small wins have been seen. Common problems involve looping and doing nothing. It's still early for these projects.
Earlier quoted context omitted.
Given that a sufficiently resourced computer ought to be able to run a subatomic-level simulation of an entire human brain, and while acknowledging the usual counterpoint vis a vis C. elegans/OpenWorm but deeming it irrelevant on longer timescales, your take seems quite arrogant. “Outside the scope of computation” is an awfully broad claim.
Prove such a simulation can be created with actual hardware, taking relativity into account.
Earlier quoted context omitted.
What makes that obvious to you? What seems uncomputable about human intelligence?
Despite our best efforts, we are deeply irrational. Our thinking is based on instinct, not on core principles; it's a top-down approach driven by feelings.
We only seem “irrational” when we are talking about a narrow view of “rationality”, i.e., as defined by the cold hard logic of machines. We do not question why we have this definition of rationality. Our “irrationality” simply seems so because we have not bothered to understand the larger complexity of our evolutionary programming. It’s the same as not bothering to understand how a car works, and then claiming that the car works on magic. If one understands how it works, then it is no longer magic. In the case of humans, we may never fully understand how we work, but we can work towards a compassionate understanding of the same.
/rant
As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
I predict that the fight will be more about defining intelligence than inventing it. No one knows what AGI is. There isn't going to be some switch that flips to take us from AI to AGI. These tools we have today will just keep getting incrementally better, and some new ones will pop up, and at some point we'll have to stop and say "yeah, this is good enough to qualify". And everyone will have their own opinion on what…
The other few things possible in next few years for past few years are: 1. Fusion 2. Self driving cars. 3. Ubiquitous, generally available and stable 8192 bit quantum computers. 4. High energy density grid scale batteries. Just few more "this maybe the... what could revolutionize..." articles away. What we forget is that it is in these people's business interest (executives making such statements) to state the exagge…
Numbers 1-4 obviously don’t have enormous recent progress nor the funding and mindshare poured into, and pouring in, as consequence.
As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
IMO, the two most important properties of human cognition are that it evolved through natural selection, and that it is embodied, meaning that it is part of a feedback loop between perception and action. I don't see why either of these things requires having a biological brain or body. It's true that current methods aren't close to achieving these things, but there's nothing in principle stopping us from creating evolved, embodied intelligences either in simulations or in robots.
Earlier quoted context omitted.
>we just see a cooling off of other endeavors as LLM becomes a known in terms of its strengths and weaknesses and it’s abilities reach a natural limit which is still general useful. This is my prediction If the training set is just the internet, it will be not much different than someone who spends all their lives in their room in front of their computer.
I don't think that's a limit. If you give me $10M and 5 years, I can tell you a half-dozen ways to train models better than GPT4, primarily by having them do richer tasks than text completion, by having them evaluate themselves in richer ways, by having workflows around them, and by having shared models perform multiple types of tasks (e.g. text, image, controls, etc.). It's not that I'm especially smart; many others…
Internet content does not go as deep as it would ideally be in some areas.
For example, do training sets contain ArXiv data? Or other stuff behind a paywall?
LLMs know very little about XX century literature.
Earlier quoted context omitted.
Prove such a simulation can be created with actual hardware, taking relativity into account.
You mean silicon-based hardware? What role does relativity play here?
Earlier quoted context omitted.
What makes that obvious to you? What seems uncomputable about human intelligence?
Despite our best efforts, we are deeply irrational. Our thinking is based on instinct, not on core principles; it's a top-down approach driven by feelings.