71–80 of 199 posts
Wow, I hope that the surveillance state will be at last 30% more efficient.
or at least ads will be 2% less irrelevant.
Adtech is still a bad joke.
How can they possibly keep the location of something like this a secret? There have to be thousands of people involved in building and maintaining it.
Could this have been the reason for chip shortage?
How far can we actually take current machine learning technologies by scaling the underlying hardware? Are we going to see some AI algorithms that are 20% better or an order of magnitude better? And what will that realistically look like to an end user? This will have cost a lot of money and maybe the news alone will push stock prices and mean its paid for itself but is it actually going to result in a substantially…
For general tasks like language modeling, we are still seeing predictable improvements (on the next-token-prediction loss) with increasing compute. We will very likely be able to scale things up by 10,000x or so and continue to see increasing performance.
But what does this mean for end users? We are probably going to see sigmoid-like curves, where qualitative features of these models (like being able to do math, or tell jokes, or tutor you in French, or provide therapy, or mediate international conflicts) will suddenly get a * lot * better at some point in the scaling curve. We saw this for simple arithmetic in the GPT-3 paper, where the small Personally I would not expect diminishing returns with increased scale, instead there will be sudden leaps in ability that will be very economically valuable. And that is why Meta and others are so interested in scaling up these models.
I can't shake the feelings that a trillion or a quadrillion parameters won't solve the fundamental shortcomings of ML models not being models of artificial intelligence. I guess there's no way of knowing until we reach AGI, but I've never heard a compelling argument for why pure ML would get us there. GPT3 seems more like an argument against that hypothesis (in my view) than for it. Even the best, most expensive mode…
I used to work at a university where my professor had been in automatic speech recognition for a long time, but basically gave up on that line of research about 10 years ago because he figured that universities simply cannot compete budget wise with the big industry players. I suppose the same will soon be true for most ML-related areas of research sooner or later, at least as far as applied ML is concerned. Already,…
I think the academic side will start shifting towards research on efficiency and speed while companies will continue to push the cutting edge. In the NLP space there's been a lot of work recently around reducing model sizes, since they've started to reach the point where model weights sometimes don't fit in the memory of most GPUs. There's also projects like MarianNMT which completely abandon Python and write heavily…
I can't shake the feelings that a trillion or a quadrillion parameters won't solve the fundamental shortcomings of ML models not being models of artificial intelligence. I guess there's no way of knowing until we reach AGI, but I've never heard a compelling argument for why pure ML would get us there. GPT3 seems more like an argument against that hypothesis (in my view) than for it. Even the best, most expensive mode…
For what it's worth, this is my view as well. And I don't think it's particularly naive. Plenty of people have researched and/or are researching aspects of how to do this. But how to combine something like a neural network, with it's distributed (and very opaque) representations, with an inference engine that "wants" to work with discrete symbols is non-obvious. Or at least it appears to be, since nobody apparently has figured out how to do it yet - at least not to the level of yielding AGI.
but I've never heard a compelling argument for why pure ML would get us there.
The simplistic argument would be that ML models are, in some sense, trying to replicate "what the brain does" and it stands to reason that if your current toy ANN's (and let's be honest - the largest ANN's built to date are toys compared to the brain) are something like the brain, then in principle if you scale them up to "brain level" (in terms of numbers of neurons and synapses), you should get more intelligence. Now on the other hand, anybody working with ANN's today will tell you that they are at best "biologically inspired" and aren't even close to actually replicating what biological neural networks do. Soo... while people like Geoffrey Hinton have gone on record as saying that "ANN's are all you need" (I'm paraphrasing, and I don't have a citation handy, sorry) I tend to think that in the short term a valid approach is exactly what you suggested. Combine ML and use it for what it's good at (pattern recognition, largely) and use "old fashioned" symbolic AI for the things that it is good at (reasoning / inference / etc.)
Now, to figure out how to actually do that. :-)
I used to work at a university where my professor had been in automatic speech recognition for a long time, but basically gave up on that line of research about 10 years ago because he figured that universities simply cannot compete budget wise with the big industry players. I suppose the same will soon be true for most ML-related areas of research sooner or later, at least as far as applied ML is concerned. Already,…
I hope a positive outcome of this will be that universities direct more of their research effort toward efficiency of network architectures and/or understandability.
If academics want to do research on expensive cutting-edge tech, they will have to join industrial labs or pool together resources, similar to particle physics or drug discovery research today.
How far can we actually take current machine learning technologies by scaling the underlying hardware? Are we going to see some AI algorithms that are 20% better or an order of magnitude better? And what will that realistically look like to an end user? This will have cost a lot of money and maybe the news alone will push stock prices and mean its paid for itself but is it actually going to result in a substantially…
It's linear for now (check GPT-2 vs GPT-3), but we're close to the point of diminishing returns.