221–230 of 329 posts
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In terms of product innovation, OpenAI is now like Google and Google is now like Xerox.
Wow, as somebody who remembers Xerox's lethargy even in the 80s and 90s, that's both an incredibly apt comparison and shockingly accurate.
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If wonder if it will drop off.
I cannot imagine a scenario where it doesn’t drop off. The massive recent improvements in GPT’s performance are a result of giving the model enormous numbers of parameters and a wealth of training data. That’s it. Surely this paradigm cannot scale up indefinitely. Moore’s law is moribund. Are we going to build a supercomputer that encircles the globe just for the purpose of trying to make the biggest LLM we can? Also…
It doesn't have to. It just has to scale up long enough to start causing real societal problems, if it isn't already there.
Also it's kind of annoying to see you dismiss any of this criticism as 'alarmist' in thread after thread when if you look back a year at best the state that we are in today was said to be at least several years away by the same people who are continuously harping on the fact that this isn't AGI yet. The point is: it doesn't have to be to do massive damage and from that perspective it might as well be. I don't particularly care if I get bitten by the cat or by the dog, I care about being bitten.
Correct me if I'm wrong but I think Wolfram/GPT integration is missing a huge opportunity: allowing Alpha to tell ChatGPT the algorithm by which it computes the answer. That would allow ChatGPT to create say Python code that solves a mathematical problem correctly and succinctly based on the Alpha algorithms. Only getting the answer is not as interesting as being able to spit out code that solves the problem in every…
Earlier quoted context omitted.
If wonder if it will drop off.
I cannot imagine a scenario where it doesn’t drop off. The massive recent improvements in GPT’s performance are a result of giving the model enormous numbers of parameters and a wealth of training data. That’s it. Surely this paradigm cannot scale up indefinitely. Moore’s law is moribund. Are we going to build a supercomputer that encircles the globe just for the purpose of trying to make the biggest LLM we can? Also…
Earlier quoted context omitted.
I cannot imagine a scenario where it doesn’t drop off. The massive recent improvements in GPT’s performance are a result of giving the model enormous numbers of parameters and a wealth of training data. That’s it. Surely this paradigm cannot scale up indefinitely. Moore’s law is moribund. Are we going to build a supercomputer that encircles the globe just for the purpose of trying to make the biggest LLM we can? Also…
> Surely this paradigm cannot scale up indefinitely. It doesn't have to. It just has to scale up long enough to start causing real societal problems, if it isn't already there. Also it's kind of annoying to see you dismiss any of this criticism as 'alarmist' in thread after thread when if you look back a year at best the state that we are in today was said to be at least several years away by the same people who are…
You didn’t say anything about societal problems. You wondered if the growth will ever stop, and I tried my best in good faith to give the reasons why I believe that it will.
If the question is “when will the models be powerful enough to cause societal problems”, then that is a completely different question and I think the answer to it is clearly “they already are”. (But not because they are superintelligent or anything close to AGI.)
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I don’t remember Google being this interesting. Ever. It was a good service and very useful, but nothing like this. It was not astonishing. The tech was mildly interesting, but most of all very useful.
I disagree. Google's search engine was orders of magnitude ahead of everyone else's when it came out. Like it actually worked vs all the others that did some mediocre keyword matching and could be moderately useful if you were good with boolean operators. Gmail, which another poster described, was also mind blowing at the time. Google Maps, and the fact that you could scroll across a map smoothly was mind blowing at…
Being able to spin a globe, find our house, visit any place in the world.
Maybe it was less useful compare to maps, but definitely more fun.
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Wolfram Alpha was (at least initially) famously bad* at actually parsing natural language. For math, it was much easier to enter raw Mathematica expression into Wolfram Alpha than structuring the question in natural language. LLMs should give it a boost, by simplifying the kind of the natural language input into the limited forms parsable by Wolfram Alpha. *: And I say this as a paying customer of Mathematica on my c…
WolframAlpha is still bad at natural language. I recently tried to get it to convert mpg to l/100km. I spent close to 10 minutes until I stumbled into a combination that gave the answer among a sea of useless conversions.
https://www.wolframalpha.com/input?i=convert+5+mpg+to+L%2Fkm