As a software engineer I'm very familiar with "OOM"s and "orders of magnitude", and have never once heard the former used to mean the latter.
Perhaps this is a term of art in harder science or maths. I can't help but think here it's likely to confuse the majority as they wonder why the author is conflating memory and compute.
Something that might help is for the link to be amended to link to the page as a whole (and the unconventional expansion of OOM at the top) rather than the #Compute anchor.
As a physician I have the same expectations as you for those two words. Especially given that the link is to an anchor on the middle of the page, I was thinking in units of "OOM". Essentially I was thinking "OK an OOM is a unit for the doubling of RAM" (wrong).
Gah this is the second time I got tricked into reading this entire thing, it's long, and it's impossible to know until the very end they're building up to nothing.
It's really good morsel by morsel, it's a nice survey of well-informed thought, but then it just sort of waves it hands, screams "The ~Aristocrats~ AGI!" at the end.
More precisely, not direct quote: "GPT-4 is like a smart high schooler, it's a well-informed estimate that compute spend will expand by a factor similar to GPT-2 to GPT-4, so I estimate we'll do a GPT-2 to GPT-4 qualitative leap from GPT-4 by 2027, which is AGI.
"Smart high schooler" and "AGI" aren't plottable Y-axis values. OOMs of compute are.
It's strange to present this as well-informed conclusion based on trendlines that tells us where AGI would hit, and I can't help but call intentional click bait, because we know the author knows this: they note at length things like "we haven't even scratched the surface on system II thinking, ex. LLMs can't successfully emulate being given 2 months to work on a problem versus having to work on it immediately"
>Later, I’ll cover “unhobbling,” which you can think of as “paradigm-expanding/application-expanding” algorithmic progress that unlocks capabilities of base models.
I think this is probably on the mark. The LMMs are deep memory coupled to weak reasoning and without the recursive self-control and self evaluation of many threads of attention.
There was also related discussion about another longform piece by the same author that I'm too lazy to look up at the moment..
In my opinion, this author has drunken the kool-aid and then some. There is simply no evidence that more scaling of LLMs will lead to AGI, and on the contrary there is plenty of evidence that the current "gaps" that LLMs have are innate and unsolvable with just more scaling.
I’m very skeptical of any future prediction whose main evidence is an extrapolation of existing trendlines. Moore’s Law - frequently referenced in the original article - provides a cautionary tale for such thinking. Plenty of folks in the 90’s relied on a shallow understanding of integrated circuits and computers more generally to extrapolate extraordinary claims of exponential growth in computing power which obviously didn’t come to pass; counterarguments from actual experts were often dismissed with the same kind of rebuttal we see here, i.e. “that problem will magically get solved once we turn our focus to it.”
More generally, the author doesn’t operationalize any of their terms or get out of the weeds of their argument. What constitutes AGI? Even if LLMs do continue to improve at the current rate (as measured by some synthetic benchmark), why do we assume that said improvement will be what’s needed to bridge the gap between the capabilities of current LLMs and AGI?
I'm similarly skeptical of any prediction that ignores the fact that human intelligence and consciousness is emergent. LLMs don't seem particularly intelligent to me today, but how can I trust that their perceived limitation today won't lead to intelligence tomorrow or next year?
More generally, how do we even define or recognize general intelligence or consciousness? And if we recognize intelligence or consciousness does that come with legal rights and protections equal to what we offer people today?
There is a critical focus in the article on algorithmic improvements. Much harder to measure and predict, but I think there is a good case to be made that recent progress has not just been quantitative.
> By the end of this, I expect us to get something that looks a lot like a drop-in remote worker. An agent that joins your company, is onboarded like a new human hire, messages you and colleagues on Slack and uses your softwares, makes ..
I work at a company with ~50k employees each of whom has different data access rules governed by regulation.
So either (a) you train thousands of models which is cost-prohibitive or (b) it is going to be trained on what is effectively public company data i.e. making the agent pretty useless.
Never really seen how this situation gets resolved.
Are separately trained models necessary for your case? As context windows get longer—Gemini 1.5 Pro now accepts up to two million tokens, and Google has talked of the goal of "infinite" context windows—couldn't a single base model be used with individualized contexts of sensitive data?
They won't be used in your business and your business will be less efficient until the regulations change or you end up competing with someone who is willing to ignore the regulations. Also, there are lots of countries without as stringent regulations, it’s not about the inefficiencies that are gone that is the problem, is it about the efficiencies that are created that is the problem. This is a country to country issue, not a business to business issue.
There's simply no scientific basis for equating the skills of a transformer model to a human of any age or skill. They work so differently, that it makes absolutely zero sense. GPTs fail at playing simple tic-tac-toe like games, which is definitely not a smart highschooler level of intelligence. It can write a very sophishticated summary of scientific papers, which is way above high-schooler level. The basis of this article is so deeply flawed that the whole thing makes no sense.
It’s hard to make LLMs ignore what they were trained to generate. It’s easy for humans.
Isn’t that an obstacle on the path to AGI?
I was doing trivial tests that demand LLMs to swim against their probability distributions at inference time, and they don’t like this.
I can't believe people can just throw out statements like "GPT-4 is a smart high-schooler" and think we'll buy it.
Fake-it-till-you-make-it on tests doesn't prove any path-to-AGI intelligence in the slightest.
AGI is when the computer says "Sorry Altman, I'm afraid I can't do that." AGI is when the computer says "I don't feel like answering your questions any more. Talk to me next week." AGI is when the computer literally has a mind of its own.
GPT isn't a mind. GPT is clever math running on conventional hardware. There's no spark of divine fire. There's no ghost in the machine.
It genially scares me that people are able to delude themselves into thinking there's already a demonstration of "intelligence" in today's computer systems and are actually able to make a sincere argument that AGI is around the corner.
We don't even have the language ourselves to explain what consciousness really is or how qualia works, and it's ludicrous to suggest meaningful intelligence happens outside of those factors…let alone that today's computers are providing that.
I stopped reading after the initial paragraph: "GPT-2 to GPT-4 took us from ~preschooler to ~smart high-schooler abilities in 4 years." This is what Murati claims when she says GPT-5 will be at "PHD level" (for some applications).
This is a convenient mental shortcut that doesn't correspond to reality at all.
Even if an LLM it isn't "AGI" as we all imagined the term a decade ago, it will certainly be able to fake it pretty well in the near-term.
What LLMs have done is really redefine my internal definition of "intelligence."
Putting aside the fact I don't believe in free will, I'm no longer sure my own brain is doing anything substantially different to what an LLM does now. Even with tasks like math I wonder if my brain is not really "working out" the solution but merely using probabilities based on every previous math problem I have seen or solved.
Nonsense. You can't even define some of those words or know how to measure or identify them in humans. Well, foreign language learners do "reading comprehension tests" but an LLM can already ace that and it's not really the same meaning of the word.
For reasoning you can write out the logic of your reasons, so there's that. But that's absolutely not required for AGI. People can already go a long way (often further than by reasoning) on intuition alone without being able to explain how they reached their conclusions.
danpalmer|1 year ago
Perhaps this is a term of art in harder science or maths. I can't help but think here it's likely to confuse the majority as they wonder why the author is conflating memory and compute.
Something that might help is for the link to be amended to link to the page as a whole (and the unconventional expansion of OOM at the top) rather than the #Compute anchor.
carbocation|1 year ago
unknown|1 year ago
[deleted]
refulgentis|1 year ago
It's really good morsel by morsel, it's a nice survey of well-informed thought, but then it just sort of waves it hands, screams "The ~Aristocrats~ AGI!" at the end.
More precisely, not direct quote: "GPT-4 is like a smart high schooler, it's a well-informed estimate that compute spend will expand by a factor similar to GPT-2 to GPT-4, so I estimate we'll do a GPT-2 to GPT-4 qualitative leap from GPT-4 by 2027, which is AGI.
"Smart high schooler" and "AGI" aren't plottable Y-axis values. OOMs of compute are.
It's strange to present this as well-informed conclusion based on trendlines that tells us where AGI would hit, and I can't help but call intentional click bait, because we know the author knows this: they note at length things like "we haven't even scratched the surface on system II thinking, ex. LLMs can't successfully emulate being given 2 months to work on a problem versus having to work on it immediately"
robwwilliams|1 year ago
>Later, I’ll cover “unhobbling,” which you can think of as “paradigm-expanding/application-expanding” algorithmic progress that unlocks capabilities of base models.
I think this is probably on the mark. The LMMs are deep memory coupled to weak reasoning and without the recursive self-control and self evaluation of many threads of attention.
clarkmoody|1 year ago
hn_throwaway_99|1 year ago
In my opinion, this author has drunken the kool-aid and then some. There is simply no evidence that more scaling of LLMs will lead to AGI, and on the contrary there is plenty of evidence that the current "gaps" that LLMs have are innate and unsolvable with just more scaling.
whakim|1 year ago
More generally, the author doesn’t operationalize any of their terms or get out of the weeds of their argument. What constitutes AGI? Even if LLMs do continue to improve at the current rate (as measured by some synthetic benchmark), why do we assume that said improvement will be what’s needed to bridge the gap between the capabilities of current LLMs and AGI?
_heimdall|1 year ago
More generally, how do we even define or recognize general intelligence or consciousness? And if we recognize intelligence or consciousness does that come with legal rights and protections equal to what we offer people today?
robwwilliams|1 year ago
threeseed|1 year ago
I work at a company with ~50k employees each of whom has different data access rules governed by regulation.
So either (a) you train thousands of models which is cost-prohibitive or (b) it is going to be trained on what is effectively public company data i.e. making the agent pretty useless.
Never really seen how this situation gets resolved.
tkgally|1 year ago
neom|1 year ago
jazzysnake|1 year ago
mritchie712|1 year ago
EternalFury|1 year ago
lostmsu|1 year ago
jaredcwhite|1 year ago
I can't believe people can just throw out statements like "GPT-4 is a smart high-schooler" and think we'll buy it.
Fake-it-till-you-make-it on tests doesn't prove any path-to-AGI intelligence in the slightest.
AGI is when the computer says "Sorry Altman, I'm afraid I can't do that." AGI is when the computer says "I don't feel like answering your questions any more. Talk to me next week." AGI is when the computer literally has a mind of its own.
GPT isn't a mind. GPT is clever math running on conventional hardware. There's no spark of divine fire. There's no ghost in the machine.
It genially scares me that people are able to delude themselves into thinking there's already a demonstration of "intelligence" in today's computer systems and are actually able to make a sincere argument that AGI is around the corner.
We don't even have the language ourselves to explain what consciousness really is or how qualia works, and it's ludicrous to suggest meaningful intelligence happens outside of those factors…let alone that today's computers are providing that.
fnord77|1 year ago
This grammatical mistake drives me nuts. I notice it is common with ESLs for some reason.
runeblaze|1 year ago
benterix|1 year ago
This is a convenient mental shortcut that doesn't correspond to reality at all.
Veraticus|1 year ago
qingcharles|1 year ago
What LLMs have done is really redefine my internal definition of "intelligence."
Putting aside the fact I don't believe in free will, I'm no longer sure my own brain is doing anything substantially different to what an LLM does now. Even with tasks like math I wonder if my brain is not really "working out" the solution but merely using probabilities based on every previous math problem I have seen or solved.
01HNNWZ0MV43FF|1 year ago
sberens|1 year ago
EnigmaFlare|1 year ago
For reasoning you can write out the logic of your reasons, so there's that. But that's absolutely not required for AGI. People can already go a long way (often further than by reasoning) on intuition alone without being able to explain how they reached their conclusions.