5 ms·

John Carmack on the Similarity of Human Learning and LLMs Training

62 points by amrrs 4y ago

m3kw9 4y ago
But we relate the words in a 3d world, llms relate words only to each other
wkdneidbwf 4y ago
surprises me a human would be exposed to a billion words. i never thought about it, but a billion is such a large number edit: oh, does he mean a billion total rather than a billion unique words? haha i r smat
ftxbro 4y ago
The context is that John Carmack got $20M to try to make AGI by 2030, based on non-LLM methods. In this tweet he is boosting the idea that there is a gigantic learning gap between the statistical efficiency of human and LLM learning, even though there may not be such a huge gap in standardized test results of LLMs and humans. This means that he might yet find a cool idea that could justify the time and money he is spending to find secret knowledge of the ancients from like pre-1990s AI publications that didn't have GPU access. Maybe it will happen! https://the-decoder.com/john-carmacks-general-artificial-intelligence-startup-gets-20-million/ https://the-decoder.com/john-carmacks-general-artificial-int...
- coffeebeqn 4y ago
  It’s definitely Good to not put all our eggs in the LLM basket. Even openAI says it’s only a part of the solution - but it has scaled more than most anyone expected. Will that continue forever and we keep getting more impressive emergent behaviors from it? I doubt that but I could certainly be wrong. It still seems to be missing any sense of what is True, not sure if that’s possible to embed in that model or if we’ll just have some human feedback hacks and eventually get a better model
  - gitfan86 4y ago
    That debate is becoming irrelevant. Sure it would be great if GTP-6 cost 50k to train instead of 100M. But even if costs 100M it will produce many billions of dollars of value, so the original investment is still relatively small.
    - scrollaway 4y ago
      Yeah but the difference between those two numbers is one is accessible to individuals (albeit wealthy ones), the other is accessible only to large-ish companies.
  - ftxbro 4y ago
    > Will that continue forever and we keep getting more impressive emergent behaviors from it? I doubt that I feel like putting the word 'forever' ruins the point. It's the most extreme strawman. It's amazing how the scaling has unlocked the emergent behaviors! When I look at the scaling graphs, I see that the ability to reduce 'perplexity' is continuing with scale and capital investment with no sign of slowing yet (it will slow eventually). I also see that reducing perplexity is continually unlocking new emergent behaviors. So I would guess that scaling will probably unlock so many more new emergent behaviors before it eventually plateaus!
  - llamaLord 4y ago
    IMO, the capabilities have not improved that much between GPT-3 vs GPT-4, the primary difference is just in the size of the context the model can handle at any one time (Max tokens). While I definitely get "longer" and slightly more "in-depth" answers from GPT-4 vs 3, it already feels like that capability growth curve is starting to plateau.
    - ftxbro 4y ago
      > IMO, the capabilities have not improved that much between GPT-3 vs GPT-4 I strongly disagree. Anyone who wants to look for themself can see the GPT 4 technical report. https://arxiv.org/pdf/2303.08774.pdf https://arxiv.org/pdf/2303.08774.pdf
- ly3xqhl8g9 4y ago
  In a way LLMs are the worst thing that could have happened in the search for artificial intelligence, instead of the harsh winter from the 1990s we are entering now a climate changed winter: warm, products to market race winter. Funnily enough, the Geoffrey Hinton of today is probably some symbols researcher, shouting in the desert, like Hinton shouted in the 1980s, that we need more than matmul. One interesting, biology-inspired mechanism, would be quorum sensing [1]: a basal cognition-like, decision-making function in which decentralized systems (bacteria, cells) start building functionality (sensing/decision) from the bottom up. There is no hint for this sort of mechanism in our current artificial 'neural networks'. Not that it should, but our cells and in general cells use these kind of 'tricks' to solve problems in all kinds of spaces (transcriptomics, morphogenetics, etc.) without requiring ridiculous amounts of energy, time, or other resources. [1] https://en.wikipedia.org/wiki/Quorum_sensing https://en.wikipedia.org/wiki/Quorum_sensing
  - joe_the_user 4y ago
    LLMs may be terrible for the search for AGI but that may be good thing. If, mind you if, LLMs are always going to semi-smart "parrots", then society will get a picture of what an AI might without the AI being able to skynet-style takeover people are maybe justifiably worried. Now, as far as whether LLM or offshoots can get to AGI, I know plenty of good arguments for them not being able to do that. I don't think the claim that they're just accumulating more abilities in each iteration in an inexplicable way is true. But I've lived long enough to know that you should never get too cocky when one is "arguing with success". So maybe. Moreover, given that LLM programming is basically just bucket chemistry, if an LLM can the ability to competently pursue long term goals, it seems like it will have a good chance of some of its goals being random cruft that will make it quite dangerous.
    - ly3xqhl8g9 4y ago
      When some hybrot [1] will write the history of the 2000s, after AES 256 is broken, we will probably find out that the Skynet takeover already happened, it just wasn't as dramatic as a James Cameron movie: it was a takeover through financialization (with enough money you can rewrite the past, so Mr. Cameron was right on the money about the time machine), it involved some suits, a glass building, and a generally apathetic, divided by design society: "as of 2020, Aladdin managed $21.6 trillion in assets" [2]. Nowadays, Mr. Sam Altman, who probably has good intentions, is speaking of OpenAI and $100 trillion [3] being unleashed into the market. There will be people wanting those $100+ trillion for themselves, and our current system is being designed by those kinds of people, biased towards and fostering their success (Blackrock, Vanguard, State Street currently barely have $30 trillion under management, with world GDP being at $101.5 trillion in 2022 [4]). Equating LLMs to parrots is offensive, to the parrots. Well, maybe not parrots, but crows are intelligent: "Scientists [5] demonstrate that crows are capable of recursion—a key feature in grammar. Not everyone is convinced" [6]. [1] https://en.wikipedia.org/wiki/Hybrot https://en.wikipedia.org/wiki/Hybrot [2] https://en.wikipedia.org/wiki/Aladdin_(BlackRock) https://en.wikipedia.org/wiki/Aladdin_(BlackRock) [3] https://www.nytimes.com/2023/03/31/technology/sam-altman-open-ai-chatgpt.html https://www.nytimes.com/2023/03/31/technology/sam-altman-ope... [4] https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal) https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nomi... [5] "Recursive sequence generation in crows", https://www.science.org/doi/10.1126/sciadv.abq3356 https://www.science.org/doi/10.1126/sciadv.abq3356 [6] https://www.scientificamerican.com/article/crows-perform-yet-another-skill-once-thought-distinctively-human https://www.scientificamerican.com/article/crows-perform-yet...
- rajman187 4y ago
  Carmack is no doubt brilliant and it shows through Oculus as a product. But AGI is not about algorithm optimization the way 3D graphics were in the 90s. I am not sure the approach he takes (let’s find a way to write this in assembly) can apply here. He’s said before that AGI is going to be a few thousands of lines of code, ultimately [1], and it’s hard to believe that’s really the case. That said, I did get a chance to speak with him one on one last year and he really emphasized a few things; the need for being product oriented and giving customers what they want rather than chasing cool engineering (über)solutions; not being careless with resources just because we have more power with modern hardware (he poked fun at React where you spin up a new thread just for an interactive button); and being aware of the inefficiencies brought on by infinite resources (# of engineers and/or funding, which make you think less critically about timelines and delivering within bounded means) [1] https://youtu.be/I845O57ZSy4 https://youtu.be/I845O57ZSy4
  - carabiner 4y ago
    The guy also tried to build a rocket to reach space via "first principles." It failed, utterly.
    - cma 4y ago
      They had a low budget relative to most of the others and reached break-even profitability but he moved on to other stuff. Where is the trying to reach space by "first principles" quote from?
    - barrysteve 4y ago
      It did the thrust vectoring thing. The rocket was ahead of the competition for a moment. He said games were one of the most complex things humans build and (with implied comparison) the mathematics and physics of rocketry hadn't changed much since the 1960s. Sounds true to me for the 2000s. Where was the utter failure and first principles?
  - phkahler 4y ago
    >> But AGI is not about algorithm optimization the way 3D graphics were in the 90s Wow, you think his ability is straight performance optimisation? A big part of his early fame was from doing research to find good algorithms to achieve his goals. He also had that stint in real time control systems... flying hovering rockets before spaceX even existed. He's a problem solver with a strong ability to sift through possible solutions for what actually works, and quite capable of devising his own solutions when is research comes up empty. But yeah, he can write assembler too.
- cpeterso 4y ago
  Is there any information available about the non-LLM methods Carmack's AGI startup is researching?
breatheoften 4y ago
Who is commenting on this and what will they say??! 1-2 trillion tokens of training for gptx class models It would take 20 years for a human to read that many tokens at 8 hrs of reading per day (although i'm pretty unclear on whether that's unique token sequences or randomly selected token pairs -- feels like a big difference between the two!!) We are all here now -- who thinks we are anywhere of note together? Personally I think that models that do not experience incremental change remain an entirely separate class of intelligence from those which evolve continuously -- but I am open to being persuaded otherwise (certainly I never thought making gpt2 bigger would be as impressive as gpt4) ...
- meindnoch 4y ago
  >1-2 trillion tokens of training for gptx class models >It would take 20 years for a human to read that many tokens at 8 hrs of reading per day Normal reading is ~200 words per minute. That's 12,000 words per hour. That's 288,000 words per day (reading for 24 hours straight). That's 105,120,000 words per year. That's 2,102,400,000 words per 20 years. A trillion is 1,000,000,000,000.
  - tonightstoast 4y ago
    Plus almost no one is reading academic papers at 200 words per minute. So for the level of material it could mean that number is halved per 20 years.
- lostmsu 4y ago
  Video from a pair of eyes is many terabytes per year though. If you compress it with best codecs. Raw video would be closer to 1PB per year. I think people overestimate the data efficiency of the biological neural networks w.r.t. transformer models.
  - actionfromafar 4y ago
    The power effiency is pretty brutal though, in favour of bio-gelpacks.
intalentive 4y ago
Humans are not exposed to "billions of words". They are exposed to continuous signals, and somehow extract meaningful representations, like "words" and "objects", out of them. LLMs are exposed to human-curated data. Let's see an LLM curate its own data out of nothing but experience of raw continuous signals. Sure, GPT trained on the internet is a nice way to condense and retrieve human-curated data. But it's not going to give us a Lt. Data or C-3P0 that can learn and adapt in real time.
- pbw 4y ago
  > Humans are not exposed to "billions of words" ... continuous signals Yes humans need to learn to extract words from signals, but that does not mean it's not correct to count the number of words they've been exposed to in their lifetime. Your comment is like saying we can't count the number of hamburgers someone has eaten, because they actually eat myofibrillar proteins, not burgers.
  - d0mine 4y ago
    Though millions seems more realistic (thousands of words per day)
- andsoitis 4y ago
  what do you think of midjourney's ability to describe a user-provided image in text? https://the-decoder.com/midjourney-new-image-tool-works-in-reverse/ https://the-decoder.com/midjourney-new-image-tool-works-in-r...
- gunshai 4y ago
  But humans are also trained on human-curated data so much so we put fairly hefty price tags on that curation. https://miro.medium.com/v2/resize:fit:1056/0*E1eNateTiDThGcYI.jpg https://miro.medium.com/v2/resize:fit:1056/0*E1eNateTiDThGcY...
- echelon 4y ago
  We've only just started. Now more capital and more minds will be pouring into solving this. Buckle up.
roenxi 4y ago
It seems likely that there is a big missing component of visual data. How many Tb of visual data (effectively video) do babies get exposed to? That is what sets up their mental model of the world, and language is fit over the top of that. It is hard to assess neural net efficiency with that difference in place.
- rvnx 4y ago
  It is going to amazing once LLMs get access to learn from real-life visual information (e.g. daily life videos without montage, or movies), and not just text.
- cma 4y ago
  > It seems likely that there is a big missing component of visual data. How many Tb of visual data (effectively video) do babies get exposed to? Vision doesn't seem relevant for what's missing in general intelligence: there are people blind from birth that nonetheless get very fluent in thought and language. It doesn't cause many significant developmental delays other than in some vision associated stuff like early motor skill acquisition, social stuff from not being able to see facial expressions, etc. The models may be missing data grounding in 3d space that people born blind do have, like proprioception, touch, and hearing. Deaf and blind from birth can cause more significant developmental delays, but becoming deaf later after some critical threshold doesn't seem to, so you are still looking at potentially only a short amount of years worth of data that seems to be needed (Hellen Keller went deaf and blind at 19 months).
deleted 4y ago
[deleted]
physPop 4y ago
Carmack is a smart guy but don't confuse his expertise in other areas with machine learning. Smells of hubris...
- SquareWheel 4y ago
  He's been heavily researching machine learning for the last few years, and formed an AGI startup last year. He's not the world's leading researcher or anything, but he's far from green in this space.
- rvnx 4y ago
  Perhaps he feels that the Metaverse thing he sold to Zuck isn't going anywhere, and now tries to ride a new wave.
cypress66 4y ago
GPT4 probably has a breadth of knowledge 1000x wider than a human, so the fact that it uses more tokens seems reasonable.
kklisura 4y ago
Q: If we need to feed additional data to the network, do we train it with existing data + new data or we can just train it on a new data? I'm asking because if we need to train it with both old and new data every time we have something new, that's not similar to human learning.
- IanCal 4y ago
  It's extremely similar to training new humans.
cma 4y ago
LeCun's reply: > more like a thousand times more. > Between 1 and 2 trillion tokens. > It would take a person 22,000 years to read through 1 trillion words at normal speed for 8 hours a day. It's kind of surprising to me that around 1000 humans could feasibly read the whole internet (as scraped for LLMs) between each other in only 22 years. The internet feels so much more massive than that. Though of course lots of the stuff on arxiv etc. is so dense there is no way you go through it at normal reading speed.
brokencode 4y ago
I wonder if it only seems like our brains need relatively little training because we have millions of years of training encoded in our DNA. No other animal can learn human language or logic to the extent that humans can, no matter how much you train them. But humans learn language easily in the first few years of life. It’s almost as if the human brain is preprogrammed with the general concepts of all languages, and it just needs to be fine tuned with specific vocabulary and grammar rules.
lairv 4y ago
LLMs weights are initialized from a normal distribution (or whatever distribution is now SOTA) while some animals can walk the second they are born
mannyv 4y ago
IMO a lot of the commenters here are missing the point, which is that LLMs are exposed to orders of magnitude more stuff than a human. Two obvious differences are that humans also learn from the context in which they live (social, environmental) and humans have to basically learn how their own wiring works. And their wiring is/can be highly variable.
dwallin 4y ago
I’m surprised to see someone of Carmack’s caliber making this mistake. If you’ve exposed a gorilla or chimp brain to the same amount of language content they still wouldn’t be able to converse like a human can. Meanwhile almost every human can utilize spoken language, despite huge variations in their training data. This implies that most of the language capabilities of the brain depend on structural elements that have been evolutionarily trained and not on the fine-tuning that a specific human achieves in their lifetime. Think of how many billions to trillions of attempts evolution had. And it was building off of models already trained (with many orders of magnitude more iterations) at the tasks of audio processing and sound recognition. Then add in the fact that we are only talking about language acquisition and all indications are that sufficiently large models (And the human brain is quite the large model) have significant benefits from cross training and it starts to become clear how false of a premise this is. 11 million bits of neural information are being pumped to our brains every second of our lives. That’s 80 gb of external data ingested during our waking hours in a single day plus who knows what sorts of internal synthetically generated data, all of this just for fine-tuning.

62 points by amrrs 4y ago

But we relate the words in a 3d world, llms relate words only to each other

surprises me a human would be exposed to a billion words. i never thought about it, but a billion is such a large number edit: oh, does he mean a billion total rather than a billion unique words? haha i r smat

ftxbro 4y ago

The context is that John Carmack got $20M to try to make AGI by 2030, based on non-LLM methods. In this tweet he is boosting the idea that there is a gigantic learning gap between the statistical efficiency of human and LLM learning, even though there may not be such a huge gap in standardized test results of LLMs and humans. This means that he might yet find a cool idea that could justify the time and money he is spending to find secret knowledge of the ancients from like pre-1990s AI publications that didn't have GPU access. Maybe it will happen! https://the-decoder.com/john-carmacks-general-artificial-intelligence-startup-gets-20-million/ https://the-decoder.com/john-carmacks-general-artificial-int...

coffeebeqn 4y ago
It’s definitely Good to not put all our eggs in the LLM basket. Even openAI says it’s only a part of the solution - but it has scaled more than most anyone expected. Will that continue forever and we keep getting more impressive emergent behaviors from it? I doubt that but I could certainly be wrong. It still seems to be missing any sense of what is True, not sure if that’s possible to embed in that model or if we’ll just have some human feedback hacks and eventually get a better model
- gitfan86 4y ago
  That debate is becoming irrelevant. Sure it would be great if GTP-6 cost 50k to train instead of 100M. But even if costs 100M it will produce many billions of dollars of value, so the original investment is still relatively small.
  - scrollaway 4y ago
    Yeah but the difference between those two numbers is one is accessible to individuals (albeit wealthy ones), the other is accessible only to large-ish companies.
- ftxbro 4y ago
  > Will that continue forever and we keep getting more impressive emergent behaviors from it? I doubt that I feel like putting the word 'forever' ruins the point. It's the most extreme strawman. It's amazing how the scaling has unlocked the emergent behaviors! When I look at the scaling graphs, I see that the ability to reduce 'perplexity' is continuing with scale and capital investment with no sign of slowing yet (it will slow eventually). I also see that reducing perplexity is continually unlocking new emergent behaviors. So I would guess that scaling will probably unlock so many more new emergent behaviors before it eventually plateaus!
- llamaLord 4y ago
  IMO, the capabilities have not improved that much between GPT-3 vs GPT-4, the primary difference is just in the size of the context the model can handle at any one time (Max tokens). While I definitely get "longer" and slightly more "in-depth" answers from GPT-4 vs 3, it already feels like that capability growth curve is starting to plateau.
  - ftxbro 4y ago
    > IMO, the capabilities have not improved that much between GPT-3 vs GPT-4 I strongly disagree. Anyone who wants to look for themself can see the GPT 4 technical report. https://arxiv.org/pdf/2303.08774.pdf https://arxiv.org/pdf/2303.08774.pdf
ly3xqhl8g9 4y ago
In a way LLMs are the worst thing that could have happened in the search for artificial intelligence, instead of the harsh winter from the 1990s we are entering now a climate changed winter: warm, products to market race winter. Funnily enough, the Geoffrey Hinton of today is probably some symbols researcher, shouting in the desert, like Hinton shouted in the 1980s, that we need more than matmul. One interesting, biology-inspired mechanism, would be quorum sensing [1]: a basal cognition-like, decision-making function in which decentralized systems (bacteria, cells) start building functionality (sensing/decision) from the bottom up. There is no hint for this sort of mechanism in our current artificial 'neural networks'. Not that it should, but our cells and in general cells use these kind of 'tricks' to solve problems in all kinds of spaces (transcriptomics, morphogenetics, etc.) without requiring ridiculous amounts of energy, time, or other resources. [1] https://en.wikipedia.org/wiki/Quorum_sensing https://en.wikipedia.org/wiki/Quorum_sensing
- joe_the_user 4y ago
  LLMs may be terrible for the search for AGI but that may be good thing. If, mind you if, LLMs are always going to semi-smart "parrots", then society will get a picture of what an AI might without the AI being able to skynet-style takeover people are maybe justifiably worried. Now, as far as whether LLM or offshoots can get to AGI, I know plenty of good arguments for them not being able to do that. I don't think the claim that they're just accumulating more abilities in each iteration in an inexplicable way is true. But I've lived long enough to know that you should never get too cocky when one is "arguing with success". So maybe. Moreover, given that LLM programming is basically just bucket chemistry, if an LLM can the ability to competently pursue long term goals, it seems like it will have a good chance of some of its goals being random cruft that will make it quite dangerous.
  - ly3xqhl8g9 4y ago
    When some hybrot [1] will write the history of the 2000s, after AES 256 is broken, we will probably find out that the Skynet takeover already happened, it just wasn't as dramatic as a James Cameron movie: it was a takeover through financialization (with enough money you can rewrite the past, so Mr. Cameron was right on the money about the time machine), it involved some suits, a glass building, and a generally apathetic, divided by design society: "as of 2020, Aladdin managed $21.6 trillion in assets" [2]. Nowadays, Mr. Sam Altman, who probably has good intentions, is speaking of OpenAI and $100 trillion [3] being unleashed into the market. There will be people wanting those $100+ trillion for themselves, and our current system is being designed by those kinds of people, biased towards and fostering their success (Blackrock, Vanguard, State Street currently barely have $30 trillion under management, with world GDP being at $101.5 trillion in 2022 [4]). Equating LLMs to parrots is offensive, to the parrots. Well, maybe not parrots, but crows are intelligent: "Scientists [5] demonstrate that crows are capable of recursion—a key feature in grammar. Not everyone is convinced" [6]. [1] https://en.wikipedia.org/wiki/Hybrot https://en.wikipedia.org/wiki/Hybrot [2] https://en.wikipedia.org/wiki/Aladdin_(BlackRock) https://en.wikipedia.org/wiki/Aladdin_(BlackRock) [3] https://www.nytimes.com/2023/03/31/technology/sam-altman-open-ai-chatgpt.html https://www.nytimes.com/2023/03/31/technology/sam-altman-ope... [4] https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal) https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nomi... [5] "Recursive sequence generation in crows", https://www.science.org/doi/10.1126/sciadv.abq3356 https://www.science.org/doi/10.1126/sciadv.abq3356 [6] https://www.scientificamerican.com/article/crows-perform-yet-another-skill-once-thought-distinctively-human https://www.scientificamerican.com/article/crows-perform-yet...
rajman187 4y ago
Carmack is no doubt brilliant and it shows through Oculus as a product. But AGI is not about algorithm optimization the way 3D graphics were in the 90s. I am not sure the approach he takes (let’s find a way to write this in assembly) can apply here. He’s said before that AGI is going to be a few thousands of lines of code, ultimately [1], and it’s hard to believe that’s really the case. That said, I did get a chance to speak with him one on one last year and he really emphasized a few things; the need for being product oriented and giving customers what they want rather than chasing cool engineering (über)solutions; not being careless with resources just because we have more power with modern hardware (he poked fun at React where you spin up a new thread just for an interactive button); and being aware of the inefficiencies brought on by infinite resources (# of engineers and/or funding, which make you think less critically about timelines and delivering within bounded means) [1] https://youtu.be/I845O57ZSy4 https://youtu.be/I845O57ZSy4
- carabiner 4y ago
  The guy also tried to build a rocket to reach space via "first principles." It failed, utterly.
  - cma 4y ago
    They had a low budget relative to most of the others and reached break-even profitability but he moved on to other stuff. Where is the trying to reach space by "first principles" quote from?
  - barrysteve 4y ago
    It did the thrust vectoring thing. The rocket was ahead of the competition for a moment. He said games were one of the most complex things humans build and (with implied comparison) the mathematics and physics of rocketry hadn't changed much since the 1960s. Sounds true to me for the 2000s. Where was the utter failure and first principles?
- phkahler 4y ago
  >> But AGI is not about algorithm optimization the way 3D graphics were in the 90s Wow, you think his ability is straight performance optimisation? A big part of his early fame was from doing research to find good algorithms to achieve his goals. He also had that stint in real time control systems... flying hovering rockets before spaceX even existed. He's a problem solver with a strong ability to sift through possible solutions for what actually works, and quite capable of devising his own solutions when is research comes up empty. But yeah, he can write assembler too.
cpeterso 4y ago
Is there any information available about the non-LLM methods Carmack's AGI startup is researching?

breatheoften 4y ago

Who is commenting on this and what will they say??! 1-2 trillion tokens of training for gptx class models It would take 20 years for a human to read that many tokens at 8 hrs of reading per day (although i'm pretty unclear on whether that's unique token sequences or randomly selected token pairs -- feels like a big difference between the two!!) We are all here now -- who thinks we are anywhere of note together? Personally I think that models that do not experience incremental change remain an entirely separate class of intelligence from those which evolve continuously -- but I am open to being persuaded otherwise (certainly I never thought making gpt2 bigger would be as impressive as gpt4) ...

meindnoch 4y ago
>1-2 trillion tokens of training for gptx class models >It would take 20 years for a human to read that many tokens at 8 hrs of reading per day Normal reading is ~200 words per minute. That's 12,000 words per hour. That's 288,000 words per day (reading for 24 hours straight). That's 105,120,000 words per year. That's 2,102,400,000 words per 20 years. A trillion is 1,000,000,000,000.
- tonightstoast 4y ago
  Plus almost no one is reading academic papers at 200 words per minute. So for the level of material it could mean that number is halved per 20 years.
lostmsu 4y ago
Video from a pair of eyes is many terabytes per year though. If you compress it with best codecs. Raw video would be closer to 1PB per year. I think people overestimate the data efficiency of the biological neural networks w.r.t. transformer models.
- actionfromafar 4y ago
  The power effiency is pretty brutal though, in favour of bio-gelpacks.

intalentive 4y ago

Humans are not exposed to "billions of words". They are exposed to continuous signals, and somehow extract meaningful representations, like "words" and "objects", out of them. LLMs are exposed to human-curated data. Let's see an LLM curate its own data out of nothing but experience of raw continuous signals. Sure, GPT trained on the internet is a nice way to condense and retrieve human-curated data. But it's not going to give us a Lt. Data or C-3P0 that can learn and adapt in real time.

pbw 4y ago
> Humans are not exposed to "billions of words" ... continuous signals Yes humans need to learn to extract words from signals, but that does not mean it's not correct to count the number of words they've been exposed to in their lifetime. Your comment is like saying we can't count the number of hamburgers someone has eaten, because they actually eat myofibrillar proteins, not burgers.
- d0mine 4y ago
  Though millions seems more realistic (thousands of words per day)
andsoitis 4y ago
what do you think of midjourney's ability to describe a user-provided image in text? https://the-decoder.com/midjourney-new-image-tool-works-in-reverse/ https://the-decoder.com/midjourney-new-image-tool-works-in-r...
gunshai 4y ago
But humans are also trained on human-curated data so much so we put fairly hefty price tags on that curation. https://miro.medium.com/v2/resize:fit:1056/0*E1eNateTiDThGcYI.jpg https://miro.medium.com/v2/resize:fit:1056/0*E1eNateTiDThGcY...
echelon 4y ago
We've only just started. Now more capital and more minds will be pouring into solving this. Buckle up.

roenxi 4y ago

It seems likely that there is a big missing component of visual data. How many Tb of visual data (effectively video) do babies get exposed to? That is what sets up their mental model of the world, and language is fit over the top of that. It is hard to assess neural net efficiency with that difference in place.

rvnx 4y ago
It is going to amazing once LLMs get access to learn from real-life visual information (e.g. daily life videos without montage, or movies), and not just text.
cma 4y ago
> It seems likely that there is a big missing component of visual data. How many Tb of visual data (effectively video) do babies get exposed to? Vision doesn't seem relevant for what's missing in general intelligence: there are people blind from birth that nonetheless get very fluent in thought and language. It doesn't cause many significant developmental delays other than in some vision associated stuff like early motor skill acquisition, social stuff from not being able to see facial expressions, etc. The models may be missing data grounding in 3d space that people born blind do have, like proprioception, touch, and hearing. Deaf and blind from birth can cause more significant developmental delays, but becoming deaf later after some critical threshold doesn't seem to, so you are still looking at potentially only a short amount of years worth of data that seems to be needed (Hellen Keller went deaf and blind at 19 months).

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physPop 4y ago

Carmack is a smart guy but don't confuse his expertise in other areas with machine learning. Smells of hubris...

SquareWheel 4y ago
He's been heavily researching machine learning for the last few years, and formed an AGI startup last year. He's not the world's leading researcher or anything, but he's far from green in this space.
rvnx 4y ago
Perhaps he feels that the Metaverse thing he sold to Zuck isn't going anywhere, and now tries to ride a new wave.

cypress66 4y ago

GPT4 probably has a breadth of knowledge 1000x wider than a human, so the fact that it uses more tokens seems reasonable.

kklisura 4y ago

Q: If we need to feed additional data to the network, do we train it with existing data + new data or we can just train it on a new data? I'm asking because if we need to train it with both old and new data every time we have something new, that's not similar to human learning.

IanCal 4y ago
It's extremely similar to training new humans.

cma 4y ago

LeCun's reply: > more like a thousand times more. > Between 1 and 2 trillion tokens. > It would take a person 22,000 years to read through 1 trillion words at normal speed for 8 hours a day. It's kind of surprising to me that around 1000 humans could feasibly read the whole internet (as scraped for LLMs) between each other in only 22 years. The internet feels so much more massive than that. Though of course lots of the stuff on arxiv etc. is so dense there is no way you go through it at normal reading speed.

brokencode 4y ago

I wonder if it only seems like our brains need relatively little training because we have millions of years of training encoded in our DNA. No other animal can learn human language or logic to the extent that humans can, no matter how much you train them. But humans learn language easily in the first few years of life. It’s almost as if the human brain is preprogrammed with the general concepts of all languages, and it just needs to be fine tuned with specific vocabulary and grammar rules.

lairv 4y ago

LLMs weights are initialized from a normal distribution (or whatever distribution is now SOTA) while some animals can walk the second they are born

mannyv 4y ago

IMO a lot of the commenters here are missing the point, which is that LLMs are exposed to orders of magnitude more stuff than a human. Two obvious differences are that humans also learn from the context in which they live (social, environmental) and humans have to basically learn how their own wiring works. And their wiring is/can be highly variable.

dwallin 4y ago

I’m surprised to see someone of Carmack’s caliber making this mistake. If you’ve exposed a gorilla or chimp brain to the same amount of language content they still wouldn’t be able to converse like a human can. Meanwhile almost every human can utilize spoken language, despite huge variations in their training data. This implies that most of the language capabilities of the brain depend on structural elements that have been evolutionarily trained and not on the fine-tuning that a specific human achieves in their lifetime. Think of how many billions to trillions of attempts evolution had. And it was building off of models already trained (with many orders of magnitude more iterations) at the tasks of audio processing and sound recognition. Then add in the fact that we are only talking about language acquisition and all indications are that sufficiently large models (And the human brain is quite the large model) have significant benefits from cross training and it starts to become clear how false of a premise this is. 11 million bits of neural information are being pumped to our brains every second of our lives. That’s 80 gb of external data ingested during our waking hours in a single day plus who knows what sorts of internal synthetically generated data, all of this just for fine-tuning.