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>How do you think a human with below average intelligence (or even with average intelligence) would fare? I don't understand what rote memorization to pass a t

>How do you think a human with below average intelligence (or even with average intelligence) would fare? I don't understand what rote memorization to pass a test has to do with intelligence. For the record Kim Kardashian passed the bar; I imagine anyone given the proper motivation and time to study could do it, it's not a hard test. If AGI is a computer passing a test, then AGI was achieved a long time ago. I don't think a human with below average intelligence could multiple 2 very large primes but I don't mistake my calculator for intelligence. A below average intelligence human can drive a car with a couple hours of training, an LLM can't do that (with a far lower power budget as well). I'm not saying AGI is impossible, but it's clear LLMs are not AGIs; it's not a matter of having a 100x more powerful LLM, just like making an Ape better at sign language won't make them better at abstract reasoning. An Ape's brain fundamentally lacks the mental machinery for higher level things that humans do. It's not a question of not being smart enough. LLMs are simply one component of the human mind.

p-e-w 3y ago
> If AGI is a computer passing a test, then AGI was achieved a long time ago. AIs couldn't even pass a third-grade reading comprehension exam until about 5 years ago. Computers being able to pass tests designed for humans is a very new thing. > it's clear LLMs are not AGIs And the main argument for that is that "it's clear". They're beating lawyers, doctors, and software engineers, but obviously, that's not real intelligence...
- qup 3y ago
  > Computers being able to pass tests designed for humans is a very new thing. Yes! Captchas so effective!
  - comex 3y ago
    Yes, it would be more accurate to say “computers being able to pass tests designed for humans by humans”, as opposed to CAPTCHAs which are automatically generated tests.
- shreyshnaccount 3y ago
  You're assuming that everything needed to write code or make arguments is purely intelligence based and has nothing to do with patterns, structural repetition and things glorified autocomplete could do, and that's not true.
  - flangola7 3y ago
    Who said anything about everything? AI could not write code AT ALL until very recently. We could invent a drug that kills 99% of cancers and the next day there would be people bemoaning that it isn't a "true" cure.
    - shreyshnaccount 3y ago
      Hey you're the one claiming intelligence and all, burden of proving it in squarely on you
      - dr_dshiv 3y ago
        Yes. Let’s define AGI as ability for a single model to pass most human professional tests (no cheating) and to provide genuine human-level flexible cognitive benefit to specialized professionals in diverse fields. Reasonable?
        shreyshnaccount 3y ago
        No, because most tests designed for humans test memory and pattern recognition, which computers already can do better than humans so it's not a useful comparison. I'd rather define it to be superhuman AGI when it not only performs better on tests with humans who can use computers during the test but also can perform everyday tasks which are not 'hard' for us humans. That is because we have the hardware in our brains to do these things, doesn't mean that it's easy in the least for a computer.
        dr_dshiv 3y ago
- WoodenChair 3y ago
  >> If AGI is a computer passing a test, then AGI was achieved a long time ago. > AIs couldn't even pass a third-grade reading comprehension exam until about 5 years ago. Computers being able to pass tests designed for humans is a very new thing. You're narrowly defining what a "test" is. And therefore the "until 5 years ago" doesn't make any sense. A test is not just written exams like a "third-grade reading comprehension exam." Is playing chess against the world champion not a test? A trad AI computer program beat him 25 years ago, not 5 years ago. Is diagnosing patients with a particular disease better than the average untrained human not a test? An expert system did that 50 years ago. Your point is not a refutation of the parent post.
ithkuil 3y ago
> it's not a matter of having a 100x more powerful LLM, I think we all can agree that even the best LLM currently is not AGI. That's not what being disputed here I think. However a 100x more powerful LLM is not just 100x better at recall. A 100x more powerful LLM is not just 100x better at being stupid hallucinatory parrot. A model that is just 100x bigger is not necessarily 100x more powerful if you define power is the ability to achieve goals. However pure language models will always lack something else: the ability to ground things in reality. I recently had a dream where I solved some problems and when I woke up I realized that those solutions were bullshit, but I also realized the whole approach of my dreaming self was very similar to what a LLM would have done.
- flangola7 3y ago
  Define grounding things in reality. We only have our 5 senses to go off of. Meta has already put out one multimodal model incorporating multiple data types, openai is undoubtedly working on it too.
  - ithkuil 3y ago
    Grounding in reality can be something as simple as what openai is experimenting with plugins or something much more integrated. It's not a matter of which senses you have, but about being able to "continuously" use them. The current LLMs are basically unfiltered raw thoughts that must be continuously refined. A similar thing happens in our brains and only a little bit of that is accessible to our consciousness
    - TeMPOraL 3y ago
      > The current LLMs are basically unfiltered raw thoughts that must be continuously refined. A similar thing happens in our brains and only a little bit of that is accessible to our consciousness Exactly. But, AFAIK, it's also the part that does the bulk of actual thinking and decision-making for us. In that sense, LLMs may be closer to AGI than people expect, because they seem to be capturing the actual core of intelligence and reasoning - and the missing bits (like long-term memory and higher-level thought stream filter/censor) may be much easier to bolt on to them.
hawk_ 3y ago
> For the record Kim Kardashian passed the bar; While I know the wider point you're trying to make, Kim Kardashian is a very smart business person. She may just not fit your narrow definition of "intelligent".
Kiro 3y ago
Are you saying that Kim Kardashian is stupid?
TeMPOraL 3y ago
> it's not a matter of having a 100x more powerful LLM, just like making an Ape better at sign language won't make them better at abstract reasoning. An Ape's brain fundamentally lacks the mental machinery for higher level things that humans do. It's not a question of not being smart enough. LLMs are simply one component of the human mind. I believe you do have a good point overall, but this here is, IMHO, a rather bad example, because I'd argue GPT-4 is already capable of abstract reasoning, and this seems to be exactly the skill that improves with increasing dimensionality of the latent space. I agree that LLMs are equivalent of a single component of a human mind. Specifically, I think they're closest equivalent to our inner voice / inner thoughts. But this part is arguably exactly the one that does most of the abstract reasoning (and most reasoning in general), so I think in fact LLMs do have the "hardware" for that specific aspect. What's lacking right now is the equivalent of the higher, "conscious" layer, that guides, filters and censors the stream of thought. That, and long-term recall. Short-term memory might atually correspond to what the context window is in LLMs. That, and fusing in all the other senses (sight, sound, smell, taste, touch, time, etc.).
chaxor 3y ago
You definitely should not make claims like 'LLMs can't drive cars'. LLMs have already been shown several times to be able to navigate as agents in different world environments. Obviously, I'm not advocating this is a good idea, and certainly light years away from safe - but as an experiment in silico, I imagine it can be done quite easily (and probably already has). The reason scientific researchers in this area are using the term 'AGI' so much is that it does fit the definition of AGI ... *For some definition of AGI*. And there lies the problem - no one can really come to a good consensus on a good definition of AGI. This is why many scientists in this area are avoiding the question altogether - the question is loaded, and is misinterpreted by the public if statements are made. So, for example, if I make the statement here that e.g. GPT-4 has intelligence which is general (AGI), it will likely be met with a rabid response from HN. However, the claim may be more dull than you're expecting. People often conflate AGI with things that are not required, such as agency, etc. This definition from [journal Intelligence Vol 24, No1, 1997] can be that "some definition" of AGI: must be able to 1) think abstractly, 2) comprehend complex ideas, 3) reason, 4) plan, 5) solve problems, 6) learn quickly from experience. Many of these GPT-4 can do, if some modifiers are allowed - for example, GPT-4 can learn quickly from experience so long as you aren't starting a 'new' GPT-4 system from scratch every time you want to interact with it. This is probably preferable, since much of the experiences it will have are personal to the individual working with it, and it would be highly undesirable to do the opposite here. Planning was difficult for the system early on, but it has appeared to learn that it is helpful to lay out a plan early on in a large task, so that appears to be a capability as well, at least on a basic level qualitatively. There is some good literature on ability to reason (and solve problems in abstract and complex ideas) from Microsoft's group on causal reasoning. In pretty much all tasks of causal reasoning the system can achieve near human performance, and LLMs as a category outperform previous SoTA from more targeted or specific systems made for causal reasoning. Anyway, I would suggest to anyone that has strong reactions to claims about AGI to realize that they are likely building up the statement to be more than it is. Perhaps similar to 'machine learning' may have been misinterpreted years ago ("A machine can learn?! There is no tomorrow!!"), what is being stated here is often more narrow than you may believe.

p-e-w 3y ago

> If AGI is a computer passing a test, then AGI was achieved a long time ago. AIs couldn't even pass a third-grade reading comprehension exam until about 5 years ago. Computers being able to pass tests designed for humans is a very new thing. > it's clear LLMs are not AGIs And the main argument for that is that "it's clear". They're beating lawyers, doctors, and software engineers, but obviously, that's not real intelligence...

qup 3y ago
> Computers being able to pass tests designed for humans is a very new thing. Yes! Captchas so effective!
- comex 3y ago
  Yes, it would be more accurate to say “computers being able to pass tests designed for humans by humans”, as opposed to CAPTCHAs which are automatically generated tests.
shreyshnaccount 3y ago
You're assuming that everything needed to write code or make arguments is purely intelligence based and has nothing to do with patterns, structural repetition and things glorified autocomplete could do, and that's not true.
- flangola7 3y ago
  Who said anything about everything? AI could not write code AT ALL until very recently. We could invent a drug that kills 99% of cancers and the next day there would be people bemoaning that it isn't a "true" cure.
  - shreyshnaccount 3y ago
    Hey you're the one claiming intelligence and all, burden of proving it in squarely on you
    - dr_dshiv 3y ago
      Yes. Let’s define AGI as ability for a single model to pass most human professional tests (no cheating) and to provide genuine human-level flexible cognitive benefit to specialized professionals in diverse fields. Reasonable?
      - shreyshnaccount 3y ago
        No, because most tests designed for humans test memory and pattern recognition, which computers already can do better than humans so it's not a useful comparison. I'd rather define it to be superhuman AGI when it not only performs better on tests with humans who can use computers during the test but also can perform everyday tasks which are not 'hard' for us humans. That is because we have the hardware in our brains to do these things, doesn't mean that it's easy in the least for a computer.
        dr_dshiv 3y ago
WoodenChair 3y ago
>> If AGI is a computer passing a test, then AGI was achieved a long time ago. > AIs couldn't even pass a third-grade reading comprehension exam until about 5 years ago. Computers being able to pass tests designed for humans is a very new thing. You're narrowly defining what a "test" is. And therefore the "until 5 years ago" doesn't make any sense. A test is not just written exams like a "third-grade reading comprehension exam." Is playing chess against the world champion not a test? A trad AI computer program beat him 25 years ago, not 5 years ago. Is diagnosing patients with a particular disease better than the average untrained human not a test? An expert system did that 50 years ago. Your point is not a refutation of the parent post.

ithkuil 3y ago

> it's not a matter of having a 100x more powerful LLM, I think we all can agree that even the best LLM currently is not AGI. That's not what being disputed here I think. However a 100x more powerful LLM is not just 100x better at recall. A 100x more powerful LLM is not just 100x better at being stupid hallucinatory parrot. A model that is just 100x bigger is not necessarily 100x more powerful if you define power is the ability to achieve goals. However pure language models will always lack something else: the ability to ground things in reality. I recently had a dream where I solved some problems and when I woke up I realized that those solutions were bullshit, but I also realized the whole approach of my dreaming self was very similar to what a LLM would have done.

flangola7 3y ago
Define grounding things in reality. We only have our 5 senses to go off of. Meta has already put out one multimodal model incorporating multiple data types, openai is undoubtedly working on it too.
- ithkuil 3y ago
  Grounding in reality can be something as simple as what openai is experimenting with plugins or something much more integrated. It's not a matter of which senses you have, but about being able to "continuously" use them. The current LLMs are basically unfiltered raw thoughts that must be continuously refined. A similar thing happens in our brains and only a little bit of that is accessible to our consciousness
  - TeMPOraL 3y ago
    > The current LLMs are basically unfiltered raw thoughts that must be continuously refined. A similar thing happens in our brains and only a little bit of that is accessible to our consciousness Exactly. But, AFAIK, it's also the part that does the bulk of actual thinking and decision-making for us. In that sense, LLMs may be closer to AGI than people expect, because they seem to be capturing the actual core of intelligence and reasoning - and the missing bits (like long-term memory and higher-level thought stream filter/censor) may be much easier to bolt on to them.

hawk_ 3y ago

> For the record Kim Kardashian passed the bar; While I know the wider point you're trying to make, Kim Kardashian is a very smart business person. She may just not fit your narrow definition of "intelligent".

Kiro 3y ago

Are you saying that Kim Kardashian is stupid?

TeMPOraL 3y ago

> it's not a matter of having a 100x more powerful LLM, just like making an Ape better at sign language won't make them better at abstract reasoning. An Ape's brain fundamentally lacks the mental machinery for higher level things that humans do. It's not a question of not being smart enough. LLMs are simply one component of the human mind. I believe you do have a good point overall, but this here is, IMHO, a rather bad example, because I'd argue GPT-4 is already capable of abstract reasoning, and this seems to be exactly the skill that improves with increasing dimensionality of the latent space. I agree that LLMs are equivalent of a single component of a human mind. Specifically, I think they're closest equivalent to our inner voice / inner thoughts. But this part is arguably exactly the one that does most of the abstract reasoning (and most reasoning in general), so I think in fact LLMs do have the "hardware" for that specific aspect. What's lacking right now is the equivalent of the higher, "conscious" layer, that guides, filters and censors the stream of thought. That, and long-term recall. Short-term memory might atually correspond to what the context window is in LLMs. That, and fusing in all the other senses (sight, sound, smell, taste, touch, time, etc.).

chaxor 3y ago

You definitely should not make claims like 'LLMs can't drive cars'. LLMs have already been shown several times to be able to navigate as agents in different world environments. Obviously, I'm not advocating this is a good idea, and certainly light years away from safe - but as an experiment in silico, I imagine it can be done quite easily (and probably already has). The reason scientific researchers in this area are using the term 'AGI' so much is that it does fit the definition of AGI ... *For some definition of AGI*. And there lies the problem - no one can really come to a good consensus on a good definition of AGI. This is why many scientists in this area are avoiding the question altogether - the question is loaded, and is misinterpreted by the public if statements are made. So, for example, if I make the statement here that e.g. GPT-4 has intelligence which is general (AGI), it will likely be met with a rabid response from HN. However, the claim may be more dull than you're expecting. People often conflate AGI with things that are not required, such as agency, etc. This definition from [journal Intelligence Vol 24, No1, 1997] can be that "some definition" of AGI: must be able to 1) think abstractly, 2) comprehend complex ideas, 3) reason, 4) plan, 5) solve problems, 6) learn quickly from experience. Many of these GPT-4 can do, if some modifiers are allowed - for example, GPT-4 can learn quickly from experience so long as you aren't starting a 'new' GPT-4 system from scratch every time you want to interact with it. This is probably preferable, since much of the experiences it will have are personal to the individual working with it, and it would be highly undesirable to do the opposite here. Planning was difficult for the system early on, but it has appeared to learn that it is helpful to lay out a plan early on in a large task, so that appears to be a capability as well, at least on a basic level qualitatively. There is some good literature on ability to reason (and solve problems in abstract and complex ideas) from Microsoft's group on causal reasoning. In pretty much all tasks of causal reasoning the system can achieve near human performance, and LLMs as a category outperform previous SoTA from more targeted or specific systems made for causal reasoning. Anyway, I would suggest to anyone that has strong reactions to claims about AGI to realize that they are likely building up the statement to be more than it is. Perhaps similar to 'machine learning' may have been misinterpreted years ago ("A machine can learn?! There is no tomorrow!!"), what is being stated here is often more narrow than you may believe.