81–90 of 241 posts
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Ask it to “reason through” a problem and then ask it to give you an answer. How’s that different from thinking?
It's just wrong. That's how you can tell. Actual reasoning leads to sensible conclusions.
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>“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. The claim that humans are word predictors is not something I would want to dispute. The claim that humans are nothing more than word predictors is obviously wrong though. When I…
Then this implies that you’d maybe think differently if LLMs could have different inputs, correct? Which they are currently doing. GPT-4 can take visual input. I totally agree that humans are far more complex than that, but just extend your timeline further and you’ll start to see how the gap in complexity / input variety will narrow.
Yes, ultimately it does imply that. Probably not the current iteration of the technology, but I believe that there will one day be AIs that will close the loop so to speak.
It will require interacting with the world not just because someone gave them a command and a limited set of inputs, but because they decide to take action based on their own experience and goals.
This articles describes much of what many youtubers explained in their videos in the recent few weeks. While I understand the core concept of 'just' picking the next word based on statistics, it doesn't really explain how chatGPT can pull off the stuff it does. E.g. when one asks it to return a poem where each word starts with one letter/next alphabet letter/the ending of the last word, it obviously doesn't 'just' pi…
I doubt this precise numbers are in the dataset of chatGPT and yet it can find the answer.
According to this paper it seems to have gain the ability as the size of the model increased (page 21): https://arxiv.org/pdf/2005.14165.pdf
" small models do poorly on all of these tasks – even the 13 billion parameter model (the second largest after the 175 billion full GPT-3) can solve 2 digit addition and subtraction only half the time, and all other operations less than 10% of the time."
That's crazy.
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>“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. The claim that humans are word predictors is not something I would want to dispute. The claim that humans are nothing more than word predictors is obviously wrong though. When I…
Then this implies that you’d maybe think differently if LLMs could have different inputs, correct? Which they are currently doing. GPT-4 can take visual input. I totally agree that humans are far more complex than that, but just extend your timeline further and you’ll start to see how the gap in complexity / input variety will narrow.
They will not be LLMs then, though. But some other iteration of AI. Interfacing current LLMs with APIs does not solve the fundamental issue, as it is still just language they are based on and use.
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a quick google reveals that all of the words in the "new" title already exist from human producers, and this is mix and matched together.
A quick google reveals the same of your comment. Are you an AI?
So the trend continues. To those deeply steeped in using computers to shift about data of average value, it heralds loss of wealth and status.
Society will adapt. People will be forced to adapt. Some will be ruined, some will climb to new heights.
Good luck all.
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>“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. The claim that humans are word predictors is not something I would want to dispute. The claim that humans are nothing more than word predictors is obviously wrong though. When I…
Then this implies that you’d maybe think differently if LLMs could have different inputs, correct? Which they are currently doing. GPT-4 can take visual input. I totally agree that humans are far more complex than that, but just extend your timeline further and you’ll start to see how the gap in complexity / input variety will narrow.
“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…
> “Humans are actually reasoning. LLMs are not.” Again, how would you measure such a thing? I would posit that reasoning is the ability to construct new, previously-unexpressed information from prior information. If ChatGPT existed 110 years ago and fed all the then-known relevant experimental data regarding subatomic particles, it would not have been able to arrive at the new notion of quantum mechanics. If it exist…
We should test it on a small scale, with synthetic examples. Not "invent Quantum Mechanics please".
And yes, people already tested it on reasonable-sized examples, and it does work, indeed. E.g. ability to do programming indicates that. Unless you believe that all programming is just rehash of what was before, it is sufficient. Examples in the "Sparks of AGI" paper demonstrate ability to construct new, previously-unexpressed information from prior information.
"It's not intelligent unless it is as smart as our top minds" is not useful. When it reaches that level you with your questions will be completely irrelevant. So you gotta come up with "as intelligent as a typical human", not "as intelligent as Einstein" criterion.
“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…
>“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. The claim that humans are word predictors is not something I would want to dispute. The claim that humans are nothing more than word predictors is obviously wrong though. When I…
I share ability to move around and feel pain with apes and cats.
What I'm interested about is ability "reason" - analyze, synthesize knowledge, formulate plans, etc.
And LLMs demonstrated those abilities.
As for movement and so on, please check PaLM-E and Gato. It's already done, it's boring.
> it's not because I'm predicting the words "I'm hungry". It's because I'm predicting that I'll be hungry.
The way LLM-based AI is implemented gives us an ability to separate the feeling part from the reasoning part. It's possible to integrate them into one acting entity, as was demonstrated in SayCan and PaLM-E. Does your understanding of the constituent parts make it inferior?
E.g. ancient people thought that emotions were processed in heart or stomach. Now that we know that emotions are processed mostly in the brain, are we less human?