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Understanding ChatGPT

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Re: Understanding ChatGPT

#131

Post by m3kw9 »

maxdoop wrote: post #67
fauigerzigerk wrote: post #63

Earlier quoted context omitted.

>“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 get scary when AI is so advanced that it can keep getting continuous input and output thru visual, audio and even feeling like pressure and temperature in a 3d setting.

Re: Understanding ChatGPT

#132

Post by m3kw9 »

calf wrote: post #13

ChatGPT is a glorified word predictor. It isn’t sentient. It doesn’t know what it’s saying, and yes, you can coax it into admitting that it wants to take over the world or saying hurtful things (although it was specially conditioned during training to try to suppress such output). It’s simply stringing words together using an expansive statistical model built from billions of sentences. Is this true though? The publi…

It isn’t sentient because it stops when there is no input. If OpenAI were implementing a continuous feedback loop of not just textual info, but one that can sense the world like we do(visual, spatial, audio, tactile) and it keeps improves as it interacts with other people and world. It gets closer.

Re: Understanding ChatGPT

#133

Post by m3kw9 »

m3kw9 wrote: post #132
calf wrote: post #13

ChatGPT is a glorified word predictor. It isn’t sentient. It doesn’t know what it’s saying, and yes, you can coax it into admitting that it wants to take over the world or saying hurtful things (although it was specially conditioned during training to try to suppress such output). It’s simply stringing words together using an expansive statistical model built from billions of sentences. Is this true though? The publi…

It isn’t sentient because it stops when there is no input. If OpenAI were implementing a continuous feedback loop of not just textual info, but one that can sense the world like we do(visual, spatial, audio, tactile) and it keeps improves as it interacts with other people and world. It gets closer.

Humans also has continuous input from our body itself all the organs, cellular etc all giving the brain continuous input

Re: Understanding ChatGPT

#134

Post by skybrian »

Bishonen88 wrote: post #2

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…

The architecture is understood, but the specifics about how they calculate which words are high-probability are mostly a mystery. Here’s a good blog post though:

We Found An Neuron in GPT-2 https://clementneo.com/posts/2023/02/11/we-found-an-neuron

If anyone knows of any other research like this, I’d love to read it.

Re: Understanding ChatGPT

#135

Post by usaar333 »

swid wrote: post #98
maxdoop wrote: post #28

“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…

One reason I hate the “glorified word predictor” phrase, is that predicting the next word involves considering what will come well after that. I saw a research paper where they tested a LLM to predict the word “a” vs “an”. In order to do that, it seems like you need to consider at least 1 word past the next token. The best test for this was: I climbed the pear tree and picked a pear. I climbed the apple tree and pick…

> In order to do that, it seems like you need to consider at least 1 word past the next token.

Why? Any large probabilistic model in your example would also predict "an" due to the high attention on the preceding "apple". (In case you are wondering, for the OpenAI GPT3 models, this is consistently handled at the scale of Babbage, which is around 3 billion params).

> One word must come next, but to do a good job modeling what that word will be, wouldn’t you need to consider further ahead than that?

Well, yes, but GPT isn't a human. That's why it needs so much more data than a human to talk so fluently or "reason".

Re: Understanding ChatGPT

#136

Post by cscurmudgeon »

maxdoop wrote: post #28

“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?

Wow. Leave it to HN commenters to arrogantly ignore research by those in the field.

1. LLMs can't reason or calculate. This is why we have ToolFormer or Plugins in the first place. Even GTP-4 is bad at reasoning. Maybe GPT-infinity will be good? Who knows.

2. They call out to tools that can calculate or reason (Humans built these tools not aliens)

3. How can humans do 2 if they can't reason?

https://arxiv.org/abs/2205.11502

More informal presentation here: https://bdtechtalks.com/2022/06/27/large-language-models-log...

Re: Understanding ChatGPT

#137

Post by usaar333 »

sebzim4500 wrote: post #38
maxdoop wrote: post #28

“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…

I'm not surprised to see your comment be downvoted, but I have yet to see a single coherent answer to this. I wish people would be more clear on what exactly they believe the difference is between LLMs are actual intelligence. Substrate? Number of neurons? Number of connections? Spiking neurons vs. simpler artifial neurons? Constant amount of computation per token vs variable? Or is it "I know it when I see it"? In w…

Meta-awareness and meta-reasoning are big ones. Such inabilities to self-validate its own answers largely preclude human level "reasoning".

It ends up being one of the best pattern matchers and translators ever created, but solves truly novel problems worse than a child.

As far as architectural details, it's a purely feed forward network where the only input is previous tokens generated. Brains have a lot more going on.

Re: Understanding ChatGPT

#138

Post by ak_111 »

maxdoop wrote: post #28

“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…

The strongest answer to almost all of your questions is "Poverty of the stimulus" (wikipedia). 4 year olds are exposed to an almost microscopically tiny amount of words relative to chatgpt (you can probably contain it in a csv file that you can open in excel), and yet can reason, even develop multilingual skills and a huge amount of emotional intelligence from the very little word tokens they are exposed to.

So whatever is driving reasoning and intelligence in humans is clearly very different to what is driving reasoning in chatgpt.

People will probably respond by saying but babies are exposed to much more data than just words, this is true, but chatgpt is learning only from words and no one has shown how you can get chatgpt to sufficiently learn what a baby learns by other kind of data. Also note that even blind babies learn language pretty quickly so this also excludes the huge amount of data you obtain from vision as putting babies at an advantage, and it is very difficult to show how sensory touch data for example contribute to babies learning to manipulate language efficiently.

Re: Understanding ChatGPT

#140

Post by web3-is-a-scam »

notahacker wrote: post #45
aqme28 wrote: post #17

> ChatGPT is a glorified word predictor. It isn’t sentient. It doesn’t know what it’s saying, and yes, you can coax it into admitting that it wants to take over the world or saying hurtful things (although it was specially conditioned during training to try to suppress such output). It’s simply stringing words together using an expansive statistical model built from billions of sentences. How do you differentiate it…

Well for a start the human mind involves a series of chemical reactions optimised by evolutionary wiring and physical world interaction towards self replication, so when a human says "I feel horny" there's a whole bunch of stuff going on in there that there's no reason to suspect is replicated in a neural network optimised for text transformation. When a silicon based hardware computes that as a response, it isn't be…

This kind of thought experiment always reminds me of Measure of a Man from Star Trek TNG.
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