Yes, neural networks can be implemented with matrix operations. What does that have to do with proving or disproving the ability to reason? You didn't post a relevant or complete thought

Your comment is like saying an audio file isn't really music because it's just a series of numbers.

I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do. Even if we analysed the human brain it would look like wires connected to wires with different resistances all over the place with some other chemical influences.

I think everyone forgets that nural networks were used in AI to replicate how animal brains work, and clearly if it worked for us to get smart then it should work for something synthetic. Well we've certainly answered that now.

Everyone being like "oh it's just a predictive model and it's all math and math can't be intelligent" are questioning exactly how their own brains work. We are just prediction machines, the brain releases dopamine when it correctly predicts things, it self learns from correctly assuming how things work. We modelled AI off of ourselves. And if we don't understand how we work, of course we're not gonna understand how it works.

Improper comparison; an audio file isn’t the basic action on data, it is the data; the audio codec is the basic action on the data

“An LLM model isn’t really an LLM because it’s just a series of numbers”

But the action of turning the series of numbers into something of value (audio codec for an audio file, matrix math for an LLM) are actions that can be analyzed

And clearly matrix multiplication cannot reason any better than an audio codec algorithm. It’s matrix math, it’s cool we love matrix math. Really big matrix math is really cool and makes real sounding stuff. But it’s just matrix math, that’s how we know it can’t think

LLMs among other things lack the whole neurotransmitter "live" regulation aspect and plasticity of the brain.

We are nowhere near a close representation of actual brains. LLMs to brains are like a horse carriage compared to modern cars. Yes they have four wheels and they move, and cars also need four wheels and move, but that is far from being close to each other.

I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do. Even if we analysed the human brain it would look like wires connected to wires with different resistances all over the place with some other chemical influences.

I think everyone forgets that nural networks were used in AI to replicate how animal brains work, and clearly if it worked for us to get smart then it should work for something synthetic. Well we've certainly answered that now.

Everyone being like "oh it's just a predictive model and it's all math and math can't be intelligent" are questioning exactly how their own brains work. We are just prediction machines, the brain releases dopamine when it correctly predicts things, it self learns from correctly assuming how things work. We modelled AI off of ourselves. And if we don't understand how we work, of course we're not gonna understand how it works.

You can prove it’s not by doing some matrix multiplication and seeing its matrix multiplication. Much easier way to go about it

https://www.anthropic.com/research/tracing-thoughts-language-model its this one

I found the aeticle in a post on the fediverse, and I can't find it anymore.

The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

This showed 2 things:

• LLM don't "know" how they work

• the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

I think it was a very interesting an meaningful analysis

Can anyone help me find this?

EDIT: thanks to @theunknownmuncher
@lemmy.world
https://www.anthropic.com/research/tracing-thoughts-language-model its this one

EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

Improper comparison; an audio file isn’t the basic action on data, it is the data; the audio codec is the basic action on the data

“An LLM model isn’t really an LLM because it’s just a series of numbers”

But the action of turning the series of numbers into something of value (audio codec for an audio file, matrix math for an LLM) are actions that can be analyzed

And clearly matrix multiplication cannot reason any better than an audio codec algorithm. It’s matrix math, it’s cool we love matrix math. Really big matrix math is really cool and makes real sounding stuff. But it’s just matrix math, that’s how we know it can’t think

I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do. Even if we analysed the human brain it would look like wires connected to wires with different resistances all over the place with some other chemical influences.

I think everyone forgets that nural networks were used in AI to replicate how animal brains work, and clearly if it worked for us to get smart then it should work for something synthetic. Well we've certainly answered that now.

Everyone being like "oh it's just a predictive model and it's all math and math can't be intelligent" are questioning exactly how their own brains work. We are just prediction machines, the brain releases dopamine when it correctly predicts things, it self learns from correctly assuming how things work. We modelled AI off of ourselves. And if we don't understand how we work, of course we're not gonna understand how it works.

Even if LLM "neurons" and their interconnections are modeled to the biological ones, LLMs aren't modeled on human brain, where a lot is not understood.

The first thing is that how the neurons are organized is completely different. Think about the cortex and the transformer.

Second is the learning process. Nowhere close.

The fact explained in the article about how we do math, through logical steps while LLMs use resemblance is a small but meaningful example. And it also shows that you can see how LLMs work, it's just very difficult

Improper comparison; an audio file isn’t the basic action on data, it is the data; the audio codec is the basic action on the data

“An LLM model isn’t really an LLM because it’s just a series of numbers”

But the action of turning the series of numbers into something of value (audio codec for an audio file, matrix math for an LLM) are actions that can be analyzed

And clearly matrix multiplication cannot reason any better than an audio codec algorithm. It’s matrix math, it’s cool we love matrix math. Really big matrix math is really cool and makes real sounding stuff. But it’s just matrix math, that’s how we know it can’t think

More than enough people who claim to know how it works think it might be "evolving" into a sentient being inside it's little black box. Example from a conversation I gave up on...
https://sh.itjust.works/comment/18759960

Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

I found the aeticle in a post on the fediverse, and I can't find it anymore.

The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

This showed 2 things:

• LLM don't "know" how they work

• the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

I think it was a very interesting an meaningful analysis

Can anyone help me find this?

EDIT: thanks to @theunknownmuncher
@lemmy.world
https://www.anthropic.com/research/tracing-thoughts-language-model its this one

EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do.

I don't think this is a fair way of summarizing it. You're making it sound like we have AGI, which we do not have AGI and we may never have AGI.

They work the exact same way we do.

Two things being difficult to understand does not mean that they are the exact same.

Let's get something straight, no I'm not saying we have our modern definition of AGI but we've practically got the original definition coined before LLMs were a thing. Which was that the proposed AGI agent should maximise "the ability to satisfy goals in a wide range of environments". I personally think we've just moved the goal posts a bit.

Wether we'll ever have thinking, rationalised and possibly conscious AGI is beyond the question. But I do think current AI is similar to existing brains today.

Do you not agree that animal brains are just prediction machines?

That we have our own hallucinations all the time? Think visual tricks, lapses in memory, deja vu, or just the many mental disorders people can have.

Do you think our brain doesn't follow path of least resistance in processing? Or do you think our thoughts comes from elsewhere?

I seriously don't think animal brains or human to be specific are that special that nurural networks are beneath. Sure people didn't like being likened to animals but it was the truth, and I as do many AI researches, liken us to AI.

AI is primitive now, yet it can still pass the bar, doctors exams, compute complex physics problems and write a book (soulless as it may be like some authors) in less than a few seconds.

Whilst we may not have AGI the question was about math. The paper questioned how it did 36+59 and it did things in an interesting way where it half predicted what the tens column would be and 'knew' what the units column was, then put it together. Although thats not how I or even you may do it there are probably people who do it similar.

All I argue is that AI is closer to how our brains think, and with our brains being irrational quite often it shouldn't be surprising that AI nural networks are also irrational at times.

“the ability to satisfy goals in a wide range of environments”

That was not the definition of AGI even back before LLMs were a thing.

Wether we’ll ever have thinking, rationalised and possibly conscious AGI is beyond the question. But I do think current AI is similar to existing brains today.

That's doing a disservice to AGI.

Do you not agree that animal brains are just prediction machines?

That's doing a disservice to human brains. Humans are sentient, LLMs are not sentient.

I don't really agree with you.

LLMs are damn impressive, but they are very clearly not AGI, and I think that's always worth pointing out.

I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do. Even if we analysed the human brain it would look like wires connected to wires with different resistances all over the place with some other chemical influences.

I think everyone forgets that nural networks were used in AI to replicate how animal brains work, and clearly if it worked for us to get smart then it should work for something synthetic. Well we've certainly answered that now.

Everyone being like "oh it's just a predictive model and it's all math and math can't be intelligent" are questioning exactly how their own brains work. We are just prediction machines, the brain releases dopamine when it correctly predicts things, it self learns from correctly assuming how things work. We modelled AI off of ourselves. And if we don't understand how we work, of course we're not gonna understand how it works.