[ACCount37]

"Not understanding or reasoning" is anthropocentric cope. There is very little practical difference between "understanding" and "reasoning" implemented in human mind and that implemented in LLMs.

One notable difference, however, is that LLMs disproportionately suck at spatial reasoning. Which shouldn't be surprising, considering that their training datasets are almost entirely text. The ultimate wordcel makes for a poor shape rotator.

All ARC-AGI tasks are "spatial reasoning" tasks. They aren't in any way special. They just force LLMs to perform in an area they're spectacularly weak at. And LLMs aren't good enough yet to be able to brute force through this innate deficiency with raw intelligence.

[HighGoldstein]

> There is very little practical difference between "understanding" and "reasoning" implemented in human mind and that implemented in LLMs.

Source?

[ACCount37]

The primary source is: measured LLM performance on once-human-exclusive tasks - such as high end natural language processing or commonsense reasoning.

Those things were once thought to require a human mind - clearly, not anymore. Human commonsense knowledge can be both captured and applied by a learning algorithm trained on nothing but a boatload of text.

But another important source is: loads and loads of mech interpret research that tried to actually pry the black box open and see what happens on the inside.

This found some amusing artifacts - such as latent world models that can be extracted from the hidden state, or neural circuits corresponding to high level abstracts being chained together to obtain the final outputs. Very similar to human "abstract thinking" in function - despite being implemented on a substrate of floating point math and not wet meat.

[freejazz]

I haven't seen LLMs perform common sense reasoning. Feel free to share some links. Your post reads like anthropomorphized nonsense.

[keeda]

One of the most astonishing things about LLMs is that they actually seem to have achieved general common-sense reasoning to a signficant extent. Example from the thread about somebody ordering 18000 waters at a drive-through: https://news.ycombinator.com/item?id=45067653

TL;DR: Even without being explicitly prompted to, a pretty weak LLM "realized" that a thousand glasses of water was an unreasonable order. I'd say that's good enough to call "common sense".

You can try it out yourself! Just pick any AI chatbot, make up situations with varying levels of absurdity, maybe in a roleplay setting (e.g. "You are a fast food restaurant cashier. I am a customer. My order is..."), and test how it responds.

[ACCount37]

What? Do you even know what "commonsense reasoning" means?

[freejazz]

Do you?

[ACCount37]

https://en.wikipedia.org/wiki/Commonsense_reasoning

[Workaccount2]

There is no source and arguing this is dumb because no one knows what reasoning or understanding is. No one.

So all we have is "Does it swim like a duck, look like a duck, quack like a duck?"

[adastra22]

I’m sympathetic to your point, but this isn’t quite fair. The field of psychology does exist.

[Workaccount2]

Neuroscience is the field that would be closest to this. But even they are empty handed with evidence and heavy with hypotheses.

[adastra22]

No, psychology is right. Psychology studies what the properties of thought are. Neuroscience studies the specific biochemical mechanisms of the brain. Psychology is the study of what mental reasoning IS, while neuroscience is the study of HOW neurons in our brain implement it.

If you are asking “ok, but what is reasoning, really? What definition of reasoning would enable us to recognize whether it is going on in this AI or not?” it is a question of psychology. Unless we are restricting ourselves to whole brain emulation only.

[salutis]

Psychology is stuck in pre-Galilean era. Even if it studies "properties of thought", as you put it, it does so without formal basis, let alone understanding from first principles. As Chomsky said, about psychology and the like, "You want to move from behavioral science to authentic science." [1]

[1] Chomsky & Krauss (2015) An Origins Project Dialogue at https://youtu.be/Ml1G919Bts0

[NooneAtAll3]

...literally benchmarks the post is all about?

practical difference is about results - and results are here

[dwallin]

Very much agree with this. Looking at the dimensionality of a given problem space is a very helpful heuristic when analyzing how likely an llm is going to be suitable/reliable for that task. Consider how important positional encodings are LLM performance. You also then have an attention model that operates in that 1-dimensional space. With multidimensional data significant transformations to encode into a higher dimensional abstraction needs to happen within the model itself, before the model can even attempt to intelligently manipulate it.

[fumeux_fume]

For many people, the difference between how a language model solves a problem and how a human solves a problem is actually very important.