Apple study exposes deep cracks in LLMs’ “reasoning” capabilities

One time I exposed deep cracks in my calculator's ability to write words with upside down numbers. I only ever managed to write BOOBS and hELLhOLE.

LLMs aren't reasoning. They can do some stuff okay, but they aren't thinking. Maybe if you had hundreds of them with unique training data all voting on proposals you could get something along the lines of a kind of recognition, but at that point you might as well just simulate cortical columns and try to do Jeff Hawkins' idea.

Did anyone believe they had the ability to reason?

The tested LLMs fared much worse, though, when the Apple researchers modified the GSM-Symbolic benchmark by adding "seemingly relevant but ultimately inconsequential statements" to the questions

Good thing they're being trained on random posts and comments on the internet, which are known for being succinct and accurate.

So do I every time I ask it a slightly complicated programming question

statistical engine suggesting words that sound like they'd probably be correct is bad at reasoning

How can this be??

They are large LANGUAGE models. It's no surprise that they can't solve those mathematical problems in the study. They are trained for text production. We already knew that they were no good in counting things.

Are you telling me Apple hasn't seen through the grift and is approaching this with an open mind just to learn how full off bullshit most of the claims from the likes of Altman are? And now they're sharing their gruesome discoveries with everyone while they're unveiling them?

The fun part isn't even what Apple said - that the emperor is naked - but why it's doing it. It's nice bullet against all four of its GAFAM competitors.

cracks? it doesn't even exist. we figured this out a long time ago.

I feel like a draft landed on Tim's desk a few weeks ago, explains why they suddenly pulled back on OpenAI funding.

People on the removed superfund birdsite are already saying Apple is missing out on the next revolution.

I hope this gets circulated enough to reduce the ridiculous amount of investment and energy waste that the ramping-up of "AI" services has brought. All the companies have just gone way too far off the deep end with this shit that most people don't even want.

Here's the cycle we've gone through multiple times and are currently in:

AI winter (low research funding) -> incremental scientific advancement -> breakthrough for new capabilities from multiple incremental advancements to the scientific models over time building on each other (expert systems, LLMs, neutral networks, etc) -> engineering creates new tech products/frameworks/services based on new science -> hype for new tech creates sales and economic activity, research funding, subsidies etc -> (for LLMs we're here) people become familiar with new tech capabilities and limitations through use -> hype spending bubble bursts when overspend doesn't keep up with infinite money line goes up or new research breakthroughs -> AI winter -> etc...

They predict, not reason....

Someone needs to pull the plug on all of that stuff.

The part of the study where they talk about how they determined the flawed mathematical formula it used to calculate the glue-on-pizza response was mindblowing.

^{(I did not read the study.)}

Are we not flawed too? Does that not makes AI...human?

Real headline: Apple research presents possible improvements in benchmarking LLMs.

Are the uncensored models more capable tho?