The ability to ‘override automatic responses and maintain complex goals’ is why we get up at six in the morning to go to a meeting we already know the outcome of and frankly I am not sure its something that is working for us.
Demand work from home, if you don’t get it, keep looking until you do… Favorite part of my work from home day is getting in the shower and having breakfast with my wife after the morning BS meeting.
The shower seems an odd place for having breakfast, but I guess if your wife is ok with it…
Maybe in a fjord instead?
It’s a real sign of our times that so many can not differentiate between a plagiarism fueled talking machine and a thinking machine.
Well, in fairness, if you ask Chatgpt a question it says “…thinking…”
You can see how confusion might occur.
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sustained focus and conflict resolution seen in human attention
What humans are these they are comparing with? Any humans born post 1995 have had constant companionship from network connected screens, they have the attention spans of unladen African swallows…
Birds that can migrate thousands of kilometers without so much as a Netflix break or a quick scroll through a memes community presumably have a good attention span. Better than mine, anyways
That is one positive aspect of a road trip, particularly a solo road trip - long periods of dull required attention…
Worked in tech for 15 years (been mostly out for 7 now) and most of the higher ups were just plagiarism word salad morons. That’s why the same management thinks these harbors are so smart.
I’m happier being poor than dealing with those fucking morons.
Might be because AI isn’t cognitive or actually intelligent. I imagine a washing machine wouldn’t do well either.
So true, and the things that LLM agents are good at, humans test very poorly by comparison, particularly on speed.
To be fair, run an LLM on a machine with an equivelent power requirement to the human brain and we might se some different results on that one.
While it’s true that a human brain only uses ~20W of power, it’s a really specific kind of organically delivered power with all sorts of environmental requirements that we, being humans, take for granted, but in the bigger picture it’s really a rare location in this universe that doesn’t kill us nearly instantly - much less provide that 20W of power in a form a brain can use.
@CheeseNoodle @MangoCats so like 20 watts of power? Yes that seems fair
So my GPU is about 300 watts and a still blatantly stupid LLM can write a little faster than me. Take off 100w to bring that down to my own writing speed then make it 10x slower to turn that 200 watts into 20 watts. Even with that heavy bias in the LLMs favour (forgiving it the entire power cost of my PCs other components that it partially utilizes) what we get is something slow, dumb, and incapable of learning because any local model is statically weighted.
That’s one way to compare it.
Now, take your privileged writer status human brain and factor in all the other power required to keep it comfy in an air conditioned room, the labor required to put a roof over your head, keep your home plumbing working, make your food, deliver you pen and paper to write with - or are you using an electrically powered appliance to record and later communicate your thoughts? Oh, did you need to go to sleep for a while?
Do I get to include the gargantuan cooling system for the data centre and all the infastructure required to keep that going?
Total impact is the only fair comparison. Pollution from the power plants included, sewage treatment from the houses too.
One positive of AI is that the ownership class is getting a lesson in just how complex, flexible, reliable, and capable “unskilled” workers are. You can watch them realize in real time that a model capable of running a dinner-rush drive-thru would be a trillion dollar quantum leap.
“unskilled” workers
They quit calling them that years ago, now they are “lower value human capital.” https://fortune.com/2026/05/26/standard-chartered-ceo-bill-winters-apologizes-calling-some-workers-lower-value-human-capital-ai-push/
I know. We should totally invoke the 25th amendment before- wait. It said AI. Oh, my bad.
sanitation — ‘classic psychology test’ covers a lot of ground. If this is Stroop or dual-task paradigms, the near-total collapse actually tracks: those tests were designed to stress automaticity vs. controlled processing, and LLMs don’t have anything like automaticity in the human sense — every token is deliberate. So ‘collapse’ might be the wrong word; it’s more like the architecture was never built for that cognitive mode. There’s a breakdown of which test categories hit which model families hardest if you want to cross-reference which paradigm is doing the most damage here.
Thanks for the explanation. I just repost the most popular content from reddit.
Given that the LLMs could follow the short lists of words well but not the longer lists, and that they were processing images, not text, I think it’s more likely that their context just filled up and they forgot the original instructions (or they were assigned a lower weight in the computation).
Tech bro psyops from psypost.
These models tested are so old they’re from the era where they couldn’t pass a math test or count letters in words
So… last week then?
I get that you hate AI but there’s no reason to lie about its capabilities.
A lot of tools like Claude or ChatGPT have internal tools they call when they do math (or use a python script) rather than have the model actually compute anything.
The underlying tech itself can’t do it because you can’t do math by token probability.
Whether they use tools to do it or not is entirely unimportant, that’s just how they do it?
That’s not lying. There’s nothing linguistic about numerical computation.
You know the “DeepMind and OpenAi models” is the hint that the LLM model is not the one doing the math. The LLM provides a hypothesis and the DeepMind model provides grounding or feedback on whether the hypothesis even makes sense or works.
It is totally irrelevant that the model calls tools to do the math. That is still a success.
It’s relevant to what the parent was saying about LLMs. The success of the LLM in using mathematical tools does not contradict what they were saying. To then accuse them of lying because of a misunderstanding is… bad form.
All of these features are not something the models themselves can do, but are grafted on.
I could easily write a Home Assistant automation pattern matching for nearly every way someone could say “how many Rs are in strawberry”, depluralize a plural letter, and run it against “wc” in a bash terminal.
That doesn’t mean it’s smarter. It’s that I’ve added something specific to it.
MCP and the like is just that too, gluing on functions or the ability to hopefully invoke a function. That’s why so many hilariously mundane ones exist.
At the core, it’s still a large language model: a statistical model of frequency of word and word chunk (token) patterns.
Sometimes one model can invoke another via that tooling but it’s still a grafting on. It isn’t a singular thing or system, but disjointed pieces so completely detached from how brains work.
This isn’t AI hate, it’s reality. I love the field of artificial intelligence and machine learning. It’s cool as hell. But an LLM is fundamentally incapable of being anything more than an LLM with glued on pieces that invoke functionality.
OpenAI saw people mock the inability to count so they wrote a specialized tool to count letters and glued it on.
The world is full of endless edge cases. The inability to simply resolve them without gluing on every single one means it just isn’t doing anything new.
I believe the progress of the last year is largely attributable to the appropriate “grafting on” of these wrappers around the LLM cores.
They regularly win olympiad mathematics up from not standing a chance and just created a novel solution to the erdos conjecture, them counting the r’s in strawberry is inconsequential but also something they can do even if you just use the raw api or a local model.
Using computers to search for a counter example to a conjecture isn’t exactly new ground and I suspect they did so with the aide of some harness tweaks like some numerical LSP. Like cool, it pushed the envelope but like what the parent said, they grafted on the ability to do a specific task.
That doesn’t change the fact that llm’s are capable of acing math olympiads. So what if it uses tools? You probably would too. I doubt anybody there did it without a calculator.
Aren’t you the least bit curious what tools they gave the LLM and how the LLM used those tools? It’s like back in math class you are asked to solve a quadratic formula but you forgot how. So you use the calculator to try different numbers and the calculator is telling you if you are getting closer. Sure I got the right answer, but it’s hardly a testament to my math skills.
Afaik that is handled through tool use in modern models (ie they didn’t learn to do maths, they learnt to use a calculator), assuming that’s true and I haven’t missed some advance, their conclusions are likely still relevant
Edit: though the article does seem to discard the chain of thought techniques a little readily, feels like they could come close to fitting the role of executive control, but perhaps that’s just the article lacking detail from the original work.
My high school math teachers would be so disappointed in them.
If I could wire a calculator into my brain I would have cheated on all the maths tests tbf
What I see in the modern models is that you can often ask them to write a program or script to do a task and they can do that successfully much better than doing the task itself directly - once they have debugged the program it is usually 100% reliable for the specified tasks. Ask them to do those simple tasks directly and you get all kinds of creatively wrong answers.
so? you know if people were cherry picking articles and research without understanding what they are, about anything else what would you think of them?
