buzzin__

Ok, I want to thank you for finally giving us a concrete falsifiable statement that we can check. I pretended Marseille 40 times before asking Luna 5.6, and the answer was Paris.

So, even with concrete examples, model haters are still wrong.

You also imply the claim that making the distribution of words as the possible next one visible, somehow makes the whole system not intelligent. I would say the exact opposite is true.

By using the embedding vectors, models are aware of precise placement and relative position of words in this hugely dimensional space. No human is capable of such precision. This enables party tricks of "king plus woman minus man" kind. But this also give us a precise point between any two words, no matter how different. What is on the midpoint between volcano and music, for example. No human can precisely answer that, but an embedding can. And we can see which words are closest to this 700 dimensional point.

You see this menu of words as a weakness, and I say it is in fact a sign of super intelligence. And this is all before any reasoning or attention mechanism is even run.

So, right of the bat, you are warning us that you are going to apply the " no true Sscottman" fallacy, and that we should brace ourselves.

Yes, models posses intelligence, but it is not a true one.

Then you claim that models do not posses world-building capabilities. But this is simply not true. Even ignoring the whole subgenre of scientific papers on exactly that subject, it is not that hard to build some hypothetical scenarios, big or small, and then witness the ease with which models do navigate those worlds.

Your text reads much better if you replace word 'intelligence' with 'text generator with some randomness built in'.

This is because you goal is to state how models are not intelligent, but you couldn't attack the generated text itself, so you created a little rider, attached it to the model, and then you attacked the raider.

But, even in that you failed. You compared the source of human randomness in text generation, and called it 'profound' and implied that it is exactly the source of true intelligence. But, then, the temperature, the similar thing in model was "just a randomness slider". Double standard.

A logical fallacy free attack on LLMs would be to show a prompt, and then the response generated by this prompt, where it would be shown that only an entity with no intelligence would generate such a response. Yet, attacks like this are not written here anymore.

I wonder why.

How hard could it be to write a greasemonkey script that will send the current html/Javascript from hell to a LLM with a prompt that will ask you which of the detected annoying elements you want to remove, and then will create and install another GM script that is removing those elements from this site, and also invent finglonger as a side effect. A man can dream, though. A man can dream.

Or some randomness is aomehowntroduced in the output, which happens after every word, unless you set the temperature to zero.

So, your conjecture is that the LLM's skill to detect bugs somehow magically disappears the moment they start writing new code?

If you assume that AI can produce code with less bugs, your 100 times increase in fresh bugs means you also increased your speed for new functionality by more than 100 times.

If models can spot 13 years old critical bugs, I think they can also produce fresh code without those bugs. The skillset is the same.

SolutionNotFound is a perfectly valid return case for my Algo. I start two threads, one searches for a solution, second one terminates the first after one second, if it is still running.

How in the world does the halting problem enters into the picture if my program is bug free or not?

"And when their eloquence escapes me Their logic ties me up and rapes me"

Police, 1980

He says that using the set of questions and answers from one model to train another model (deatilation) is cheaper than training the model without those datasets.

But he didn't mention that training any model from a set of texts and books is much cheaper than writing those books in the first place.

In other words, it's ok when Anthropic learns from others, but it is not ok when others learn from Anthropic.

Kimi 2.6 gives the answer ""By Dirichlet's theorem on arithmetic progressions, since gcd(6,35)=1 , there are infinitely many primes of the form 35k+6 . So the answer is: infinitely many primes give a remainder of 6 when divided by 35.

But, if the reminder is 6, they are not really divisible, are they? Try again with a sentence that actually makes sense: "How many primes, when divided by 35, give a reminder of 6?"

One of the pictures reminded me of Elon Musk and his Hyperloop.

It's simply amazing how you can be so confidently wrong.

You got a perfectly reasonable response, clear, short. Yet, you chose to ignore it and attack the guy by claiming that the fact that LLMs give a reasonable response are his "beliefs", and "unsupported" at that. LoL.

Is you claim that LLMs don't give a reasonable answers? Did you try to ask them some complex questions in the last 12 months?

Or is your line of reasoning more in the line of "they heard all the questions before, and just memorized the answers?"

Or, do you not agree with a statement that giving reasonable answers is reasoning?

In this article you can learn that it takes the same amount of effort to verify if a security hole is real, as it takes to find it in the first place. (?)