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A recent experience with ChatGPT 5.5 Pro

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Re: A recent experience with ChatGPT 5.5 Pro

#191

Post by kybernetikos »

illiac786 wrote: post #122
ziotom78 wrote: post #62

I am a physics professor and often use Gemini to check my papers. It is a formidable tool: it was able to find a clerical error (a missing imaginary unit in a complex mathematical expression) I was not able to find for days, and it often underlines connections between concepts and ideas that I overlooked. However, it often makes conceptual errors that I can spot only because I have good knowledge of the topic I am di…

Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn. It does not, and it is for the human brain a formidable task to remember that something as smart as an LLM does not learn. I keep catching myself making the same mistake. It’s also because it is so annoying to have to manage the memory of the LLM with custom prompts/instructions manually. I have not yet played with the lon…

Current LLM architecture doesn't learn - and you're right this is a huge piece that normal folks fail to understand, since in many ways, it's the opposite of what years of AI research has been trying to create.

However, I think it's important to remember that LLMs are embedded in larger systems, and those larger systems do learn.

Re: A recent experience with ChatGPT 5.5 Pro

#193

Post by charleshn »

MrDrDr wrote: post #166

> "Even though I can motivate it in retrospect, ChatGPT’s idea to use h^2-dissociated sets to control relations of order at most h feels quite ingenious. As far as I can tell, this idea is completely original." The question that keep bothering me is can an LLM generate an idea that is truly novel? How would/could that actually happen? But then that leads to the question - what are we actually doing when we think? Per…

Yes, they can.

Some people like to parrot "next token prediction", "LLMs can only interpolate", and other nonsense, but it is obviously not true for many reasons, in particular since we introduced RL.

Humans do not have the monopoly on generating novel ideas, modern AI models using post training, RL etc can come to them in the same way we do, exploration.

See also verifier's law [0]: "The ease of training AI to solve a task is proportional to how verifiable the task is. All tasks that are possible to solve and easy to verify will be solved by AI."

This applied to chess, go, strategy games, and we can now see it applying to mathematics, algorithmic problems, etc.

It is incredibly humbling to see AI outperform humans at creative cognitive tasks, and realise that the bitter lesson [1] applies so generally, but here we are.

[0] https://www.jasonwei.net/blog/asymmetry-of-verification-and-...

[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Re: A recent experience with ChatGPT 5.5 Pro

#195

Post by DiogenesKynikos »

quantummagic wrote: post #177
port11 wrote: post #165

Earlier quoted context omitted.

Gemini’s smug and over-confident “this is the gold standard in 2026” definitely leaves little space for nuance if you don’t know the subject matter. Human students would, hopefully, know they don’t know everything.

> Gemini’s smug... Anthropomorphizing these systems is dangerous, whether coming from the bullish or bearish perspective. The output is statistically generated by a machine lacking the capability to be smug.

It's only "statistically generated" in the same way that your brain is just "neurons firing." That's the low-level description of what's happening, but on a higher level, it's correct to say that it's being smug.

Re: A recent experience with ChatGPT 5.5 Pro

#196

Post by dist-epoch »

ionwake wrote: post #124

one thing I was wondering, is, if LLMs are word completions seemingly coming up with new solutions could this just be because stuff that was kept secret and now - is no longer is due to ingestion? I dont know enough about it tho

why would you keep secret this particular mathematical idea? it's not extraordinarily important, it's not on the path to some other major result, doesn't seem useful in financial trading. even author calls it good reasonable problem for a PhD thesis.

Re: A recent experience with ChatGPT 5.5 Pro

#197

Post by casey2 »

I think mathematicians like LLMs because this is the first time we have something like a computer for the kinds of math most people do, high level, hand wavy abstractions that are (relatively) easy for people to grok but hard to explain to traditional computers.

Re: A recent experience with ChatGPT 5.5 Pro

#198

Post by stingraycharles »

illiac786 wrote: post #122
ziotom78 wrote: post #62

I am a physics professor and often use Gemini to check my papers. It is a formidable tool: it was able to find a clerical error (a missing imaginary unit in a complex mathematical expression) I was not able to find for days, and it often underlines connections between concepts and ideas that I overlooked. However, it often makes conceptual errors that I can spot only because I have good knowledge of the topic I am di…

Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn. It does not, and it is for the human brain a formidable task to remember that something as smart as an LLM does not learn. I keep catching myself making the same mistake. It’s also because it is so annoying to have to manage the memory of the LLM with custom prompts/instructions manually. I have not yet played with the lon…

> Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn.

I think this is a bit pedantic. Obviously the parent you’re replying to is referring to the concept of “in-context learning”, which is the actual industry / academic term for this. So you feed it a paper, and then it can use that info, and it needs steering / “mentoring” to be guided into the right direction.

Heck the whole name of “machine learning” suggests these things can actually learn. “reasoning” suggests that these things can reason, instead of being fancy, directed autocomplete. Etc.

In other news: data hydration doesn’t actually make your data wet. People use / misuse words all the time, and that causes their meaning to evolve.

Re: A recent experience with ChatGPT 5.5 Pro

#199

Post by northzen »

ziotom78 wrote: post #62

I am a physics professor and often use Gemini to check my papers. It is a formidable tool: it was able to find a clerical error (a missing imaginary unit in a complex mathematical expression) I was not able to find for days, and it often underlines connections between concepts and ideas that I overlooked. However, it often makes conceptual errors that I can spot only because I have good knowledge of the topic I am di…

Hi ziotom! I wonder about you work in 3D Cifford Algebras. May you share some links to the research you do? I also have interest in this topic I research on my own.

Just in case if you don't want to disclose your name my email is northzen@gmail.com

Re: A recent experience with ChatGPT 5.5 Pro

#200

Post by kasey_junk »

stingraycharles wrote: post #198
illiac786 wrote: post #122

Earlier quoted context omitted.

Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn. It does not, and it is for the human brain a formidable task to remember that something as smart as an LLM does not learn. I keep catching myself making the same mistake. It’s also because it is so annoying to have to manage the memory of the LLM with custom prompts/instructions manually. I have not yet played with the lon…

> Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn. I think this is a bit pedantic. Obviously the parent you’re replying to is referring to the concept of “in-context learning”, which is the actual industry / academic term for this. So you feed it a paper, and then it can use that info, and it needs steering / “mentoring” to be guided into the right direction. Heck the wh…

I agree it’s pedantic and personally don’t get bent out of shape with people anthropomorphizing the llms. But I do think you get better results if keep the text prediction machine mental model in your head as you work with them.

And that can be very hard to do given the ui we most interact with them in is a chat session.

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