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A new Google model is nearly perfect on automated handwriting recognition

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Re: A new Google model is nearly perfect on automated handwriting recognition

#151

Post by chrischen »

roywiggins wrote: post #132

My task today for LLMs was "can you tell if this MRI brain scan is facing the normal way", and the answer was: no, absolutely not. Opus 4.1 succeeds more than chance, but still not nearly often enough to be useful. They all cheerfully hallucinate the wrong answer, confidently explaining the anatomy they are looking for, but wrong. Maybe Gemini 3 will pull it off. Now, Claude did vibe code a fairly accurate solution t…

But these models are more like generalists no? Couldn’t they simply be hooked up to more specialized models and just defer to them the way coding agents now use tools to assist?

Re: A new Google model is nearly perfect on automated handwriting recognition

#152

Post by BobbyTables2 »

snickerbockers wrote: post #104
brulard wrote: post #88

Earlier quoted context omitted.

This is quite a narrow view of how the generation works. AI can extrapolate from the training set and explore new directions. It's not just cutting pieces and gluing together.

uhhh can it? I've certainly not seen any evidence of an AI generating something not based on its training set. It's certainly smart enough to shuffle code around and make superficial changes, and that's pretty impressive in its own way but not particularly useful unless your only goal is to just launder somebody else's code to get around a licensing problem (and even then it's questionable if that's a derived work or…

You have to realize AI is trained the same way one would train an auto-completer.

Theres no cognition. It’s not taught language, grammar, etc. none of that!

It’s only seen a huge amount of text that allows it to recognize answers to questions. Unfortunately, it appears to work so people see it as the equivalent to sci-fi movie AI.

It’s really just a search engine.

Re: A new Google model is nearly perfect on automated handwriting recognition

#153

Post by famouswaffles »

imiric wrote: post #133
famouswaffles wrote: post #89

Earlier quoted context omitted.

>Here's a thought experiment: if modern machine learning systems existed in the early 20th century, would they have been able to produce an equivalent to the theory of relativity? How about advance our understanding of the universe? Teach us about flight dynamics and take us into space? Invent the Turing machine, Von Neumann architecture, transistors? Only a small percentage of humanity are/were capable of doing any…

> LLMs are great, but they're not (yet?) as capable as our best and brightest (and in many ways, lag behind the average human) in most respects, so why would you expect such genius now ? I'm not expecting novel scientific theories today . What I am expecting are signs and hints of such genius. Something that points in the direction that all tech CEOs are claiming we're headed in. So far I haven't seen any of this yet…

>I'm not expecting novel scientific theories today. What I am expecting are signs and hints of such genius.

Like I said, what exactly would you be expecting to see with the capabilities that exist today ? It's not a gotcha, it's a genuine question.

>And, I'm sorry, I don't buy the excuse that these tools are not "yet" as capable as the best and brightest humans.

There's nothing to buy or not buy. They simply aren't. They are unable to do a lot of the things these people do. You can't slot an LLM in place of most knowledge workers and expect everything to be fine and dandy. There's no ambiguity on that.

>They contain the sum of human knowledge, far more than any individual human in history.

It's not really the total sum of human knowledge but let's set that aside. Yeah so ? Einstein, Newton, Von Newman. None of these guys were privy to some super secret knowledge their contemporaries weren't so it's obviously not simply a matter of more knowledge.

>Are they not intelligent, capable of thinking and reasoning?

Yeah they are. And so are humans. So were the peers of all those guys. So why are only a few able to see the next step ? It's not just about knowledge, and intelligence lives in degrees/is a gradient.

>If all this is true, surely we should be seeing incredible results produced by this technology. If not by itself, then surely by "amplifying" the work of the best and brightest humans.

Yeah and that exists. Terence Tao has shared a lot of his (and his peers) experiences on the matter.

https://mathstodon.xyz/@tao/115306424727150237

https://mathstodon.xyz/@tao/115420236285085121

https://mathstodon.xyz/@tao/115416208975810074

>And yet... All we have to show for it are some very good applications of pattern matching and statistics, a bunch of gamed and misleading benchmarks and leaderboards, a whole lot of tech demos, solutions in search of a problem, and the very real problem of flooding us with even more spam, scams, disinformation, and devaluing human work with low-effort garbage.

Well it's a good thing that's not true then

Re: A new Google model is nearly perfect on automated handwriting recognition

#154

Post by BoorishBears »

kazinator wrote: post #136
brulard wrote: post #88

Earlier quoted context omitted.

This is quite a narrow view of how the generation works. AI can extrapolate from the training set and explore new directions. It's not just cutting pieces and gluing together.

Positively not. It is pure interpolation and not extrapolation. The training set is vast and supports an even vaster set of possible traversal paths; but they are all interpolative. Same with diffusion and everything else. It is not extrapolation that you can transfer the style of Van Gogh onto a photographl it is interpolation. Extrapolation might be something like inventing a style: how did Van Gogh do that? And, s…

This is knock against you at all, but in a naive attempt to spare someone else some time: remember that based on this definition it is impossible for an LLM to do novel things and more importantly, you're not going to change how this person defines a concept as integral to one's being as novelty.

I personally think this is a bit tautological of a definition, but if you hold it, then yes LLMs are not capable of anything novel.

Re: A new Google model is nearly perfect on automated handwriting recognition

#155

Post by [deleted] »

fooker wrote: post #137
neom wrote: post #87

Earlier quoted context omitted.

I raised this point with her, she said there are times it would be ambiguous for both her and the model, and she thinks it would be dangerous for her to be influenced by it. I'm not a professional historical researcher so I'm not sure if her concern is valid or not.

As a scientist, I don't think this is valid or useful. It's very much a first year PhD line of thought that academia stamps out of you. This is the 'RE' in research, you specifically want to know and understand what others think of something by reading others' papers. The scientific training slowly, laboriously prepares you to reason about something without being too influenced by it.

[deleted]

Re: A new Google model is nearly perfect on automated handwriting recognition

#156

Post by veegee »

terminalshort wrote: post #143
jstummbillig wrote: post #40

Earlier quoted context omitted.

I remain confused but still somewhat interested as to a definition of "novel", given how often this idea is wielded in the AI context. How is everyone so good at identifying "novel"? For example, I can't wrap my head around how a) a human could come up with a piece of writing that inarguably reads "novel" writing, while b) an AI could be guaranteed to not be able to do the same, under the same standard.

If a LLM had written Linux, people would be saying that it isn't novel because it's just based on previous OS's. There is no standard here, only bias.

[dead]

Re: A new Google model is nearly perfect on automated handwriting recognition

#157

Post by throwup238 »

nestorD wrote: post #126
throwup238 wrote: post #31

Earlier quoted context omitted.

> The fun part has been build tools to turn Claude code and Codex CLI into capable research assistant for that type of projects. What does that look like? How well does it work? I ended up writing a research TUI with my own higher level orchestration (basically have the thing keep working in a loop until a budget has been reached) and document extraction.

I started with a UI that sounded like it was built along the same lines as yours, which had the advantage of letting me enforce a pipeline and exhaustivity of search (I don't want the 10 most promising documents, I want all of them). But I realized I was not using it much because it was that big and inflexible (plus I keep wanting to stamp out all the bugs, which I do not have the time to do on a hobby project). So I…

> It is not perfect (if a search surfaces 300 promising documents, it will not check all of them, and it often misunderstands things due to lacking further context)

This has been the biggest problem for me too. I jokingly call it the LLM halting problem because it never knows the proper time to stop working on something, finishing way too fast without going through each item in the list. That’s why I’ve been doing my own custom orchestration, drip feeding it results with a mix of summarization and content extraction to keep the context from different documents chained together.

Especially working with unindexed content like colonial documents where I’m searching through thousands of pages spread (as JPEGs) over hundreds of documents for a single one that’s relevant to my research, but there are latent mentions of a name that ties them all together (like a minor member of an expedition giving relevant testimony in an unrelated case). It turns into a messy web of named entity recognition and a bunch of more classical NLU tasks, except done with an LLM because I’m lazy.

Re: A new Google model is nearly perfect on automated handwriting recognition

#158

Post by pksebben »

zahlman wrote: post #27
pksebben wrote: post #16

Earlier quoted context omitted.

I don't know if I'd call it an 'amazing feat', but claude had me pause for a moment recently. Some time ago, I'd been working on a framework that involved a series of servers (not the only one I've talked to claude about) that had to pass messages around in a particular fashion. Mostly technical implementation details and occasional questions about architecture. Fast forward a ways, and on a lark I decided to ask in…

Are you sure it isn't just a case of a write-up of the project appearing in the training data?

was my own project, so i don't see how it could have been. Private repo, unfinished, i gave it the name.

Re: A new Google model is nearly perfect on automated handwriting recognition

#159

Post by pksebben »

omega3 wrote: post #37
pksebben wrote: post #16

Earlier quoted context omitted.

I don't know if I'd call it an 'amazing feat', but claude had me pause for a moment recently. Some time ago, I'd been working on a framework that involved a series of servers (not the only one I've talked to claude about) that had to pass messages around in a particular fashion. Mostly technical implementation details and occasional questions about architecture. Fast forward a ways, and on a lark I decided to ask in…

Perhaps you have the memory feature enabled: https://support.claude.com/en/articles/11817273-using-claude...

I probably do, and this is what I think happened. Mind you, it's not magic, but to hold that information with enough fidelity to pattern-match the structure of the underlying function was something I would find remarkable. It's a leap from a lot of the patterns I'm used to.

Re: A new Google model is nearly perfect on automated handwriting recognition

#160

Post by fragmede »

snickerbockers wrote: post #104
brulard wrote: post #88

Earlier quoted context omitted.

This is quite a narrow view of how the generation works. AI can extrapolate from the training set and explore new directions. It's not just cutting pieces and gluing together.

uhhh can it? I've certainly not seen any evidence of an AI generating something not based on its training set. It's certainly smart enough to shuffle code around and make superficial changes, and that's pretty impressive in its own way but not particularly useful unless your only goal is to just launder somebody else's code to get around a licensing problem (and even then it's questionable if that's a derived work or…

Isn't that what's going on with synthetic data? The LLM is trained, then is used to generate data that gets put into the training set, and then gets further trained on that generated data?
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