151–160 of 309 posts
In case the article author sees this, the "HTML transcription" link is broken - it goes to https://aistudio-preprod.corp.google.com/prompts/1GUEWbLIlpX... which is a Google-employee-only URL.
Love how employee portals for many companies essentially never get updated design wise over the decades, lol. That page styling and the balls certainly take me back.
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You'll need to explain the IMO results, then.
Human legs and car tires can both take a human and a car respectively to the finish line of a 200 meter track course, the car tires do so considerably quicker than a pair of human legs. But nobody needs to describe the tire‘s running abilities because of that, nor even compare a tire to a leg. A car tire cannot run, and it is silly to demand an explanation for it.
I do some electrical drafting work for construction and throw basic tasks at LLMs. I gave it a shitty harness and it almost 1 shotted laying out outlets in a room based on a shitty pdf. I think if I gave it better control it could do a huge portion of my coworkers jobs very soon
Well It is the first model to get partial-credit on an LLM image test I have. Which is counting the legs of a dog. Specifically, a dog with 5 legs. This is a wild test, because LLMs get really pushy and insistent that the dog only has 4 legs. In fact GPT5 wrote an edge detection script to see where "golden dog feet" met "bright green grass" to prove to me that there were only 4 legs. The script found 5, and GPT-5 the…
It always feels to me like these types of tests are being somewhat intentionally ignorant of how LLM cognition differs from human cognition. To me, they don't really "prove" or "show" anything other than simply - LLMs thinking works different than human thinking. I'm always curious if these tests have comprehensive prompts that inform the model about what's going on properly, or if they're designed to "trick" the LLM…
Interesting "ScreenSpot Pro" results: 72.7% Gemini 3 Pro 11.4% Gemini 2.5 Pro 49.9% Claude Opus 4.5 3.50% GPT-5.1 ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use https://arxiv.org/abs/2504.07981
I was surprised at how poorly GPT-5 did in comparison to Opus 4.1 and Gemini 2.5 on a pretty simple OCR task a few months ago - I should run that again against the latest models and see how they do. https://simonwillison.net/2025/Aug/29/the-perils-of-vibe-cod...
According to the calculator on the pricing page (it's inside a toggle at the bottom of the FAQs), GPT-5 is resizing images to have a minor dimension of at most 768: https://openai.com/api/pricing/ That's ~half the resolution I would normally use for OCR, so if that's happening even via the API then I guess it makes sense it performs so poorly.
These OCR improvements will almost certainly be brought to google books, which is great. Long term it can enable compressing all non-digital rare books into a manageable size that can be stored for less than $5,000.[0] It would also be great for archive.org to move to this from Tesseract. I wonder what the cost would be, both in raw cost to run, and via a paid API, to do that. [0] https://annas-archive.org/blog/criti…
This is a really interesting "data flywheel" -- better model >> more usable data >> even better model
I do some electrical drafting work for construction and throw basic tasks at LLMs. I gave it a shitty harness and it almost 1 shotted laying out outlets in a room based on a shitty pdf. I think if I gave it better control it could do a huge portion of my coworkers jobs very soon
"AI could never replace the creativity of a human" "Ok, I guess it could wipe out the economic demand for digital art, but it could never do all the autonomous tasks of a project manager" "Ok, I guess it could automate most of that away but there will always be a need for a human engineer to steer it and deal with the nuances of code" "Ok, well it could never automate blue collar work, how is it gonna wrench a pipe i…
> "the economic demand for digital art"
You twisted one "goalpost" into a tangential thing in your first "example", and it still wasn't true, so idk what you're going for. "Using a wrench vs preliminary layout draft" is even worse.
If one attempted to make a productive observation of the past few years of AI Discourse, it might be that "AI" capabilities are shaped in a very odd way that does not cleanly overlap/occupy the conceptual spaces we normally think of as demonstrations of "human intelligence". Like taking a 2-dimensional cross-section of the overlap of two twisty pool tubes and trying to prove a Point with it. Yet people continue to do so, because such myopic snapshots are a goldmine of contradictory venn diagrams, and if Discourse in general for the past decade has proven anything, it's that nuance is for losers.
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> Categories of _what_, exactly? Precisely. At least apples and oranges are both fruits, and it makes sense to compare e.g. the sugar contents of each. But an LLM model and the human brain are as different as the wind and the sunshine. You cannot measure the windspeed of the sun and you cannot measure the UV index of the wind. Your choice of the words here was rather poor in my opinion. Statistical models do not have…
Wind and sunshine are both types of weather, what are you talking about?
What I am trying to say is that the intrinsic properties of the brain and an LLM are completely different, even though the extrinsic properties might appear the same. This is also true of the wind and the sunshine. It is not unreasonable to (though I would disagree) that “cognition” is almost the definition of the sum of all intrinsic properties of the human mind (I would disagree only on the merit of animal and plant cognition existing and the former [probably] having similar intrinsic properties as human cognition).
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I just tried to get Gemini to produce an image of a dog with 5 legs to test this out, and it really struggled with that. It either made a normal dog, or turned the tail into a weird appendage. Then I asked both Gemini and Grok to count the legs, both kept saying 4. Gemini just refused to consider it was actually wrong. Grok seemed to have an existential crisis when I told it it was wrong, becoming convinced that I ha…
An interesting test in this vein that I read about in a comment on here is generating a 13 hour clock—I tried just about every prompting trick and clever strategy I could come up with across many image models with no success. I think there's so much training data of 12 hour clocks that just clobbers the instructions entirely. It'll make a regular clock that skips from 11 to 13, or a regular clock with a plaque saying…
https://gemini.google.com/share/b3b68deaa6e6
I thought giving it a setting would help, but just skip that first response to see what I mean.
I do some electrical drafting work for construction and throw basic tasks at LLMs. I gave it a shitty harness and it almost 1 shotted laying out outlets in a room based on a shitty pdf. I think if I gave it better control it could do a huge portion of my coworkers jobs very soon
I would really love a magic wand to make things like AVEVA and AutoCAD not so painful to use. You know who should be using tools to make these tools less awful? AVEVA and AutoCAD. Engineers shouldn't be having to take on risk by deferring some level of trust to third party accelerators with poor track records.