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Gemini 3 Pro: the frontier of vision AI

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Re: Gemini 3 Pro: the frontier of vision AI

#201

Post by vunderba »

XenophileJKO wrote: post #137
vunderba wrote: post #77

Earlier quoted context omitted.

Anything that needs to overcome concepts which are disproportionately represented in the training data is going to give these models a hard time. Try generating: - A spider missing one leg - A 9-pointed star - A 5-leaf clover - A man with six fingers on his left hand and four fingers on his right You'll be lucky to get a 25% success rate. The last one is particularly ironic given how much work went into FIXING the ol…

It mostly depends on "how" the models work. Multi-modal unified text/image sequence to sequence models can do this pretty well, diffusion doesn't.

Multimodal certainly helps but "pretty well" is a stretch. I'd be curious to know what multimodal model in particular you've tried that could consistently handle generative prompts of the above nature (without human-in-the-loop corrections).

For example, to my knowledge ChatGPT is unified and I can guarantee it can't handle something like a 7-legged spider.

Re: Gemini 3 Pro: the frontier of vision AI

#202

Post by golem14 »

reducesuffering wrote: post #81
knollimar wrote: post #33

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…

Carl Sagan has entered the chat: https://www.youtube.com/watch?v=6_-jtyhAVTc&t=450s

Re: Gemini 3 Pro: the frontier of vision AI

#204

Post by nl »

JamesSwift wrote: post #119
danielvaughn wrote: post #57

Earlier quoted context omitted.

I don’t know much about AI, but I have this image test that everything has failed at. You basically just present an image of a maze and ask the LLM to draw a line through the most optimal path. Here’s how Nano Banana fared: https://x.com/danielvaughn/status/1971640520176029704?s=46

I just oneshot it with claude code (opus 4.5) using this prompt. It took about 5 mins and included detecting that it was cheating at first (drew a line around the boundary of the maze instead), so it added guardrails for that: ``` Create a devenv project that does the following: - Read the image at maze.jpg - Write a script that solves the maze in the most optimal way between the mouse and the cheese - Generate a new…

Programs can solve mazes and LLMs can program. That's a different thing completely.

Re: Gemini 3 Pro: the frontier of vision AI

#205

Post by JamesSwift »

nl wrote: post #204
JamesSwift wrote: post #119

Earlier quoted context omitted.

I just oneshot it with claude code (opus 4.5) using this prompt. It took about 5 mins and included detecting that it was cheating at first (drew a line around the boundary of the maze instead), so it added guardrails for that: ``` Create a devenv project that does the following: - Read the image at maze.jpg - Write a script that solves the maze in the most optimal way between the mouse and the cheese - Generate a new…

Programs can solve mazes and LLMs can program. That's a different thing completely.

That just seems like an arbitrary limitation. Its like asking someone to do answer a math calculation but "no thinking allowed". Like, I guess we can gauge if a model just _knows all knowable things in the universe_ using that method... but anything of any value that you are gauging in terms of 'intelligence', is going to actually be validating their ability to go "outside the scope" of what they actually are (an autocomplete on steroids).

Re: Gemini 3 Pro: the frontier of vision AI

#206

Post by flyinglizard »

JamesSwift wrote: post #205
nl wrote: post #204

Earlier quoted context omitted.

Programs can solve mazes and LLMs can program. That's a different thing completely.

That just seems like an arbitrary limitation. Its like asking someone to do answer a math calculation but "no thinking allowed". Like, I guess we can gauge if a model just _knows all knowable things in the universe_ using that method... but anything of any value that you are gauging in terms of 'intelligence', is going to actually be validating their ability to go "outside the scope" of what they actually are (an aut…

We know there are very simple maze solving algorithms you could code in few lines of Python but no one could claim that constitutes intelligence. The difference is between applying intuitive logic and using a predetermined tool.

Re: Gemini 3 Pro: the frontier of vision AI

#207

Post by adastra22 »

runarberg wrote: post #184
adastra22 wrote: post #139

Earlier quoted context omitted.

LLMs are very good at generalizing beyond their training (or context) data. Normally when they do this we call it hallucination. Only now we do A LOT of reinforcement learning afterwards to severely punish this behavior for subjective eternities. Then act surprised when the resulting models are hesitant to venture outside their training data.

Hallucination are not generalization beyond the training data but interpolations gone wrong. LLMs are in fact good at generalizing beyond their training set, if they wouldn’t generalize at all we would call that over-fitting, and that is not good either. What we are talking about here is simply a bias and I suspect biases like these are simply a limitation of the technology. Some of them we can get rid of, but—like a…

What, may I ask, is the difference between "generalization" and "interpolation"? As far as I can tell, the two are exactly the same thing.

In which case the only way I can read your point is that hallucinations are specifically incorrect generalizations. In which case, sure if that's how you want to define it. I don't think it's a very useful definition though, nor one that is universally agreed upon.

I would say a hallucination is any inference that goes beyond the compressed training data represented in the model weights + context. Sometimes these inferences are correct, and yes we don't usually call that hallucination. But from a technical perspective they are the same -- the only difference is the external validity of the inference, which may or may not be knowable.

Biases in the training data are a very important, but unrelated issue.

Re: Gemini 3 Pro: the frontier of vision AI

#208

Post by grugnog »

Workaccount2 wrote: post #48

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…

Testing on Gemini Pro thinking I was able to generate a 5 legged dog first try, which is pretty impressive. Pasting that into a new session it counted 4 at first, but when I asked it to count more carefully it came back with 5 (and a detailed description of the positioning of each limb). Interestingly it suggested the anomaly could have been caused by AI! This is a good test I think - we seem right on the threshold of it being doable.

Re: Gemini 3 Pro: the frontier of vision AI

#209

Post by maczwei »

Workaccount2 wrote: post #48

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…

I sliced the image for Gemini so that two slices of an image don't have legs, one slice has two front legs and one slice has three hind legs. Then Gemini 3 Pro answered correctly that the dog has 5 legs. Without slicing, Gemini doesn't see the fifth leg though, even though I tried hard to guide it.

Re: Gemini 3 Pro: the frontier of vision AI

#210

Post by Andrex »

Workaccount2 wrote: post #48

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…

I bet if you'd show that image to a human they'd need a little time to figure out what the heck they were looking at. Humans might need additional guesses, too. Five-legged dogs aren't common, but well-endowed dogs may be.
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