271–280 of 309 posts
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I actually did this prompt and found that it worked with a single nudge on a followup prompt. My first shot got me a wine glass that was almost full but not quite. I told it I wanted it full to the top - another drop would overflow. The second shot was perfectly full.
The correction I expect to give to an intern, not a junior person.
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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…
Draw a millipede as a dog: Gemini responds: Conceptualizing the "Millipup" https://gemini.google.com/share/b6b8c11bd32f Draw the five legs of a dog as if the body is a pentagon https://gemini.google.com/share/d74d9f5b4fa4 And animal legs are quite standardized https://en.wikipedia.org/wiki/List_of_animals_by_number_of_l... It's all about the prompt. Example: Can you imagine a dog with five legs? https://gemini.google…
Earlier quoted context omitted.
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…
I feel a weird mix of extreme amusement and anger that there's a fleet of absurdly powerful, power-hungry servers sitting somewhere being used to process this problem for 2.5 minutes
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 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…
What is " a dog" to Gemini?
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 just can't imagine we are close to letting LLMs do electrical work. What I notice that I don't see talked about much is how "steerable" the output is. I think this is a big reason 1 shots are used as examples. Once you get past 1 shots, so much of the output is dependent on the context the previous prompts have created. Instead of 1 shots , try something that requires 3 different prompts on a subject with uncertain…
I think they'll never be great at switchgear rooms but apartment outlet circuitry? Why not?
I have a very rigid workflow with what I want as outputs, so if I shape the inputs using an LLM it's promising. You don't need to automate everything; high level choices should be done by a human.
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Even so, that doesn’t take away from my point. Traditional specialized models can do these things already, for much cheaper and without expensive optimization. What traditional models cannot do is the toy aspect of LLM, and that is the only usecase I see for this technology going forward. Lets say you are right and these things will be optimized, and in, say, 5 years, most models from the big players will be able do…
Having one tool that you can use to do all of these things makes a big difference. If I'm a financial analyst at a company I don't need to know how to implement and use 5 different specialized ML models, I can just ask one tool (that can still use tools on the backend to complete the task efficiently)
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What I meant was that what LLMs are doing is very similar to curve fitting, so I think it is not wrong to call it interpolation (curve fitting is a type of interpolation, but not all interpolation is curve fitting). As for bias, sampling bias is only one many types of biases. I mean the UNIX program YES(1) has a bias towards outputting the string y despite not sampling any data. You can very easily and deliberately p…
Training is kinda like curve fitting, but inference is not. The inference algorithm is random sampling from a next-token probability distribution. It’s a subtle distinction, but I think an important one in this case, because if it was interpolation then genuine creativity would not be possible. But the attention mechanism results in model building in latent space, which then affects the next token distribution.
My reasons to subscribing to the latter camp is that when you have a distribution and you fit things according to that distribution (even when the fitting is stochastic; and even when the distribution belongs in billions of dimensions) you are doing curve fitting.
I think the one extreme would be a random walk, which is obviously not curve fitting, but if you draw from any other distribution then the uniform distribution, say the normal distribution, you are fitting that distribution (actually, I take that back, the original random walk is fitting the uniform distribution).
Note I am talking about inference, not training. Training can be done using all sorts of algorithms, some include priors (distributions) and would be curve fitting, but only compute the posteriors (also distributions). I think the popular stochastic linear descent does something like this, so it would be curve-fitting, but the older evolutionary algorithm just random walks it and is not fitting any curve (except the uniform distribution). What matters to me is that the training arrives at a distribution, which is described by a weight matrix, and what inference is doing is fitting to that distribution (i.e. the curve).
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 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…
Earlier quoted context omitted.
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…
Draw a millipede as a dog: Gemini responds: Conceptualizing the "Millipup" https://gemini.google.com/share/b6b8c11bd32f Draw the five legs of a dog as if the body is a pentagon https://gemini.google.com/share/d74d9f5b4fa4 And animal legs are quite standardized https://en.wikipedia.org/wiki/List_of_animals_by_number_of_l... It's all about the prompt. Example: Can you imagine a dog with five legs? https://gemini.google…
Asymmetry is as hard for AI models as it is for evolution to "prompt for" but they're getting better at it.