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GPT-5

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871–880 of 1001 posts

Re: GPT-5

#871

Post by tim333 »

Telemakhos wrote: post #734

I am thoroughly unimpressed by GPT-5. It still can't compose iambic trimeters in ancient Greek with a proper penthemimeral cæsura, and it insists on providing totally incorrect scansion of the flawed lines it does compose. I corrected its metrical sins twice, which sent it into "thinking" mode until it finally returned a "Reasoning failed" error. There is no intelligence here: it's still just giving plausible output.…

I too can't compose iambic trimeters in ancient Greek but am normally regarded as of average+ intelligence. I think it's a bit of an unfair test as that sort of thing is based of the rhythm of spoken speech and GPT-5 doesn't really deal with audio in a deep way.

Re: GPT-5

#872

Post by verytrivial »

highfrequency wrote: post #331

It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. It's interesting to note that at least so far, the trend has been the opposite: as time goes on and the models get better, the performance of the different company's gets clustered closer together. Right now GPT-5, Claude Opus, Grok 4, Gemini 2.5 Pro all seem quite good across the board (ie the…

Well, it is perhaps frequently suggested by those Ai firms raising capital that once one of the Ai companies reaches an AGI threshhold ... It as rallying call. "Place your bets, gentlemen!"

Re: GPT-5

#873

Post by froh42 »

Wow, I just got GPT-5. Tried to continue the discussion of my 3D print problems with it (which I started with 4o). In comparison GPT-5 is an entitled prick trying to gaslight me into following what it wants.

Can I have 4o back?

Re: GPT-5

#874

Post by optimalsolver »

MagicMoonlight wrote: post #509

It's pretty good. I asked it to make a piece of warehouse software for storing cobs of corn and it instantly pumped out a prototype. I didn't ask it for anything in particular but it included JSON importing and exporting and all kinds of stuff. It's going to be absolute chaos. Compsci was already mostly a meme, with people not able to program getting the degree. Now we're going to have generations of people that can'…

*what people crave.

Re: GPT-5

#875

Post by mustaphah »

joshmlewis wrote: post #641

It's a really good model from my testing so far. You can see the difference in how it tries to use tools to the greatest extent when answering a question, especially compared to 4.1 and o3. In this example it used 6! tool calls in the first response to try and collect as much info as possible. https://promptslice.com/share/b-2ap_rfjeJgIQsG

Is there any value in using XML elements to guide the model instead of simple text (e.g., "Recommendation criteria:")?

Re: GPT-5

#876

Post by Davidzheng »

markb139 wrote: post #565

Ha. I asked it to write some code for the Raspberry Pi RP2350. It told me there might be some confusion as there is no official product release of the RP2350. If it doesn’t know that, then what else doesn’t it know?

Scarily close to satire of humans in denial about AI capabilities (not saying that it's the case here but I can imagine easily such arguments when AI is almost everywhere superhuman)

Re: GPT-5

#877

Post by asterix_pano »

iammrpayments wrote: post #435
Topfi wrote: post #281

> 400,000 context window > 128,000 max output tokens > Input $1.25 > Output $10.00 Source: https://platform.openai.com/docs/models/gpt-5 If this performs well in independent needle-in-haystack and adherence evaluations, this pricing with this context window alone would make GPT-5 extremely competitive with Gemini 2.5 Pro and Claude Opus 4.1, even if the output isn't a significant improvement over o3. If the output qu…

You also have to count the cost of having to verify your identity to use the API

you can also use https://nano-gpt.com/ if privacy is necessary

Re: GPT-5

#878

Post by retinaros »

AgentMatrixAI wrote: post #566

I'm not really convinced, the benchmark blunder was really strange but the demos were quite underwhelming, and it appears this was reflected by a huge market correction in the betting markets as to who will have the best AI by end of the year. What excites me now is that Gemini 3.0 or some answer from Google is coming soon and that will be the one I will actually end up using. It seems like the last mover in the LLM…

The demos were awful. It felt like watching sloppy vibe coded css UIs

Re: GPT-5

#879

Post by coldtea »

highfrequency wrote: post #331

It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. It's interesting to note that at least so far, the trend has been the opposite: as time goes on and the models get better, the performance of the different company's gets clustered closer together. Right now GPT-5, Claude Opus, Grok 4, Gemini 2.5 Pro all seem quite good across the board (ie the…

>It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest.

Both the AGI threshold with LLM architecture, and the idea of self-advancing AI, is pie in the sky, at least for now. These are myths of the rationalist cult.

We'd more likely see reduced returns and smaller jumps between version updates, plus regression from all the LLM produced slop that will be part of the future data.

Re: GPT-5

#880

Post by torginus »

highfrequency wrote: post #331

It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. It's interesting to note that at least so far, the trend has been the opposite: as time goes on and the models get better, the performance of the different company's gets clustered closer together. Right now GPT-5, Claude Opus, Grok 4, Gemini 2.5 Pro all seem quite good across the board (ie the…

Honestly for all the super smart people in the LessWrong singularity crowd, I feel the mental model they apply to the 'singularity' is incredibly dogmatic and crude, with the basic assumption that once a certain threshold is reached by scaling training and compute, we get human or superhuman level intelligence.

Even if we run with the assumption that LLMs can become human-level AI researchers, and are able to devise and run experiments to improve themselves, even then the runaway singularity assumption might not hold. Let's say Company A has this LLM, while company B does not.

- The automated AI researcher, like its human peers, still needs to test the ideas and run experiments, it might happen that testing (meaning compute) is the bottleneck, not the ideas, so Company A has no real advantage.

- It might also happen that AI training has some fundamental compute limit coming from information theory, analogous to the Shannon limit, and once again, more efficient compute can only approach this, not overcome it

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