Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

GPT-5

openai.com

Post reply on HN
Print view View original

811–820 of 1001 posts

Re: GPT-5

#811

Post by sibeliuss »

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.…

Pure failure:

"You’ve given:

Moon in the 10th house (from the natal Ascendant)

Venus in the 1st house (from the natal Ascendant)

Step-by-step: From the natal Ascendant’s perspective

Moon = 10th house

Venus = 1st house

Set Moon as the 1st house (Chandra Lagna)

The natal 10th house becomes the 1st house in the Chandra chart.

Therefore, the natal 1st house is 3rd house from the Moon:

10th → 1st (Moon)

11th → 2nd

12th → 3rd (which is the natal 1st)

Locate Venus from the Moon’s perspective

Since Venus is in the natal 1st, and natal 1st is 3rd from Moon,

Venus is in the 3rd house from Chandra Lagna.

Answer: From Chandra Lagna, Venus is in the 3rd house."

Re: GPT-5

#812

Post by xpe »

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.

That's only one part of it. Some forecasters put probabilities on each of the four quadrants in the takeoff speed (fast or slow) vs. power distribution (unipolar or multipolar) table.

Re: GPT-5

#813

Post by jtfrench »

de6u99er wrote: post #809
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…

This is just more of the same. My guts tell me Deepmind will crack AGI.

My gut says similar. They've been on a roll. Genie 3 looks pretty wild.

Re: GPT-5

#814

Post by saddat »

If Grol , Claude , ChatGPT seemingly still all scale , yet their Performance feels similar, could this mean that the Technology path is narrow, with little differentiations left ?

Re: GPT-5

#815

Post by vrighter »

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…

they are improving exponentially... but the exponent is less than 1...

Re: GPT-5

#816

Post by illiac786 »

fidotron wrote: post #277

Going by the system card at: https://openai.com/index/gpt-5-system-card/ > GPT‑5 is a unified system . . . OK > . . . with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say “think hard about this” in the prompt).…

I do agree that the current evolution is moving further and further away from AGI, and more toward a spectrum of niche/specialisation.

It feels less and less likely AGI is even possible with the data we have available. The one unknown is if we manage to get usable quantum computers, what that will do to AI, I am curious.

Re: GPT-5

#817

Post by boomfunky »

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 real last mover is Apple, because boy are they not moving.

Re: GPT-5

#818

Post by illiac786 »

diogolsq wrote: post #572
doctoboggan wrote: post #61

Watching the livestream now, the improvement over their current models on the benchmarks is very small. I know they seemed to be trying to temper our expectations leading up to this, but this is much less improvement than I was expecting

Law of diminishing returns. We’re talking about less than a 10% performance gain, for a shitload of data, time, and money investment.

Yeah I think that throwing more and more compute at the same training data produces smaller and smaller gains.

Maybe quantum compute would be significant enough of a computing leap to meaningfully move the needle again.

Re: GPT-5

#819

Post by quatonion »

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…

I know right, if I didn't know any better one might think they are all customized versions of the same base model.

To be honest that is what you would want if you were digitally transforming the planet with AI.

You would want to start with a core so that all models share similar values in order they don't bicker etc, for negotiations, trade deals, logistics.

Would also save a lot of power so you don't have to train the models again and again, which would be quite laborious and expensive.

Rather each lab would take the current best and perform some tweak or add some magic sauce then feed it back into the master batch assuming it passed muster.

Share the work, globally for a shared global future.

At least that is what I would do.

Re: GPT-5

#820

Post by ako »

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…

I know there's an official AGI definition, but it seem to me that there's too much focus on the model as the thing where AGI needs to happen. But that is just focusing on knowledge in the brain. No human knows everything. We as humans rely on a ways to discover new knowledge, investigation, writing knowledge down so it can be shared, etc.

Current models, when they apply reasoning, have feedback loops using tools to trial and error, and have a short term memory (context) or multiple short term memories if you use agents, and a long term memory (markdown, rag), they can solve problems that aren't hardcoded in their brain/model. And they can store these solutions in their long term memory for later use. Or for sharing with other LLM based systems.

AGI needs to come from a system that combines LLMs + tools + memory. And i've had situations where it felt like i was working with an AGI. The LLMs seem advanced enough as the kernel for an AGI system.

The real challenge is how are you going to give these AGIs a mission/goal that they can do rather independently and don't need constant hand-holding. How does it know that it's doing the right thing. The focus currently is on writing better specifications, but humans aren't very good at creating specs for things that are uncertain. We also learn from trial and error and this also influences specs.

Post reply on HN