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

OpenAI, Google and Anthropic are struggling to build more advanced AI

bloomberg.com

Post reply on HN
Print view View original

241–250 of 622 posts

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#241

Post by handfuloflight »

tartoran wrote: post #49
j_maffe wrote: post #36

Earlier quoted context omitted.

> I get the vague impression that so far researchers haven’t found any advantage to such systems — anything you can do with a group of AI agents can be emulated with a single one. It’s like chaining up perceptrons hoping to get more expressive power for free. Emergence happens when many elements interact in a system. Brains are literally a bunch of neurons in a complex network. Also research is already showing promis…

That's wishful thinking at best. Throw it all in a bucket and it will get infected with being and life.

Don't see where your parent comment said or implied that the point was for being and life to emerge.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#242

Post by fragmede »

luckydata wrote: post #232
Xenoamorphous wrote: post #159

Earlier quoted context omitted.

> Here is an example of a task that I do not believe this generation of LLMs can ever do but that is possible for a human That’s possible for a highly intelligent, extensively trained, very small subset of humans.

does it mean people that can build languages and compilers are not humans? What is the point you're trying to make?

It means that's a really high bar for intelligence, human or otherwise. If AGI is "as good as a human, and the test is a trick task that most humans would fail at (especially considering the weasel requirement that it additionally has to be faster), why is that considered a reasonable bar for human-grade intelligence.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#243

Post by famouswaffles »

swatcoder wrote: post #74
AlwaysRock wrote: post #17

Earlier quoted context omitted.

I think your definition is off from what most people would define AGI as. Generally, it means being able to think and reason at a human level for a multitude/all tasks or jobs. "Artificial General Intelligence (AGI) refers to a theoretical form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to that of a human being." A…

On the contrary, I think you're conflating the narrow jargon of the industry with what "most people" would define. "Most people" naturally associate AGI with the sci-tropes of self-aware human-like agents. But industries want something more concrete and prospectively-acheivable in their jargon, and so that's where AGI gets redefined as wide task suitability. And while that's not an unreasonable definition in the cont…

>But industries want something more concrete and prospectively-acheivable in their jargon, and so that's where AGI gets redefined as wide task suitability.

The term itself (AGI) in the industry has always been about wide task suitability. People may have added their ifs and buts over the years but that aspect of it never got 'redefined'. The earliest uses of the term all talk about how well a machine would be able to perform some set number of tasks at some threshold.

It's no wonder why. Terms like "consciousness" and "self-awareness" are completely useless. It's not about difficulty. It's that you can't do anything at all with those terms except argue around in circles.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#244

Post by xpe »

guluarte wrote: post #110

Well, there have been no significant improvements to the GPT architecture over the past few years. I'm not sure why companies believe that simply adding more data will resolve the issues

> Well, there have been no significant improvements to the GPT architecture over the past few years.

A lot hangs on what you mean by "significant". Can you define what you mean? And/or give an example of an improvement that you don't think is significant.

Also, on what basis can you say "no significant improvements" have been made? Many major players have published some of their improvements openly. They also have more private, unpublished improvements.

If your claim boils down to "what people mean by a Generative Pre-trained Transformer" still has a clear meaning, ok, fine, but that isn't the meat of the issue. There is so much more to a chat system than just the starting point of a vanilla GPT.

It is wiser to look at the whole end-to-end system, starting at data acquisition, including pre-training and fine-tuning, deployment, all the way to UX.

P.S. I don't have a vested interest in promoting or disparaging AI. I don't work for a big AI lab. I'm just trying to call it like I see it, as rationally as I can.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#245

Post by lokimedes »

yk wrote: post #221
LASR wrote: post #115

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…

To a certain extent I think we get a better understanding what llms can do, and my estimation for the next ten years is more like best UI ever rather than llms will replace humanity. Now best UI ever is something that can certainly deliver a lot of value, 80% of all buttons in a car should be replaced by actually good voice control, and I think that is were we are going to see a lot of very interesting applications:…

To each their own, but I don’t look forward to having my kids yelling, a podcast in my ears and having to explain to my tumbler that wool must be spun at 1000 RPM. Humans have varying preferences when it comes to communication and sensing, making our machine interactions favor the extroverted talkative exhibitionists is really only one modality.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#246

Post by chongli »

hackinthebochs wrote: post #96
vundercind wrote: post #86

Earlier quoted context omitted.

I thought maybe they were on the right track until I read Attention Is All You Need. Nah, at best we found a way to make one part of a collection of systems that will, together, do something like thinking. Thinking isn’t part of what this current approach does. What’s most surprising about modern LLMs is that it turns out there is so much information statistically encoded in the structure of our writing that we can u…

I see takes like this all the time and its so confusing. Why does knowing how things work under the hood make you think its not on the path towards AGI? What was lacking in the Attention paper that tells you AGI won't be built on LLMs? If its the supposed statistical nature of LLMs (itself a questionable claim), why does statistics seem so deflating to you?

Because it can't apply any reasoning that hasn't already been done and written into its training set. As soon as you ask it novel questions it falls apart. The big LLM vendors like OpenAI are playing whack-a-mole on these novel questions when they go viral on social media, all in a desperate bid to hide this fatal flaw.

The Emperor has no clothes.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#247

Post by olalonde »

jedberg wrote: post #20
irrational wrote: post #13

> The AGI bubble is bursting a little bit I'm surprised that any of these companies consider what they are working on to be Artificial General Intelligences. I'm probably wrong, but my impression was AGI meant the AI is self aware like a human. An LLM hardly seems like something that will lead to self-awareness.

Whether self awareness is a requirement for AGI definitely gets more into the Philosophy department than the Computer Science department. I'm not sure everyone even agrees on what AGI is, but a common test is "can it do what humans can". For example, in this article it says it can't do coding exercises outside the training set. That would definitely be on the "AGI checklist". Basically doing anything that is outside…

I feel the test for AGI should be more like: "go find a job and earn money" or "start a profitable business" or "pick a bachelor degree and complete it", etc.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#248

Post by sdesol »

jihadjihad wrote: post #205
afro88 wrote: post #195

Earlier quoted context omitted.

> potential applications > if you ... > for example ... Yes there seems to be lots of potential. Yes we can brainstorm things that should work. Yes there is a lot of examples of incredible things in isolation. But it's a little bit like those youtube videos showing amazing basketball shots in 1 try, when in reality lots of failed attempts happened beforehand. Except our users experience the failed attempts (LLM repli…

> Except our users experience the failed attempts (LLM replies that are wrong, even when backed by RAG) and it's incredibly hard to hide those from them. This has been my team's experience (and frustration) as well, and has led us to look at using LLMs for classifying / structuring, but not entrusting an LLM with making a decision based on things like a database schema or business logic. I think the technology and to…

> "let's only allow the LLM to do things we know it is rock-solid at."

Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool:

https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples...

5 models are asked if the sentence is correct and GPT-4o got it wrong all 5 times. It keeps complaining that GitHub is spelled like Github, when it isn't. Note, only 2 weeks ago, Claude 3.5 Sonnet did the same thing.

I do believe LLM is a game changer, but I'm not convinced it is designed to be public-facing. I see LLM as a power tool for domain experts, and you have to assume whatever it spits out may be wrong, and your process should allow for it.

Edit:

I should add that I'm convinced that not one single model will rule them all. I believe there will be 4 or 5 models that everybody will use and each will be used to challenge one another for accuracy and confidence.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#249

Post by robrenaud »

LASR wrote: post #115

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…

> For example, combining a human-moderated knowledge graph with an LLM with RAG allows you to build "expert bots" that understand your business context / your codebase / your specific processes and act almost human-like similar to a coworker in your team.

I'd love to hear about this. I applied to YC WC 25 with research/insight/an initial researchy prototype built on top of GPT4+finetuning about something along this idea. Less powerful than you describe, but it also works without the human moderated KG.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#250

Post by ppeetteerr »

crystal_revenge wrote: post #127
LASR wrote: post #115

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…

I don't think we've even started to get the most value out of current gen LLMs. For starters very few people are even looking at sampling which is a major part of the model performance. The theory behind these models so aggressively lags the engineering that I suspect there are many major improvements to be found just by understanding a bit more about what these models are really doing and making re-designs based on…

The reason people are holding out is that the current generation of models are still pretty poor in many areas. You can have it craft an email, or to review your email, but I wouldn't trust an LLM with anything mission-critical. The accuracy of the generated output is too low be trusted in most practical applications.
Post reply on HN