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

391–400 of 622 posts

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

#391

Post by rco8786 »

larodi wrote: post #332
sdesol wrote: post #248

Earlier quoted context omitted.

> "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…

Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet resu…

There’s another option here though. Human supervised tasks.

There’s a whole classification of tasks where a human can look at a body of work and determine whether it’s correct or not in far less time than it would take for them to produce the work directly.

As a random example, having LLMs write unit tests.

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

#392

Post by tick_tock_tick »

sincerecook wrote: post #165
iandanforth wrote: post #47

A few important things to remember here: The best engineering minds have been focused on scaling transformer pre and post training for the last three years because they had good reason to believe it would work, and it has up until now. Progress has been measured against benchmarks which are / were largely solvable with scale. There is another emerging paradigm which is still small(er) scale but showing remarkable res…

> That's full multi-modal training with embodied agents (aka robots). 1x, Figure, Physical Intelligence, Tesla are all making rapid progress on functionality which is definitely beyond frontier LLMs because it is distinctly different. Cool, but we already have robots doing this in 2d space (aka self driving cars) that struggle not to kill people. How is adding a third dimension going to help? People are just refusing…

I ride in self driving cares basically once a week in SF (Waymo). It's always felt safer then a Uber and makes ways less risky maneuvers.

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

#393

Post by gchamonlive »

We should put a model in an actual body and let it in the world to build from experiences. Inference is costly though, so the robot would interact during a period and update it's model during another period, flushing the context window (short term memory) into its training set (long term memory).

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

#394

Post by Bjorkbat »

aresant wrote: post #72

Taking a hollistic view informed by a disruptive OpenAI / AI / LLM twitter habit I would say this is AI's "What gets measured gets managed" moment and the narrative will change This is supported by both general observations and recently this tweet from an OpenAI engineer that Sam responded to and engaged -> "scaling has hit a wall and that wall is 100% eval saturation" Which I interpert to mean his view is that model…

I agree that existing benchmarks are no longer useful now that there's basically nothing left in them that seems to stump LLMs.

But when I hear that models are failing to meet expectations, I imagine what they're saying is that the researchers had some sort of eval in mind with room to grow and a target, and that the model in question failed to hit the target they had in mind.

Honestly, problem with sentiments like these is on Twitter is that you can't tell if they're being sincere or just making a snarky, useless remark. Probably a mix of both.

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

#395

Post by niobe »

simonw wrote: post #212
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…

Right. I've been saying for a while that if all LLM development stopped entirely and we were stuck with the models we have right now (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1/2, Qwen 2.5 etc) we could still get multiple years worth of advances just out of those existing models. There is SO MUCH we haven't figured out about how to use them yet.

> There is SO MUCH we haven't figured out about how to use them yet.

I mean, it's pretty clear to me they're a potentially great human-machine interface, but trying to make LLMs - in their current fundamental form - a reliable computational tool.. well, at best it's an expensive hack, but it's just not the right tool for the job.

I expect the next leap forward will require some orthogonal discovery and lead to a different kind of tool. But perhaps we'll continue to use LLMs as we knownthem now for what they're good at - language.

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

#396

Post by bbor »

mrandish wrote: post #381
bbor wrote: post #228

Earlier quoted context omitted.

Great question. Im very confident in my answer, even though it’s in the minority here: we’re not even close to exhausting the potential. Imagine that our current capabilities are like the Model-T. There remains many improvements to be made upon this passenger transportation product, with RAG being a great common theme among them. People will use chatbots with much more permissive interfaces instead of clicking throug…

> why are so many experts so worried about AGI? FYI, I find this line of reasoning to be unconvincing both logically and by counter-example ("why are so many experts so worried about the Y2K bug?") Personally, I don't find AI foom or AI doom predictions to be probable but I do think there are more convincing arguments for your position than you're making here.

Fair enough, well put to both of these responses! I’m certainly biased, and can see how the events that truly scare me (after already assessing the technology on my own and finding it to be More Important Than Fire Or Electricity) don’t make very convincing arguments on their own.

For us optimistic doomers, the AI conversation seems similar to the (early-2000s) climate change debate; we see a wave of dire warnings coming from scientific experts that are all-to-often dismissed, either out of hand due to their scale, or on the word of an expert in an adjacent-ish field. Of course, there’s more dissent among AI researchers than there was among climate scientists, but I hope you see where I’m coming from nonetheless — it’s a dynamic that makes it hard to see things from the other side, so-to-speak.

At this point I’ve pretty much given up convincing people on HackerNews, it’s just cathartic to give my piece and let people take it or leave it. If anyone wants to bring the convo down from industry trends into technical details, I’d love to engage tho :)

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

#397

Post by vidarh »

sdesol wrote: post #248
jihadjihad wrote: post #205

Earlier quoted context omitted.

> 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…

I do contract work on fine-tuning efforts, and I can tell you that most humans aren't designed to be public-facing either.

While LLMs do plenty of awful things, people make the most incredibly stupid mistakes too, and that is what LLMs needs to be benchmarked against. The problem is that most of the people evaluating LLMs are better educated than most and often smarter than most. When you see any quantity of prompts input by a representative sample of LLM losers, you quickly lose all faith in humanity.

I'm not saying LLMs are good enough. They're not. But we will increasingly find that there are large niches where LLMs are horrible and error prone yet still outperform the people companies are prepared to pay to do the task.

In other words, on one hand you'll have domain experts becoming expert LLM-wranglers. On the other hand you'll have public-facing LLMs eating away at tasks done by low paid labour where people can work around their stupid mistakes with process or just accepting the risk, same as they currently do with undertrained labor.

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

#398

Post by ehnto »

JumpCrisscross wrote: post #303
HarHarVeryFunny wrote: post #295

Earlier quoted context omitted.

Current AI coding assistants are best at writing functions or adding minor features to an existing code base. They are not agentic systems that can develop an entire solution from scratch given a specification, which in my experience is more typcical of the work that is being outsourced. AI is a tool, whose full-cycle productivity benefit seems questionable. It is not a replacement for a human.

> they are not agentic systems that can develop an entire solution from scratch given a specification, which in my experience is more typcical of the work that is being outsourced If there is one domain where we're seeing tangible progress from AI, it's in working towards this goal. Difficult projects aren't in scope. But most tech, especially most tech branded IT, is not difficult. Everyone doesn't need an inventory…

There have been off the shelf solutions for so many common software use cases, for decades now. I think the reason we still see so much custom software is that the devil is always in the details, and strict details are not an LLMs strong suit.

LLMs are in my opinion hamstrung at the starting gate in regards to replacing software teams, as they would need to be able to understand complex business requirements perfectly, which we know they cannot. Humans can't either. It takes a business requirements/integration logic/code generation pipeline and I think the industry is focused on code generation and not that integration step.

I think there needs to be a re-imaging of how software is built by and for interaction with AI if it were to ever take over from human software teams, rather than trying to get AI to reflect what humans do.

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

#399

Post by layer8 »

nomel wrote: post #288
layer8 wrote: post #146

Earlier quoted context omitted.

Being aware of its own limitations, for example. Or being aware of how its utterances may come across to its interlocutor. (And by limitations I don’t mean “sorry, I’m not allowed to help you with this dangerous/contentious topic”.)

> Or being aware of how its utterances may come across to its interlocutor. I think this behavior is being somewhat demonstrated in newer models. I've seen GPT-3.5 175B correct itself mid response with, almost literally: > > Wait, that's not right, that . > . Later models seem to have much more awareness of, or "weight" towards, their own responses, while generating the response.

I'm assuming the "Wait" sentence is from the user. What I mean is that when humans say something, they also tend to have a view (maybe via the famous mirror neurons) of how this now sounds to the other person. They may catch themselves while speaking, changing course mid-sentence, or adding another sentence to soften or highlight something in the previous sentence, or maybe correcting or admitting some aspect after the fact. LLMs don't exhibit such an inner feedback loop, in which they reconsider the effect of the ouput they are in the process of generating.

You won't get an LLM outputting "wait, that's not right" halfway through their original output (unless you prompted them in a way that would trigger such a speech pattern), because no re-evaluation is taking place without further input.

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

#400

Post by bbor »

gchamonlive wrote: post #393

We should put a model in an actual body and let it in the world to build from experiences. Inference is costly though, so the robot would interact during a period and update it's model during another period, flushing the context window (short term memory) into its training set (long term memory).

There are people trying this, both in simulated spaces and real ones - look into the “embodiment” camp if interested to see how they’re doing! There’s many experts who think AGI is unreachable without this, and I think the unexpected intuitive capabilities of LLMs are great support for that thesis, albeit in a non-spatial way.

Kant describes two human “senses”: the intensive sense of time, and the extensive sense of space. In this paradigm, spatial experience would be inextricably tied to all forms of logic, because it helps train the cognitive faculties that are intrinsically tied to all complex (discriminative?) thought.

Post reply on HN