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

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

#221

Post by yk »

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: Hey washing machine, this is two t-shirts and a jeans. (The washing machine can then figure out it's program by itself, I don't want to memorize the table in the manual.)

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

#222

Post by ben_w »

hobs wrote: post #102
joe_the_user wrote: post #85

Earlier quoted context omitted.

Tesla are all making rapid progress on functionality which is definitely beyond frontier LLMs because it is distinctly different Sure, that's tautologically true but that doesn't imply that beyondness will lead to significant leaps that offer notable utility like LLMs. Deep Learning overall has been a way around the problem that intelligent behavior is very hard to code and no wants to hire many, many coders needed t…

And worth noting that Tesla faked a ton of its robot footage already, they might be making progress but their physical human robotics does not seem advanced at the moment.

Indeed.

Even assuming the recent robot demo was entirely AI, the only single thing they demonstrated that would have been noteworthy was isolating one voice in a noisy crowd well enough to respond; everything else I saw Optimus do, has already been demonstrated by others.

What makes the uncertainty extra sad, is that a remote controllable humanoid robot is already directly useful for work in hazardous environments, and we know they've got at least that… but Musk would rather it be about the AI.

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

#223

Post by VeejayRampay »

afro88 wrote: post #195
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…

> 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…

really agree with this and I think it's been the general experience: people wanting LLMs to be so great (or making money off them) kind of cherry picking examples that fit their narrative, which LLMs are good at because they produce amazing results some of the time like the deluxe broken clock that they are (they're right many many times a day)

at the end of the day though, it's not exactly reliable or particularly transformative when you get past the party tricks

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

#224

Post by machiaweliczny »

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…

Long context is a scam. Claude is best but it’s still gets lost with longer context

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

#225

Post by Der_Einzige »

dmd wrote: post #166
ngai_aku wrote: post #164

Earlier quoted context omitted.

You’re solving novel problems all day every day?

Pretty much, yes. My job is pretty fun; it mostly entails things like "take this horrible file workflow some research assistant came up with while high 15 years ago and turn it into a newer horrible file format a NEW research assistant came up with (also while high) 3 years ago" - and automate this in our data processing pipeline.

Due to WFH, the weed laws where tech workers live, and the fast tolerance building of cannabis in the body - I estimate that 10% of all code written by west coast tech workers is done “while high” and that estimate is likely low.

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

#227

Post by nuancebydefault »

vundercind wrote: post #196
foxglacier wrote: post #171

Earlier quoted context omitted.

> think or actually “understand” anything It doesn't matter if that's happening or not. That's the whole point of the Chinese room - if it can look like it's understanding, it's indistinguishable from actually understanding. This applies to humans too. I'd say most of our regular social communication is done in a habitual intuitive way without understanding what or why we're communicating. Especially the subtle infor…

There’s a distinction in behavior of a human and a Chinese room when things go wrong—when the rule book doesn’t cover the case at hand. I agree that a hypothetical perfectly-functioning Chinese room is, tautologically, impossible to distinguish from a real person who speaks Chinese, but that’s a thought experiment, not something that can actually exist. There’ll remain places where the “behavior” breaks down in ways…

I would argue maybe people also are not thinking but simply processing. It is known that most of what we do and feel goes automatically (subconsciously).

But even more, maybe consciousness is an invention of our 'explaining self', maybe everything is automatic. I'm convinced this discussion is and will stay philosophical and will never get any conclusion.

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

#228

Post by bbor »

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…

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 through menus.

But all of that’s just the start, the short term, the maturation of this consumer product; the really scary/exciting part comes when the technology reaches saturation, and opens up new possibilities for itself. In the Model-T metaphor, this is analogous to how highways have (arguably) transformed America beyond anyone’s wildest dreams, changing the course of various historical events (eg WWII industrialization, 60s & 70s white flight, early 2000s housing crisis) so much it’s hard to imagine what the country would look like without them. Now, automobiles are not simply passenger transportation, but the bedrock of our commerce, our military, and probably more — through ubiquity alone they unlocked new forms of themselves.

For those doubting my utopian/apocalyptic rhetoric, I implore you to ask yourself one simple question: why are so many experts so worried about AGI? They’ve been leaving in droves from OpenAI, and that’s ultimately what the governance kerfluffle there was. Hinton, a Turing award winner, gave up $$$ to doom-say full time. Why?

My hint is that if your answer involves less then a 1000 specialized LLMs per unified system, then you’re not thinking big enough.

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

#229

Post by epicureanideal »

mikeyouse wrote: post #189
sdenton4 wrote: post #172

Earlier quoted context omitted.

Curiously, Moore's law was predictable enough over decades that you could actually plan for the speed of next year's hardware quite reliably. For LLMs, we don't even know how to reliably measure performance, much less plan for expected improvements.

Moores law became less of a prediction and more of a product road map as time went on. It helped coordinate investment and expectations across the entire industry so everyone involved had the same understanding of timelines and benchmarks. I fully believe more investment would’ve ‘bent the curve’ of the trend line but everyone was making money and there wasn’t a clear benefit to pushing the edge further.

Or maybe it pushed everyone to innovate faster than they otherwise would’ve? I’m very interested to hear your reasoning for the other case though, and I am not strongly committed to the opposite view, or either view for that matter.

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

#230

Post by bbor »

machiaweliczny wrote: post #224
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…

Long context is a scam. Claude is best but it’s still gets lost with longer context

I have no data, but I whole-heartedly agree. Well, perhaps not “scam”, but definitely oversold. One of my best undergrad professors taught me the adage “don’t expect a model to do what a human expert cannot”, and I think it’s still a good rule of thumb. Giving someone an entire book to read before answering your question might help, but it would help way, way more to give them a few paragraphs that you know are actually relevant.
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