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Agents Are Not Enough

arxiv.org

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Re: Agents Are Not Enough

#91

Post by Terr_ »

lugu wrote: post #87
wkat4242 wrote: post #65

Earlier quoted context omitted.

Totally agree. An LLM won't be an AGI. It could be part of an AGI, specifically the human interface part. That's what an LLM is good at. The rest (knowledge oracle, reasoning etc) are just things that kinda work as a side-effect. Other types of AI models are going to be better at that. It's just that since the masses found that they can talk to an AI like a human they think that it's got human capabilities too. But i…

I am not sure what you mean by LLM when you say they are professional bullshitter. While it was certainly true for model based on transformers just doing inference, recent models have progressed significantly.

> I am not sure what you mean by LLM when you say they are professional bullshitter.

Not parent-poster, but an LLM is a tool for extending a document by choosing whatever statistically-seems-right based on other documents, and it does so with no consideration of worldly facts and no modeling of logical prepositions or contradictions. (Which also relates to math problems.) If it has been fed on documents with logic puzzles and prior tests, it may give plausible answers, but tweaking the test to avoid the pattern-marching can still reveal that it was a sham.

The word "bullshit" is appropriate because human bullshitter is someone who picks whatever "seems right" with no particular relation to facts or logical consistency. It just doesn't matter to them. Meanwhile, a "liar" can actually have a harder job, since they must track what is/isn't true and craft a story that is as internally-consistent as possible.

Adding more parts around and LLM won't change that: Even if you add some external sensors, a calculator, a SAT solver, etc. to create a document with facts in it, once you ask the LLM to make the document bigger, it's going to be bullshitting the additions.

Re: Agents Are Not Enough

#92

Post by HarHarVeryFunny »

georgestrakhov wrote: post #28

IMHO, the word agent is quickly becoming meaningless. The amount of agency that sits with the program vs. the user is something that changes gradually. So we should think about these things in terms of how much agency are we willing to give away in each case and for what gain[1]. Then the ecosystem question that the paper is trying to solve will actually solve itself, because it is already the case today that in many…

An agent, or something that has agency, is just something that takes some action, which could be anything from a thermostat regulating the temperature all the way up to an autonomous entity such as an animal going about it's business.

Hugging Face have their own definitions of a few different types of agent/agentic system here:

https://huggingface.co/docs/smolagents/en/conceptual_guides/...

As related to LLMs, it seems most people are using "agent" to refer to systems that use LLMs to achieve some goal - maybe a fairly narrow business objective/function that can be accomplished by using one or more LLMs as a tool to accomplish various parts of the task.

Re: Agents Are Not Enough

#93

Post by Terr_ »

wkat4242 wrote: post #65
tonetegeatinst wrote: post #59

Somewhat related but here's my take on super intelligence or AGI. I have worked with CNN,GNN and other old school AI methods, but don't have the resources to build a real SOT LLM, but I do use and tinker with LLM's occasionally. If AGI or SI(super intelligence)/is possible, and that is an if...I don't think LLM's are going to be this silver bullet solution Just as we have in the real world of people who are dedicated…

Totally agree. An LLM won't be an AGI. It could be part of an AGI, specifically the human interface part. That's what an LLM is good at. The rest (knowledge oracle, reasoning etc) are just things that kinda work as a side-effect. Other types of AI models are going to be better at that. It's just that since the masses found that they can talk to an AI like a human they think that it's got human capabilities too. But i…

> It's just that since the masses found that they can talk to an AI like a human

In a way it's worse: Even the "talking to" part is an illusion, and unfortunately a lot of technical people have trouble remembering it too.

In truth, the LLM is an idiot-savant which dreams up "fitting" additions to a given document. Some humans have prepared a document which is in the form of a a theater-play or a turn-based chat transcript, with a pre-written character that is often described as a helpful robot. Then the humans launch some code that "acts out" any text that looks like it came from that fictional character, and inserts whatever the real-human-user types as dialogue for the document's human-character.

There's zero reason to believe that the LLM is "recognizing itself" in the story, or that is is choosing to self-insert itself into one of the characters. It's not having a conversation. It's not interacting with the world. It's just coded to Make Document Bigger Somehow.

> they think that it's got human capabilities too

Yeah, we easily confuse the character with the author. If I write an obviously-dumb algorithm which slaps together a story, it's still a dumb algorithm no matter how smart the robot in the story is.

Re: Agents Are Not Enough

#94

Post by nuancebydefault »

tonetegeatinst wrote: post #59

Somewhat related but here's my take on super intelligence or AGI. I have worked with CNN,GNN and other old school AI methods, but don't have the resources to build a real SOT LLM, but I do use and tinker with LLM's occasionally. If AGI or SI(super intelligence)/is possible, and that is an if...I don't think LLM's are going to be this silver bullet solution Just as we have in the real world of people who are dedicated…

Indeed! That's what I have been thinking for a while but I never had the occasion and or breath to write it down, and you explained it concisely. Finally some 'confirmation' 'bias'...

Re: Agents Are Not Enough

#95

Post by lukeplato »

tonetegeatinst wrote: post #59

Somewhat related but here's my take on super intelligence or AGI. I have worked with CNN,GNN and other old school AI methods, but don't have the resources to build a real SOT LLM, but I do use and tinker with LLM's occasionally. If AGI or SI(super intelligence)/is possible, and that is an if...I don't think LLM's are going to be this silver bullet solution Just as we have in the real world of people who are dedicated…

I don't see why a mixture of experts couldn't be distilled into a single model and unified latent space

Re: Agents Are Not Enough

#96

Post by phaedrus »

tonetegeatinst wrote: post #59

Somewhat related but here's my take on super intelligence or AGI. I have worked with CNN,GNN and other old school AI methods, but don't have the resources to build a real SOT LLM, but I do use and tinker with LLM's occasionally. If AGI or SI(super intelligence)/is possible, and that is an if...I don't think LLM's are going to be this silver bullet solution Just as we have in the real world of people who are dedicated…

But the G in AGI stands for General. I think the hope is that there is some as-yet-undiscovered algorithm for general intelligence. While I agree that deferring to a subsystem that is an expert in that type of problem is the best way to handle problems, I would hope that it is possible that that central coordinator not just be able to delegate but design new subsystems as needed. Otherwise what happens when you run out of types of expert problem solvers to use (and still haven't solved the problem well)?

One might argue maybe a mixture of experts is just the best that can be done - and that it's unlikely the AGI be able to design new experts itself. However where do the limited existing expert problem solvers come from? Well - we invented them. Human intelligences. So to argue that an AGI could NOT come up with its own novel expert problem solvers implies there is something ineffable about human general intelligence that can't be replicated by machine intelligence (which I don't agree with).

Re: Agents Are Not Enough

#97

Post by shekhargulati »

kgeist wrote: post #6
GiorgioG wrote: post #3

Earlier quoted context omitted.

re: LLM as UI: Given that I don't trust LLMs to be deterministic, I wouldn't trust them to make the correct API call every time I tell it to do X.

I think most users have a fixed set of workflows which usually don't change from day to day, so why not just use LLMs as a macro builder with a natural language interface (and which doesn't require you to know the product's UI well beforehand): - you ask LLM to build a workflow for your problem - the LLM builds the workflow (macro) using predefined commands - you review the workflow (can be an intuitive list of comma…

This is the same approach we took when we added LLM capability to a low code tool Appian. LLM helped us generate the Appian workflow configuration file, user reviews it and make changes if required, and then finally publishes it.

Re: Agents Are Not Enough

#99

Post by Upvoter33 »

tonetegeatinst wrote: post #59

Somewhat related but here's my take on super intelligence or AGI. I have worked with CNN,GNN and other old school AI methods, but don't have the resources to build a real SOT LLM, but I do use and tinker with LLM's occasionally. If AGI or SI(super intelligence)/is possible, and that is an if...I don't think LLM's are going to be this silver bullet solution Just as we have in the real world of people who are dedicated…

"If AGI ... is possible"

I don't get this line of thinking. AGI already exists - it's in our heads!

So then the question is: is what's in our heads magic, or can we build it? If you think it's magic, fine - no point arguing. But if not, we will build it one day.

Re: Agents Are Not Enough

#100

Post by ethbr1 »

simonw wrote: post #7
adpirz wrote: post #5

Earlier quoted context omitted.

You posted on X a while back asking for a crowdsourced definition of what an "agent" was and I regularly cite that thread as an example of the fact that this word is so blurry right now.

I really need to write that up in one place - closest I've got is this section from my 2024 review https://simonwillison.net/2024/Dec/31/llms-in-2024/#-agents-...

The language problem around agents is that most companies are describing them solely from a human/UX perspective.

'You ask it to do something, and it does it'

That makes it difficult to differentiate the more critical 'how' options in the execution process. From that perspective: deterministic integrations, LLM+tools, LAM, etc are more descriptive categories, each with their own capabilities, strengths, and weaknesses.

Or to put it a different way, if the term doesn't tell you what something is good and bad at, it's probably an underspecified term.

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