81–90 of 165 posts
Maybe I just don’t understand the article but I really have 0 clue how they go about making their conclusions and really don’t understand what they are saying. I think the 5 issues they provide under “Cognitive Architectures” are severely underspecified to the point where they really don’t _mean_ anything. Because the issues are so underspeficifed I don’t know how their proposed solution solves their proposed problem…
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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-...
This is exactly the problem and these two categories nicely sum up the source of the confusion.
I consider myself in the former camp. The AI needs to determine my intent (book a flight) which is a classification problem, extract out the relevant information (travel date, return date, origin city, destination city, preferred airline) which is a Named Entity Recognition problem, and then call the appropriate API and pass this information as the parameters (tool usage). I'm asking the agent to perform an action on my behalf, and then it's taking my natural language and going from there. The overall workflow is deterministic, but there are elements within it that require some probabilistic reasoning.
Unfortunately, the second camp seems to be winning the day. Creating unrealistic expectations of what can be accomplished by current day LLMs running in a loop while simultaneously providing toy examples of it.
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> People have been talking about agents for at least 2 years. WAY longer than that. What's come to the forefront specifically in the last year or two is very specific subset of the overall agent landscape. What I like to call "LLM Agents". But "Agents" at large date back to at least the 1980's if not before. For some of the history of all of this, see this page and some of the listed citations: https://en.wikipedia.o…
Right - the term "user-agent" shows up in the HTTP/1.0 spec from 1996: https://datatracker.ietf.org/doc/html/rfc1945 and there's plenty of history of debates about the meaning of the term before then. In 1994 people were already complaining that the term that had no universal agreed definition: https://simonwillison.net/2024/Oct/12/michael-wooldridge/
Agent is a funding and marketing term imho Soon it will be AI Microservices
Who wants to invest in my startup, its a Microagent service architectures orchestration platform. All you do is define the inputs, write the agents algorithms, apply agency by inputting a decision tree (ifs and conditionals) and then a function to format output! And the best part? You do all of it in YAML! /sarcasm, hopefully obviously
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They are deterministic at 0 temperature
(Disclaimer: I know literally nothing about LLMs.) Wouldn't there still be issues of sensitivity, though? Like, wouldn't you still have to ensure that the wording of your commands stays exactly the same every time? And with models that take less discrete data (e.g. ChatGPT's new "advanced voice model" that works on audio directly), this seems even harder.
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…
With time, they will get a lot better. IMO, the biggest hurdles the agents currently lack is good implementation of function calling capabilities. LLM's should be used as reasoning engines and everything else should be offloaded to tool use. This will drastically reduce hallucinations and errors in math and all the other areas.
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…
It doesn't have to, the LLM just needs access to a computer. Then it can write the code for Stockfish and execute it. Or just download it, the same way you or I would.
> True AGI or SI would stop trying to be a grand master of everything but rather know what best method/model should be applied to a given problem.
Yep, but I don't see how that relates to LLMs not reaching AGI. They can already write basic Python scripts to answer questions, they just need (vastly) more advanced scripting capabilities.
Maybe I just don’t understand the article but I really have 0 clue how they go about making their conclusions and really don’t understand what they are saying. I think the 5 issues they provide under “Cognitive Architectures” are severely underspecified to the point where they really don’t _mean_ anything. Because the issues are so underspeficifed I don’t know how their proposed solution solves their proposed problem…
Of course it's going to be vague and presumptuous. It's more of a high-level executive summary for tech-adjacent folks than an actual research paper.