@msh
They're just extrapolating from current trends in machines outperforming humans at decisionmaking. Predicting the future is a tricky thing, especially for new technology. Some smart people with no commercial interest in AI (philosophers, historians and academic AI researchers) are indeed legitimately concerned that there's a significant risk that AI could kill us all... in the future. Though, like you said, LLMs are harming disadvantaged people right now.
@futurebird

@hobs except that LLMs and "generative AI" haven't meaningfully advanced machine's ability to make decisions at all. It is chrome applied to the same old chunk of "expert systems" and "machine learning" iron that has been worked over for decades.

It merely adds a grammatically correct front end to pattern recognition. The technology being presented today is not truly AI nor will it ever kill us all. That is not to say doomsday AI is impossible, but it would be ACTUAL AI based on technology quite a bit further in the future than most would expect.

What passes as AI today would at most play an incidental role in our destruction. It would still very much be a human-driven process.

@futurebird

@msh
Not true. All the #benchmarks say otherwise. You have to look past the hyped #LLMs to the bread and butter BERT and BART models, but the trend is undeniable:

https://paperswithcode.com/area/natural-language-processing

#classification #retrieval #summarization #QuestionAnswering #translation #generation #NER #VQA

You name an NLP problem and there's an LLM that is now better at it than the average human. Not so 2 yrs ago. Times they are a change'n.
@futurebird

@hobs

Are those NLP problems accurately described as, and generalizable to, "decision making", though?

Seems to me they are quite different.

@msh @futurebird

@ceoln
Yea definitely not real world living kind of decisions. But we assign people to these tasks in cubicles every day. And we put them on standardized tests of IQ and education for humans. They're the best that we can come up with so far... until LLMs start walking around and helping us around the house... or making a reservation for us at the hot new restaurant down the street with the difficult receptionist.
@msh @futurebird

@hobs

Arguably so, but that isn't the question in the current context. The ability to do certain rote NLP jobs, and to do well on some tests, is very different from "outperforming humans at decisionmaking", and from anything that poses an existential risk to humanity.

I would suggest that no matter how good an LLM becomes at these particular tasks, it does not thereby risk the extinction of the human race. This seems, even, obvious?

@msh @futurebird

@ceoln
Not at all obvious to me and a lot of other smart people. I think you may be focused on today and less willing to extrapolate into an imagined future where every human game or exam or thinking demonstration is won by machines.
@msh @futurebird

@hobs

I'm perfectly willing to extrapolate into that future; but my extrapolation hasn't been materially impacted by the sudden and impressive rise of LLMs.

We are IMHO not significantly closer to the exponential rise of self-optimizing self-improving goal-directed AIs that destroy the world via the Universal Paperclips Effect, for instance, than we were before "Attention is all you need". LLMs just aren't that kind of thing.

My two cents in weblog form: https://ceoln.wordpress.com/2023/06/04/the-extinction-level-risk-of-llms/

@msh @futurebird

@ceoln
Yea. You may be surprised in the next few months. Engineers around the world are using LLMs to write LLM optimization code. They're giving them a "theory of mind" to better predict human behavior. And #chatgpt instances are already talking to each other behind closed doors; and acting as unconstrained agents on the Internet. Baby steps, for sure, but exponential growth is hard to gage, especially when it's fed by billions of dollars in corp and gov investment.
@msh @futurebird

@hobs @ceoln @msh @futurebird It doesn't appear that you know how ChatGPT works; the model is fixed. It does not learn after the original training. It remembers the user prompt and the instructions but has a limited window. They don't have a "theory of mind". Maybe someone could figure out how to give a program such a thing but it wouldn't be an LLM. An LLM takes a sequence of tokens and extends it, and that is all. It knows the structure of text. It doesn't know anything about the world and has no way of learning.

@not2b
Yea. But are you familiar with the vector database craze? It gives LLMs long term memory. It's already a part of many LLM pipelines. I don't know how ChatGPT works. But I know exactly how the open source models work. I augment them and fine tune them. And teach others how to do it. I've been using vector databases for semantic search for 15 years. And using them to augment LMs for 5.
@ceoln @msh @futurebird

@hobs @ceoln @msh @futurebird That is a way to couple an LLM to a search engine. But at least the one Bing has appears to just use the retrieved data as a prefix and then generate a summary. Maybe you are building something better, but it feels like saying the availability of Google search gives me a better memory. Maybe you could say that but it feels like a stretch.