These AIs are incredible when it comes to question/answer, but with simple planning they fall apart. I feel like it's something that could be trained for more specifically, but yea you quickly end up being in a situation where you are nervous to go to sleep with AI unsupervised working on some task.
They tend to go off on tangents very easily. Like one time it was building a web page, it tried testing the wrong URL, thought the web server was down, ripped through the server settings, then installed a new web server, before I shut it down. AI like computer programs work fast, screw up fast, and compound their errors fast.
> with simple planning they fall apart
They are not remotely close to acting autonomously. Most don't even act well at all for much of anything but gimmicky text generation. This hype is so overblown.
Do you think these step changes in autonomy have stopped? Why?
It's the same when I have a conversation with it, then tell it to ignore something I said and it keeps referring to it. That part of the conversation seems to affect its probabilities somehow, throwing it off course.
The fact that these things work at all is amazing, and the fact that they can be RLHF'ed and prompt-engineered to current state of the art is even more amazing. But we will probably need more sophisticated systems to be able to build agents that resemble thinking creatures.
In particular, humans seem to have a much wider variety of "memory bank" than the current generation of LLM, which only has "learned parameters" and "context window".
Two steps deeper; even a mere Markov chain replicates the patterns rather than being limited to pure quotation of the source material, attention mechanisms do something more, something which at least superficially seems like reason.
Not, I'm told, actually Turing compete, but still much more than mere replication.
> It's the same when I have a conversation with it, then tell it to ignore something I said and it keeps referring to it. That part of the conversation seems to affect its probabilities somehow, throwing it off course.
Yeah, but I see that a lot in real humans, too. Have noticed others doing that since I was a kid myself.
Not that this makes the LLMs any better or less annoying when it happens :P
They feel like they are asymptotically approaching just a bit better quality than GPT-4.
Given every major lab except Meta is saying "this might be dangerous, can we all agree to go slow and have enforcement of that to work around the prisoner's dilemma?", this may be intentional.
On the other hand, because nobody really knows what "intelligence" is yet, we're only making architectural improvements by luck, and then scaling them up as far as possible before the money runs out.
Both are sufficient even in isolation.
I also have a tendency to get side tracked and the only remedy was to force myself to occasionally pause what I'm doing and then reflect, usually during a long walk.
Inter-agent tasks is a fun one. Sometimes it works out, but a lot of the time they just end up going back and forth talking, expanding the scope endlessly, scheduling 'meetings' that will never happen, etc..
A lot of AI 'agent systems' right now add a ton of scaffolding to corral the AI towards success. The scaffolding is inversely proportional to the sophistication of the model. GPT-3 needs a ton, Opus needs a lot less.
Real autonomous AI you should just be able to give a command prompt and a task and it can do the rest. Managing it's own notes, tasks, goals, reports, etc.. Just like if any of us were given a command shell and task to complete.
Personally I think it's just a matter of the right training. I'm not sure if any of these AI benchmarks focus on autonomy, but if they did maybe the models would be better at autonomous tasks.
sounds like "a straight shooter with upper management written all over it"
What we should do is train AI on self-help books like the '7 habits of highly productive people'. Let's see how many paperclips we get out of that.
When I read a book, for example, I do not keep all of it in my short-term working memory, but I also don't entirely forget what I read at the beginning by the time I get to the end: it's something in between. More layered forms of memory would probably allow us to return to smaller context windows.
And then in terms of reading a book, a model's training could be updated with the book, right?
At least it just decided to replace the web server, not itself. We could end up in a sorcerer’s apprentice scenario if an AI ever decides to train more AI.
2. It breaks out and in the process uses the breakout to trigger the spread of and further prompts against copies of itself.
Current models are still way too dumb to do most of this themselves, but simple worms (e.g. look up the Morris worm) require no reasoning and aren't very complex, so it won't necessarily take all that much when coupled with someone probing what they can get it to do.
I could pick out all kinds of useful software that are significantly simpler than GPT-4, but accomplish very sophisticated tasks that GPT-4 could never accomplish.