4 ms·
Human behaviour is goal-directed because humans have executive function. When you turn off executive function by going to sleep, your brain will spit out dreams
by strken 7mo ago
Human behaviour is goal-directed because humans have executive function. When you turn off executive function by going to sleep, your brain will spit out dreams. Dream logic is famous for being plausible but unhinged.
I have the feeling that LLMs are effectively running on dream logic, and everything we've done to make them reason properly is insufficient to bring them up to human level.
- whoamii 7mo agoSome of my best code comes from my dreams though.
- satvikpendem 7mo agoA prompt for an LLM is also a goal direction and it'll produce code towards that goal. In the end, it's the human directing it, and the AI is a tool whose code needs review, same as it always has been.
- basch 7mo agoId argue humans have some sort of parallelness going on that machines dont yet. Thoughts happening at multiple abstraction levels simultaneously. As I am doing something, I am also running the continuous improvement cycle in my head, at all four steps concurrently. Is this working, is this the right direction, does this validate? You could build layers and layers of LLMs watching the output of each others thoughts and offering different commentary as they go, folding all the thoughts back together at the end. Currently, a group of agents acts more like a discussion than something somewhat omnipotent or omnitemporal.
- spiderfarmer 7mo agoAnd yet LLM’s are incredibly useful as they are right now.
- strken 7mo agoAnd yet they're going to be better in a decade, which will require understanding why they aren't perfect today.
- nemo44x 7mo agoLLMs are literally goal machines. It’s all they do. So it’s important that you input specific goals for them to work towards. It’s also why logically you want to break the problem into many small problems with concrete goals.
- andai 7mo agoDo you only mean instruct-tuned LLMs? Or the base (pretrained) model too?
- nemo44x 7mo agoThe entire system and the agent loop allows for more complex goal resolution. The LLM models language (obviously) and language is goal oriented so it models goal oriented language. It’s an emergent feature of the system.
- tsunamifury 7mo agoIt’s amazing how much you get wrong here. As LLM attention layers are stacked goal functions. What they lack is multi turn long walk goal functions — which is being solved to some degree by agents.
- strken 7mo agoI don't argue that thinking and attention are missing. I argue that they are trying to do the job of human executive function but aren't as good at it.
- seanmcdirmid 7mo agoIsn’t a modern LLM with thinking tokens fairly goal directed? But yes, we hallucinate in our sleep while LLMs will hallucinate details if the prompt isn’t grounded enough.
- tovej 7mo agoAssuming this is not a rhetorical question: no, it is not. The only "goal" is to maximize plausibility.
- seanmcdirmid 7mo agoAgain, how is that different from humans? I’m not going around trying to prove my code correct when I write it manually.
- tovej 7mo agoI write code to solve a problem. Not code that looks like it solves the problem if a non-technical client squints at it. And if you don't prove your code, do you not design at all then? Do you never draw state diagrams? Every design is an informal proof of the solution. Rarely I write formal proofs. Most of the time I write down enough for myself to be convinced that the desing solves the problem.
- abdullahkhalids 7mo agoYou are correct. However, humans sometimes do write stuff that "looks like it solves the problem". A prime example of this is a student who doesn't know how to answer a question. So they make up a plausible sounding answer. As a exam grader, you can easily tell when a student has the mindset of "solving a problem" but made a mistake, and when they had the mindset of "looks like it solves the problem" and just wrote some stuff.
- seanmcdirmid 7mo agoYes, you can dedicate extra tokens to draw state diagrams, the LLM can actually do that, if you don't have it generating one or more design documents before you are writing code you are doing that wrong. I still don't get how that is different from what humans are doing. > Most of the time I write down enough for myself to be convinced that the desing solves the problem. Again, why do you assume we aren't doing the same thing with LLMs? 1. Spec given 2. Ask LLM to write a bunch of design documents based off of spec 3. Ask LLM to identify edge cases 4. Ask LLM to device edge cases in to a test plan involving N tests 5. Ask LLM to write tests 6. Ask LLM to write commented code 7. Ask LLM to run tests on code, and determine on failing tests if test or code is wrong, go back to the appropriate step to fix test and/or code. Whenever I hear someone here on HN imply that the only way to code with an AI is via vibe coding I just die a bit more inside.