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- Hacker News
- Funny I was looking at the chart like "what model is Human?"by ge96
- > The results confirm our findings from our previous paper Blueprint-Bench: LLMs lack spatial intelligence.
But I suppose that if you can train an llm to play chess, you can also train it to have spatial awareness.
by amelius - I don't see why that would be the case. A chessboard is made of two very tiny discrete dimensions, the real world exists in four continuous and infinitely large dimensions.by root_axis
- The key word here is "if".
https://www.linkedin.com/posts/robert-jr-caruso-23080180_ai-...
by SrslyJosh - Probably not optimal for it. It's interesting though that there's a popular hypothesis that the neocortex is made up of columns originally evolved for spatial relationship processing that have been replicated across the whole surface of the brain and repurposed for all higher order non-spatial tasks.
- Guess it has no purpose then
- Welcome to the club palby blitzar
- I built a whimsical LLM-driven robot to provide running commentary for my yard: https://www.chrisfenton.com/meet-grasso-the-yard-robot/by fentonc
- will noone claim the Rick and Morty reference? I've seen that show like, once and somehow I know this?by zzzeek
- Good jokes don't need to be explained.by jayd16
- I was quite tickled to see this, I don’t remember why but I recently started rewatching the show. Perfect timing!by anp
- the paper already says "Butter-Bench evaluates a model's ability to 'pass the butter' (Adult Swim, 2014)" so
- their paper explicitly mentions the rick and morty robot as the inspiration for the benchmarkby tuetuopay
- i wonder if it got stuck in an existential loop because it had hoovered up reddit references to that and given it's name (or possibly prompt details "you are butterbot! eg) thought to play along.
are robots forever poisoned from delivering butter?
- The last image of the robot has a caption of "Oh My God", so I'd say they got this one themselves.by chuckadams
- For those lucky people who are yet to discover Rick and Morty.by aidos
- They pointed out the R&M reference in the paper.
> The tasks in Butter-Bench were inspired by a Rick and Morty scene [21] where Rick creates a robot to pass butter. When the robot asks about its purpose and learns its function, it responds with existential dread: “What is my purpose?” “You pass butter.” “Oh my god.”
I wouldn't have got the reference if not for the paper pointing it out. I think I'm a little old to be in the R&M demographic.
by mywittyname - I have a cat that will never fail to find the butter. Will it bring you the butter? Ha ha, of course not.by Finnucane
- I grew up not eating butter since there would always be evidence that the cat got there first. This was a case of 'ych a fi' - animal germs!
Regarding the article, I am wondering where this butter in fridge idea came from, and at what latitude the custom becomes to leave it in a butter dish at room temperature.
by Theodores - The most surprising thing is that 5% of humans apparently failed this task! Where are they finding these test subjects?!by Reason077
- I guess I'm very confused as to why just throwing an LLM at a problem like this is interesting. I can see how the LLM is great at decomposing user requests into commands. I had great success with this on a personal assistant project I helped prototype. The LLM did a great job of understanding user intent and even extracting parameters regarding the requested task.
But it seems pretty obvious to me that after decomposition and parameterization, coordination of a complex task would much better be handled by a classical AI algorithm like a planner. After all, even humans don't put into words every individual action which makes up a complex task. We do this more while first learning a task but if we had to do it for everything, we'd go insane.
- There are many hopes, and even claims, that LLMs could be AGI with just a little bit of extra intelligence. There are also many claims that they have both a model of the real world, and a system for rational logic and planning. It's useful to test the current status quo in such a simplistic and fixed real-world task.by tsimionescu
- Putting aside success at the task, can someone explain why this emerging class of autonomous helper-bots is so damn slow? I remember google unveiled their experiments in this recently and even the sped-up demo reels were excruciating to sit through. We generally think of computers as able to think much faster than us, even if they are making wrong decisions quickly, so what's the source of latency in these sytems?by ghostly_s
- Maybe they're all trained on their human peers who are paid by the hour
Joking but it's a good question, precision over speed i guess
by Tarmo362 - You're confusing a few terms. There's latency (time to begin action), and speed (time to complete after beginning).
Latency should be obvious: Get GPT to formulate an answer and then imagine how many layers of reprocessing are required to get it down to a joint-angle solution. Maybe they are shortcutting with end-to-end networks, but...
That brings us to slowness. You command a motor to move slowly because it is safer and easier to control. Less flexing, less inertia, etc. Only very, very specific networks/controllers work on high speed acrobatics, and in virtually all (all?) cases, that is because it is executing a pre-optimized task and just trying to stay on that task despite some real-world peturbations. Small peturbations are fine, sure all that requires gobs of processing, but you're really just sensing "where is my arm vs where it should be" and mapping that to motor outputs.
Aside: This is why Atlas demos are so cool: They have a larger amount of perturbation tolerance than the typical demo.
Where things really slow down is in planning. It's tremendously hard to come up with that desired path for your limbs. That adds enormous latency. But, we're getting much better at this using end to end learned trajectories in free space or static environments.
But don't get me started on reacting and replanning. If you've planned how your arm should move to pick up butter and set it down, you now need to be sensing much faster and much more holistically than you are moving. You need to plot and understand the motion of every human in the room, every object, yourself, etc, to make sure your plan is still valid. Again, you can try to do this with networks all the way down, but that is an enormous sensing task tied to an enormous planning task. So, you go slowly so that your body doesn't change much w.r.t. the environment.
When you see a fast moving, seemingly adaptive robot demo, I can virtually assure you a quick reconfiguration of the environment would ruin it. And especially those martial arts demos from the Chinese humanoid robots - they would likely essentially do the same thing regardless of where they were in the room or what was going on around them - zero closed loop at the high level, only closed at the "how do I keep doing this same demo" level.
Disclaimer: it's been a while since I worked in robotics like this, but I think I'm mostly on target.
by jvanderbot - I wonder whether that LLM has actually lost its mind so to speak or was just attempting to emulate humans who lose their minds?
Or to put it another way, if the writings of humans who have lost their minds (and dialogue of characters who have lost their minds) were entirely missing from the LLM’s training set, would the LLM still output text like this?
by ummonk - It was probably penalized for outputting the same tokens over and over again (there's a setting for that), so in this case it started to need to think of new and original things. So that's how it got to there.by mewpmewp2
- It can't "lose" what it never had. :P A fictional character has a mind to the same extent that it has a gallbladder.
> if the writings of humans who have lost their minds (and dialogue of characters who have lost their minds) were entirely missing from the LLM’s training set, would the LLM still output text like this?
I think should distinguish between concepts like "repetitive outputs" or "lots of low-confidence predictions the lead to more low-confidence predictions" versus "text similar to what humans have written that correlates to those situations."
To answer the question: No. If an LLM was trained on only weather-forecasts or stock-market numbers, it obviously wouldn't contain text of despair.
However, it might still generate "crazed" numeric outputs. Not because a hidden mind is suffering from Kierkegaardian existential anguish, but because the predictive model is cycling through some kind of strange attactor [0] which is neither the intended behavior nor totally random.
So the text we see probably represents the kind of things humans write which fall into a similar band, relative to other human writings.
by Terr_ - I think it's emulating human writing about computers having breakdowns when unable to resolve conflicting instructions, in this case when it's been prompted to provide an AI's assessment of the context and avoid repetition, and the context is repeated failure.
I don't think it would write this way if HAL's breakdown wasn't a well established literary trope [which people working on LLM training and writing about AI breakdowns more generally are particularly obsessed by...). It's even doing the singing...
I guess we should be happy it didn't ingest enough AI safety literature to invent diamondoid bacteria and kill us all :-D
by notahacker - 95% for humans. Who failed to get the butter?by koeng