[HN Gopher] Deep Neural Nets: 33 years ago and 33 years from now...
___________________________________________________________________
Deep Neural Nets: 33 years ago and 33 years from now (2022)
Author : gsky
Score : 258 points
Date : 2023-08-26 00:13 UTC (22 hours ago)
(HTM) web link (karpathy.github.io)
(TXT) w3m dump (karpathy.github.io)
| Nevermark wrote:
| The most fundamental change is the difference in _what_ models
| are being trained on.
|
| Little images of characters is a trivia type problem, very
| different from training on the linguistic and visual
| communication of essentially the whole human race.
|
| Another 33 years of expanded computing resources won't be
| training models to mimic the behavior and knowledge of humanity.
|
| That problem ( _us!_ ) will have been reduced to a toy problem
| long before then.
| visarga wrote:
| I think AI models will evolve by generating synthetic data,
| filtering and improving it, and then retraining. Possibly with
| external systems in the loop - code execution, search, human,
| simulation or robot. Quality won't degrade because there will
| be a lot of effort put into data filtering and diversity. We
| can always improve on a model by giving it more time.
|
| Model architecture doesn't matter compared to the dataset. Any
| model from a class can learn the same skills from the same
| data, but change the data and they all change their abilities -
| the intelligence is in the data.
|
| The future is data engineering, not model architecturing. Human
| culture, by analogy, evolves faster than human biology. The
| data is evolving faster than the model. And we are seeing a
| drastic reduction in novel architectures in AI, diverse
| datasets applied to the same transformer models in recent
| years. Even among the transformers, very few variants are
| largely used, thousands of them abandoned.
|
| I like to think of it as language evolution by memetics being
| the real engine behind intelligence. We and AI are riding the
| language exponential together.
| eru wrote:
| > Model architecture doesn't matter compared to the dataset.
| Any model from a class can learn the same skills from the
| same data, but change the data and they all change their
| abilities - the intelligence is in the data.
|
| You might be right in the same sense that big-O notation is
| 'right'. Constant factor can matter; especially once you have
| to take energy use into account.
| ResearchCode wrote:
| Come close to solving the toy problem of autonomous driving
| first, we're still waiting.
| Nevermark wrote:
| I don't know. I find pessimistic views, like you are
| expressing, very strange.
|
| My Tesla drives and navigates itself most of the time. 90-95%
| at least, just not 100%.
|
| As apposed to cars 10 or more years ago which didn't do any
| of that.
|
| To me it is much like the "God of the Gaps" when tremendous
| progress on a big problem is dismissed negatively, due to the
| (continuously shrinking) gaps of what it can't do.
| ResearchCode wrote:
| We are already five years late in autonomous vehicles
| replacing all truck drivers. We will see if we even have
| autonomous driving "long before" 33 years have passed.
| "AGI" (rebranded AI after "AI" failed to deliver) will of
| course still not be a thing.
| Nevermark wrote:
| I have no idea what "late" technology means.
|
| And not delivering AGI yet is a problem?
|
| What are these broad technology schedule based criticisms
| founded on?
|
| I really want to understand this viewpoint!
|
| Hopefully not the over-optimism of anyone who uses
| optimistic timelines as a motivational force. That's not
| real data. Or a suitable benchmark for human progress.
| cma wrote:
| Waymo and Cruise are operating without drivers which is
| much more impressive, even if in limited areas.
| Nevermark wrote:
| I don't know if it is much more impressive than Tesla,
| given Tesla's "limited areas" don't seem very limiting in
| my experience.
|
| But I think all these complementary takes on the problem,
| with significant year-to-year progress by all three
| firms, are fantastic.
|
| That used to be considered a fast learning curve!
| breckinloggins wrote:
| I really enjoyed this article. My only critique is that the 2055
| predictions are "meta-linear". In other words: the author avoids
| the (probable) mistake of taking our current tech and linearly
| regressing the numbers 33 years forward, but the predictions
| still suggest a kind of "worldline symmetry" with the present
| date at the origin.
|
| It's quite possible that none of these predictions will come true
| simply because the timeframe is large enough for many
| unanticipated breakthroughs and roadblocks.
|
| Maybe someone will figure out a much, much simpler foundational
| architecture than "perceptrons++", maybe we'll all be training
| clouds of 3D gaussians, maybe quantum computers will finally take
| off and we don't even have the nouns for the building blocks
| we'll use.
|
| On the negative side perhaps we hit a hard scaling limit (in
| hardware or training) that we didn't see coming. Or a
| civilizational setback.
|
| All that said, though, if I were a betting man I wouldn't exactly
| wager against the article's conclusions; they're probably the
| best we can extrapolate knowing only the past and present state
| of affairs.
| Nevermark wrote:
| I think you are right, the next 33 years are likely to be very
| different.
|
| I would lean to them being even more dramatic, due to the
| opportunity to advance algorithms, not just resources.
|
| On the more obvious side, most libraries are not yet taking
| full advantage of many known gradient optimization techniques.
| It's been so much easier to just add data & processing that
| there is an overhangs of tools to still apply.
|
| And large successful models are telling us important things.
|
| For instance, it is clear that language models are learning a
| kind of logic of language similar to how we process thoughts,
| allowing highly disparate types information to be woven
| together sensibly.
|
| At some point, identifying the nature of that processing could
| radically simplify language processing.
|
| That is just one opportunity for radical architecture and
| algorithm advances, and it would be revolutionary.
| [deleted]
| version_five wrote:
| This was really good. The only thing I didn't see explicitly
| discussed, although I guess it's obvious, is that what's
| different 33 years later is the inputs the models operate on. The
| '89 sota model used 16x16 greyscale images, today we have single
| digit megapixel color images, in 30 years, a desktop will be able
| to train Clip in 90 seconds, but what will the sota models be
| trained on?
| eigenvalue wrote:
| We might have megapixel images that we can easily get with
| phone cameras, but virtually all vision models in common use
| take 224x224 resolution images as input, or maybe 384x384.
| Anything higher resolution than that just gets resampled down.
| It seems that you are better off using your compute budget on a
| bigger "brain" than on better "eyes" for now.
| version_five wrote:
| I don't think that's current. Certainly the object detection
| models work on bigger images, and the datasets they're
| pretrained on e.g. coco are not 224x224. I think standard
| models pretrained on imagenet, like the Resnets usually have
| everything resized to 224x224, and so they favor this kind of
| scaling.
| ramblerman wrote:
| Millions of hours of data captured by headsets like the vision
| pro?
|
| Not sure all the things it captures, but a model could be
| trained on the combination of audio/video/spatial/iris/what
| have you...
| retrac wrote:
| Human behaviour in a way far more general than which token we
| might next type. To mimick humans as closely as might be
| possible with the basic deep learning method, train something
| that can predict human behaviour in general. Training would
| require billions to quadrillions of hours of video and audio
| and probably many other inputs, from many different people,
| engaged in the full variety of human activity.
| quickthrower2 wrote:
| Humans have a brain that physically changes especially in
| childhood, so that is potentially a massive advantage.
| reducesuffering wrote:
| Why? An adult by 25 only has 146k hours of video experience
| "training," most of it repeated, derivative, and
| unproductive. And their encoded genes can be observed in
| their genome, so don't need to be retrained by millions of
| years of evolution.
| thaw13579 wrote:
| Much of that time also includes physical interaction with
| the world, which makes it far more valuable because it can
| improve performance in a focused way.
| [deleted]
| canjobear wrote:
| Neural nets seem to learn much slower than humans. Even
| GPT-2 has seen orders of magnitude more tokens of language
| than a human experiences in a lifetime. At least as far as
| language is concerned, humans are able to extract a lot
| more information from their training data.
| mkaic wrote:
| Humans are also extensively pretrained by billions of
| years of evolution, so by starting from scratch GPT is
| admittedly disadvantaged from the get-go.
| georgeg23 wrote:
| But the human genome is only 3 gigabytes and the vast
| majority of that is unlikely to be encoding brain
| structure.
| retrac wrote:
| "Only" 3 gigabytes.
|
| The lambda calculus (a system we know is capable of
| infinite self-complexity, learning, etc. with the right
| program) can be described in a few hundred bits. And a
| neural net can be described in the lambda calculus in
| perhaps a few thousand bits.
|
| Also, we have no idea how "compressed" the genome is.
| georgeg23 wrote:
| Basic structure can be encoded yes (and obviously is
| given brains have consistent structure), but the weights
| or parameters, presuming that brains learn via synaptic
| weights, obviously do not fit in the genome.
|
| Compression still must obey information theory.
| canjobear wrote:
| True, but we don't know how to recreate that kind of
| pretraining.
| edgyquant wrote:
| Humans do not train on video, the idea of a video, or even
| a frame, is a high level abstraction within the human
| brain.
| alanbernstein wrote:
| How better to learn to do menial physical tasks like house
| cleaning, and produce picking?
| dang wrote:
| Related:
|
| _Deep Neural Nets: 33 years ago and 33 years from now_ -
| https://news.ycombinator.com/item?id=30673821 - March 2022 (5
| comments)
| antirez wrote:
| Always refreshing reading Andrej Karpathy: the more he knows
| stuff, the more he explores the fundamentals of the science of ML
| in a direct and simple way. The field is full of papers that for
| a very hard to reproduce gain in some new convoluted architecture
| (in the hope to beat some state of art result) will happily fill
| 50 useless pages trying to make their works "serious".
| mark_l_watson wrote:
| Great article. I lived through the early days of artificial
| neural networks. I was on a DARPA advisory panel for neural
| network tooling in the mid 1980s, wrote the first version of the
| SAIC ANSim commercial product, and created the simple back-prop
| model that was deployed in the bomb detector my company built
| under contract to the FAA. I also managed a 'conventional' deep
| learning team at Capital One 5-6 years ago.
|
| My world has been very exciting in the last 18 months. I spend as
| much time as I can exploring self hosted LLMs, APIs from Hugging
| Face, OpenAI, etc.
|
| My mind is blown even thinking about tech 33 years from now!
| bilsbie wrote:
| It's interesting in that time we almost completely lost interest
| in neural networks and then came back around to them.
| sroussey wrote:
| I had to retake my AI class at university several times because
| I just didn't agree on the "AI is symbolic search" aspect.
|
| Now though, I'm sure people are taking LLMs and putting them
| together to do forward and backward chaining.
| jacquesm wrote:
| Thank Hinton for that. It's a pity we don't have a Nobel for
| software.
|
| But a Turing award is pretty neat as well.
| Sharlin wrote:
| In this case there are good reasons for the resurgence, but
| that's really the case with pretty much anything software-
| related. Except the fashion cycles tend to be shorter with more
| mainstream technologies.
| m-i-l wrote:
| So should we spend the next 33 years doing the same things, just
| with more data and more compute power? That would be the logical
| conclusion of the breathless "I can't believe it is finally
| happening in my lifetime" and "we just need bigger models and
| more data" enthusiasm for LLMs when they first appeared. But can
| we really simply brute force our way to AGI?
|
| Remember, 33 years ago "connectionist AI" wasn't the dominant AI
| paradigm, and "symbolic AI" wasn't the only other approach either
| - there were others, like "robotic functionalism" (the idea that
| you couldn't have true intelligence with interacting with the
| physical world). Maybe in 33 years some of these other approaches
| will have a resurgence, perhaps in combination with connectionist
| approaches. Or maybe they'll even be some entirely new approach.
| Funtech007 wrote:
| [flagged]
| fnord77 wrote:
| > Our datasets and models today [2055] look like a joke. Both are
| somewhere around 10,000,000X larger.
|
| will there really be 10 million times 400 million images floating
| around then?
| ramblerman wrote:
| I think you are limiting yourself by thinking of the dataset of
| the future as just being more and bigger images.
|
| Perhaps it will be trained on whole videos, or a combination of
| different inputs from agents that move about in the real world
| / or a video game.
| Lucasoato wrote:
| Maybe the real game changer in the future will be the ability
| to train the same model on very different kind of inputs like
| video, images, text, audio... Imagine also all these data
| cleaning tasks are already automated, you just need to feed
| the model PDFs and automatically a support model will extract
| all the relevant metadata... or probably you'll just be able
| to select a set of books from an online library and your
| model will train on them as well (of course for a non trivial
| subscription lol)
| sroussey wrote:
| Generate as many as you need.
| sroussey wrote:
| Oh, also curious... today, how many individual image frames
| from video are there just from Tesla vehicles?
| alpaca128 wrote:
| Training models from generated content degrades them over
| time.
| pyinstallwoes wrote:
| Yet science fiction pushes civilization towards novelty
| Philpax wrote:
| The generated results can come from other means - for
| example, pretraining on rendered CG imagery is quite
| popular in the computer vision world, especially for
| problems where acquiring ground truth data in the real
| world is quite difficult.
| djantje wrote:
| Maybe, but the input in 2055 will be more something in the form
| of continuous/realtime data input streams.
| ben_w wrote:
| 10e6*400e6/8e9/365/18 = 76 images per person per waking hour;
| it's not implausible given how many cameras there are and how
| many moments people might snap to share with remote friends --
| I can easily believe we'll have always-on video chat with
| multiple people in AR glasses by that point.
| anonzzzies wrote:
| Most images are not shared though; just snapped. In the past
| you had photo albums no-one ever looked in. And that weren't
| that many pics; now , whenever, people (old and young) take
| 100s of pictures, on iPhones often by holding the button so
| it zaps 100s of them in a few seconds.
| ben_w wrote:
| > Most images are not shared though
|
| Not _yet_.
|
| As the joke goes:
|
| People in the 60s:
|
| I better not say that or the government will wiretap my
| house
|
| People today:
|
| Hey wiretap, do you have a recipe for pancakes?
| cma wrote:
| Maybe you won't receive your "world coin" universal income
| dividend unless you livestream 24/7.
| alexmuro wrote:
| It's crazy how little has changed and how much had changed. I
| remember what a revelation "the unreasonable effectiveness of
| RNNs" was when I was read it and it feels like we live in a
| different world.
| benreesman wrote:
| I think we could collectively more constructive and sober
| conversation if we kept that 2015 bit of work as a sort of
| baseline.
|
| The new stuff is better, by a lot, and with implications more
| to come.
|
| But those of us paying attention then had a frame of reference
| where "so much better it's crazy" still stops short of "it's
| out of control".
|
| It's a lot better.
| dsign wrote:
| It's not clear that compute will scale as it did for the next 33
| years. But it doesn't really need to.
|
| I read the article and I was thinking "my God, I remember I used
| MSE that weekend in my pet ML project and it really didn't work
| out that well; wrong loss function." Our current crop of LLMs, or
| the one next year, will be perfectly able to tell me how I can
| improve my code and graphs, which means that I can deploy some
| expert-level techniques that otherwise would be "locked" to me by
| 50000 hours of "mastery acquisition".
|
| A part of me is telling me that we humans are doomed, and that in
| 33 years we would have created a world in which we humans are
| irrelevant. But another part tells me that if we avoid that fate
| and all the other dooms, the future might just be quite bright.
| somewhereoutth wrote:
| > or the one next year
|
| We have heard, and will continue to hear, this sort of thing
| rather a lot. The last 5 yards are the hardest, but without
| them the previous 5 miles are of limited utility.
| phkahler wrote:
| >> The original network trained for 3 days on a SUN-4/260
| workstation.
|
| This is exactly why I didn't start experimenting with this stuff
| back then. I read some articles and had the interest, but having
| no access to existing training data or "fast" computers was
| really a show stopper. This article really convinced me that the
| amazing results today are mostly due to hardware advances.
|
| I will add my own view that 1) hardware will not be advancing
| anywhere near so much in the future. And 2) training and
| inference have to be done together like real brains do. Then the
| AI will learn from experience while deployed and you can clone
| the best ones later.
| 0xDEF wrote:
| >This article really convinced me that the amazing results
| today are mostly due to hardware advances.
|
| For LLMs that is true. But many other things like Whisper,
| Stable Diffusion etc. could in theory have been made a decade
| earlier.
| mk_stjames wrote:
| Something else I find exciting, starting with one of the
| reflections-
|
| The original training took 3 days on a Sun 4/260 workstation; I
| can't find specifics but I believe that era of early SPARC
| workstations would likely pull about 200 watts in total (the CPU
| wasn't super high powered but the whole system, running with the
| disks and the monitor etc would pull about that).
|
| So 200 watts * 72 hours = 14400 watt-hours of energy.
|
| Karpathy trained the equivalent on a Macbook, not even fully
| utilized, in 90 seconds. Likely something around 20 watts * 0.025
| hours = 0.5 watt-hours.
|
| An energy efficiency improvement of nearly 30000x.
| Yajirobe wrote:
| > watt-hours
|
| You mean joules (up to a constant factor)?
| renonce wrote:
| A watt-hour is 3600 joules but watt-hours or kilowatt-hours
| is commonly used because it's easier to calculate.
| Aardwolf wrote:
| 30k doesn't even sound like that much to me given Moore's law.
| I'd expect more improvement since 1989. Supercomputer
| performance increased more than a million since then
| fooker wrote:
| Amdahl's law
| cma wrote:
| 33 years ago is 2000/1999
| fHr wrote:
| quickest maffs
| Philpax wrote:
| Um... you might want to check your tens digit.
| swores wrote:
| My (wrong) intuition on reading your comment was that you
| were over-estimating the expected growth in performance over
| that time period, but actually after checking the maths based
| on Moore's Law, i.e. doubling every two years (though of
| course I understand that was a rough estimate, more of a
| concept prediction than expected to be precise) you're right
| so I'll share the maths for anyone else whose intuition might
| be as poor as mine:
|
| Doubling every 2 years = compound annual growth rate (CAGR)
| of ~41.42% CAGR = ((End Value / Start
| Value)^(1 / Number of Years)) - 1 ((2 / 1)^(1 /
| 2)) - 1 = 0.41421356237
|
| Therefore in 34 years since then: 1 * (1 +
| 0.41421356237)^34 = ~131,072
|
| So x30k is ~4.4x less than 131k. Then again, that's
| equivalent to ~x1.833 every two years, compared to Moore's
| Law of x2 every two years, so only ~8% less growth per two
| years, which coming back to the fact that Moore's Law is a
| rough estimate concept not an exact fact, doesn't seem to far
| off!
| jacquesm wrote:
| The rest of that difference can easily be explained by the
| difference in the class of hardware used. A desktop made
| today vs a laptop is roughly that factor 4. Not sure if
| back then there would have been laptops that you could have
| done this on for a more apples-to-apples comparison.
|
| Modern laptops give great efficiency, when I went for solar
| power here the first thing to go was the desktop computer.
| I still have it, but it hasn't run in over a year and the
| elderly thinkpad that is now my daily driver uses far less
| power and still has enough compute to serve my modest
| needs. But if I would dive into something requiring much
| more compute I'd have to start the desktop again.
| Unfortunately power management is not such that computers
| can really throttle down to 'miser mode' when you don't
| need it, it's a good step but not as good as the jump
| between desktop and laptop.
| swores wrote:
| Hadn't thought of that, good point
| DesiLurker wrote:
| also the 'memory wall', remember memory b/w did not grow
| at pace with moore's law. sure there are ways to mitigate
| it but that eat into chip budget and reflect when real
| world performance is calculated.
| jacquesm wrote:
| Yes, true and in a way that wall is still there. The way
| GPUs are limited in how much RAM they have because there
| is a way to sell you that memory at a multiple of the
| cost.
|
| Imagine a GPU with a 128G or even 256G slot based memory
| section that is sold unpopulated. 8 SODIMM slots or so.
| 2Gkashmiri wrote:
| imagine that we are discussing "proving" a law with
| historical data from our POV but at the time, it must've
| seem like a theory at best or comical at the least.
|
| 8% less growth is not the point. The "law" has stood the
| test of time which says something about the guy and his
| vision
| ben_w wrote:
| It isn't much, but as the link says, the neural network they
| were reimplementing is _too small_ to take advantage of
| modern hardware.
| fouronnes3 wrote:
| This is very interesting, because I've always thought that all
| NN performance should be measured in a unit with energy in the
| denominator.
| eru wrote:
| It totally depends on what you want to use a measure for.
| Just like neither height or volume alone will tell you what
| will fit in your car.
|
| By any measure that puts energy used by the brain in the
| denominator, humans are probably dumber than ants. But that
| doesn't mean those measures are always accurate.
|
| (For contemporary neural networks, you also have to
| distinguish training costs from inference costs.)
| nojvek wrote:
| To add more context, humans are 100W biological machines.
| Brain is ~20% of that power - 20W.
|
| The greatest form of general intelligence at 20W.
|
| A MacBook Air is ~30W.
|
| https://www.jackery.com/blogs/knowledge/how-many-watts-a-
| lap...
| beebmam wrote:
| You're leaving out the training requirements
| renonce wrote:
| It is "the greatest" because we only appreciate
| intelligence that we ourselves understand. A 0.0001W
| calculator calculates arithmetic faster than any human
| brain.
| chubot wrote:
| I dispute that, if the metric is a chess game between an
| ant and a human
| quickthrower2 wrote:
| For inference that could be useful, but the energy is not for
| the model it is for at least the tuple of: model, model
| architecture and compilation, and hardware chosen.
| ilaksh wrote:
| Maybe a better idea of the future is to look at what LeCun is
| working on now as a future program. He wants to change quite a
| lot in order to move towards more animal-like cognitive
| abilities.
|
| LeCun is not even really interested in supervised learning
| anymore, for example.
|
| https://youtu.be/vyqXLJsmsrk?si=8n0ylC6qdLX06CmY
|
| Note that the talk is not really primarily about ChatGPT even
| though that's in the title. The new ideas are a little bit in.
| The beginning of the talk is just him explaining how unimpressed
| he is with LLMs. Which I think is a misjudgement but that doesn't
| mean his plan doesn't have merit.
| golol wrote:
| I don't understand why Yann is so focused on his "Animals are
| smarter than AI" analogy. If compute wasn't so limited,
| couldn't we just train a transformer on video, audio and text
| data? I don't see why it would not learn the basic physical
| structure of our world just like a language transformer learns
| the grammatical and other structures of language. Then with
| this prettaines transformer you can build an agent and use some
| reinforcement learning too. I feel very confident that this
| would mirror the level of intelligence of non-human animals
| quite well.
| nojvek wrote:
| Animals are indeed smarter than AI for certain aspects of
| intelligence (physical intelligence).
|
| Evolution has optimized animal brain and bodies to survive
| and take care of the next generation. They have a good grasp
| of environment, where they are, where food is, where
| predators are, basic communication if they live as a group.
| Babies grow up and start learning.
|
| Our current AI is extremely power hungry compared to a brain.
| Cruise & Waymo put large power hungry supercomputers in cars.
| The computing system costs 100k+. They still make silly
| mistakes like crashing into fire trucks, blocking roads,
| driving into wet cement etc.
|
| ChatGPT and friends make silly mistakes for trivial math
| problems that require a few hierarchical planning steps.
|
| All in all, brains have some form of symbolic computation and
| reasoning that we haven't been able to replicate with current
| AI algorithms.
|
| I'm not saying we'll never be able to but current AI is
| really hyped. Kinda like crypto boom of 2019.
|
| There are some really hard algorithmic problems to be solved.
|
| Google, Microsoft, Meta could have 1000x more computing power
| and data, however in the grand space or all algorithms there
| exists a learning algorithm that is probably >10000X more
| efficient at generalized modeling and reasoning than what we
| have.
|
| The proof that we (20W biological generally intelligent
| computers) exist validates the hypothesis that there is a lot
| of advancement we can still do at the algorithm part.
| jerpint wrote:
| There have been many attempts to do multi modal pre training,
| the difficulty is finding the right combination of data for
| it to be "useful" and "scalable". It's not trivial to just
| train a transformer on video, text, audio, etc. mainly due to
| O(N^2) on the token counts, time components with video, etc
| quickthrower2 wrote:
| I know intuition is often wrong, but to me a mix of dedicated
| visual processing AI and language AI and sound AI all somehow
| interacting with each other would be a good way to make an
| "animal" like AI rather than throw loads of attention heads
| at everything all at once.
| version_five wrote:
| The odds of the next material advance in ML/AI coming from one
| of its pioneers are zero. Not to say we shouldn't listen to
| what LeCun has to say (the other ones have basically lost it)
| but focusing on him is a bad way of imagining the future.
| vendiddy wrote:
| AI layperson here. Is there something like an MRI scan for a
| neural network?
|
| Imagining I could take a foundation model, run it on my
| specialized task, and measure which regions of the neural network
| light up.
|
| Then I could carve out unused regions of the network to create a
| more lightweight model.
|
| Or is this a silly idea?
| RivieraKid wrote:
| Possible future development I'm excited about is something like
| GPT but for real world interaction - i.e. robots that take input
| from sensors and are able to physically navigate and manipulate
| the world.
|
| Fine-tuning would then be used for specific environments (human
| hair, an apartment) and robots (robotic barber, cleaning robot).
| ultra_nick wrote:
| Google put an LLM into a robot a few months ago. This is the
| second paper I've seen on it.
|
| https://arxiv.org/abs/2306.08647
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