[HN Gopher] Nvidia bets on robotics to drive future growth
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Nvidia bets on robotics to drive future growth
Author : pella
Score : 162 points
Date : 2024-12-30 10:21 UTC (2 days ago)
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| pella wrote:
| https://archive.md/jqMKX
| veunes wrote:
| Robotics has long been an area of promise but (I think) limited
| returns
| spot5010 wrote:
| Care to elaborate? I feel the. the real power of AI will be
| unlocked when AI can sense and interact with the world.
| malux85 wrote:
| Yeah but the limiting factor for ages has been software and
| batteries, both of which have been improving a lot in the last
| 5-8 years.
| Animats wrote:
| Yes. I've known people with robotics startups, and have visited
| some of them. They're all gone now. But that was all prior to
| about 2015.
|
| Robots are a branch of industrial manufacturing machinery. That
| is not, historically, a high-margin business. It also demands
| high reliability and long machine life.
|
| Interestingly, there's a trend towards renting robots by the
| working hour. It's a service - the robot company comes in, sets
| up robot workers, services them as needed, and monitors them
| remotely. The robot company gets paid for each operating hour.
| Pricing is somewhat below what humans cost.[1]
|
| [1] https://bernardmarr.com/robots-as-a-service-a-technology-
| tre...
| ethbr1 wrote:
| Having been involved in similar financial arrangements in
| software automation, years ago, it makes sense.
|
| The end user usually doesn't have the expertise to even
| maintain the systems, nor does it make sense for them to do
| it in-house.
|
| Charging per item of work (operating hour or thing processed)
| allows use of consultants but keeps incentives aligned
| between all parties (maximize uptime/productivity).
| ksec wrote:
| That is interesting. I assume robots here means something
| close to humanoid for rent, and be programmed to take some or
| most of the human's job and not robots in terms of industrial
| manufacturing machinery?
| Animats wrote:
| Robot, as used here, is, in more modern forms, one or more
| arms with a vision system. Or some kind of mobile base for
| moving things around.
| e_y_ wrote:
| They're industrial robot arms, not humanoids, although the
| concept of android workers getting paid an hourly fee or
| "wage" (going to their masters, an android rental
| corporation) would be fascinating.
| bregma wrote:
| Even just a robot arm with an appropriate sensors and
| hand attachment could replace human employees in the
| world's oldest profession. Consider what drove the video
| industry if you're looking to invest.
| rapsey wrote:
| > Yes. I've known people with robotics startups, and have
| visited some of them. They're all gone now. But that was all
| prior to about 2015.
|
| Lots of dotcom busts in the late 90s were concepts that
| worked 10-15 years later. We just did not have broadband and
| smartphones. Battery and AI tech is quite likely to be the
| missing piece robotics lacked in the past.
| alephnerd wrote:
| > Battery and AI tech is quite likely to be the missing
| piece robotics lacked in the past.
|
| Cheap semiconductors as well.
|
| Fabricating a chip on a 28nm and 48nm process is extremely
| commodified nowadays. These are the same processes used to
| fabricate an Nvidia Tesla or an i7 or Xeon barely a decade
| ago, so the raw compute power available at extremely
| commodified prices is insane.
|
| Just about every regional power has the ability to
| fabricate an Intel i7 or Nvidia Tesla equivalent nowadays.
|
| And most regional powers have 3-7 year plans to build
| domestic 14nm fabrication capacity as well now. A number of
| firms like Taiwan's PSMC have made a killing selling the
| end-to-end IP and workflow for fabrication.
| petra wrote:
| Given that robot-as-a-service removes the biggest barriers
| for companies not buying robots, why aren't we seeing a huge
| growth in "employed" robots?
| contingencies wrote:
| Robotics is everywhere but you don't see it. The joke is we
| call it something else when it works. Large corporations with
| successful margin-supporting automation systems have every
| intent and reason to keep them secret. See for example ASML.
| JFingleton wrote:
| Here in the UK there's been a boom in robot delivery over the
| years:
|
| https://www.starship.xyz/
|
| "Slow but steady" I would call it.
| peppertree wrote:
| After using FSD 13 for 2 weeks I'm convinced we are close to
| solving self driving. Too bad the everyone lost interest and now
| robotics is the hot new thing.
| mdorazio wrote:
| Waymo already solved self driving years ago. Tesla still has a
| long way to go.
| echelon wrote:
| Is there anyone even close to Waymo in this game? Is Waymo
| going to own the entire market?
| krupan wrote:
| Cruise is close to Wayno, but nobody is willing to invest
| in Cruise anymore
| AlotOfReading wrote:
| Cruise doesn't exist anymore (or rather shortly won't).
| The teams are being folded into GM to work on other
| things.
| IncreasePosts wrote:
| Why would waymo own the entire market? Sure, they might be
| the first ones there, but every year recreating what is
| "good enough" should be cheaper and cheaper.
| 05 wrote:
| Baidu Apollo. They also have a commercial fleet without in-
| car safety drivers (they use remote operators for real time
| monitoring, though, so hard to say how hands-off it really
| is)
| rapsey wrote:
| Waymo works in US grid cities on highly modified cars. I
| know people love hating Musk, but it is still very much up
| in the air if Waymo will be a better solution than what
| Tesla or Wayve is doing.
| creer wrote:
| What does "grid" have to do with anything at this point?
| Mapping was done a million years ago and try and see if
| "grid" helps you understand the lane and traffic light
| system in San Francisco (which tourists need to figure
| out in real time - they are hard enough on the locals.)
| rapsey wrote:
| Nice easy intersections. Wide two way streets. Put a
| waymo in Rome and I will be impressed.
| sbuttgereit wrote:
| I ride Waymos in San Francisco that traverse longer
| twisting "two-way" roads in the San Francisco hills (look
| at the neighborhoods around Mount Davidson). In these
| cases, the road, while two way, more often than not only
| has space which allows a single car to pass at a time;
| the rest of the space is taken by cars parked on either
| side of the roadway. The Waymo cars. at least during my
| rides, handled these situations well.
|
| While it's not Rome, the operating areas for Waymo, at
| least in San Francisco, are not all grids of modern wide
| streets either.
| doublepg23 wrote:
| Isn't that the difference though? I've never even seen a
| Waymo and I've been successfully driven by Teslas many times.
| ksec wrote:
| >n February 2024, a driverless Waymo robotaxi struck a
| cyclist in San Francisco.[132] Later that same month, Waymo
| issued recalls for 444 of its vehicles after two hit the same
| truck being towed on a highway
|
| I am not entirely sure that is solved. And certainly not
| years ago. And it is only close in US where the data are
| trained. Doesn't mean it could be used in Japan ( where they
| are doing testing now ) driving on the different of the road
| with very different culture and traffics.
| szvsw wrote:
| Citing a specific (tragic) incident isn't really great
| evidence in re: safety. You have to normalize by something
| like accidents/mile driven and compare to comparable
| services (taxi/uber etc) - having said that I couldn't
| quickly find any sources either positive or negative on
| those stats (besides Waymo PR docs) so I'm not saying
| you're necessarily wrong. just wanted to point out the
| obvious flaw with citing anecdotal evidence for something
| like this.
|
| You could easily use the same logic to say humans haven't
| solved driving yet either!
| vasco wrote:
| A big part of me believes the only extra safety they give
| is they drive much slower. This in itself might be the
| solution for human deaths on the road.
| Dig1t wrote:
| Crashes per mile is multiple times lower than the human
| rate for both Waymo and Tesla. If your definition of solved
| is that there will be 0 collisions ever then the problem
| will never be solved. But if we have a system that is much
| better at driving than most humans, I think that qualifies
| it as good enough to start using.
| lm28469 wrote:
| In a very small subset of cities, road conditions, weather
| condition, &c. Basically US grid cities with 300 days of sun
| per year
| karlgkk wrote:
| That's not the limiting factor, fwiw. It's an operations
| problem for them at this point. Freeways are a big
| contentious point as well.
| e_y_ wrote:
| Waymo is pretty good (but not perfect) as far as safety, but
| there's too many ways it can get stuck. Including vandalism
| from humans like "coning". And if a significant number of
| them are on the road, it could gum up traffic when that
| happens.
|
| I still think it'll do well because even if you need to hire
| 1 person to remotely monitor every 10 cars (I doubt Waymo has
| anywhere near that many support staff) it's still better than
| having to pay 10 drivers who may or may not actually be good
| at driving. But to really take over they'll need to be much
| more independent.
| myvoiceismypass wrote:
| Have you been a passenger in a Waymo? My only ride felt safer
| than every uber / Lyft driver I have ever had pretty much, so
| wondering how it compares to a beta thing you have to be able
| to take over in an instant.
| hackcasual wrote:
| Last time I was in SF I took 3 waymo rides and attempted a
| fourth. The attempted one was cancelled after 15 minutes of
| waiting for it being 2 minutes away. As best as I can tell,
| the waymo was stuck at an intersection where power had been
| lost and didn't understand it needed to treat it like a 4 way
| stop.
|
| 2 rides went fine though neither was particularly
| challenging. The third though the car decided to head down a
| narrow side street where a pickup in front was partially
| blocking the road making a dropoff. There was enough space to
| just squeeze by and it was clear the truck expected the car
| to. A few cars turned in behind the waymo, effectively
| trapping it in as it didn't know how to proceed. The dropoff
| eventually completed and it was able to pull forward
| cbsks wrote:
| Note that Nvidia is also working on self driving. The Jetson
| robotics platform is based on the same SoC as the DRIVE
| platform, but is a separate product.
| mhh__ wrote:
| Although the idea of self driving is obviously cool I think
| it's good that robotics take priority (if such a thing is
| possible) e.g. think of it like the invention of the washing
| machine as a liberating force on the world.
| sangeeth96 wrote:
| I'm not sure if you're generalizing to a specific region in
| your assessment but regardless, I doubt this is anywhere close
| to a solved problem given the crashes/incidents (so far) still
| associated with the tech and the dependencies IIRC on street
| signs and other markers.
|
| re: region, I'd like to see it take on more challenging
| conditions, like in India for example where things are chaotic
| even for human drivers. I doubt that it'll survive over here.
| bobsomers wrote:
| As someone who worked in V&V for AV systems for a decade, it's
| exactly the kind of thinking displayed here that has held back
| real assessment of AV safety for years.
|
| There is absolutely no meaningful signal about a system's
| safety that can be derived from one person using a system for
| two weeks.
|
| At best it can only demonstrate that a system is wildly unsafe.
|
| There is a very large chasm of 9s between one person being able
| to detect an unsafe system in two weeks of use and actually
| having a truly safe system.
| krisoft wrote:
| > Too bad the everyone lost interest and now robotics is the
| hot new thing.
|
| Self driving is robotics. Simple as that.
| amelius wrote:
| Quite simple robotics actually. Especially if you use Lidar.
| Basically IF (object present) THEN (do not go there) style of
| simplicity. Of course in reality there are lots of cases to
| consider, but each one of these cases is not rocket science.
|
| Building a robot that can cook or fold a t-shirt, for
| example, is much harder.
| n144q wrote:
| And it only takes a (near) accident in 5 more minutes' driving
| to completely negate that.
|
| Your observation from this short time window isn't enough to
| prove the usefulness of something as serious as life and death.
| seydor wrote:
| Cars are robots without arms
| ram_rattle wrote:
| I personally think apart from GPU and compute for intelligence
| for meaningful robotics to take off we still have lot of things
| to crack like better battery, better affordable sensors,
| microelectronics etc, I'm pretty sure we will get there but I
| don't think one company can do it.
| 01100011 wrote:
| Better battery isn't really an issue for factories. Same with
| sensors if you're saving the cost of employing a human,
| especially for dangerous work.
| michaelt wrote:
| True - and of course factories don't mind if a robot costs
| $40,000 if the payback time is right.
|
| But factory robots haven't propelled Kuka, Fanuc, ABB, UR,
| Staubli and peers to anything like the levels of success
| nvidia is already at. A market big enough to accommodate
| several profitable companies with market caps in the tens of
| billions might not drive much growth for a company with a
| trillion-dollar market cap.
|
| nvidia has several irons in the fire here. Industrial robot?
| Self-driving car? Creepy humanoid robots? Experimental
| academic robots? Whatever your needs are, nvidia is ready
| with a GPU, some software, and some tutorials on the basics.
| Onavo wrote:
| > _But factory robots haven 't propelled Kuka, Fanuc, ABB,
| UR, Staubli and peers to anything like the levels of
| success nvidia is already at. A market big enough to
| accommodate several profitable companies with market caps
| in the tens of billions might not drive much growth for a
| company with a trillion-dollar market cap._
|
| That's because the past year of robotics advancements (e.g.
| https://www.physicalintelligence.company/blog/pi0,
| https://arxiv.org/abs/2412.13196) has been driven by
| advances in machine learning and multimodal foundation
| models. There has been very little change in the actual
| electronics and mechanical engineering of robotics. So it's
| no surprise that the traditional hardware leaders like Kuka
| and ABB are not seeing massive gains so far. I suspect they
| might get the Tesla treatment soon when the Chinese
| competitors like unitree start muscling into the humanoid
| robotics space.
|
| Robotics advancements are now AI driven and software
| defined. It turned out that adding a camera and tying a big
| foundation model to a traditional robot is all you need.
| Wall-E is now experiencing the ImageNet moment.
| michaelt wrote:
| _> There has been very little change in the actual
| electronics and mechanical engineering of robotics. So it
| 's no surprise that the traditional hardware leaders like
| Kuka and ABB are not seeing massive gains so far._
|
| Perhaps I wasn't explicit enough about the argument I was
| trying to make.
|
| Revenue in business is about selling price multiplied by
| sales volumes, and I'm not sure factory robot sales
| volumes are big enough to 'drive future growth' for
| nvidia.
|
| According to [1] there were 553,000 robots installed in
| factories in 2023. Even if every single one of those half
| a million robots needed a $2000 GPU that's only $1.1
| billion in revenue. Meanwhile nvidia had revenue of 26
| billion in 2023, and 61 billion in 2024.
|
| Many of those robots will be doing basic, routine things
| that don't need complex vision systems. And 54% of those
| half a billion robot arms were sold in China - sanctions
| [2] mean nvidia can't export even the 4090 to China, let
| alone anything more expensive. Machine vision models are
| considered 'huge' if they reach half a gigabyte -
| industrial robots might not need the huge GPUs that LLMs
| call for.
|
| So it's not clear nvidia can increase the price per GPU
| to compensate for the limited sales volumes.
|
| If nvidia wants robotics to 'drive future growth' they
| need a bigger market than just factory automation.
|
| [1] https://ifr.org/img/worldrobotics/2023_WR_extended_ve
| rsion.p... [2]
| https://www.theregister.com/2023/10/19/china_biden_ai/
| Onavo wrote:
| You are forgetting that the "traditional" factory robots
| are the way they are because of software limitations. Now
| that the foundation models have mostly solved basic
| robotic limitations, there's going to be a lot more
| automation (and job layoffs). Your traditional factory
| robotics are dumb and mostly static. They are mostly
| robotic arms or other type of conveyor belt centric
| automation. The new generation of VLM enabled ones offers
| near-human levels of flexibility. Actual android type
| robotics will massively increase demand for GPUs, and
| this is not even accounting for non-heavy industry use
| cases in the service industry e.g. cleaning toilets,
| folding clothing at a hotel. They are already being done
| by telepresence, full AI automation is just the next
| step. Here's an example from a quick google:
|
| https://www.reddit.com/r/interestingasfuck/comments/1h1i1
| z1/...
| dogma1138 wrote:
| Factories don't mind if the robot costs $4,000,000 or even
| $40,000,000 I really don't think people understand how much
| an industrial robots from the likes of KUKA cost...
| rafaelmn wrote:
| I think it's more about how much of that 40m$ is nvidia
| and how many units can you deploy ?
| michaelt wrote:
| I agree that you can get to some big cost figures if
| you're talking about a full work cell with multiple
| robots, conveyors, end effectors, fancy sensors, high-
| tech safety systems, and staff costs.
|
| But if you're just buying the arm itself? There are
| quality robot arms, like the EUR38,928 UR10e [1], that
| are within reach of SMEs. No multi-million-dollar budget
| required.
|
| [1] https://shop.wiredworkers.io/en_GB/shop/universal-
| robots-ur1...
| ANewFormation wrote:
| It seems such costs would become prohibitive quite
| quickly? Stuff with moving parts breaks, and I'd expect
| ongoing maintenance costs to be proportional to the unit
| cost. Pair in the fact that most factories run on thin
| margins but massive volume, and it would seem cost is
| very much an issue.
| blueboo wrote:
| It's hard to say when we're still looking for a first real
| household robot. But a car-priced (60k?) housekeeper bot will
| be very popular.
|
| And those duties can be achieved with today's mechanics -- they
| just need good control, which is now seeing ferocious progress
| whatever1 wrote:
| What is the new breakthrough in robotics that is gpu driven ?
| There are subsets of the overall problem that can be solved by a
| gpu (eg object detection) but the whole planning and control algo
| scheme seems to be more or less the same as it has been for the
| past decades. These typically involve non-convex optimization so
| not much gpu benefit.
| pseudosudoer wrote:
| There are search spaces that are quite large that are used in
| optimal control. GPUs can be used to drastically accelerate
| finding a solution.
|
| As an example, imagine you are given a height map, a 2D
| discrete search space overlayed in the height map, 4 legs, and
| robot dynamics for every configuration of the legs in their
| constrained workspace. Find the optimal toe placement of the 4
| legs. Although a GPU isn't designed exactly to deal with this
| sort of problem, if it's framed as a reduction problem it still
| significantly out performs a multi core CPU.
| whatever1 wrote:
| These are sparse systems, factorization is not a strength of
| the gpu architectures. Typically adding more cpu cores is a
| better investment rather than trying to parallelize it
| through gpu. Nvidia has been trying for some time to make
| progress with cuSparse etc, although not much has been
| achieved in the space.
|
| Maybe they try a completely different approach with
| reinforcemnt learning and a ton of parallel simulations?
| mattlondon wrote:
| > What is the new breakthrough in robotics that is gpu driven ?
| There are subsets of the overall problem that can be solved by
| a gpu (eg object detection) but the whole planning and control
| algo scheme seems to be more or less the same as it has been
| for the past decades
|
| I think the "object detection" goes quite far beyond the
| classic "objection detection" bounding boxes etc we're used to
| seeing. So not just a pair of x,y coords for the bounding box
| for e.g. a mug of coffee in the robot's field of view, but what
| is the orientation of the mug? where is the handle? If the
| handle is obscured, can we infer where it might be based on
| what we understand for what a mug typically looks like and plan
| our gripper motion towards it (and at 120hz etc)? Is it a solid
| mug, or a paper cup (affects grip strength/pressure)? Etc etc.
| Then there is the whole thing about visually show the robot
| once what you are doing, and it automatically "programs" itself
| to repeat the tasks in a generalised way etc. Then you could
| probably spawn 100 startups just on hooking up a LLM to tell a
| robot what to do in a residential setting (make me a coffee,
| clear up the kitchen, take out the trash etc)
|
| This has all been possible before of course, but could it be
| done "on device" in a power efficient way? I am guessing they
| are hoping to sell a billion or two chips + boards to be built
| directly into things to do so so that your next robotic vacuum
| or lawn mower or whatever will be able to respond to you
| yelling at it and not mangle your pets/small children in the
| process.
|
| I eagerly await the day when I have a plug and play robot
| platform that can tell the difference between my young children
| and a fox, and attack the fox shitting/shredding something
| small and fluffy in the garden but ignore the kids
| iancmceachern wrote:
| It's the edge computing.
|
| Similar to autonomous vehicles, doing complex multi sensor
| things very quickly.
|
| Surgical robotics is a great example, lots of cool use cases
| coming out in that field.
| XenophileJKO wrote:
| I think it is what nobody has answered yet.. virtualized
| training/testing. I watched a presentation by their research
| team. This is a HUGE force multiplier. Don't underestimate how
| much this changes robotic foundational model training.
| krasin wrote:
| > What is the new breakthrough in robotics that is gpu driven ?
| There are subsets of the overall problem that can be solved by
| a gpu (eg object detection) but the whole planning and control
| algo scheme seems to be more or less the same as it has been
| for the past decades. These typically involve non-convex
| optimization so not much gpu benefit.
|
| In the past two years two very important developments appeared
| around imitation learning and LLMs. Some starting points for
| this rabbit hole:
|
| 1. HuggingFace LeRobot: https://github.com/huggingface/lerobot
|
| 2. ALOHA: https://aloha-2.github.io/
|
| 3. https://robotics-transformer2.github.io/
|
| 4. https://www.1x.tech/discover/1x-world-model
| YeGoblynQueenne wrote:
| We've been here many times before. Imitation learning doesn't
| generalise and that makes it useless in practice.
|
| Aloha is a great example of that. It's great for demos, like
| the one where their robot "cooked" (not really) one shrimp,
| but if you wanted to deploy it to real peoples' houses you'd
| have to train it for every task in every house over a few
| hours at a time. And "a task" is still at the level of "cook
| (not really) one shrimp". You want to cook (not really)
| noodles? It's a new task and you have to train it all over
| again from scratch. You want it to fold your laundry? OK but
| you need to train it on each piece of laundry you want it to
| fold, separately. You want it to put away the dishes? Without
| exaggeration you'd have to train it to handle each dish
| separately. You want it to pick up the dishes from the
| kitchen? Train for that. You want it to pick up the dishes
| from the living room? Train for that. And so on.
|
| It sucks so much with miserable disappointment that it could
| bring on a new AI winter on its own, if Google was dumb
| enough to try and make it into a product and market it to
| people.
|
| Robot maids and robot butlers are a long way away. Yeah but
| you can cook one shrimp (not really) with a few hours of
| teleoperation training in your kitchen only. Oh wow. We could
| never cook (not really) one shrimp before. I mean we could
| but this uses RL and so it's just one step from AGI.
|
| It's nonsense on stilts.
| krasin wrote:
| I generally agree with your analysis of the current state
| of art but strongly disagree with the overall conclusion of
| where it leads us.
|
| I believe it will take on the order of 100M hours of
| training data of doing tasks in real world (so, not just
| Youtube videos), and much larger models than we have now to
| make general-purpose robotics working, but I also believe
| that this will happen.
|
| I've saved your comment to my favorites and hope to revisit
| it in 10 years.
| YeGoblynQueenne wrote:
| Thanks, that'll be interesting :)
| michaelt wrote:
| Two decades ago, I was trying to use classical machine vision
| to tell the difference between cut and uncut grass, to guide a
| self-driving lawnmower.
|
| I concluded that it couldn't be done with classical machine
| vision, and that this "neural network" nonsense wasn't going to
| catch on. Very slow, computationally inefficient, full of
| weirdos making grandiose claims about "artificial intelligence"
| without the results to back it up, and they couldn't even
| explain how their own stuff worked.
|
| These days - you want to find the boundary between cut and
| uncut grass, even though lighting levels can change and cloud
| cover can change and shadows can change and reflections can
| change and there's loads of types of grass and grass looks
| different depending on the angle you look from? Just label some
| data and chuck a neural network at it, no problemo.
| justmarc wrote:
| Funnily now with with the advent of GPS+RTK lawnmower robots,
| fancy AI is not even needed anymore. They follow a very
| exact, pre-determined patterns and paths, and do a great job.
| michaelt wrote:
| Yeah GPS+RTK was what I went with in the end.
|
| Didn't work as well as I'd hoped back in those days though,
| as you could lose carrier lock if you got too close to
| trees (or indeed buildings), and our target market was golf
| courses which tend to have a lot of trees. And in those
| days a dual-frequency RTK+IMU setup was $20k or more, which
| is expensive for a lawnmower.
| justmarc wrote:
| No tool is perfect for every job. That said, the
| positioning of the RTK unit is crucial. Possibly look for
| a mower which can work with multiple RTK units, or
| reposition your existing one for better coverage.
|
| I find that even though signals get significantly weaker
| under trees, mine still works wonderfully in a complex
| large garden scenario. It will depend on your exact
| unit/model, as well as their firmware and how it chooses
| to deal with these scenarios.
| madaxe_again wrote:
| That, and you can now train the perfect lawnmower in an
| entirely virtual environment before dropping it into a
| physical body. You do your standard GAN thing, have a network
| that is dedicated to creating the gnarliest lawn mowing
| problems possible, bang through a few thousand generations of
| your models, and then hone the best of the best. There are
| some really astonishing examples that have been published
| this last year or so - like learning to control a hand from
| absolute first principles, and perfecting it.
|
| This is all pretty much automated by nvidia's toolkits, and
| you can do it cheaply on rented hardware before dropping your
| pretrained model into cheap kit - what a time to be alive.
| blagie wrote:
| FYI: A comment like this one is more helpful with links.
| There's one below with a few. If you happen to read this,
| feel free to respond, or to hit "edit" and add them.
| blagie wrote:
| > These days - you want to find the boundary between cut and
| uncut grass, even though lighting levels can change and cloud
| cover can change and shadows can change and reflections can
| change and there's loads of types of grass and grass looks
| different depending on the angle you look from? Just label
| some data and chuck a neural network at it, no problemo.
|
| If only.
|
| Having been faced with the same problem in the real world:
|
| 1) There isn't a data bank of millions of images of cut /
| uncut grass
|
| 2) If there were, there's always the possibility of sample
| bias. E.g. all the cut photos happen to have been taken early
| in the day, of uncut late in the day, and we get a "time-of-
| day" detector. Sample bias is oddly common in vision data
| sets, and machine learning can look for very complex sample
| bias
|
| 3) With something like a lawnmower, you don't want it to kill
| people or run over flowerbeds. There can be actual damages.
| It's helpful to be able to understand and validate things.
|
| Most machine vision algorithms I actually used in projects
| (small n) made zero use of neural networks, and 100% of
| classical algorithms I understand.
|
| Right now, the best analogy to NLP is BERT. At that point,
| neural techniques were helpful for some tasks, and achieved
| stochastically interesting performance, but were well below
| the level of general uses, and 95% of what I wanted to do
| used classical NLP. IF I had a large data set AND could do
| transfer training from BERT AND didn't need things to work
| 100% of the time, BERT was great.
|
| Systems like DALL-e and the reverse are moving us in the
| right direction. Once we're at GPT / Claude / etc.-level
| performance, life will be different, and there's a light at
| the end of the tunnel. For now, though, the ML machine is
| still a pretty limited way to go.
|
| Think of it this way. What's cheaper:
|
| 1) A consulting project for a human expert in machine vision
| (tens or hundreds of thousands of dollars)
|
| 2) Hiring cheap contractors to build out a massive dataset of
| photos of grass (millions of dollars)
| krisoft wrote:
| > What's cheaper
|
| If you don't have the second how can you trust the first?
| Without the dataset to test on your human experts will
| deliver you slop and be confident about it. And you will
| only realise the many ways their hand finessed algorithms
| fail once you are trying to field the algorithm.
|
| > With something like a lawnmower, you don't want it to
| kill people or run over flowerbeds.
|
| Best to not mix concerns though. Not killing people with an
| automatic lawnmover is about the right mechanical design,
| appropriately selected slow speed, and bumper sensors. None
| of this is an AI problem. We don't have to throw out good
| engineering practices just because the product uses AI
| somewhere. It is not an all or nothing thing.
|
| The flowerbed avoidance question might or might not be an
| AI problem depending on design decisions.
|
| > Hiring cheap contractors to build out a massive dataset
| of photos of grass (millions of dollars)
|
| I think that you are over estimating the effort here. The
| database doesn't have to be so huge. Transfer learning and
| similar techniques reduced the data requirements by a lot.
| If all you want is a grass height detector you can place
| stationary cameras in your garden, collect a bunch of data
| and automatically label them based on when you moved the
| grass. That will obviously only generalise to your garden,
| but if this is only a hobby project maybe that is all you
| want? If this is a product you intend to sell for the
| general public then of course you need access to a lot of
| different gardens to test it on. But that is just the
| nature of product testing anyway.
| blagie wrote:
| > If you don't have the second how can you trust the
| first? Without the dataset to test on your human experts
| will deliver you slop and be confident about it.
|
| One of the key things is that if you don't understand how
| things work, your test dataset needs to be the world. A
| classical system can be analyzed, and you can pick a test
| dataset which maximally stresses it. You can also
| engineer environments where you know it will work, and 9
| times out of 10, part of the use of classical machine
| vision in safety-critical systems is to understand the
| environments it works in, and to only use it in such
| environments.
|
| Examples:
|
| - Placing the trackball sensor inside of the mouse (or
| the analogue for a larger machine) allows the lighting
| and everything else to be 100% controlled
|
| - If it's not 100% controlled, in an industrial
| environment, you can still have well-understood
| boundaries.
|
| You test beyond those bounds, and you understand that it
| works there, and by interpolation, it's robust within the
| bounds. You can also analyze things like error margin
| since you know if an edge detection is near the threshold
| or has a lot of leeway around it.
|
| One of the differences with neural networks is that you
| don't understand the failure modes, so it's hard to know
| the axes to test on. Some innocuous change in the
| background might throw it completely. You don't have
| really meaningful, robust measures of confidence, so you
| don't know if some minor change somewhere won't throw
| things _. That means your test set needs to be many
| orders of magnitude bigger.
|
| _ For nitpickers: You can do sensitivity analysis, look
| at how strongly things activate, or a dozen other things,
| but the keywords there were "robust" and "meaningful."
| michaelt wrote:
| _> If you don't have the second how can you trust the
| first? Without the dataset to test on your human experts
| will deliver you slop and be confident about it._
|
| 1. Test datasets can be a lot smaller than training
| datasets.
|
| 2. For tasks like image segmentation, having a human look
| at a candidate segmentation and give it a thumbs up or a
| thumbs down is much faster than having them draw out the
| segments themselves.
|
| 3. If labelling needs 20k images segmented at 1 minute
| per image but testing only needs 2k segmentation results
| checked at 5 seconds per image, you can just do the
| latter yourself in a few hours, no outsourcing required.
| michaelt wrote:
| _> If only._
|
| I will admit that "no problemo" made it sound easier than
| it actually is. But in the past I considered it _literally
| impossible_ whereas these days I 'm confident it is
| possible, using well known techniques.
|
| _> There isn 't a data bank of millions of images of cut /
| uncut grass_
|
| True - but in my case I literally already had a robot
| lawnmower equipped with a camera. I could have captured a
| hundred thousand images pretty quickly if I'd known it was
| worth the effort.
|
| _> With something like a lawnmower, you don 't want it to
| kill people or run over flowerbeds._
|
| I agree - at the time I was actually exploring a hybrid
| approach which would have used landmarks for navigation
| when close enough to detect the landmarks precisely, and
| cut/uncut boundary detection for operating in the middle of
| large expanses of grass, where the landmarks are all
| distant. And a map for things like flowerbeds, and a LIDAR
| for obstacle tracking and safety.
|
| So the scope of what I was aiming for was literally
| cut/uncut grass detection, not safety-of-life human
| detection :)
| blagie wrote:
| Out of curiosity: Why would you need cut/uncut grass
| detection? If you have all the other stuff in place,
| what's the incremental value-add? It seems like you
| should be able to cut on a regular schedule, or if you
| really want to be fancy, predict how much grass has grown
| since you last cut it from things like the weather.
| michaelt wrote:
| I wanted to steer the mower along the cut/uncut grass
| boundary, just like a human operator does. Image
| segmentation into cut/uncut grass would be the input to a
| steering control feedback loop - much like lane-following
| cruise control.
|
| I hoped by doing so I could produce respectable results
| without the need to spend $$$$$ on a dual-frequency RTK
| GPS & IMU system.
| plaidfuji wrote:
| I don't think people fully appreciate yet how much of LLMs'
| value comes from their underlying dataset (I.e. the entire
| internet - probably .. quadrillions..? of tokens of text)
| rather than the model + compute itself.
|
| If you're trying to predict something within the manifold
| of data on the internet (which is incredibly vast, but not
| infinite), you will do very well with today's LLMs.
| Building an internet-scale dataset for another problem
| domain is a monumental task, still with significant
| uncertainty about "how much is enough".
|
| People have been searching for the right analogy for "what
| type of company is Open AI most like?" I'll suggest they're
| like an oil company, but without the right to own oil
| fields. The internet is the field, the model is the
| refining process (which mostly yield the same output but
| with some variations - not dissimilar from petroleum
| products).. and the process / model is a significant asset.
| And today, Nvidia is the only manufacturer of refining
| equipment.
| ramblenode wrote:
| This is an interesting analogy. Of course oil extraction
| and refining are very complex, but most of the value in
| that industry is simply the oil.
|
| If you take the analogy further, while oil was necessary
| to jumpstart the petrochemical industry, biofuels and
| synthetic oil could potentially replace the natural stuff
| while keeping the rest of the value chain in tact (maybe
| not economical, but you get the idea). Is there a post-
| web source of data for LLMs once the well has been
| poisoned by bots? Maybe interactive chats?
| amelius wrote:
| The fun thing with DL is that you don't have to optimize stuff
| with complicated math. You just train it, and it will generate
| solutions. Maybe not the perfect solutions, but don't let
| perfect be the enemy of good.
| imtringued wrote:
| Actually, it is quadratic programming that is big in robotics.
| QP is powerful enough that you can formulate your task, but
| also fast enough that you can run it in the control loop in
| real time.
| emn13 wrote:
| The article lists 2: firstly, simply that ML models are now
| feasible at a scale they weren't only a few years ago.
| Secondly, compute power is now better enough that it can now
| simulate more realistic environments which enables sim-based
| (pre)training to work better. That second one is potentially
| particularly alluring to nvidia given how it plays on two of
| their unique strengths - AI and graphics.
| m_ke wrote:
| VLA - Vision Language Action models
|
| https://arxiv.org/abs/2406.09246
|
| It turns out you can take a vision language foundational model
| that has a broad understanding of visual and textual knowledge
| and fine tune it to output robot actions given a sequence of
| images and previous actions.
|
| This approach beats all previous methods by a wide margin and
| transfers across tasks.
| dartos wrote:
| Unstructured Sensory input driven by these large neural
| networks, if I had to guess.
|
| To be able to visually determine weight, texture, and how
| durable something is can be done with those systems so long as
| we have a training set.
| mmmore wrote:
| My understanding is that it's in vogue to use deep learning for
| complex control problems, and the results are fairly
| impressive. The idea is to train robotic motion end to end with
| RL. Not an expert so I don't know the strength and weaknesses
| versus classical approaches.
|
| https://blogs.nvidia.com/blog/eureka-robotics-research/
|
| https://arxiv.org/abs/2108.10470
| asadalt wrote:
| if they really want to bet on robotics, I want them to release a
| $10 variant of jetson board.
| adrian_b wrote:
| A few weeks ago they have reduced the price of the Orin Nano
| development kit from $500 to $250, while also increasing a few
| of the performance limits that cripple it in comparison with
| the more expensive Orin models.
|
| Previously it was far too overpriced for most uses (except for
| someone developing a certified automotive device), but at the
| new price and performance it has become competitive with the
| existing alternatives in the same $150 to $300 price range,
| which are based on Intel, AMD, MediaTek, Qualcomm or Rockchip
| CPUs.
| bradfa wrote:
| When they reduced the price of the dev kit, they priced it
| below the low volume sales price of the cheapest Orin Nano
| 4GB module. Presumably the module prices go down when you buy
| in bulk but for small volumes it was (is still?) cheaper to
| buy the dev kit and throw away the carrier than to just buy
| the module. Granted the dev kits went out of stock pretty
| quick.
| jononor wrote:
| For 10 USD you get an ESP32S3 board, which can do basic
| computer vision tasks. For example using OpenMV or emlearn-
| micropython. For 15-20 USD you can get a board that includes an
| OV2640 camera. Examples would be XIAO ESP32S3 Sense, LilyGo T
| Camera S3 or "ESP32-S3-CAM" board from misc manufacturers.
| asadalt wrote:
| yes that's what i am working on these days but there is a
| need for a generally available neural chip (see google's
| coral as one attempt). in my tests, esp32s3 is very very slow
| for any model with conv2d involved.
|
| i just want a tiiiny gpu for $10 so i can run smaller models
| at higher speed than possible with xtensa/rp2040 having
| limited simd support etc.
| jononor wrote:
| Are you utilizing the SIMD and acceleration instructions in
| the S3? What kind of performance are you seeing?
|
| Neural accelerators are coming into MCUs. The just released
| STM32N6 is probably among the best. Alif with the U55/U85
| has been out for a little while. Maxim MAX78000 has a CNN
| accelerator out for a couple of years. More will come in
| the next few years - though not from Nvidia any time soon.
| albert_e wrote:
| https://archive.ph/uKeHc
| cess11 wrote:
| Since cops, guards and military officers are itching to get
| autonomous guns it's probably a reasonable move. The genocide of
| palestinians has showed that people operated gun drones aren't
| distance enough, the operators cost a lot in psych treatment and
| personnel churn.
| Qiu_Zhanxuan wrote:
| Unironically, I think this is one of the "main benefits" that
| the disconnected people in places of power seems to covet. They
| won't have a human operator that will do their dirty jobs and
| potentially leaks the truth out of guilt.
| ANewFormation wrote:
| I'm bearish on 'ai war' but this sounds like a huge positive
| if it comes to fruition because war would become more about
| two sides trying to kill each each sides leaders, instead of
| these leaders sending masses of doe eyed young people off to
| die for them.
|
| If politicians had real skin in the game there'd be far less
| war.
| cess11 wrote:
| That's already being done. Saddam Hussein was quickly
| killed, and what followed? Same goes for Ghaddaffi. Israel
| has killed a lot of leaders and is clearly not satisfied
| with having done that.
|
| What they want is border and population control that
| involves very few ordinary citizens, in large part in
| expectation of something like hundreds of millions of
| climate refugees. After having spent a couple of years
| killing and maiming poor people with almost nowhere to go
| you tend to need quite a bit of medical care and usually
| join the anti-war movement regardless if you got a college
| degree out of it or not.
|
| I find it likely we'll see gun mounted robodog patrols
| along occidental borders within ten years from now, after
| having tested it on populations elsewhere.
| dankobgd wrote:
| took them 16 years to fix night light bug in the driver but yeah
| robots are the future
| stephc_int13 wrote:
| I think that most people are underestimating Nvidia strategy.
|
| Their bet is that AI will unlock robotics use and they don't want
| to be simply compute providers, they want to innovate on the
| whole chain, software, hardware, services, everything.
|
| Their position is quite unique as their R&D is basically financed
| by their future competitors, they are making bank while going
| where the puck will be.
| nthingtohide wrote:
| I think Nvidia should try to create an compute-analogue of wifi
| routers where computation will be offloaded to smart-home gpu-
| servers. This strategy will cement their future for eternity.
| dartos wrote:
| Why sell to consumers when you can sell to large corporations
| at 10x the price?
| talldayo wrote:
| Because consumers will pay ~3-5x MSRP if the hype is big
| enough.
| jszymborski wrote:
| Corps aren't impervious to hype either.
| david-gpu wrote:
| Something in that spirit is already making progress: compute
| attached to cell phone towers, called "edge computing".
|
| Because you are pooling computer resources across many more
| users you have better amortized cost than if you had a
| workstation on every home that was idle 99% of the time. Of
| course, latency and bandwidth to the cell tower is worse than
| wifi, but better than if the compute is done on a remote
| server.
|
| I have no idea of which model will succeed for which use
| cases, but the idea is sound.
| giancarlostoro wrote:
| Not sure how well this would scale if every users needing
| to compute all at once. Which all it takes is some meme new
| app that requires heavy computation and then everyone
| downloads it.
| david-gpu wrote:
| It has the same scalability issues and the same
| scalability solutions as cell towers themselves, doesn't
| it? Densely populated areas have more cell towers and
| thus smaller cells.
|
| Again: which computation paradigm works best depends on
| the use case. It is not an all or nothing situation.
| nthingtohide wrote:
| Smart mirror, smart appliances, smart robots (of various
| kinds), all could benefit from such a central compute
| server which can work without internet. All appliances will
| have voice interface, and will require heavy intelligence
| so it is better if the model is downloaded to a central
| place at home. This model also means all home appliances
| can be equally smart with no restriction on form factor of
| the appliances themselves. Nvidia could model itself around
| current mobile companies selling OS, models, everything in
| a nice compute-rack package.
| Teever wrote:
| I think there's a definite itch to scratch with this kind of
| stuff and you can see hobbyists already tinkering on the
| edges with things like home assistant and the homelabs
| community.
|
| As much as there are market drivers to make the cloud
| attractive to businesses and legitimate reasons why cloud
| solutions are better than alternatives there is a real desire
| in a growing number of people to have a solution that they
| can tinker with but that also has a polished UI so they don't
| have to tinker with it when they don't want to.
| penjelly wrote:
| funny I landed on this idea myself but ended up thinking it
| had no value
| amelius wrote:
| > they don't want to be simply compute providers
|
| I'm still surprised they did not create an App Store for AI.
| Basically lock everything down and make developers pay a % of
| their revenue, Apple style.
| alephnerd wrote:
| > Basically lock everything down and make developers pay a %
| of their revenue, Apple style
|
| At enterprise scale, locked down marketplaces don't work.
| They act as an forcing factor for larger organizations to
| build in house because no one wants vendor lock-in or to lose
| money via an arbitrage.
|
| This is a major reason why you'll see large deals pushing for
| enhanced customization options or API parity, as larger
| customers have the ability to push back against vendor lock-
| in.
|
| Furthermore, a relatively open market (eg. NGC) acts as a
| loss-leader by allowing a community to develop using a
| corporate standard, thus allowing you to build stickiness
| without directly impacting a customer's bottom-line
|
| Fundamentally, a company driven by Enterprise revenue (eg.
| Nvidia) will have a different marketplace structure from a
| B2C product such as Apple's App Store where purchasers have
| little power.
| amelius wrote:
| > At enterprise scale, locked down marketplaces don't work.
|
| If this was true, we'd have more mobile computing
| platforms. Large enterprises publish in Apple's AppStore.
| alephnerd wrote:
| > Large enterprises publish in Apple's AppStore.
|
| Purchasers from Apple's App Store are primarily
| individual consumers. It is a B2C play.
|
| Monetizing an Nvidia marketplace such as NGC would be
| foolhardy as the primary users/"purchasers" are
| organizations with budgets and procurement power. It is
| an Enterprise B2B player.
|
| In enterprise sales, the power differential between (mid-
| and upper-market) customers and vendors is in the
| customer's favor, as they have significant buying power
| and thus a higher user acquisition cost. The upside is
| revenue is much higher, margins are better, and you can
| differentiate on product as commodification is difficult.
|
| This is less so in consumer facing sales as customers
| have significantly weaker buying power, but conversely
| have a much lower user acquisition cost at scale. Hence,
| a growth-based GTM approach is critical, as you need
| customers in aggregate to truly unlock revenue at scale.
| talldayo wrote:
| > If this was true, we'd have more mobile computing
| platforms.
|
| If you think smartphone customers and server customers
| are evaluating hardware based on the same criteria, then
| why isn't Apple the leading datacenter hardware OEM?
|
| > Large enterprises publish in Apple's AppStore.
|
| Yeah? Where's my _Pro Tools_ download on the App Store?
| Where 's my _Cinema4D_ download? Can I get _Bitwig
| Studio_ from there? Hell, is _iTerm2_ or _Hammerspoon_
| even available there?
|
| Large enterprises very explicitly _don 't_ publish on the
| MacOS App Store because it is a purely raw deal. If
| you're developing a cross-platform app (which most large
| enterprises do), then you've already solved all the
| problems the App Store offers to help with. It's a
| burdonsome tax for anyone that's not a helpless indie,
| and even the indies lack the negotiating power that makes
| the App Store profitable for certain enterprises.
| amelius wrote:
| > If you think smartphone customers and server customers
| are evaluating hardware based on the same criteria, then
| why isn't Apple the leading datacenter hardware OEM?
|
| Because Apple doesn't want to be in the B2B space.
| talldayo wrote:
| Let it never be said they didn't learn their lesson from
| XServe, eh?
| akutlay wrote:
| I think AWS showed us that it could work for enterprises
| too.
| alephnerd wrote:
| Listing fees for AWS Marketplace are marginal compared to
| the overall margins of Enterprise SaaS, as 90% are the
| expected target margins in Enterprise SaaS - hence why
| 80% discounts are fairly common in enterprise sales.
|
| More tactically, excessive charging on marketplace pushes
| vendors away from selling on AWS Marketplace and makes
| them develop alternative deployment methods, which reduce
| the stickiness of AWS, as hyperscalers are commodified
| nowadays.
|
| Motorola learned that the hard way 40 years ago when
| pushing excessively restrictive OEM and Partnership rules
| compared to IBM.
|
| AWS is only as strong as it's Partnership ecosystem, as
| companies that are purchasing tend to use 80-90 different
| apps along with their cloud.
|
| Basically, Enteprise Sales shows hallmarks of a Stag Hunt
| Game, so a mutually beneficial pricing strategy amongst
| vendors (AWS, AWS Partners such as Nvidia, MSP) is ideal.
| amelius wrote:
| > At enterprise scale, locked down marketplaces don't work.
| They act as an forcing factor for larger organizations to
| build in house because no one wants vendor lock-in or to
| lose money via an arbitrage.
|
| But you can say exactly the same thing about large
| companies publishing (consumer apps) in the App Store. Why
| would they want vendor lock-in?
| detourdog wrote:
| Apple works with consumers who need that simplification. The
| developers need the market. "AI" in its current form isn't
| really a consumer product to be sold in that way. Consumers
| aren't purchasing Nvidia products to improve their life
| developers are.
| detourdog wrote:
| The hard part to pull off with this strategy is that a truly
| wide spread "robot" platform I think depends on the
| commodification of the IP driving the platform.
|
| I think there are many early innovators that fail in later
| stage growth because of this issue.
| mlepath wrote:
| I have worked with Jetson Orin platform, and honestly Nvidia has
| something that is really easy to work with there. The Jetsons are
| basically a full GPU (plus some stuff) at very low power. If I
| were tasked with building a robot it would likely be the first
| place I look.
| leetrout wrote:
| They are OK. If you need advanced vision - yes, because CUDA.
|
| But off the shelf mini PCs are much more user friendly for
| existing software IME.
|
| Thankfully ARM being so wide spread and continuing to grow this
| wont matter as much.
| blihp wrote:
| Maybe you've had a different experience with GPU drivers on
| ARM for Linux than most of the rest of us? (i.e. it's the
| fact that nVidia actually has Linux support on ARM that is
| the real appeal)
| talldayo wrote:
| > But off the shelf mini PCs are much more user friendly for
| existing software IME.
|
| I'd love you to point me in the direction of an off-the-shelf
| mini PC that has 64gb of addressable memory and CUDA support.
| leetrout wrote:
| Now if we could get a robotics platform like ROS that actually
| cares about modern dev patterns and practices from dev's slapping
| keyboards through production deployment with decent smoke tests,
| easy versioned artifacts and no need to understand linux
| packaging details...
|
| Coming from web / app dev this was my very least favorite part of
| working on the software side of robotics with ROS.
| alephnerd wrote:
| > Coming from web / app dev this was my very least favorite
| part of working on the software side of robotics with ROS
|
| To be brutally honest, you aren't the primary persona in the
| robotics space.
|
| If you have limited resources (as any organization does), the
| PM for DevEx will target customers with the best "bang-for-
| buck" from a developer effort to revenue standpoint.
|
| Most purchasers and users in the robotics and hardware space
| tend to be experienced players in the hardware, aerospace, and
| MechE world, which has different patterns and priorities from a
| purely software world.
|
| If there is a case to be made that there is a significant
| untapped market, it makes sense for someone like you to go it
| on your own and create an alternate offering via your own
| startup.
| scottLobster wrote:
| Sounds like NVIDIA doesn't know what the hell the future is going
| to look like but hopes it's something to do with robotics, and is
| taking some the boatloads of money from the past few years to
| build out product lines for every conceivable robotics need. Good
| for them I guess.
|
| The article references a "ChatGPT moment" for physical robotics,
| but honestly I think the Chat GPT moment has kind of come and
| gone, and the world still runs largely as it ever did. Probably
| not the best analogy, unless they're just talking about buckets
| of VC money flowing into the space to fund lots of bad ideas,
| which would be good for NVIDIA financially.
|
| As an admitted non-expert in this field, I guess the one thing
| that really annoys me about articles like this is the lack of a
| concrete vision. It's like Boston Dynamics and their dancing
| robots, which while impressive, haven't really amounted to much
| outside of the lab. The last thing I remember reading was a
| military prototype to carry stuff for infantry that ended up
| being turned down because it was too loud.
|
| The article even confirms this general perspective, ending with
| "As of right now, we don't have very effective tools for
| verifying the safety and reliability properties of machine
| learning systems, especially in robotics. This is a major open
| scientific question in the field," said Rosen."
|
| So whatever robot you're developing is incredibly complex, to be
| trusted with heavy machinery or around consumers directly, while
| being neither verifiably safe nor reliable.
|
| Sorry, but almost everything in this article sounds like a
| projection of AI-hype onto physical robotics, with all the
| veracity of "this is good for Bitcoin". Sounds like NVIDIA is
| doing right by its shareholders though.
| akutlay wrote:
| I just finished reading Daron Acemoglu and Simon Johnson's book
| "Power and Progress" where they talk about how the leaders in the
| technology space is (unfortunately) able to set the direction of
| the technology according to their goals, not humanity's goals.
| This is an excellent example of such power. NVIDIA wants to
| expand its business and pushes the industry to use more and more
| AI, which highly depends on their cards. Now all the VCs put
| billions of dollars towards this goal, thousands of Phds spend
| all their time, and companies change direction of business to
| catch the AI hype. Not necessarily because we decided this is the
| best for humanity, just because it's the best for NVIDIA.
| EarthIsHome wrote:
| The ChatGPT, LLMs, generative AI, and other hyped usecases have
| been the driving force for Nvidia: it injected huge sums of money
| into their R&D, which also stimulated the economy as developers
| ran to build build build in order to keep up with the demand for
| datacenters, which in turn required more infrastructure building
| to satiate the thirst and power needs of datacenters, etc.
| Before, ChatGPT, I recall the hype was blockchain, crypto, and
| NFTs; and maybe before that, it was "big data."
|
| As the LLM, generative AI, etc. bubble begins to deflate due to
| investors and companies finding it hard to make profits from
| those AI usecases, Nvidia needs to pivot. This article indicates
| that Nvidia is hedging on robotics as the next driving force that
| will continue to sustain the massive interest in their products.
| Personally, I don't see how robotics can maintain that same
| driving force for their products, and investors will find it hard
| to squeeze profit out of it, and they'll be back to searching for
| another hype. It's like Nvidia is trying to create a market to
| justify their products and continued development, similar to what
| Meta has tried, to spectacular failure, with the Metaverse for
| their virtual products.
|
| After the frenzy that sustained these compute products
| transitioned from big data, to crypto, and now, to AI, I'm
| curious what the next jump will be; I don't think the "physical
| AI" space of robotics can sustain Nvidia in the way that they're
| hoping.
| infecto wrote:
| The part that is hard for me to parse is there is hype but
| there is also a significant amount of value being extracted by
| using LLMs and other products coming from this new wave.
| Everytime I read opinions like yours it's hard to make sense of
| it because there is value in the tooling that exists. It cannot
| be applied to everything and anything but it does exist.
| gmays wrote:
| Comparing AI to crypto doesn't really work due to the utility
| of AI. If you believe that there haven't been meaningful use
| cases from the recent generative AI surge, then you might be
| out of touch.
|
| On the investment side, it's hard to say that since ROIC is
| still generally up and to the right. As long as that continues,
| so will investment.
|
| Then biggest gap I see is expected if you look at past trends
| like mobile and the internet: In the first wave of new tech
| there's a lot of trying to do the old things in the new way,
| which often fails or gives incremental improvements at best.
|
| This is why the 'new' companies seem to be doing the best. I've
| been shocked at so many new AI startups generating millions in
| revenue so quickly (billions with OpenAI, but that's a special
| case). It's because they're not shackled to past products,
| business models, etc.
|
| However, there are plenty of enterprise companies trying to
| integrate AI into existing workflows and failing miserably.
| Just like when they tried to retrofit factories with
| electricity. It's not just plug and play in most cases, you
| need new workflows, etc. That will take years and there will be
| plenty more failures.
|
| The level of investment is staggering though, and might we see
| a crash at some point? Maybe, but likely not for a while since
| there's still so much white space. The hardest thing with new
| technologies like this is not to confuse the limits of our
| imagination with the limits of reality (and that goes both
| ways).
| bfrog wrote:
| What breakthrough are people expecting in robotics I wonder?
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