Nvidia bets on robotics to drive future growth

https://www.ft.com/content/7c3dafa8-ffb9-4ca8-b677-ab3cc2afbdcb

by pella • 2 years ago

188 146 2 years ago

ft.com

pella• 2 years ago

https://archive.md/jqMKX

veunes• 2 years ago

Robotics has long been an area of promise but (I think) limited returns

spot5010• 2 years ago

Care to elaborate? I feel the. the real power of AI will be unlocked when AI can sense and interact with the world.

malux85• 2 years ago

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.

Animats3• 2 years ago

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• 2 years ago

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).

ksec2• 2 years ago

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?

rapsey3• 2 years ago

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.

petra• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

Waymo already solved self driving years ago. Tesla still has a long way to go.

echelon• 2 years ago

Is there anyone even close to Waymo in this game? Is Waymo going to own the entire market?

doublepg23• 2 years ago

Isn't that the difference though? I've never even seen a Waymo and I've been successfully driven by Teslas many times.

ksec2• 2 years ago

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.

lm284692• 2 years ago

In a very small subset of cities, road conditions, weather condition, &c. Basically US grid cities with 300 days of sun per year

e_y_2• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

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__• 2 years ago

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• 2 years ago

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• 2 years ago

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.

krisoft2• 2 years ago

Too bad the everyone lost interest and now robotics is the hot new thing.

Self driving is robotics. Simple as that.

amelius7• 2 years ago

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• 2 years ago

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• 2 years ago

Cars are robots without arms

ram_rattle• 2 years ago

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• 2 years ago

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.

michaelt8• 2 years ago

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.

blueboo• 2 years ago

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

whatever12• 2 years ago

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• 2 years ago

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.

whatever12• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

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.

krasin2• 2 years ago

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

YeGoblynQueenne2• 2 years ago

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.

michaelt8• 2 years ago

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.

justmarc2• 2 years ago

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.

madaxe_again• 2 years ago

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.

blagie4• 2 years ago

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)

fakedang• 2 years ago

What is classical machine vision? For an image recognition problem, wouldn't you use a conventional neural network? (And I might be a bit outdated here, considering that CNNs were used for image recognition a decade ago).

amelius7• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

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.

dartos2• 2 years ago

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• 2 years ago

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

jvanderbot2• 2 years ago

I've been doing this for 10+ years, and have seen GPU-based calculations slowly eat away at the following problems:

* Mapping. Nowadays generating a dense grid of costs can be done insanely fast on GPU. There's just no excuse to not use a GPU on every robot so it can build a fast map, unless you move at snail speed.

* Computer Vision. Classical depth mapping is best done on a GPU. Classical computer vision object detection has fallen away to the rise in ML-based CV for segmentation. Some (IMHO) overzealous practitioners are trying to eat away at estimation and tracking, which IMHO will recede a little since there was nothing wrong with the estimators (just Bayesian stats) to begin with, it was always the measurements. Still, for detection (and sometimes association), ML on GPU is the way to go and that will very likely not change. It has gotten so good that you can get away without using other sensors and just deploying a vision system (though I don't recommend it, but this is what Tesla does). This is an obvious case for one (or one more) GPU on every robot.

* Planning - End to end planning is eating traditional planning now, similar to CV. There are some areas where this is an obvious win (e.g., complex manipulation tasks), and some areas where some overzealous overreach is happening (e.g., simpler planning tasks like routing). But ML on GPUs is here to stay for all planning tasks, especially when estimating costs from complex data, even if a classical planner uses those costs. And I'd be remiss if I didn't mention policy-based planning, which does a huge amount of training to generate essentially a fast lookup table for actions. Deployment of these types of planners often requires a very good estimator to determine what state you are in - and this is a great area for ML, mapping real world messy data to a clean state lookup. I think this can typically be done without a GPU, due to training prior to deployment, but if you have a GPU already (see prior two), you will find this is a good use of it.

* Low-level planning / Controls - Shares a small overlap with above, but mostly concerned with fast responses to transient data and stabilizing the system. I've heard, but not seen directly, that learned policies are coming into vogue here. But regardless, it is a common thread that a network can assist with estimating costs and states to allow a traditional controls system to operate more reliably. I doubt this will necessitate a GPU, but like above, will gladly use it if required and available.

To add to this, consider that we're generally not talking about discrete, gaming-type GPUs, we're talking about purpose built robotics-targeted embedded systems that speak native CUDA. The Jetson family, in particular.

asadalt2• 2 years ago

if they really want to bet on robotics, I want them to release a $10 variant of jetson board.

adrian_b2• 2 years ago

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• 2 years ago

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.

jononor2• 2 years ago

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.

asadalt2• 2 years ago

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.

albert_e• 2 years ago

https://archive.ph/uKeHc

cess112• 2 years ago

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• 2 years ago

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.

ANewFormation2• 2 years ago

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.

dankobgd• 2 years ago

took them 16 years to fix night light bug in the driver but yeah robots are the future

stephc_int13• 2 years ago

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.

nthingtohide2• 2 years ago

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.

dartos2• 2 years ago

Why sell to consumers when you can sell to large corporations at 10x the price?

david-gpu2• 2 years ago

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.

Teever• 2 years ago

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• 2 years ago

funny I landed on this idea myself but ended up thinking it had no value

amelius7• 2 years ago

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.

alephnerd5• 2 years ago

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.

detourdog2• 2 years ago

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.

QuadmasterXLII• 2 years ago

I mean, you’re almost describing Google’s TPU sales approach, and we can all see how that goes

detourdog2• 2 years ago

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• 2 years ago

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.

leetrout2• 2 years ago

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• 2 years ago

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)

talldayo4• 2 years ago

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.

leetrout2• 2 years ago

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.

alephnerd5• 2 years ago

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• 2 years ago

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.

akutlay2• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

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• 2 years ago

What breakthrough are people expecting in robotics I wonder?

Nvidia bets on robotics to drive future growth

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