As someone who works in the semiconductor industry, I'm worried that joining an AI hardware company is a risky move that will falter just like General Magic and other hyped hardware startups.
Have any other examples of failed hardware? I love reading about these, most of which I get from r/shittykickstarters, but I'd like to have a rich vein of failed SV startups
Thinking Machines and Transmeta come first to my mind. I'm foggy on details around Thinking Machines, but I vividly remember hype and lustrous lies/promises media pushed about Transmeta.
Intel itself was once a risky company to join, as the whole concept of a microprocessor seemed both stupid and crazy at the same time. Their 4004 series calculator controller was a useless toy compared to more serious computer hardware at the time and their 8008 was marginally less toy-like but equally useless for enterprise computing.
Nvidia is heavily pushing AI right now, and it could be their biggest market in the future. But they're not a startup: Nvidia is valued at $50B. Their core market is selling high-end graphics cards for hard-core gamers. Even if their AI business failed completely tomorrow, it's still a viable company.
I would love to hear any opinions on how one might invest in this, the no-brainer Nvidia has had a huge run already, although I wouldn't be surprised if it was double from here in a year's time.
It really depends on how the market shapes up. You might see one software company rise to dominance where they interoperate with different platforms interchangeably, or you might see a single hardware vendor come to dominate a number of much smaller software vendors.
NVidia is the horse to bet on now, but it remains to be seen how they can can expand beyond their current model of leaning heavily on their GPU technology.
As AI becomes more pervasive you'll want to have it integrated into smaller, more power conscious devices, such as would be the case in robotics. Does NVidia have a solution here that scales down? Clearly they're focused on scaling up, as that's where the money is today.
They don't necessarily have to scale down to get the benefits of AI in side smaller devices such as robotics. There's no reason why the robotics devices can't or won't be equipped with a variety of radios to facilitate communication.
The AI agent can easily be somewhere centralized where power isn't a big deal.
Then the robot stops being analogous to an animal with it's own brain and starts being something more like an appendage.
Or maybe they'll be like those magical mops in Fantasia.
I'm thinking more like warehouse or factory that has a central rack-server and all the robots in the factory are wirelessly connected to it.
People look at a humanoid robot and they see an entity that is similar to a human but made of silicone and steel instead of meat and bone. I think it's more accurate to compare the robots in this factory to the hands and eyes of a large disembodied rackmount brain.
Does NVidia have a solution here that scales down? Clearly they're focused on scaling up, as that's where the money is today.
Yes, they have invested heavily in this. Everytime they talk about their "car/auto segment" (which is all the time if you listen to their investor calls) it's mostly about scale-down.
Cars, especially the sort that will need to do self-driving, will have massive batteries in them. I'm talking about Roomba-sized devices where they can't power a full GPU worth of gear.
Where did you get the nonsensical idea that they are going to use desktop GPUs in cars? The board they specifically designed for self driving cars merely needs a few watts for 500 GFLOPS. Why would this not be suitable for a "Roomba-sized device"?
They may have large batteries if they are electric cars, but it won't be for the GPUs.
The NVidia PX2 platform[1] - which is what the Telsa self-driving features uses[2] - is available in a 10W config. I presume this isn't the full self-driving mode, but the Jetson TK1 can do full image tracking and recognition in less than 30W (and that is a 2 year old platform).
If Intel is smart they will invest heavily into library development. Researchers use whatever is fast and right now cuda is fast, not just because nvidia has the best GPUs (it's about even) but because the primitives in cuda are so much faster. Matrix multiplication on the same hardware is like 3x faster in cuda than opencl or competing libraries and using the neutral networking primitives is even faster. Intel needs to invest in good, low level libraries so researchers can hack on their platform, build new things, etc. Ultimately I think researchers drive what platform gets widely used since training takes so much longer than inference.
I bought both NVIDIA and AMD. AMD has a big GPU patent portfolio as well. AMD trades at about 1/5th NVIDIA and could be a takeover target for a bigger player trying to move into AI.
In my opinion NVIDIA needs a moat. CUDA is a good start but they need proprietary data that's hard to create. I thought they should buy Yahoo which would give them a large and unique data set that NVIDIA users could tie into through an API.
Won't get anywhere with a dataset. You need dozens or hundreds of them, and they are usually created using Amazon Turk, so anyone with some money can create one. And they need to be commissioned by the AI researchers, because they know best what they want to study.
NVIDIA needs to become more efficient and build deep learning hardware instead of graphics cards that do faster computation than CPU. If only they could create a small form AI processor for robotics that could do vision at 500+ frames per second. Take a look at this video from 7 years ago which does 1Khz vision: https://youtu.be/-KxjVlaLBmk?t=158
This. Intel has a lot of catchup to do there. NVidia's drivers and libs are simply on a different level. For example I've seen the same OpenGL code execute faster on an ancient Tegra 4 (mobile) platform than on an Intel Haswell with HD Graphics (which is supposed to beat the Tegra 4 by miles according to raw specs).
I think memristers are an HP thing. Intel's competing product is 3D cross point, which, according to marketing is a persistent memory technology that's higher density than DRAM and faster than flash.
That means memristors are used to store neural network weights, and a signal passing through a memristor would be effectively multiplied (scaled) by the weight value.
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[ 3.3 ms ] story [ 101 ms ] threadWe're in the 8088 stage of AI right now.
Maybe I'm biased, but I can count far more company fortunes that were made doing a simple thing (at the right time) than a complex thing.
They are just good enough as integrated GPUs for the occasional gamer and that is it.
NVidia is the horse to bet on now, but it remains to be seen how they can can expand beyond their current model of leaning heavily on their GPU technology.
As AI becomes more pervasive you'll want to have it integrated into smaller, more power conscious devices, such as would be the case in robotics. Does NVidia have a solution here that scales down? Clearly they're focused on scaling up, as that's where the money is today.
The AI agent can easily be somewhere centralized where power isn't a big deal.
Then the robot stops being analogous to an animal with it's own brain and starts being something more like an appendage.
Or maybe they'll be like those magical mops in Fantasia.
Obviously cars are not really power or space limited and they can't rely in connectivity. But it's funny to think about anyway.
People look at a humanoid robot and they see an entity that is similar to a human but made of silicone and steel instead of meat and bone. I think it's more accurate to compare the robots in this factory to the hands and eyes of a large disembodied rackmount brain.
Also can't remember the last time I saw a USB port jammed on sideways.
Yes, they have invested heavily in this. Everytime they talk about their "car/auto segment" (which is all the time if you listen to their investor calls) it's mostly about scale-down.
https://m.f.ix.de/scale/geometry/1280/q50/imgs/18/1/6/4/4/6/...
The NVidia PX2 platform[1] - which is what the Telsa self-driving features uses[2] - is available in a 10W config. I presume this isn't the full self-driving mode, but the Jetson TK1 can do full image tracking and recognition in less than 30W (and that is a 2 year old platform).
[1] http://www.nvidia.com/object/drive-px.html
[2] https://blogs.nvidia.com/blog/2016/10/20/tesla-motors-self-d...
[3] http://developer.download.nvidia.com/embedded/jetson/TK1/doc...
In my opinion NVIDIA needs a moat. CUDA is a good start but they need proprietary data that's hard to create. I thought they should buy Yahoo which would give them a large and unique data set that NVIDIA users could tie into through an API.
NVIDIA needs to become more efficient and build deep learning hardware instead of graphics cards that do faster computation than CPU. If only they could create a small form AI processor for robotics that could do vision at 500+ frames per second. Take a look at this video from 7 years ago which does 1Khz vision: https://youtu.be/-KxjVlaLBmk?t=158
We need that.
Nope it's just some rebranded x86 cores.
Neuromemristive,although I haven't heard the term before, means using memristors to compute artificial neural networks.
What does that mean?
https://www.wired.com/2014/08/ibm-unveils-a-brain-like-chip-...
More details about Nervana architecture. https://www.nervanasys.com/nervana-engine-delivers-deep-lear...
https://news.ycombinator.com/item?id=12257376