181–190 of 207 posts
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DOJ and FTC is appointed by White House and they make sure they align with their ideologies. There is a reason Lina Khan goes after companies while her republican counterpart wont. Thank God the supreme court will kneecap all these useless agenda-driven agencies
They are recommended by the White House and confirmed by Congress. It is far closer to a bi-partisan choice than you are making it seem.
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It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky that this accelerator also turned out to work pretty well for AI. At best, he saw that he needed a more general compute platform for GPUs but OpenCL was a thing so he wasn’t alone in recognizing this. There’s eve…
> It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky it's actually specifically not questionable because it's been covered by journalists etc. https://www.newyorker.com/magazine/2023/12/04/how-jensen-hua... > Within a couple of years, every entrant in the ImageNe…
> In marketing cuda, Nvidia had sought a range of customers, including stock traders, oil prospectors, and molecular biologists. At one point, the company signed a deal with General Mills to simulate the thermal physics of cooking frozen pizza. One application that Nvidia spent little time thinking about was artificial intelligence. There didn’t seem to be much of a market.
> Despite the snub, Hinton encouraged his students to use cuda, including a Ukrainian-born protégé of his named Alex Krizhevsky, who Hinton thought was perhaps the finest programmer he’d ever met. In 2012, Krizhevsky and his research partner, Ilya Sutskever, working on a tight budget, bought two GeForce cards from Amazon. Krizhevsky then began training a visual-recognition neural network on Nvidia’s parallel-computing platform, feeding it millions of images in a single week. “He had the two G.P.U. boards whirring in his bedroom,” Hinton said. “Actually, it was his parents who paid for the quite considerable electricity costs.”
Basically Hinton thought CUDA was a better platform and kept encouraging his acolytes to utilize it with NVidia completely ignoring it. For what it’s worth ChatGPT and LLMs are not the start of the story. I remember seeing Hinton present his research in 2009 and thought it was a revolutionary step forward. It took a much longer amount of time for NVidia to notice that they should be marketing CUDA to AI. They did so around the same time that Google started building their own inference accelerators for Tensorflow. So at that point NVidia was getting multiple signals already from a big customer that there’s something here.
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> It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky it's actually specifically not questionable because it's been covered by journalists etc. https://www.newyorker.com/magazine/2023/12/04/how-jensen-hua... > Within a couple of years, every entrant in the ImageNe…
I mean your article literally suggests that CUDA was a misstep. It failed to bring supercomputing to the masses until they got bailed out by AI coinciding with needing supercomputer powers. > In marketing cuda, Nvidia had sought a range of customers, including stock traders, oil prospectors, and molecular biologists. At one point, the company signed a deal with General Mills to simulate the thermal physics of cooking…
Yes, it's probably true that in 2009 or whatever it wasn't on NVIDIA's radar as being a major R&D priority. It was by 2014-2015, which is what I cited.
You claimed it couldn't be proven that Jensen specifically targeted AI as a priority, and it's clear that by 2015 or so he was, at a time when AMD was not even taking GPGPU itself seriously as a development priority let alone focusing on a specific niche or field like AI.
> It's questionable whether or not he anticipated this specific market ...
again: no, it is not, literally he's been focused on this specific market for a decade and we have the receipts.
if your point is that he didn't see it fifteen years ago instead of ten years ago... ok I guess you win then, discussion concluded.
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I didnt read what you said because you didn't say anything. You just linked to his blog while carefully making sure to point out it was CD who wrote it. That's literally dictionary definition of idolatry.
Idolatry: noun; the worship of idols. It has a decent number of up votes, so clearly some people think it worth posting. As I said, you don't have to read it, and I don't really care if you do. I'm not sure how linking to something with a reference is worship, but ok. You seem very angry.
What you're doing, explicitly, is placing more value on name than idea. A fallacy. I'm obviously not saying "worship of a minor deity" literally, but colloquially.
You're right, I can read it or not. Same with you, you can take valid criticism or ignore it and be all the poorer for it.
You can also continue admitting you have no rebuttal as you hyper-focus on the _diction of the conversation_ instead of the _conversation_, like how to define worship and idolatry in a casual conversation.
Nvidia solved a hard problem and their competitors all failed. I spent years with an AMD card growing progressively more annoyed at their self-inflicted apparent inability to multiply matrices on demand. Nvidia had nothing to do with their failure, unless they had some sort of high-level mole in AMD's driver teams. Hitting the only successful company in a difficult field with legal assaults is not the obvious path to…
It's a bit ironic to talk about lawsuits over monopolies in an emerging AI market, that hardly existed 24 months ago, while ignoring the two worst monopolies in different domains, led by Microsoft and Google.
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> No law can ever be written to capture every possible application of the underlying spirit. Thus your ability to escape the spirit is directly correlated with how many lawyers you can hire to find loopholes in the text (or just flat out lie). The assumption here is that the rules would be complicated and provide lots of opportunities for gamesmanship. Now suppose the rule is "no company shall have more than 30% mark…
Please clearly define the meaning of “you” in your simple law. Literally me? Okay, I don’t own 50% of the market—my company does. Oh you mean my company? Okay, my company doesn’t own 50% of the market—each of my companies only control 25%. Oh you mean me and my companies? Okay, well I only own one company and my wife owns the other one. Oh you mean… I think you get the point… Now move on to all the other words you us…
But we know that the interpretation phase has a lot of wiggle room (the US constitution is a regular parade of this sort of thing - the Roe v. Wade fight or the abuse of the commerce clause for example). When they go bad these things aren't loopholes as much as they are just ignoring the written law with a polite fiction and it is up to different interest groups to work out where the power lies to get what they want. Political reality vs. the theoretical rule of law ideal.
But that is necessarily independent of what is written in the law itself. If a group has enough power to overrule the written law then what you write in the law won't be able to stop them.
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> There is no moat Isn't the CUDA API a fairly significant moat?
I don't think so, no. I had an AMD card; I didn't feel like the lack of CUDA was slowing me down. I managed to implement everything I tried on the CPU just fine. My problem was that every approach I tried to implement my own stuff on the GPU using things like Vulkan or OpenCL led to crashes or frustration. In fairness I didn't know how to do basic operations on a graphics card with OpenGL and would be relying on supe…
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There probably isn't anything in CUDA that makes it special. They are well optimised math libraries and the math for most of the important stuff is somewhat trivial. AI seems to be >80% matrix multiplication - well optimised BLAS is tricky to implement, but even a bad implementation would see all the major libraries support AMD. The vendor "lock in" is because it takes a few years for decisions to be expressed in mar…
> The vendor "lock in" is because it takes a few years for decisions to be expressed in marketable silicon and literally only Nvidia was trying to be in the market 5 years ago. It's crazy, because even 10 years ago it was already obvious that machine learning was big and is only going to become more important. AlphaGo vs Lee Sedol happened in 2016. Computer vision was making big strides. 5 years ago, large language m…
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Honestly I don't get how the current situation could develop. Video games have used compute shaders forever, and those work just fine on other cards. I remember doing some quite involved stuff for my thesis using OpenCL a decade ago, and it worked just fine. Nowadays OpenCL is dead for some reason... I just don't get what is there in CUDA that makes it so special. As far as I remember, a GPGPU API consists of a shade…
Writing and porting kernels between different GPU paradigms is relatively trivial, that's not the issue (although I find the code much clunkier in everything other than CUDA). The problem is that the compiler toolchains and GPU accelerated libraries for FFT, BLAS, DNN, etc. which come bundled with CUDA are pretty terrible or non-existent for everything else, and the competitors are so far away from having a good answ…
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People have been saying that GPUs randomly happened to be good at ML since at least 2014 when Nervana was founded. It was clear 12 years ago to anyone paying attention that DL was revolutionizing the world and that NVIDIA GPUs were at the center of the revolution. Whatever random chance factored into NVIDIA's success is outweighed by the decade+ of pedal-to-the-metal development NVIDIA has undertaken while its compet…
The history page for CUDA is pretty accessible [1]. It originated from experiments at Stanford in 2000 with Ian taking on leadership of CUDA development in 2004. > In pushing for CUDA, Jensen Huang aimed for the Nvidia GPUs to become a general hardware for scientific computing. CUDA was released in 2006. Around 2015, the focus of CUDA changed to neural networks.[8] Credit to Jensen for pivoting, but I recall hearing…
[1] https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1575717 [2] https://dl.acm.org/doi/pdf/10.1145/1390156.1390170