Anyone else found it odd that the article says "For all the improvements evident in contemporary surgical technology, electron microscopic images actually confirm that the edge of Neolithic obsidian blades exceed today’s steel scalpels in sharpness" and then cites a paper about obsidian blades from 1982?
Has there been no improvement in sharpness in 42 years? Or are Neolithic obsidian blades just that much sharper?
There are glass fractured blades that form the same edges and diamond scalpels too, all of them show brittleness issues to lateral moves especially. The physics of steel cutting edges get very complicated in general as can be shown by an example of how human hair can damage steel here: https://www.reddit.com/r/EngineeringPorn/s/6xHn8TQEtK
I tend to geek out over the topic of cutting as it’s endlessly complex from biological matter all the way to cnc.
From a pure sharpness perspective, you can't actually get any sharper, but it turns out there are many other physical properties (toughness, ease of sterilization, repeatability of manufactured tolerances at-scale, etc.) of a practical cutting edge where steel overwhelmingly beats amorphous ceramics.
FWIW, many surgical operations done today that require a very high-precision cut use numerically controlled lasers. Unfortunately, lasers will never work for certain procedures where cauterization would hamper healing or tissue reintegration.
They are fragile I've heard, and quite likely they dull quickly (don't know), but I'm not saying they're 'better' than steel; it's not a competition. Horses/courses.
LilyPond is great, but the writing process for a lot of music involves a lot of playback, so integration with a half-decent playback engine is really useful. On top of that, you can do almost everything in Dorico and Sibelius with keyboard shortcuts, so they are very power-user-friendly (which is what I like about LaTeX).
Plus, as professional software used by people along (mostly not very much) money with it, productivity is key. Lilypond loses badly here.
I can enter 200 or 300 bars in Dorico in the time I could do 20 in Lilypond - and that’s at the rate I could manage when I was using lilypond regularly.
I also think the output is nicer, which also matters here.
I do "professional" engraving as a side gig, mostly turning scratch people's make on lined paper into actual sheet music, and I exclusively use lilypond
Lilypond makes gorgeous music. However, getting things besides music to look good is painful at best and sometimes impossible. I spent hours trying to figure out how to get a good looking lead sheet setup (music, chord name, lyrics). Especially font sizes and spacing. Ugh. Good luck getting an annotation (such as "intro" or "chorus") anywhere less than about 2em from the top of the staff...
I think they've changed things in the five years since then, so I think I'd have to do it all over again.
I feel like I pay a lot for news. I pay for:
* WSJ
* NYTimes
* Economist
* LATimes
* SJ Mercury News
* Apple News
And yet I constantly run into paywalls (which I circumvent). How much per month does the news industry think is fair for me to pay?
I wish I could just pay a fee per article I read. I think the business model is broken because there are too many individual entities and they all want a subscription. And this makes no sense in the age of the internet.
This is why my next car won’t be a Tesla (after driving one for the past 8 years). It’s sad, they get so many things right but they are dangerously irresponsible.
Yep, agreed. I'm on my third Tesla, and probably my last. The cars are still very structurally safe, but operationally they're getting more dangerous because of changes like the UI, FSD feeling actually worse than it used to (I had it on my Model 3, but I couldn't transfer it to my Model X, and it wasn't good enough on the 3, why would it be good enough on the X which actually had FEWER sensors?) and the absolute insistence than vision-only is safer than vision plus radar and ultrasonic. I understand that the radar they had was limited, but you improve the radar, don't drop it entirely.
Also, I haven't been an Elon fan since 2018 or so, but I was still able to look past his shenanigans when I bought this a few years ago, but he's gone straight over the cliff since then and keep falling, so this is almost definitely the last one I'm getting.
The point is: if you sue claiming this model breaks the law, you lose your license to use it.
Apache 2.0 has a similar restriction:
“ If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.”
True, although it's unusual to see it for copyright not patents.
That said, the far bigger issue is the end of the same clause 2.1:
> NVIDIA may update this Agreement to comply with legal and regulatory requirements at any time and You agree to either comply with any updated license or cease Your copying, use, and distribution of the Model and any Derivative Model
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 competitors decried AI as an overhyped bubble.
I have been there for this history, working on ML on GPUs at NVIDIA for a few years before Jensen decided to productize my little research project, CUDNN.
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 about CUDA networks from Google tech talks in 2009 and realizing they would be huge. It wasn't anything unique to realize NNs were a huge innovation but it did take another 5 years for it to mature enough and for it to become clear that GPUs could be useful for training and whatnot. Additionally, it's important to remember that Google had a huge early lead here & worked closely with Nvidia since CUDA was much more mature than OpenCL (due to intentional sabotage or otherwise) and Nvidia's chips satisfied the compute needs of that early development.
So it was more like Google leading Nvidia to the drinking well and Nvidia eventually realizing it was potentially an untapped ocean and investing some resources. Remember, they also put resources behind cryptocurrency when that bubble was inflating. They're good at opportunistically taking advantage of those bubbles. It was also around this time period that Google realized they should start investing in dedicated accelerators with their TPUs because Nvidia could not meet their needs due to lack of focus (+ dedicated accelerators could outperform) leading to the first TPU being used internally by 2015 [2].
Pretending like Jensen is some unique visionary seeing something no one else in the industry didn't is insane. It was a confluence of factors and Jensen was adept at navigating his resources to take advantage of it. You can appreciate Nvidia's excellence here without pretending like Jensen is some kind of AI messiah.
I was at NVIDIA at that time working on ML on GPUs. Jensen is indeed a visionary. It’s true as you point out that NVIDIA paid attention to what its customers were doing. It’s also true that Ian Buck published a paper using the GPU for neural networks in 2005 [1], and I published a paper using the GPU for ML in 2008 while I did my first internship at NVIDIA [2]. It’s just not true that NVIDIA’s success in AI is all random chance.
CUDA won big because it made a big bet. Were it that OpenCL was ubiquitous and at feature-parity with CUDA, maybe there would be more than one player dealt-in at the table today. But everyone else folded while Nvidia ran the dealer for 10 long years.
Innumeracy in this area drives me nuts. Case 17 in https://www.eia.gov/analysis/studies/powerplants/capitalcost... for instance specs out a solar installation that can store a bit more than an hour of its peak output energy in batteries and quotes $2,175 per kWh.
People are going to compare that to Case 9 and a $7,681 kWh for a dual installation of AP-1000s and declare it is game over but you're going to need a lot more than one hour of storage to get through the night and when you add that and consider the need that you'll either need storage or overcapacity to get through the winter the cost is going to closer to the AP-1000. Personally I am pretty irked that that paper gives a number for the capital cost of a PV system which is not dependent on the location because you could a difference by more than a factor of two in how much energy the same array could produce in different spots.
It's one of those things that adds to a culture of people just talking past each other.
Cerebras yields 46225 * .93 = 43000 square millimeters per wafer
NVIDIA yields 58608 * .92 = 54000 square millimeters per wafer
I don't know if their numbers are correct but it is a strange thing for a startup to brag that it is worse than a big company at something important.
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