92 karma · joined June 11, 2016
It's much easier and cheap to make a finetune or LoRA than to train from scratch to adapt it to your use case. So it's not quite like source vs binary in software.
Surveyor 1 was the first automated vertical soft-landing rocket AFAIK, in 1966. DC-X was the first using turbo-pump engines.
He also "held two internships in Silicon Valley: one at energy storage startup Pinnacle Research Institute, which investigated electrolytic supercapacitors for energy storage, and another at Palo Alto–based startup Rocket Science Games."[1] , has some software patents (software patents should be abolished) from his time at Zip2, and made and sold a simple game when he was twelve.
So he has a little experience working directly at the low level with his physics degree and coding knowledge, but of course it was not his talent in those that made him a billionaire, it might even have been the opposite. So there is indication for the "at all" but not on how talented. I guess one versed in BASIC can read the source of his game, but that was when he was twelve...
But yeah, nowadays he has thousands of engineers working under him, of course he is going to delegate. The the important thing is the system engineering, making sure the efforts are going in the right direction and well coordinated. He seems knowledgeable and talented enough at that. Evidence for SpaceX: https://old.reddit.com/r/SpaceXLounge/comments/k1e0ta/eviden...
[1] https://web.archive.org/web/20191228213526/https://www.cnbc.... , https://fortune.com/longform/book-excerpt-paypal-founders-el...
Also, before you thought he knew very little about his many companies, implying no distinction, but now you adjusted up his knowledge about two companies, but inexplicably down for the others.
You also imply he should give equal attention to all of them, ignoring some of them are bigger, more important, or simply more interesting to him. Is equal attention the optimal strategy here, or you would be getting an F grade if you suggested that?
He didn't need to invest a lot of time to make a good investment in DeepMind, that was then bought by Google, for example. Investing in what you know and understand is a good investment advice, but so is to diversify your portfolio and to not spend too much time optimizing your investments in lieu of everything else.
Some of his "investments" are more like spending on a hobby (as destructive as it can be, in the case of twitter for example... or constructive like SpaceX), so not even bound by those rules...
Someone being capable in one field doesn't means he isn't a insufferable jerk or a moron in other fields. I don't understand this impulse to paint someone as completely black or completely white.
I imagine that they choose a fixed number of recurrent iterations during training for parallelization purposes. Not depending on the previous step to train the next is the main revolution about transformers vs LSTM (plus the higher internal bandwidth). But I agree that it might not be the most efficient model to train due to all that redundant work at large r.
A big part of those $5 billion was not having to pay sales taxes on possible future Gigafactory production, for example, but that gigafactory never reached the initally planned size AFAIK.
I do recognize that as a royalty free and well supported architecture, very flexible with all the optional extensions, it do have a better shot than others at becoming the standard architecture. But the sheer amount of closed source software written for architectures that keep track of arithmetic flags that would need to be emulated is daunting.
Of course, if you have nuclear weapons already on Mars that can be remotely triggered from Earth this doesn't apply, but hopefully we can avoid that...
The quality overal will likely be better to, as the noise level can be lowered. It's possible to do a better denoise with temporal info than simply averaging the noise like a long exposure does.
There are a few system languages that uses GC, like Nim and D. Of course with the option to do manual memory management where necessary, and allocating things on the stack whenever possible. Nim also gives option for several diferent types of GCs and memory allocators, where each one can be more performant for different tasks. Maximum GC pause can also be configurable, at the cost of temporarily using more memory than you should until the GC manages to catch up.
Of course, you can always manually craft arenas and such to be faster and avoid fragmentation, at the cost of much more effort.
In special, I was reminded of Rocket Girls by the Polaris Dawn mission recently. EVA from a capsule, highly eliptical earth orbit, all private mission, etc.
Would such donation even be legal currently in the US? And AFAIK neither Tesla nor Musk ever donated a car before, for any reason or to anyone else in those 20+ years.
On the other hand, he is the largest private donator to Ukraine, via SpaceX and Starlink.
https://investigatemidwest.org/2020/12/04/buy-it-or-else-ins...
https://inthedirt.substack.com/p/when-the-neighbor-kills-you...
> Hazards Fixed:
> V(P)COMPRESS store to memory is fixed. (3 cycles/store to non-overlapping addresses)
> The super-alignment hazard is fixed.
I initially tried searching for the string, but the () thwarted that.
It used to be 142 cycles/instruction for "vpcompressd [mem]{k}, zmm" in zen4.
> Falcon Heavy rocket, having three launches under its belt, has proven more powerful than originally anticipated. Previously, it was thought that launching Europa Clipper on a Falcon Heavy would require a “kick” stage — essentially a small booster attached to the top of the rocket. The Falcon Heavy’s impressive performance has made that unnecessary. Moreover, mission designers at Jet Propulsion Laboratory have found a path to Jupiter called a MEGA trajectory: after launch on a Falcon Heavy, Europa Clipper would fly to Mars for a gravity assist, and then return to Earth for another, and then on to the Jovian system. (The mission previously believed that the rocket would necessitate a Venus gravity assist, which would require special thermal protection for the spacecraft.)
> The window for a MEGA launch opens in 2024 and would take only three years longer than an SLS flight. A Falcon Heavy expendable launch is about $150 million. A single SLS launch is now estimated to cost $2 billion.
Source: https://www.supercluster.com/editorial/europa-clipper-inches...