Manabu-eo
Joined 10 years ago
Joined 10 years ago
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.