1,022 karma · joined December 15, 2014
Or more charitably, Sequoia presented with something more professional looking, but completely incorrect.
I think many people believe they don't even have enough USD to start with, eg because some of their assets are loans collateralised by crypto (such as the Celsius 1B loan).
I believe both are true - Tether printed USDT against non-cash, and used some of the actual cash to buy dodgy assets for more yield.
I believe you're right just curious about the mechanism.
You can also replace "crypto fund" by any crypto project that uses leverage to deliver extraordinary returns - as long as everything goes up.
- Kristian Andersen offered to abuse his power as editor to stealthily edit a preprint. He is the main author of the most notable paper supporting a natural origin, and has been one of the major resources of the natural origins camp. Now his scientific integrity is seriously questioned.
- Peter Daszak appears much more focused on finding new sources of grant money than on doing good science: "What was needed, he exhorted his staff, was a “change in culture” as “part of [a] mentaility [sic] to get money,”
- The Wuhan Institute of Virology is more of a second zone lab than a top lab: "The WIV was also viewed as subpar, especially when compared with the Harbin Veterinary Research Institute. Harbin was China’s Harvard, said the former DARPA official. The WIV was more like a safety school"
- EcoHealth Alliance and Daszak were seen by DARPA as amateurs in virology, not at all the expert frontline organisation that's been suggested before: "EcoHealth Alliance was viewed as a “ragtag group” and a “middle guy,” a backseat collaborator willing to get on an Air China jet, eat terrible food, and stay in bad hotels" "EcoHealth Alliance had “bolted on” a serious scientist, Ralph Baric".
I think some researchers are refusing to accept the idea that machine learning is very much an experimental science today, and the (very cool) mathematics of kernels, SVMs, empirical risk minimization, bayesian statistics, etc. are simply no longer useful in the large scale regime.
My prediction is that there will be a useful "deep learning theory" in the future, but it will look a lot more like physics (such as Kaplan's scaling laws) than early 21st century machine learning mathematics/statistics.
https://www.washingtonpost.com/business/2020/04/06/bailout-c...
https://www.dallasnews.com/business/airlines/2021/04/09/pay-...
https://www.bloomberg.com/news/features/2018-10-04/the-big-h...
On modern parachutes it won't actually get rid of an unopened main parachute. A main parachute that refuses to open at all is actually scarier than a poorly opened one, because it could get open anytime. Including after you have opened the reserve one. Then you have two parachutes open, and they can get into surprising unstable aerodynamic configurations (a downplane). And you can't easily get rid of an unopened main parachute.
Source: I learned skydiving this summer
I'm about 80/20 on a lab leak.
On your second point, she has stated repeatedly and explicitly that her position is that she doesn't know what the origin of the virus is, natural origin is not ruled out either, and that all she is asking for is that both theories are credibly investigated, for example here [1]. Of course she talks a lot more about the lab leak, but that's largely because she feels this side has not been discussed sufficiently, which was certainly true until 2/3 months ago.
On your third point, you're right that she truncated the quote, but I think her interpretation is the one many readers would come to: "lab leak=conspiracy theory". The quote is dishonestly structured to start with, lumping together lab leak with 5G/Bill Gates silliness. It's the same mistake that the Andersen paper made lumping lab leak with bioweapon. I wouldn't count that as a win on either side.
[1] https://twitter.com/ayjchan/status/1411049136762327043?s=21
They won't generalize after training to transformations of the data that they haven't been trained on, even simple ones such as rotations, whereas humans will.
So I would argue that vision model do use the "underlying structure", and even that one of their problems is that they make use of some of the "underlying structures" that are not actually important, such as image luminosity, rotations etc. I think people usually augments the data with these transformations beforehand during preprocessing to enforce invariance.
In related news: https://www.cnbc.com/2021/02/01/new-chart-shows-china-gdp-co...
Think I'm exaggerating? This has already happened in the UK, RIPA bill was passed to "fight terrorism and child abuse" and ended up used by local officials to identify people who let their dog shit on the sidewalk.
https://en.wikipedia.org/wiki/Regulation_of_Investigatory_Po...