Another compounding issue is that they had to package the vector into two parts, which then have to both infect the same cell to get any effect. Which means you have to at least double the dose to get similar coverage compared to a single AAV vector (and actually more than double). Seems like a easy recipe for liver toxicity. Which is why most companies doing AAV therapy either target the liver or the eye (where AAV doesn't escape to the liver).
I feel like the parents were not well enough informed of the risks, and the PI rushed the therapy to be famous. Not the first time this has happened, and not the last, sadly.
> He used xAI to move the Twitter debt so it couldn't be taken away for failure to pay debts.
Exactly. I think it was obvious he was shifting the debt around when xAI merged w/ X right after xAI raised a large funding round and had cash in the bank (which it could use to pay down the X debt).
Probably in a desperate attempt to stall margin calls on the debt, which would cause him to have to sell his Tesla stock, which might start the freefall in stock price, creating a negative feedback loop and cratering his empire. See also the news about Tesla shifting manufacturing to robotics.
Of course they are. Why would you want revenue? If you show revenue, people will ask 'HOW MUCH?' and it will never be enough. The company that was the 100xer, the 1000xer is suddenly the 2x dog. But if you have NO revenue, you can say you're pre-revenue! You're a potential pure play... It's not about how much you earn, it's about how much you're worth. And who is worth the most? Companies that lose money!
The down rounds have started for those companies that either haven’t found product market fit, or aren’t in contention on leaderboards. Especially those companies that are a OpenAI wrapper and can be easily replaced by a new, more broadly applicable, foundation model or a new modality model.
The padding in healthcare is part of the system. One part is to have high prices so insurance can negotiate them down. And for hospitals in particular, prices are padded to subsidize emergency care for the indigent (which they have to provide without regard to ability to pay; thanks Reagan).
Then take a Cauchy or a t-distribution. Basically anything with a longer tail than exp(x^2). The Gaussian summary will be misleading because of the tails.
They’ve chosen the path of commoditizing their complement. To ensure that ML capabilities are not a differentiating factor in the market, make ML capabilities a commodity available to everyone at the marginal cost.
Everyone talks about the chips, but Nvidia's true competitive advantage is CUDA. Only recently has PyTorch added AMD support, and benchmarks I've seen have shown that AMD has a lot of work to catch up.
From a practitioners perspective, I view this type of behavior as tuning the model more towards the exploitation side of the exploration/exploitation trade-off. I think a lot of recommendation engines do this (looking at you YouTube) because it’s more profitable.
My wife and I visited Paris last summer and biked everywhere. It felt very safe, not as safe as Copenhagen but still safe. I hope that with more time, as more drivers get used to bikes on the road it will get safer and more people will bike, creating a positive feedback loop.
I completely agree with your assessment and logic, but the parent posted suggested that socialization of healthcare (e.g. ObamaCare) is contributing to auto rates. That I don't understand.
I feel like the parents were not well enough informed of the risks, and the PI rushed the therapy to be famous. Not the first time this has happened, and not the last, sadly.