Maybe that it would be interesting to know what the numbers actually are. There is the common saying that you are more likely to get in an accident on the way to the airport than traveling on the plane, but knowing what the likelihood of both of those as well as the likelihood of an accident on a max could show this all to be hysteria or actually well founded.
Take it like this: If you have to ride share and you hear that a common car used by drivers of Uber is having an increased failure rate, not passing inspections and have been temporarily banned from being on the road until more is learned; I think a pretty fair response is to try and avoid entering that car regardless of the ratio of incidents when driving that car, and when biking to the dealership to get it
>I think a pretty fair response is to try and avoid entering that car regardless of the ratio of incidents when driving that car, and when biking to the dealership to get it
"avoid" is doing a lot of the heavy lifting here though. If doing so doesn't cost me anything, then avoiding that car is a non-brainer. However in real life nothing is really costless. Avoiding that car at the very least would cause you to wait longer. If that car is your daily driver, and you don't have a backup, then avoiding that car might cost you hundreds per month. That's why you need to factor in statistics and figure what the absolute risk is, and whether it's worth "avoiding".
Right it would be a bigger problem if I owned and depended on this hypothetical car but my hypothetical self only needs it for Ubers. Maybe I’ll switch to Lyft or local Taxi service. That might make me wait more but the point is that this isn’t a hysterical response, much like monitoring and avoiding the 737 MAX until further notice isn’t hysterical. It’s simply sensible.
To not first consider the actual absolute safety level before considering reasons that might change relatively is not sensible. You can't just come up with reasons to fear something and conclude it's therefore best to avoid, that's completely sidestepping any reasoning about the actual risk and replacing it with emotion.
Taken another way: That your Uber driver is not as safe as others is not, in itself, a reason to reschedule your life to avoid them on the principle there is a safer option somewhere else. It's reason to ask the question how unsafe are they actually and is that level of difference vs normal something the extra inconvenience is worthwhile for. The point of someone mentioning cars is, if you actually quantify the risk change, it's likely you're rescheduling your flights around a risk which is less concerning than the rest of your day to day life and things you accept for getting to the airport in the first place. Without quantifiably discounting that you're letting news headlines dictate your life by fear instead of acting sensibly.
But we can't be sure unless we know the actual stats. A lot of news operates by driving sensational stories, the more hysteria and doom they invoke, the more views they get. Are we talking 1% more likely or 0.00000001% more likely to get in an accident?
How much money did he manage to milk from the company? With that light four year prison sentence where he makes nothing, he'll probably still end up making more than us during his work + prison sentence if the penalty phase is just as light.
We don't know if they had a rigorous internal debate where they evaluated all of the options or if it was a decision made from above. We don't know how the chips perform, if there are alternatives, and if there are alternatives, how the price compares.
Baby boomer mass retirement. This will increase the selling of shares across the board, since boomers will need to liquidate their assets to finance their living expenses when they are no longer taking salaries.
Sounds plausible, any idea what proportion of the large index funds they are thought to own? In the same vein, I wonder if pension funds can pose a risk or if they are self perpetuating enough to not cause any fire sales.
Baby Boomers have been retiring for quite some time already. The Baby Boom was from 1946 to 1964[1], so the oldest Boomers are now 77 (well past the usual retirement age) and the ones in the middle are around 68 (many have retired already).
External plugins on the backend that are hidden from users? How does it work? Something like an api call to wolfram or is it another self hosted model?
Is it the tech that is worth that much or the tech and the people that built the tech when future iterations of the product rely so heavily on the people? Losing 95 percent of the employees will definitely impact delivering future products and that will definitely impact future profits and that has to impact valuation.
It's one of the most innovative and impactful companies in a long time. I'm sure they'll be able to run gpt4, but good luck delivering gpt 5 (or 6 depending on how far along 5 is) when you lost entire teams that dreamt up and built previous iterations.
What is the goal of environmentalism? Avoiding short term harm or fostering sustainability in the long term?
You don't think reaching out further into the universe will give more people the pale blue dot viewpoint and could cause more people to care more about earth when they realize how fragile and insignificant it is?
What about moving industry off the earth in the distant future? Wouldn't that be good for earth in the long run?
I have no experience in this area, but I feel like the ideas are the hard part in music. These are rough drafts and some of them have promise if they can be refined in an iterative fashion with the user at the helm.
You can generate very interesting music simply by working with MIDI as opposed to sampled audio (slashing complexity by orders of magnitude!) and starting from a good model architecture. Daniel D. Johnson's (formerly known as Hexahedria, hired by Google Brain) model biaxial-rnn-music-composition is from 2015, requires very few resources for training or inference, and still delivers compelling, SOTA-or-close results wrt. improvising ("noodling") classical piano. Github https://github.com/danieldjohnson/biaxial-rnn-music-composit... , you may also want to check out user kpister's recent port to Python 3.x and aesara: https://github.com/kpister/biaxial-rnn-music-composition (Hat tip: https://news.ycombinator.com/item?id=30328593 )
