Readit NewsAI companies will someday just be called companies, much like how tech companies are just companies.
(Paraphrase != endorsement.)
Are you worried about the high valuations of the big AI companies (OpenAI, Nvidia, etc)? Then sure, that will correct over time. There will likely be 1-2 big winners.
But if you're talking about AI in general... right now is the least amount of money companies will be spending on AI ever. It will only go up. This isn't crypto.
Bitcoin is at $92k.
If theatres pivoted to competing first on format rather than exclusive access to recent releases, and managed to do well in that regime, I'm sure Netflix and other new media would be more than happy to indulge. Seems unlikely, though, doesn't it? The demand exists but I would be surprised if it was a quarter the size.
Additionally, the New Glenn fairings are very large for their weight capacity. New Glenn has 3x the fairing volume compared to the Falcon Heavy, but can throw less mass. So many expected that BO designed it this way because they expected to increase performance of their engines in the future, making the weight/volume ratio of their fairing more balanced.
New Glenn has 45t of capacity now. Increasing thrust by 15% should increase that to 51t, thus making New Glenn 7x2 also just barely a Super Heavy booster. Perhaps they didn't call that out because that would overshadow the 9x4 announcement.
Increasing thrust by 15% doesn't just increase payload by 15%. I don't know a simpler way to estimate this than to run a simulation, and I don't have one with numbers I can toggle.
I just don't buy this at all
>"The new iPad Pro adds ... a breakthrough LiDAR Scanner that delivers cutting-edge depth-sensing capabilities, opening up more pro workflows and supporting pro photo and video apps." [1]
Yes of course the specs of LiDAR on a car are higher but if apple are putting it on iPads I just don't buy the theory that an affordable car-spec LiDAR is totally out of the realm of the possible. One of the things istr Elon Musk saying is that one of the reasons they got rid of the LiDAR is the problem of sensor fusion - what do you do when the LiDAR says one thing and the vision says something different.
[1] https://www.apple.com/uk/newsroom/2020/03/apple-unveils-new-...
Am I crazy or have I heard this same announcement from Google and others like 5 times at this point?
Is this... just to be clever? Why not
(!x)=/:!x
https://en.wikipedia.org/wiki/Lindley%27s_paradox#The_lack_o...
Indeed, Bayesian approaches need effort to correct bad priors, and indeed the original hypothesis was bad.
That said. First, in defense of the prior, it is infinitely more likely that the probability is exactly 0.5 than it is some individual uniformly chosen number to each side. There are causal mechanisms that can explain exactly even splits. I agree that it's much safer to use simpler priors that can at least approximate any precise simple prior, and will learn any 'close enough' match, but some privileged probability on 0.5 is not crazy, and can even be nice as a reference to help you check the power of your data.
One really should separate out the update part of Bayes from the prior part of Bayes. The data fits differently under a lot of hypotheses. Like, it's good to check expected log odds against actual log odds, but Bayes updates are almost never going to tell you that a hypothesis is "true", because whether your log loss is good is relative to the baselines you're comparing it against. Someone might come up with a prior on the basis that particular ratios are evolutionarily selected for. Someone might come up with a model that predicts births sequentially using a genomics-over-time model and get a loss far better than any of the independent random variable hypotheses. The important part is the log-odds of hypotheses under observations, not the posterior.