it's economically unreasonable to drive for more than one apps simultaneously. Now, it could be economically reasonable to drive for more than one apps at many points in the past.
If Lyft and Uber are so easily exchangeable, why is Lyft is still a minority in the US while spending more money?
like i said it's reasonable to drive for all at different points in the past but it's unreasonable to drive for all 4 at the same time. Think about it, incentive-wise, if you complete 50 trips you got $x , if you complete 100 trips you got $2x. If you only got 50 trips in you, why are you splitting them between two apps 25 trips each and got $0 incentive?
Almost every driver I ride with is switching between the Lyft and Uber apps to see which one is offering better rates/destinations. The incentives appear to be insufficient to offset rate disparity in this market - unclear if that holds true everywhere.
I'm not at all well-versed in statistics, but it seems that without enough extra cash to consistently one-up the competition in terms of incentive structures, a ride share company could not make the driver's choice of which app to use a non-random event. So yes, a driver wouldn't necessarily choose all four at once, but they also wouldn't consistently pick one.
It's a well known fact that the biggest factor in taxi revenues is how much paid miles/km you can do per day. The single biggest factor to increase your revenues is increasing that number.
So unless the incentives are massive, a driver will always look for the next ride by any means necessary. It's nearly never worth it to "wait for a better option".
Uber is a cancer that has been amassing and burning that investment. Who knows? Lots of smart people at Uber maybe they can figure out how to turn a profit but I maintain that won’t happen until human drivers are not part of the equation or they pivot to something entirely different.
That's a long way away. The self driving technology has plateaued after the initial controlled tech demos and isn't looking likely to be useful in the general way that Uber needs any time soon. Maybe they can get it working in some very favourable routes but not enough to balance the books. Some really hard problems still exist with algos and sensors and the costs will be high for a long time. If the bet is on autonomous vehicles it's a very long bet.
No, paper millionaires. You're only an actual millionaire once you sell your stock. You become a paper millionaire when the IPO happens and you gain liquidity and price increases.
According to your logic there is no such thing as an actual millionaire. No sane person keeps a million dollars sitting in cash in a savings account, they invest it somewhere (equity, bonds, real estate, etc).
> Uber has a global operations, is in multiple streams of business and has diversity across business lines
This can be a reason why one would be more interested in Lyft. Uber seems like a distracted player who's losing money on many other markets and businesses, not to mention hundreds of millions of dollars on self-driving cars (and flying cars?!). Lyft is much cheaper (15B valuation), while Uber is much more expensive (120B?). If I invest 1B in Uber, my money would vanish in 1 quarter (yes they're losing 1B/quarter). Those 1B dollars would be split to invest in flying cars, uber eats freight bike/scooter, battles in India Middle East. On the other hand, if I invest 1B in Lyft, I'm sure those 1B would go towards gaining market shares in the US which is by far the most important market for the two players.
Second of all, personally I think if Lyft failed and the stock dropped by half. Some other dominant players would look to acquire Lyft. I'm thinking about Google's Waymo One plus Lyft's network. Apple seems to have a lot of cash to burn also, and they're also developing SDC. On the other hand, Uber's share price has to drop more than 10x in order for it to come close to a reasonable acquisition price.
There's a long version and a short version. The long version is you have to learn to write mathematics by yourself. Start with an intro course and start deriving theorems by yourself. Do not look at the proofs. At this stage, details are very important and can't be overlooked. You need to be your own critic and keep asking why and how to every single detail and step until you can convince yourself that you would be able to naturally come up with the theorem and proof. Continue doing this to higher level courses. This is how I learned Math since middle school all the way throughout graduate school.
The short version is you have to ask the right questions. Naturally for every theorem or equation, there are 3 big questions:
1) What does the theorem/equation say? What's the intuition behind it?
2) Why is it true?
3) How does one come up with it?
One must ask these questions in the exact order. To understand what the equation really means, you should break it down further to smaller components. What is this variable? What does it represent? What is the intuition behind what it represents? What's the implication when the variable increases, decreases, etc? Do that for every single component in the equation/theorem. One should fully understand the intuition and clearly describe all quantities before trying to look at the equation/theorem as a whole.
To understand why an equation/theorem is true you need to build up a repertoire of theorems related to the quantities of interest. The bigger your repertoire, the easier you can prove or disprove something. The more advanced way is to build up intuition around the quantities of interest then come up with intuitive hypotheses. The hypotheses are often easier to prove/disprove. The process repeats.
This answer matches my experiences very closely. When you feel you brain begging you to gloss over the equation and move on - that's a red flag that you need to slow down, exercise discipline over your concentration, and figure out what's going on. It might take a few days for the intuition to settle. Personally I've known it to take a couple of years.
edit: patience, self-forgiveness, and a willingness to accept frustration are important traits. You might spend a whole week banging your head against the wall, feeling like you're making no progress, and then one day everything falls beautifully into place. That doesn't mean you did something correctly on the final day - it means you did everything correctly for the whole week before. Don't view a difficult and unrewarding day as wasted time. You're building something very difficult and that takes a bewildering amount of time.
- model free methods have seen great success in terms of learning high dimensional tasks however it suffers from being sample inefficient. In other words, it takes too long for real robots. Examples of these methods are TRPO, PPO, ES, etc
- model based methods is an order of magnitude more efficient, and thus, are more practical on real world robots. However, these methods have high bias and most working models are simple in terms of representation power, e.g. GP, time varying linear, mixture of Gaussians,. Examples are PILCO, GPS, PETS, etc
Of course, SOTA is a lot more complicated but it's a short explanation to your observation.
From the description it does sound like blink dagger and the range here refers to radiance or necro's heartstopper. It's definitely not "previously undiscovered". Also, the article makes it sound like we saw another AlphaGo's 3-3 invasion, 5th line shoulder-hit kind of moment. We did not. This is more similar to AlphaGo and Fan Hui match, except imagine AlphaGo lost to Fan Hui. The bots did make a lot of interesting moves in 5 invincible chicken meta. The bots appeared very weak in normal meta (constantly check rosh for no reason, inefficient use of ults, don't get me started on warding, etc)
Yup, I didn't learn much from my image analysis class but the professor made sure that over fitting and under fitting concepts were firmly lodged in our heads.
The surprising thing to note here is how much optimism the street has in $twtr. Just one quarter of small beat and the stock jumped 100%. After the drop today (to $34) it's still too high for $twtr which hasn't actually proved anything since the stock was high teens low 20s. If anything, their live streaming effort is pretty much down the drain, and Anthony Noto, who's pretty much the heart and soul of Twitter operation, left. I'm super surprised the stock is still mid 30s.
