The guy said: "I’ve seen some great movies with really abysmal Rotten Tomatoes scores,” I'll ask you since I can't ask him. Like what? What low scoring movies (and we're talking below 30% on RT) did you actually like?
Rotten Tomatoes is an aggregator, and so it is very limited. You have to look at the individual reviews to figure out why the reviewer didn't like the movie. That said, if a movie is sitting at 27%, there is a really good reason. There is something wrong with a movie that is less than 50%. Maybe you will like it, but there is a flaw in there somewhere that caused most people that review movies to not like it.
My movie watching time is severely limited. My book reading, exercising, hobby and other time are severely limited. So, I have to decide pretty severely what I'm going to watch. Are there movies with a 30% rating that I might like? Maybe. But, why on earth would I bother going to see it, when there are movies with 70, 80, 90% ratings to go see?
By national law enforcement (FBI) and others (NSA, CIA), I'm sure it is. However, your ISP won't have access to it, so they won't be selling it. If the Feds have your browsing info, you are unlikely to get a popup saying 'Looks like you are shopping for a hooker, try these!' Or whatever it is that you've been googling for. And, more importantly, it won't be popping up when your significant other, parents, children, siblings are browsing.
The Feds are scary and we need to have better ways to prevent them from abusing civil liberties. But I think that commercial interests are really bad too, and are more likely to actually negatively affect your life.
Well, it's really machine learning, and of course machine learning is 'just' pattern matching of a sort. The important thing is that the ML system (in particular deep learning) is learning what to match. Before deep learning, the features that object recognition used were usually hand-created, consisting of SIFT points, HOGs, etc. and then the image would be represented by some array of these features that could then be classified (using a SVM or other technique). the deep learning part is that it learns what the features are, how to group them (intermediate representation), and then how to classify them.
The summary references Fei-Fei Li. Google her, read her papers. She's a leader in the field and is the originator of ImageNet. It's real machine learning.
DEKA made the iBot wheelchair for years. Not this advanced, but a whole lot better than a standard one. At $25,000, it was too expensive and didn't sell much. This would be even more expensive and would sell less. You would think that $25k would be not too much to give someone their freedom, but people that need them just can't afford them.
The problem, unfortunately, is not technology, it's money.
Actually, buses DO clock a lot of miles/kilometers.
Apparently we disagree about what a lot of miles is. An 18-wheeler will do, say, 8 hours at 70 mph; that's a long way. 150mi is something that a current electric car can do. A bus can have a higher percentage of it's mass devoted to the batteries. So, range is no problem whatsoever in this scenario (looping). A long range run could easily be too far, but that's not most of them.
Yes, the battery will weigh more than a engine + fuel. You'd have to do the math to determine if it is favorable, if you include efficiency, cost of electrical, maintenance, cost of gas, regen, etc.
The casinos know who has problems, just like they know they tamper with the air to make people feel more euphoric
How do they tamper with the air? Do they increase the level of oxygen? That's insidious! It would also make cigarettes burn faster, assuming people were allowed to smoke indoors.
Only, people are allowed to smoke indoors, as long as it is in a casino. See http://www.riverfronttimes.com... for example. (Note: not true of all locations and casinos; however, in general, casinos are able to carve out special exemptions for themselves.)
If you are good, you will be assigned all the work. If you are real good, you will get out of it.