You still missed it. They said that they have bought the faceglory domain in "all possible languages", not with all possible TLDs, nor did they say that they have all of those domains active (although I am not sure why they would not have them active...possibly because they do not want people navigating to them until they have a webpage in the appropriate language).

IIt has IMAP. SM's email is bascially based off Thunderbird's.

https://wiki.mozilla.org/SeaMo...

Soon for v2.35. They're having build issues. No to the lame features like Firefox is getting AFAIK.

Where are the new places to replace Reddit, /., etc.? :P

Battery life and cost are big factors only following range anxiety.

Often the 10 year + life is cited for many of the hybrids such as the Prius. The long life is only obtained through battery maintenance. The state of charge is kept between 50 and 80% most of the time.

In an electric, that would severely limit range to preserve battery life.

To get maximum range, EV's often top off the battery (100% charge) which shortens the life and deep cycles them, also shortening the life. Think about other devices you deep cycle on a regular basis with the same battery technology. How long does your cell phone, laptop, tablet, etc last on a charge the first year and after 3 years of use. Do you expect an EV to get the same distance after 3 years of daily commute? Give me an EV with a guarantee of >80% capacity after 8 years or 100,000 miles and I am so on it. Making it only 60% of the way to work after 3 years is not going to cut it.

Everyone has the right to express their disgust with you...

Yes. Absolutely.

...and take whatever measures they like in response.

No. Not even close.

the trolls keep telling us that there is "no right to be offended"

Well, perhaps, but I've never run into it. What I have run into, and said myself, is that "there is no right to not be offended."

The version you quote is ridiculous. The version I give you is profoundly defensible.

Actually, the oil industry IS paying for the global warming alarmists.

I would suggest that you learn something about science. You do not do science by consensus. You do science by facts. The facts are that, so far, all of the AGW models have FAILED to accurately predict future temperature changes and most of them have even failed to predict what has actually happened with temperatures when started with the data of a point in the past.

Or the 7th Guest.

So you can compile your own code but you can't share it with your friends without paying up?

In other words, you don't know what you are talking about, but you heard this really neat meme, that, if it were true, would be a slam dunk for the opinion you hold. The problem of course is that the "98% of the scientific community" claim is not supported by any actual studies. The actual study said that 97% of papers on climatology published in peer reviewed journals supported anthropogenic global warming. The thing is that the study counted any paper on climatology which did not explicitly express the the position that anthropogenic global warming was NOT true as supporting the theory, even when the subject of the paper was not connected to that theory in any way.

One could point out that there are fewer instances of white males being miscategorized.

White males are just about the easiest faces to categorize. They tend to have short hair that doesn't obscure facial features or create oddball shapes that confuse the classifiers. Their skin tone makes photographing them and finding edges extracting features easier than it is with darker skinned people. White people have a greater variety of eye colors that can be used to distinguish among them. "White guy face" is just about the optimal case for this problem. If I had to come up with a worst case that was also a photo of a fairly "common" person, I'd go with "dark skinned, brown eyed person wtih long hair and facial hair." That's a pretty clean sweep of all of the variables that make this a hard problem.

Good decisions? Sorry, but releasing poorly tested software like this was obviously a bad decision. The bad outcomes were a direct result of their poor decision making.

How good does the cutting edge of object recognition need to be before it's not "poorly tested" anymore, especially when it's for a silly photo app and not a medical or military application? I never hear this type of thing from people who have actually had to solve these types of problems. The reality is that objects are going to be confused with other objects. Lots of them, once we're talking about hundreds of millions or billions of samples. Some cases will fail with great regularity and patterns. The unfortunate fact here was that the pattern happened to coincidentally have really embarrassing cultural connotations.

This is one of the things I don't miss about working in machine vision. We'd run our algorithm over a zillion images and it would correctly handle all of them save a small handful and that small handful would be filed as bugs. OK, maybe we'll be able to handle that small handful at the expense of a smaller handful next time around. But the pass/fail criteria for the tool is in its overall results, not in the outliers.

The developers building vision algorithms don't typically create their own datasets. They purchase archives of images, and a lot of these problems stem from how many samples of each type are in those archives. The Google team likely has a giant database of human faces that it works with, and the ethnic frequencies are probably either the result of choices made by whatever origanization compiled it (and for whatever reason they compiled it) or the ethnic breakdown of the userbase of some app they used to grab the data. It's extremely unlikely that either of those will produce the same number of samples of every ethnic type.

It's also one thing if this was a program just designed to distinguish between different people. But it looks like it's trying to recognize objects of all sorts and distinguish between people and just about everything else. That's a hard problem, and the only response to this sort o thing is to take a regular failure case and feed it back into the training data so you can hit the next regular failure case. Hopefully it will be less coincidentally embarrassing, but it will definitely be there. Perhaps confusing bald men with balloons or something like that.

But I also think people underestimate how much skin color affects machine vision problems. I spent years in the biometrics industry and one consistent fact is that people with darker skin just don't provide as much easy-to-recognize detail as people with lighter skin. There will be more misclassifications as long as the image is taken using the visible spectrum. To a computer extracting features, dark skinned people and gorillas are both human-ish face shapes with a particular color range and somewhat indistinct geometry due to weak contrast and shadows. Distingushing between those two sets just isn't as easy as distinguishing between fair-skinned blondes and gorillas. You can make that decision just by looking at the color histograms and not even bothering with geometry.