A little too late for my tastes.

I work at Google, and have no idea where you came up with your claims.

While I cannot disprove that HR is running sentiment analysis, we have company-wide surveys every year that they could use, biannual reviews by co-workers, and quarterly short reviews from managers. All of those probably have much higher signal/noise ratio than rummaging through peoples' email. Also, they type of people who can do that kind of NLP are probably better off working on NLP-related areas that help the company such as Android, Search, or Ads.

This is not at all true. First of all, for internal communications hardly anyone uses phones anymore -- chat, voice chat, and hangouts are a simpler and faster options. The only people frequently on the phone are those talking to external people (sales folks, customer reps, etc). I guess those "calls could be monitored for quality" but that has little to do with the panopticon-like claim you are making.

Also, in the context of meetings, people say the phrase "let's take this offline" all the time, indicating that they don't want to start some (possibly long) side discussion in a meeting with multiple participants.

The original sources in the TFA don't agree with you:

This fingerprint is constructed by a smartphone app which snaps a series of photos of the user as they read web pages, emails or tweets. It then creates a file – called a spatiogram – that captures the spatial distribution of colours, textures and patterns (vertical or horizontal stripes, say) of the clothes they are wearing. This combination of colour, texture and pattern analysis makes someone easier to identify at odd viewing angles or over long distances.

Usefully, in terms of protecting people's privacy, the fingerprint changes every time you change your clothes, so you can be anonymous again whenever you wish.

"A person's visual fingerprint is only temporary, say for a day or an evening," says Nelakuditi.

Also from the TFA:

In early tests using 15 volunteers, the team identified people 93 per cent of the time, even when they had their backs to the headset user.

I guess it is easier to make stuff up than to read.

...and it's a damn shame. I miss this aspect of what the Democrats used to stand for. Now, both parties are led by hawks.

The example using a Windows drive letter was probably not added by an engineer, since the use of Windows in engineering is very close to zero. Regardless of who added it, it still serves its purpose as a short example, so there wasn't a reason to change it.

FWIW, Google's most common internal date format is:
      YYYYMMDDxHHMMSS
Where x is "s" for standard and "d" for daylight savings time. It sorts correctly even across DST shifts.

Of course, using dates for any reason other than informational hints is not considered a good idea -- time and date will vary over the globe at any given point. All data files will have internal records with unambiguous timestamps, but the name gives hints to someone doing maintenance work. If need be, it is also good enough for very coarse cleanup rules (delete stuff > 30 days).

If the government started raiding your bank account, the correct reaction would not be to store your money in your mattress, nor to ask that banks hold less money.

The government can't search the inside of my locked car just because it is parked on the street, but when effectively the same thing happens electronically it's open season.

Property laws need to be updated to reflect the reality that many people want to store their email, photos, and other account data on a server somewhere.

The lawmakers have quite purposely dragged their feet on updating the relevant laws, while law enforcement uses outdated legal analogies to exploit loopholes in laws that didn't foresee the present reality.

There seems to be very little misunderstanding if I just type your actual question:
    https://www.google.com/search?q=convert+from+a+WPF+Visual+to+a+Windows+Metafile

One thing that I think trips up people who used web search for a long time is that you drop words you don't think are important for keyword searches, but that actually hurts now that search engines use more than keywords. Keyword spam killed keyword search a decade ago, and regular people could not use pure keyword search anyway; so now (whether we like it or not) all search engines try to operate at more of a semantic level. If you go in with that mindset you can still find almost anything within a few tries.

Try this query instead:
    https://www.google.com/#q=men's+blazers
The entire first page is full of items that are exactly what you are looking for.

As the web and search engines both evolve, you may need to change the way you search to get the same information. Something that worked before may not work now, and the critical words or phrases to get the best results are still there but they aren't the same as what they were in the past.

In your particular example, the exclusion is far too weak, as "women's blazers" matches [blazers -ladies], and once you start using unusual queries (very few people will use exclusions) the search engine will tend to be more literal since it doesn't have the statistics of many previous searchers using those terms to go on.

Google Now is an example of a product that could not exist without data sharing. The premise is that it cross references data to make timely suggestions, such as letting you know when you should leave for the airport if you have a flight, and if your flight is on time. It can do this even though you never explicitly told it you have a flight or made a calendar entry.

Well, the 90s are over too, and we have larger datasets now. With "large scale" SVMs still being measured in 10s of thousands of examples, you can see why companies with 4 orders of magnitude more *users* (let alone data items to classify) would need to use better scaling techniques. The older algorithms, when coupled with more modern minimizers, tend to fare well in comparison to the much smaller models you can train with more advanced techniques.

Also, as a researcher, you should recognize the adage about the actual order of importance for getting machine learning to work:
(1) picking the right features.
(2) getting enough data
(3) the learning algorithm

People love to talk at length about picking "the best" #3, when really you need to consider answers for #3 that let you do well on #2 and #1.

While I was a bit surprised to hear this Google project used networks (though not backprop trained NNs btw, which was the 80s fad), Andrew Ng is on the author list and he's a pretty smart guy (if you've done anything with reinforcement learning in the past 10 years you've probably run across his work). So I'm pretty sure they considered various options before they built something to run on 16K cpu cores.

You can read the ICML paper here:
http://research.google.com/pubs/pub38115.html

Here's an example where you can really tell the difference when compared to the earlier lower-bitrate sample.

Well, Google does give plenty of notification that a service is getting retired and lets you download all your data, so the data's only gone if you are too lazy to download it with 6+ months of notice.

Actually, ~1/2

FB is looking to land around 100B, as of today Google's market cap is $205B.

While I can't say what bar you'd have to meet for "proven", it has been tested on much busier roads than you are alluding to:
    http://www.youtube.com/watch?v=64w-v-RJpk8

Well, one cannot prove a negative, but look at this:
    http://www.google.com/apps/intl/en/edu/privacy.html
which includes the statement:
No advertising to students, faculty, or staff. We offer Google Apps for Education to schools for free. It's also completely ad-free -- which means your school's content is not processed by Google's advertising systems.