Tech News is a blog created by Wasim Akhtar to deliver Technical news with the latest and greatest in the world of technology. We provide content in the form of articles, videos, and product reviews.
The French data protection authority, the Commission Nationale de l’Informatique et des Libertes (CNIL), has said it has fined Alphabet subsidiary, Google, €100,000 for failing to scrub search results widely enough to comply with an EU privacy ruling.
The US based tech firm has been in a long running dispute with several European Union data protection authorities for the last 2 years, ever since the European Court of Justice ruled in 2014 that EU citizens had a legitimate right to ask search engines, not just Google, but also the likes of Microsoft’s Bing to permanently remove incorrect information about themselves.
The ruling quickly became known as the ‘right to be forgotten’
The ‘right to be forgotten,’ has proved to be a administrative headache for Google, as the only way for them to actually comply with the ruling was to ‘delist’ search results that popped up under name searches
While Google did comply with the original 2014 ruling, it only scrubbed results across its European website search results, i.e. Google.de in Germany, Google.ie in Ireland, and Google.fr in France.
Google have argued that to go any further the consequences would have a negative effect on the free flow of information worldwide, not least to mention the fact that it is a time consuming and costly affair for Google. No, not at all…
CNIL said that all links under the “right to be forgotten” “must be carried out on all of the data processing and thus on all search engine’s domains…Contrary to what Google asserts, delisting on all domains doesn’t limit the freedom of expression in that it doesn’t involve any removal of Internet content.”
For its part, Google spokesman, Al Venrey was quoted as saying that the company had, and was working tirelessly to comply with the EU ruling, “thoughtfully and comprehensively in Europe…. But as a matter of principle, we disagree with the CNIL’s assertion that it has the authority to control the content that people can access outside France, and we plan to appeal their ruling.”
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Google Play Books comes with an absolutely fantastic interface especially for those who read on a daily basis. Although, wouldn’t it be better if you could upload any file on it? Sure, but how do you do that? Google allows you to easily upload your own files and read them across all your devices. Here we walk you through the steps of doing so.
Upload any PDF/ePub file to Google Books
First of all, you’ll need to upload the file to Google Books. You can either upload a PDF or an ePub ( EBook format ) document. If you have a Doc or a Docx file, it can be easily converted to PDF through any online tool or on the contrary, we would recommend turning it in ePub format.
To convert a Doc file into an ePub file, you’ll need to upload it on Google Drive and head over to Google Docs past that. There, you’ll find the document among other files, open it and through “Files” option, download it as an EPUB publication. Now that, you have a file that you would like to read on Google Books, let’s move on to uploading.
To upload the desired file onto Google Play Books, open the “My Books” section in your account and tap on the “Upload files” button. Drag the file on Chrome or browse for it on your computer. Once the upload process completes, you’ll be able to read on Google Books app whether it’s on your mobile or the website.
Read EPUB/PDF files on Google Books
If you’re reading on your phone, the newly added material will be available in the “My Library” section in the left drawer. Make sure you’re logged in with the same Google account, though. On the website, you can find them on the “My Books” page.
Features including the “Night Light”, “Bookmarks”, “Text settings” will all be supported when you open the particular book, document or anything else. Personally, I love the smooth page animation it brings to the table, something which any other application fails to offer.
That was all. Do let us know if we missed something in the comments section down below.
We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out.
So. About that. Turns out I was kinda … totally completely wrong. The number of possible moves, or "problem space", of Chess is indeed astonishingly large, estimated to be 1050:
Deep Blue was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. How many orders of magnitude? In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second. And that was enough to defeat Kasparov, the highest ever ranked human player – until 2014 at least. Even though one of its best moves was the result of a bug.
200,000,000
In 2006, about ten years later, according to the Fritz Chess benchmark, my PC could evaluate only 4.5 million chess positions per second.
4,500,000
Today, about twenty years later, that very same benchmark says my PC can evaluate a mere 17.2 million chess positions per second.
17,200,000
Ten years, four times faster. Not bad! Part of that is I went from dual to quad core, and these chess calculations scale almost linearly with the number of cores. An eight core CPU, no longer particularly exotic, could probably achieve ~28 million on this benchmark today.
28,000,000
I am not sure the scaling is exactly linear, but it's fair to say that even now, twenty years later, a modern 8 core CPU is still about an order of magnitude slower at the brute force task of evaluating chess positions than what Deep Blue's specialized chess hardware achieved in 1997.
But here's the thing: none of that speedy brute forcing matters today. Greatly improved chess programs running on mere handheld devices can perform beyond grandmaster level.
In 2009 a chess engine running on slower hardware, a 528 MHz HTC Touch HD mobile phone running Pocket Fritz 4 reached the grandmaster level – it won a category 6 tournament with a performance rating of 2898. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second.
As far as chess goes, despite what I so optimistically thought in 2006, it's been game over for humans for quite a few years now. The best computer chess programs, vastly more efficient than Deep Blue, combined with modern CPUs which are now finally within an order of magnitude of what Deep Blue's specialized chess hardware could deliver, play at levels way beyond what humans can achieve.
Chess: ruined forever. Thanks, computers. You jerks.
Despite this resounding defeat, there was still hope for humans in the game of Go. The number of possible moves, or "problem space", of Go is estimated to be 10170:
Deep Blue was a statement about the inevitability of eventually being able to brute force your way around a difficult problem with the constant wind of Moore's Law at your back. If Chess is the quintessential European game, Go is the quintessential Asian game. Go requires a completely different strategy. Go means wrestling with a problem that is essentially impossible for computers to solve in any traditional way.
A simple material evaluation for chess works well – each type of piece is given a value, and each player receives a score depending on his/her remaining pieces. The player with the higher score is deemed to be 'winning' at that stage of the game.
However, Chess programmers innocently asking Go players for an evaluation function would be met with disbelief! No such simple evaluation exists. Since there is only a single type of piece, only the number each player has on the board could be used for a simple material heuristic, and there is almost no discernible correlation between the number of stones on the board and what the end result of the game will be.
Analysis of a problem this hard, with brute force completely off the table, is colloquially called "AI", though that term is a bit of a stretch to me. I prefer to think of it as building systems that can learn from experience, aka machine learning. Here's a talk which covers DeepMind learning to play classic Atari 2600 videogames. (Jump to the 10 minute mark to see what I mean.)
As impressive as this is – and it truly is – bear in mind that games as simple as Pac-Man still remain far beyond the grasp of Deep Mind. But what happens when you point a system like that at the game of Go?
DeepMind built a system, AlphaGo, designed to see how far they could get with those approaches in the game of Go. AlphaGo recently played one of the best Go players in the world, Lee Sedol, and defeated him in a stunning 4-1 display. Being the optimist that I am, I guessed that DeepMind would win one or two games, but a near total rout like this? Incredible. In the space of just 20 years, computers went from barely beating the best humans at Chess, with a problem space of 1050, to definitively beating the best humans at Go, with a problem space of 10170. How did this happen?
Well, a few things happened, but one unsung hero in this transformation is the humble video card, or GPU.
What's not clear in this table is that after 2007, all the big advances in FLOPS came from gaming video cards designed for high speed real time 3D rendering, and as an incredibly beneficial side effect, they also turn out to be crazily fast at machine learning tasks.
The Google Brain project had just achieved amazing results — it learned to recognize cats and people by watching movies on YouTube. But it required 2,000 CPUs in servers powered and cooled in one of Google’s giant data centers. Few have computers of this scale. Enter NVIDIA and the GPU. Bryan Catanzaro in NVIDIA Research teamed with Andrew Ng’s team at Stanford to use GPUs for deep learning. As it turned out, 12 NVIDIA GPUs could deliver the deep-learning performance of 2,000 CPUs.
Let's consider a related case of highly parallel computation. How much faster is a GPU at password hashing?
Radeon 7970
8213.6 M c/s
6-core AMD CPU
52.9 M c/s
Only 155 times faster right out of the gate. No big deal. On top of that, CPU performance has largely stalled in the last decade. While more and more cores are placed on each die, which is great when the problems are parallelizable – as they definitely are in this case – the actual performance improvement of any individual core over the last 5 to 10 years is rather modest.
But GPUs are still doubling in performance every few years. Consider password hash cracking expressed in the rate of hashes per second:
GTX 295
2009
25k
GTX 690
2012
54k
GTX 780 Ti
2013
100k
GTX 980 Ti
2015
240k
The latter video card is the one in my machine right now. It's likely the next major revision from Nvidia, due later this year, will double these rates again.
(While I'm at it, I'd like to emphasize how much it sucks to be an 8 character password in today's world. If your password is only 8 characters, that's perilously close to no password at all. That's also why why your password is (probably) too damn short. In fact, we just raised the minimum allowed password length on Discourse to 10 characters, because annoying password complexity rules are much less effective in reality than simply requiring longer passwords.)
Distributed AlphaGo used 1202 CPUs and 176 GPUs. While that doesn't sound like much, consider that as we've seen, each GPU can be up to 150 times faster at processing these kinds of highly parallel datasets — so those 176 GPUs were the equivalent of adding ~26,400 CPUs to the task. Or more!
Even if you don't care about video games, they happen to have a profound accidental impact on machine learning improvements. Every time you see a new video card release, don't think "slightly nicer looking games" think "wow, hash cracking and AI just got 2× faster … again!"
I'm certainly not making the same mistake I did when looking at Chess in 2006. (And in my defense, I totally did not see the era of GPUs as essential machine learning aids coming, even though I am a gamer.) If AlphaGo was intimidating today, having soundly beaten the best human Go player in the world, it'll be no contest after a few more years of GPUs doubling and redoubling their speeds again.
AlphaGo, broadly speaking, is the culmination of two very important trends in computing:
Huge increases in parallel processing power driven by consumer GPUs and videogames, which started in 2007. So if you're a gamer, congratulations! You're part of the problem-slash-solution.
We're beginning to build sophisticated (and combined) algorithmic approaches for entirely new problem spaces that are far too vast to even begin being solved by brute force methods alone. And these approaches clearly work, insofar as they mastered one of the hardest games in the world, one that many thought humans would never be defeated in.
Based on our experience with Chess, and now Go, we know that computers will continue to beat us at virtually every game we play, in the same way that dolphins will always swim faster than we do. But what if that very same human mind was capable of not only building the dolphin, but continually refining it until they arrived at the world's fastest minnow? Where Deep Blue was the more or less inevitable end result of brute force computation, AlphaGo is the beginning of a whole new era of sophisticated problem solving against far more enormous problems. AlphaGo's victory is not a defeat of the human mind, but its greatest triumph.
While there are multiple online services available that can fetch videos, they are often filled with ads, have limitations on downloads and quality and overall provide a bad experience. Moreover, only a few of them supports downloading YouTube playlists. As such, 4K Video Downloader does a fantastic job of downloading videos and playlists without any constraints.
What is a YouTube Playlist?
YouTube Playlist is a simple collection of videos on a topic stitched together. Once you start a video from the playlist, it will automatically play the next video relieving you from the manual task of searching and playing again and again.
Anyone can make a playlist as they are a convenient way to organize videos. All you need is to use the “Add to” button at the bottom of the video to create or add that video to a playlist, then use this page to edit the name, description or privacy. Apart from creating your own playlists, YouTube also gives you the ability to save playlists created by others.
How to Search for YouTube Playlist?
Generally, when you search for any video, YouTube often shows you playlist in the results with the number of videos in the list. To specifically search for only playlists, use the operator “,playlist” along with the search query or apply the filter along with the keywords on YouTube.
Searching is very useful for instances when you want to watch videos in a consecutive manner. For example, playlists for tutorials or lectures – a bunch of different playlist with how-to videos and talks on certain topics will immediately pop up and you can continuously learn.
4K Video Downloader is a unique tool that downloads YouTube playlists with one mouse click. It’s a free desktop application suitable for Windows, Mac and Linux, that downloads sole YouTube videos, playlists with subtitles and even whole channels in high resolution and various different video and audio formats.
Just copy and paste the link to a YouTube playlist of your liking and the app will get all the videos from the list to your computer in seconds
Why do we like 4K Video Downloader?
The tool is packed with features that make it a great choice among other similar tools –
#. You can directly download public playlists with any format of your choice.
#. The tool allows you to download subtitles or closed captions along with the video in a separate (dot)srt file.
#. It even works with streams from YouTube Gaming.
#. You can subscribe to channels right from the convenience of the tool.
#. It works for YouTube channels as well. As simple as entering the channel URL and selecting the videos from the channel to download.
There is also a Smart mode which will help you download video with just a single click. You pre-select download format, quality, and folder where the video will be saved and these settings will be applied whenever you download videos.
News has emerged from several different sources that Apple has struck a deal with what some might see as its arch rival, Google, to store some users iCloud data on Google’s own cloud storage servers.
The deal is being reported as a real coup for Google, which is seen to significantly lag behind both Amazon and Microsoft for providing 3rd party cloud storage to others.
According to the BBC, Apple sealed the new deal with Google’s Cloud Platform division in the final quarter last year. The BBC has also stated that it has been able to “independently confirm the arrangement.”
The arrangement might come as a surprise to those who think of Apple and Google as rivals. It is not the first time however that these 2 titans of the technological world have worked together.
Court transcripts leaked to the web at the beginning of 2016 have revealed that Google allegedly paid Apple $1 billion to become the default search engine of choice for the mobile version of Apple’s Safari browser, back in 2014
But if a week is a long time in politics, then 2 years is almost geological when it comes to technology. Since then, Apple has markedly been reducing its dependence upon Google for search queries in recent times. Microsoft’s Bing is now the default search engine for Siri alongside Spotlight search. With the last iOS.9 software updates, Safari has also started accumulating search results from Spotlight as well
The new deal between Google and Amazon is thought to be worth between $400-$600 million. Previously, Apple had used Amazon’s cloud storage solution as its preferred online platform.
The news has apparently caused some concern for Amazon investors, since the new deal also follows recent announcements that Spotify and Dropbox had also moved to different platforms.
The latest move by Apple may itself be only temporary. Apple has been investing heavily in its own data centers, and may soon move its “i” data there in an effort to reduce costs.
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