If you need a scalable recommendation library you probably look at MlLib from Spark. Is it always a good choice? Is it the best solution for You? In this presentation I help you to understand what are strong and week points of MlLib and when MlLib is not for you. It could happens (and probably will), that MlLib is not for you.

This downward trend in new job creation in new technology industries is particularly evident starting in the Computer Revolution of the 1980s. For example, a study by Jeffery Lin suggests that while about 8.2% of the US workforce shifted into new jobs during the 1980s which were associated with new technologies; during the 1990s this figured declined to 4.4%. Estimates by Thor Berger and Carl Benedikt Frey further suggest that less than 0.5% of the US workforce shifted into technology industries that emerged throughout the 2000s, including new industries such as online auctions, video and audio streaming, and web design.

Japan is spending more than 1 billion to resurrect the area around the wrecked Fukushima No. 1 nuclear plant as the country's "Innovation Coast." The region is trying to capitalize on technology developed in the five years spent cleaning up the worst nuclear disaster since Chernobyl, including Hitachi Ltd. and Toshiba Corp. robots that slither like snakes or cruise through radioactive water like speed boats to investigate the flooded reactors. Fukushima Prefecture -- like Beirut or post-bankruptcy Detroit -- is ripe to develop a strong tech community, according to Samhir Vasdev, an innovation consultant at the World Bank. "To lead the future from Fukushima, we must overcome our failures," Fukushima Gov. Masao Uchibori said at the Foreign Press Center in Tokyo last month. "Creating new industries will attract new people, which will be vital to revitalizing the region."

It seems it may only be a matter of time before the best Go player on the planet is a computer. AlphaGo beat the European champion in Go and was driven by machine learning, a technology that has underpinned the recent major advances in artificial intelligence in computer vision, speech recognition and language translation.1 Machine learning is a data driven approach to artificial intelligence. AlphaGo learnt how to play Go by many games played against itself, and by observing a large history of games played by professional players. The end result is that by the time of its first match against the European Champion AlphaGo had already played many more games of Go than any human could possibly play in their lifetime. And since that win AlphaGo has been actively learning to improve itself. Relentlessly playing all day and all night in an effort to ready itself to play the world champion.

That was the result of the match between Google's AlphaGo and human champion Lee Sedol at the fiendishly complex game of Go, and it came with a disconcerting question: what next? Where will the machines claim their next victory: putting you out of a job; solving the mysteries of science; bettering human abilities in the bedroom? AlphaGo's success was down to artificial intelligence (AI): the computer program taught itself how to improve its game by playing millions of matches against itself. But the trouble with using games such as chess and Go as measures of technological progress is that they are competitions. There's a winner and there's a loser – and this month's biggest tech news story had a clear victor.

Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars, an auto industry source has said. "They wanted autonomous cars," the source, who declined to be named, said. "It seemed like they were shopping around." Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet. Volkswagen's Audi, Daimler's Mercedes-Benz, BMW and car industry suppliers Bosch and Continental are all working on technologies for autonomous or semi-autonomous cars.

Shedding drivers would save Uber a lot of money. Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars, an auto industry source has said. "They wanted autonomous cars," the source, who declined to be named, said. "It seemed like they were shopping around." Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet.

We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix completion, robust principal component analysis or factor analysis. It is also important in machine learning: unsupervised representation learning can often be studied through matrix factorization. We use the tools of statistical mechanics - the cavity and replica methods - to analyze the achievability and computational tractability of the inference problems in the setting of Bayes-optimal inference, which amounts to assuming that the two matrices have random independent elements generated from some known distribution, and this information is available to the inference algorithm. In this setting, we compute the minimal mean-squared-error achievable in principle in any computational time, and the error that can be achieved by an efficient approximate message passing algorithm. The computation is based on the asymptotic state-evolution analysis of the algorithm. The performance that our analysis predicts, both in terms of the achieved mean-squared-error, and in terms of sample complexity, is extremely promising and motivating for a further development of the algorithm.

Inbox by Gmail has a few useful features but probably the most interesting is Smart Reply, which was expanded to the desktop this week. As impressive as the A.I.-powered suggestions for replies to your emails are, they're just not very useful yet. Sure, Smart Replies tend to offer relevant options, but they're not very useful in most real-world cases. Open an invitation to party and you might get'Sounds good!,' 'Can't wait,' and Sorry, I can't be there!' as the suggestions. By the time I've added'Hi Bob,' and a few other bits of personalization, I might as well have written the whole thing myself.

The number of new malware variations that pop up each day runs somewhere between 390,000 (according to AV-TEST Institute) and one million (according to Symantec Corporation). These are new strains of malware that have not been seen in the wild before. Even if we consider just the low end figure, the situation is still dire. Google Now is about to get a lot better in the future, aiming to serve Android users even when they're offline. With smartphones increasingly gaining ground, digital assistants have become widely popular and heavyweight companies are competing to deliver the best software in this category.