This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. Do you think you are smart enough to work for Google? I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems.

No longer the exclusive domain of data-reliant businesses like Google, Microsoft, and Amazon, machine learning has been making its way into the masses as an essential approach to data. Today, machine learning is understood and accepted by a more mainstream audience, and has become a measurable driver for big business benefits both on and offline. There are three key reasons why machine learning has become one of the top 10 strategic technology trends that will shape digital business opportunities through 2020. First, the volume of data companies now collect is so massive that many companies struggle to make sense of it and fail to take advantage of it. Second, the computing power required to process these exploding data assets, previously exclusive to the Googles of this world, is now widely available to smaller businesses.

One of the things I love about my job is seeing how today's technologies can enable new ways of doing things that make a real impact on people's lives. Case in point: The dyslexia-screening tool developed by Optolexia, which was named a "Future Swedish Innovator" by SvD, one of the largest newspapers in Sweden. Taking advantage of the cloud computing and machine learning Optolexia aims to help schools identify students at risk for dyslexia significantly earlier than current screening tests. Its solution is a great example of a project that falls into the upper left quadrant of the four-block diagram tool I covered in a previous blog, which is to say that it's a project that benefits tremendously from being in the cloud with relatively low risk and it was able to be implemented quickly. As many as 10–15 percent of school-age children are dyslexic, and the International Dyslexia Association estimates there are 1 billion people with dyslexia worldwide.

We cover many emerging markets in the startup ecosystem. Previously, we published posts that summarized Financial Technology, Internet of Things, Bitcoin, and MarTech in six visuals. This week, we do the same with Artificial Intelligence (AI). At this time, we are tracking 855 AI companies across 13 categories, with a combined funding amount of 8.75billion. To see all of our AI related posts, check out our blog!

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.