Daniel Faggella is founder of TechEmergence, a news and advice website for entrepreneurs and investors interested in the intersection of technology and the mind. "Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten." These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning. Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation.

When Google's algorithm AlphaGo beat South Korean Go Grandmaster Lee Se-dol by 4-1 last week, it was a significant event in the world of algorithms and artificial intelligence. This is because it represented a new form of artificial intelligence: intuitive artificial intelligence, something which is remarkably more challenging than standard artificial intelligence. The disruption happening thanks to algorithms is happening all around us. The largest taxi company in the world, Uber, owns no taxis, but uses smart algorithms to connect drivers and passengers. The largest telephone company in the world, WhatsApp, has no telecom infrastructure, but sends over 35 billion message per day.

Last week, Google's in house Artificial Intelligence department announced a breakthrough. Their software has learnt to play a game. Not just any game, but Go. It is thousands of years old, and considered by many to be the hardest board game in the world with more potential move combinations than you can shake a stick at and a requirement to see patterns and combinations and to play by'feel'. The complexity of the game is one thing, but the real point here is that Google's AI engine has learnt to play the game.

There is no meaningful sense in which there is an abstract notion of purpose. That is, purpose is something that comes from history. One of the things that might be true about computation, might be true about our world, that would be disappointing, is maybe we go through all this history and biology and civilization and so on, and at the end of the day, the answer is 42 or something. That's the end, so to speak. We got to the answer.

Doha, Qatar - Faced with shrinking budgets, greater competition and increasingly selective audiences, leaders of international media organisations gathering vowed to share ideas about how best to gather and present news. "We are 20 years into the digital revolution of the media," Gary Pruitt, president of the Associated Press news agency, told a gathering in Doha of around 300 journalists from across the world. "Demand for news will only grow from here, but the supply of news will also grow. Much of it will not be of very high quality." He said that "a key component to innovation at AP is to increase work and investment into media and technology startups", adding that the agency has tested drones for news gathering purposes, and has used "robot journalism" to produce reports without human intervention.

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.