Fab Labs are workspaces where people with common interests, often in computer science, machining and hardware development, science, and digital fields can meet, socialize, collaborate, innovate, and invent. "To prevent the concentration of value and power in just a few hands, we have to find ways to balance the benefits and risks of digital platforms (including industry platforms) by ensuring openness and opportunities for collaborative innovation," Schwab writes, regarding the possible economic ramifications of the 4ID. MENA countries also graduate fewer students in STEM (Science, Technology, Engineering, Mathematics) fields annually, compared to other regions. Cofounders Ali Rajai (left) and Salman Oraibi (right) of Fab Lab Bahrain are co-designing hardware innovations.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

Students at Lebanese American University (LAU) participate in a hardware design workshop that leverages the tools of the fourth Industrial Revolution. We are in the midst of the greatest industrial revolution in human history. The Fourth Industrial Revolution (4ID) is an economic transformation a thousand-times wider and deeper than anything that has come before it. "The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril," according to Professor Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF). The 4ID is characterized by the confluence of next generation technologies like: quantum computing, artificial intelligence and machine learning, autonomous transportation and robotics, the Internet of Things, additive manufacturing including 3D printing, biotechnology, and more generally; the merging of the digital and physical worlds.

Technically speaking, PCA uses orthogonal projection of highly correlated variables to a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This linear transformation is defined in such a way that the first principal component has the largest possible variance. It accounts for as much of the variability in the data as possible by considering highly correlated features. Each succeeding component in turn has the highest variance using the features that are less correlated with the first principal component and that are orthogonal to the preceding component.

Technological history saw a new advancements in a match of Go between an artificial intelligence developed by Google and 18-time world champion, Lee Sedol in Seoul, South Korea. Five matches were held over a span of a week, starting on March 9 and ending March 15. "Yesterday, I was surprised," said Sedol after his defeat in game two, "but today, more than that, I'm speechless…there was not a moment in time when I felt that I was leading the game." AlphaGo, the AI machine, claimed victory 4–1 against Sedol. The win was a shock as experts had predicted that Go would not be conquered by a machine for another decade.

AlphaGo, the computer system Google engineers trained to master the ancient game of Go, needed only one move to make it abundantly clear it has left humans in its dust. The move came Thursday, in the second game of AlphaGo's 4-1 landmark victory over South Korean Lee Sedol, one of the world's best Go players. About an hour into Thursday's match, AlphaGo placed one of its stones in a nontraditional spot on the board that surprised those watching. "I don't really know if it's a good or bad move," said Michael Redmond, a commentator on a live English broadcast. Redmond, one of the Western world's best Go players, could only crack a bemused smile.

Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis. For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We've done the same in the past for Machine Learning and NLP terms, which you might also find interesting. In the human brain, a neuron is a cell that processes and transmits information.

Deep Learning is, as with all technology, neither inherently good nor bad. However, it is not just lunatics in foil hats who are worried that self aware computers could spell danger. The CEO and co-founder of DeepMind himself, Demis Hassabis, has acknowledged that the advanced techniques his own group is pioneering may cause AI to spiral out of human control, and could need to be constrained, while his co-founder, Shane Legg, considers a human extinction due to artificial intelligence the top threat in this century. As a result, contingencies have been put in place. DeepMind investor Elon Musk has just spent 10 million on a study of AI dangers, and Hassabis and his co-founders put in the conditions of Google's takeover that there be an outside board of advisors to monitor the progress of the company's AI efforts.

You--ve likely heard the news that the Google DeepMind --AlphaGo-- computer not only beat a human expert at the game of Go, defeating the European Go champion, Fan Hui in five straight games, but also beat the reigning world champion grandmaster, South Korea--s Lee Sedol, 4 games to 1. Go is considered to be a significantly more difficult game for a computer to tackle than chess, if only because of the vastly greater number of possible moves over a much larger playing field. Chess has on the order of 1040 possible legal and realistic positions in a 40-move game; Go can have up to 10360, give or take a few tens of orders of magnitude. When Deep Blue beat world chess champion Gary Kasparov back in 1997, it did it with a brute force approach -- a massively parallel computer that would typically search to a depth of between six and eight moves, and up to a maximum of about 20 moves in some situations. It was an expert system (not AI), with separate programing modules/libraries for openings, end games, and middle game strategy and tactic evaluation. All the legal moves and rules had to be programmed into it, and it could not learn as it went (although its programmers made adjustments after each game).

It was oversold in the past. Easy to see why – the potential gains were (and still are) enormous and any indication you were on the right track meant people would throw money at you. And if you then couldn't deliver anything monetizable, the money people would shred you and your reputation. DL systems have achieved near-human performance in at least 6 problem domains (signal processing, low-level speech understanding, image understanding, text understanding, Atari games, and Go). From now on, we can use incremental improvements and we can reliably measure our progress.