The doomsday scenarios spun around this theme are so outlandish -- like The Matrix, in which human-created artificial intelligence plugs humans into a simulated reality to harvest energy from their bodies -- it's difficult to visualize them as serious threats. Meanwhile, artificially intelligent systems continue to develop apace. Self-driving cars are beginning to share our roads; pocket-sized devices respond to our queries and manage our schedules in real-time; algorithms beat us at Go; robots become better at getting up when they fall over. It's obvious how developing these technologies will benefit humanity. But, then -- don't all the dystopian sci-fi stories start out this way? One is overly credulous scare-mongering. But the other extreme is equally dangerous -- complacency that we don't need to think about these issues, because humanity-threatening AI is decades or more away.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

You can learn a lot about neural networks and deep learning models by observing their performance over time during training. Keras is a powerful library in Python that provides a clean interface for creating deep learning models and wraps the more technical TensorFlow and Theano backends. In this post you will discover how you can review and visualize the performance of deep learning models over time during training in Python with Keras. Display Deep Learning Model Training History in Keras Photo by Gordon Robertson, some rights reserved. Keras provides the capability to register callbacks when training a deep learning model.

This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. The book explains this'why'; that is, it explains the purpose and progress of statistical research, through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.

More than anything else, IBM's Watson supercomputer is probably best known for one thing: Appearing on Jeopardy!, the legendary TV game show, in 2011. With an internet connection and the ability the buzz in quicker than a human opponent could, Watson destroyed Jeopardy!'s longest-tenured champions, Ken Jennings and Brad Rutter, in devastating fashion throughout a week of games at the IBM campus. If you were to ask the average person if they know about IBM's supercomputer, there's no doubt that an affirmative answer would involve cleaning up on a gameshow. There are now more than 30 different Watson services as part of what IBM calls the Watson Developer Cloud, including a tool that discerns tone in writing. On top of that, there are consumer tools like Chef Watson, where the supercomputer helps generate recipes based on available ingredients.

In the next few days, humanity's ego is likely to take another hit when the world champion of the ancient Chinese game Go is beaten by a computer. Currently Lee Sedol – the Roger Federer of Go – has lost two matches to Google's AlphaGo program in their best-of-five series. If AlphaGo wins just one more of the remaining three matches, humanity will again be vanquished. Back in 1979, the newly crowned world champion of backgammon, Luigi Villa, lost to the BKG 9.8 program seven games to one in a challenge match in Monte Carlo. In 1994, the Chinook program was declared "Man-Machine World Champion" at checkers in a match against the legendary world champion Marion Tinsley after six drawn games. Sadly, Tinsley had to withdraw due to pancreatic cancer and died the following year.

Today was a day for the history books. The UK has voted to leave the European Union and opened a deep crack in the heart of Europe. As a consequence of this result, Prime Minister David Cameron will step down by October urging for a fresh leadership. At this point nobody knows the repercussions of these results. Will the Brexit hurt the economy of the UK and ignite a new recession?

For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. So the UK has just given itself a national headache. Whether you think the Brexit was the right decision or a dangerous and unmitigated screw-up (as I do), the consequences of the referendum will be non-trivial and take years to complete. But the mechanics of the UK exiting the European Union aside, the question of how people now feel about the Brexit is interesting. Are they awash in jubilation or has buyer's remorse set in?

Google is starting to piece together a cloud platform strategy beyond just being a lower-cost option than the competition, but it's a considerable risk, banking on a set of services many enterprises likely won't use for years to come. Machine learning and deep analytics are the latest trend to gain attention in the cloud market, and Google has latched on wholeheartedly -- it sees these tools a way to differentiate in the market, by externalizing what has driven it to be one of the largest corporations in the world. The most full-throated endorsement of this strategy emerged at the GCP Next user conference back in March. Eric Schmidt, chairman of Google parent company Alphabet Inc., talked in broad strokes about creating an internet operating system, adding that in five years, every major IPO will be for companies using machine learning. "The platform is not the end; it's the bottom, and above it is machine learning," Schmidt said.

Making Apple services even smarter and more personalized entails processing troves of information because intelligence is driven by big data. The fact that iOS 9's proactive features don't tap into the cloud has served Apple well thus far. But since Google Assistant came to light, people have been wondering if Apple can compete without resorting to raw data collection Google is infamous for. An en vogue statistical method, Differential Privacy helps Apple deliver smarter services without compromising privacy of their users. It's a relatively unproven technique with lots of potential which hasn't been used to boost Apple's services before iOS 10 and macOS Sierra.