Since the inception of modern fighter planes, it has always been an unspoken assumption that at some point, machines would be flying the aircraft instead of human pilots. This assumption became even more solid once Artificial Intelligence (AI) technology started to pick up. Now, new test results via simulations have shown that AIs are superior to human pilots, particularly when using fighter planes. Engineers that graduated from the University of Cincinnati programmed an AI that was able to outmaneuver and outfly fighter pilots, Wired reports. The program is called "ALPHA," and through multiple simulations against former United States Air Force Colonel Gene Lee, the AI came out significantly ahead.

A very good piece by Tom Simonite in the MIT Technology Review looks at the implications of Intel's announcement that it will slow the rate at which it increases the density of transistors in microprocessors. In one important way, this is an "end of Moore's Law," which predicted that the speed of microprocessors would steadily double every two years, making computation logarithmically cheaper for the foreseeable future. The timescales are getting longer, and may get longer still. Some pin their hopes on fundamental breakthroughs that restore the tempo of Moore's Law, but even in the absence of such a breakthrough, there is still lots of new things that are both plausible and exciting and don't require fundamental, unpredictable scientific discoveries. One such advancement is in fundamental computer science, specifically in "parallelization."

As anyone who endures a call with an automated customer "help line" quickly learns, robots have a frustrating inability to understand sarcasm. It only takes a few minutes of the pseudo-friendly automaton cycling through endless menu selections to trigger a lunge for the 0 button and a desperate plea for "AGENT!" We're even becoming accustomed to the idea that self-driving cars will soon lower our insurance rates and enable virtually uninterrupted texting during waking hours. Automation is now so ubiquitous that it's strange to think that we're entirely at ease with the robots, until we have to talk to them. But don't blame the machines.

U.S. transportation authorities are investigating the deadly collision of a Tesla Model S car. And many reports say the fatal crash has heightened concern about self-driving cars. As NPR's Sonari Glinton points out, what Tesla's Model S has are self-driving features, autonomous elements meant to assist drivers rather than replace them. Virtually all major car and tech companies are pursuing self-driving technology as the future of transportation. But Tesla and Google are the earliest innovators, taking very different approaches.

The idea that university CS programs are taking bright young minds and fashioning them into algorithm and data structure whiz-kids defies the observations of almost any incoming CS student or their instructor. Many CS freshmen enter college already having a passion for computers and likely a privileged amount of access to technology and mentorship. Like myself, they were given computers as children by parents who were themselves close to technology. They have computer usage skills (how to configure your machine, how to fix basic computer problems) and have parents (or tutors) who introduced them to programming. For those without that background, freshman CS can prove very challenging.

December's ICCV 2015 conference in Santiago, Chile has come and gone, but that's no reason not to know about its top papers. Get an update on which computer vision papers and researchers won awards.

Python Developer - Artificial Intelligence/Machine Learning/API Python Developer urgently needed by the fastest growing healthcare startup in Europe for a brand new team building an incredible new product which will be used to predict illness and risk factors for their customers. The Python Developer who join this team will be building their new predictive engine to help them analyse future risk and illness. Data will be taken form a variety of sources like wearables and test results. This will be achieved through a combination of machine learning and deep learning algorithms. There may also be some work on their Back End microservices environment so any API exposure would be a bonus.

To learn more about creating a modern IT environment, click: http://aka.ms/GuideModernIT. For any novice in machine learning – the biggest challenge is to determine the algorithm to use to train the model. This tutorial attempts to identify the various use-cases for training models and which algorithm one can use in a particular use-case.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

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