Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

While the human brain is often seen as the ultimate model for robotic intelligence, scientists are also learning plenty from the neurobiological structures and processes of more humble creatures, from fruit flies to rodents. Take the fruit fly – or rather, the maggot that grows up to be a fruit fly. Drosophila fruit fly larvae have fewer than 10 000 neurons – compared to about 100 billion in the human brain. But they display a range of complex orientation and learning behaviours that computational theory does not adequately explain at present. By studying how the larvae change their response to stimuli such as smells when these are associated with reward or punishment, the EU-funded MINIMAL project aims to unpick the exact mechanism underlying learning processes.

Computers have slowly started to encroach on activities we previously believed only the brilliantly sophisticated human brain could handle. IBM's Deep Blue supercomputer beat Grand Master Garry Kasparov at chess in 1997, and in 2011 IBM's Watson beat former human winners at the quiz game Jeopardy. But the ancient board game Go has long been one of the major goals of artificial intelligence research. It's understood to be one of the most difficult games for computers to handle due to the sheer number of possible moves a player can make at any given point. Researchers at Google DeepMind, the Alphabet-owned artificial intelligence research company, announced today that it had created an artificial intelligence system that has beat a professional Go player at the game.

Visit us in Chicago on Thursday, June 2nd at 6:30pm to see a Machine Learning presentation by Jeremy Watt, instructor of the upcoming Metis course titled Machine Learning: Algorithms & Applications and author of Machine Learning Refined. This is an Open House for Jeremy's upcoming 6-week evening course at Metis, which starts on July 11th and will be held on Monday and Wednesday evenings from 6:30 - 9:30pm through August 17th . Please RSVP if you'd like to see a demonstration from Jeremy, and to learn more about the course structure and outcomes. Pizza and drinks will be served. Jeremy holds a PhD in Computer Science and Electrical Engineering from Northwestern University where he conducted research in machine learning and computer vision while actively consulting with partners in finance and insurance, as well as startups in the e-commerce and healthcare space.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Researchers have shown that an informatics-based adaptive strategy, when used with experiments, can propel the discovery of new materials with targeted properties, states a paper recently published in the journal Nature Communications. Turab Lookman, lead researcher and a physicist and materials scientist with the Physics of Condensed matter and Complex Systems group at Los Alamos National Laboratory, says that, "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target." "Finding new materials has traditionally been guided by intuition and trial and error," Lookman adds."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To achieve this goal, Lookman and fellow scientists at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China made use of machine learning to make the process faster. The team designed and developed a framework that employs uncertainties to guide the next experiments towards looking for a shape-memory alloy with very low thermal dissipation.

WIRE)--Cylance, the American company that is revolutionizing cybersecurity through the use of artificial intelligence to proactively prevent advanced persistent threats and malware, announced that it has signed an original equipment manufacturer (OEM) agreement with MOTEX to integrate MOTEX LanScope, a leading endpoint systems management solution, and CylancePROTECT, Cylance's innovative artificial intelligence (AI) and machine learning based endpoint malware prevention product. Cylance has produced the world's most advanced malware detection and attack prevention technology, which is protecting hundreds of global organizations and millions of computers today. Draper Nexus Ventures, a leading US-Japan cross border investment firm, is helping Cylance accelerate its entry into the Japanese market. "It's a global phenomenon that traditional security cannot protect endpoints from advanced threats. The Japanese market is not an exception and we have identified a great opportunity there," said Hiro Rio Maeda, managing director for Draper Nexus.

In what ways could NLG be used in healthcare? What will NLG mean for patients? NLG can be used to empower patients, so that they understand their medical conditions and can make better choices about their healthcare. NLG can also help patients do a better job of looking after themselves: this includes lifestyle changes, self-management of chronic conditions, and complying with treatment regimes. For example, many diabetics have sensors which measure blood sugar levels, but they struggle to use this information to manage their diabetes because often they don't understand it, and can overreact and indeed panic when they see their blood sugar change.

It is also being used to identify the drugs that particular patients are more likely to respond to, most recently with Berg, who are hoping to identify the biological makeup of cancer patients who are likely to respond best to their drugs. Berg CEO and co-founder Niven Narain is confident of its success - 'With use of Berg's Interrogative Biology platform, we will be applying our precision medicine approach where output from this trial will allow us to match patients to this given combination based on their biological profile.'

The annual March Machine Learning Mania competition sponsored by SAP challenged Kagglers to predict the outcomes of every possible match-up in the 2016 men's NCAA basketball tournament. Nearly 600 teams competed, but only the first place forecasts were robust enough against upsets to top this year's bracket. In this blog post, Miguel Alomar describes how calculating the offensive and defensive efficiency played into his winning strategy. I earned a Master's Degree in Computer Science from UIB in Mallorca, Spain. For nearly 20 years, I have been involved in software development, business intelligence and data warehousing.