For anyone that thinks artificial intelligence (AI) belongs firmly in the future, it's worth taking a moment to remember that the technology has its roots squarely in the past; 60 years in the past to be precise. Six decades ago, delegates gathered at the first Dartmouth Conference to hear about an exciting new world of "artificial intelligence". It's said amongst IT historians that this was the first time the term was ever used. Today there are a lot of practical applications for AI but many people can't imagine a real world example that doesn't end with a group of crazy megalomaniac robots taking over the planet and wiping out humanity. But, as with many scientific advances, the reality is a lot simpler, modest and, dare we say, down-to-earth. Take Facebook's recent announcement about launching a range of chatbots.

It's no secret that much of the wisdom of the world lies in unstructured data, or the kind that's not necessarily quantifiable and tidy. So it is in cybersecurity, and now IBM is putting Watson to work to make that knowledge more accessible. Towards that end, IBM Security on Tuesday announced a new year-long research project through which it will collaborate with eight universities to help train its Watson artificial-intelligence system to tackle cybercrime. Knowledge about threats is often hidden in unstructured sources such as blogs, research reports and documentation, said Kevin Skapinetz, director of strategy for IBM Security. "Let's say tomorrow there's an article about a new type of malware, then a bunch of follow-up blogs," Skapinetz explained.

Nimble enough to remove a canister from a cinderblock. The ways robots progress don't always mirror the stages of childhood development, but it's eerie when they do. We've seen robots stumble to walk, watched as they learned the basics of language, and now we can see one dexterous enough to move color disks from one peg to another, stacking them in the correct order. It can also do more complex tasks, like opening safety lids on pill bottles. Aptly named the "Highly Dexterous Manipulation System", the robot was developed by Resquared Robotics, with funding from the Army and Navy.

MIT scientists have built a hybrid human/artificial intelligence (AI) machine that they claim can learn how to detect 85% of cyber attacks – that's roughly three times better than previous benchmarks – while reducing false positive rates by a factor of 5. Nitesh Chawla, professor of computer science at Notre Dame University, said in a statement from MIT that the machine "has the potential to become a line of defense against attacks such as fraud, service abuse and account takeover." Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx demonstrated the platform, called AI2, in a paper titled "AI2: Training a big data machine to defend". As the researchers describe the current state of the art, today's security systems are typically driven by either humans – so-called "analyst-driven solutions" – or by machine. The problem with security systems based on fixed rules is that they miss attacks that don't match those rules. Machine-learning approaches, as the name suggests, rely on an adaptive process that can trigger annoying numbers of false positives.

Co-author Paul Leiby, Distinguished Research Scientist at Oak Ridge National Laboratory, said: "Because automation has the potential to provide convenient, lower cost mobility, we see it could have large implications for transportation demand, energy use and resulting CO2 emissions, by both passengers and freight. For example, low cost automated trucking could shift more freight away from efficient railways to trucks. To make continued progress in reducing carbon emissions from light-duty vehicles and large trucks in the face of expanded mobility, it will be essential to couple vehicle automation with the extensive use of advanced low-carbon vehicles, like electric or hydrogen vehicles."

For years, machine learning has been used for image, video, and text recognition, as well as serving as the power behind recommendation engines. Today, it's being used to fortify cybersecurity, ensure public safety, and improve medical outcomes. It can also help improve customer service and make automobiles safer. "Machine learning allows you to look at volumes of data and do volumes of calculations that a person really can't do," said Lisa Dolev, founder and CEO of operational intelligence solutions provider Qylur, in an interview. Machine learning can identify patterns that humans tend to overlook or may be unable to find as fast in vast amounts of data.

You may be familiar with the name Robert Wachter, M.D. He's written six books and hundreds of journal articles; he chairs the department of medicine at the University of California, San Francisco; and he's a leading advocate for patient safety. He's perhaps best known for having coined the term "hospitalist," and for having defined and promoted hospital medicine as a recognized primary care subspecialty. In a new book, The Digital Doctor: Hope, Hype and Harm at the Dawn of Medicine's Computer Age (McGraw-Hill Education, 2015), Wachter takes a deep dive into the turbulent waters of medical technology as informed by artificial intelligence, or AI. "While computers are preventing many medical errors," he observes, "they are also causing new kinds of mistakes, some of them whoppers." It was one of those -- at his own institution, UCSF -- that inspired Wachter to examine where AI has got us and where it's taking us.

Over the past several years, Riot Games, which produces the immensely popular League of Legends, has been experimenting with artificial intelligence (AI) and predictive analytics tools to find the online trolls and make their games more sportsmanlike. "We used to think that online gaming and toxic behavior went hand in hand," explains Jeffrey Lin, lead game designer of social systems at Riot Games. "First, put the tools in the hands of the community and second, build machine learning systems that leverage the scale of data--contributed from the community through reports--to combat the problem." To learn how Big Data, automation and artificial intelligence will shape the future, download the HPE white paper "Big Data in 2016."

Millions of young online gamers today are accustomed to battling bad guys. But their biggest foes are often their fellow players. Many online gaming sites are rife with creepy bigotry, harassment and even death threats. So how do you root out the rotten apples? Over the past several years, Riot Games, which produces the immensely popular League of Legends, has been experimenting with artificial intelligence (AI) and predictive analytics tools to find the online trolls and make their games more sportsmanlike.

Technicians from the SWIFT global financial network connecting it to Bangladesh's central bank made it easier for hackers to attack the bank, Bangladeshi police and a bank official have told Reuters. The technicians worked on Bangladesh's Real-time Gross Settlement (RTGS) system, used to transfer money among Bangladeshi banks, three months before hackers attempted to steal US 951 million from the central bank. The work opened up "a lot of loopholes" in bank computer systems, said the head of the criminal investigation department leading the investigation. Bangladeshi police want to interview the SWIFT technicians to find out whether their actions were intentional or negligent, Mohammad Shah Alam told Reuters. Police and bank officials told the news agency that a number of actions last October left the bank more vulnerable after the RTGS system was set up and connected to the SWIFT network, which provides messaging services to around 11,000 financial institutions worldwide. SWIFT staff declined to speak to Reuters about the allegations, and did not immediately respond to requests from IDG News Service for comment.