Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.

If you play video games, imagine how much you sometimes empathize with the character you're controlling. You may even forget the separation between the two of you, experiencing the world as that character. Consider whether your control in these moments is different, both at a high level and in tiny movements, than what the character would do if you had simply written down a set of rules for it to act by. In that difference lies the promise of this method for developing character AI. In psychology research methodology, there's a broad consensus that if you want to know what someone would do in a situation, you don't ask them what they would do.

Over the past year I've reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and TensorFlow. If I had cast my net even wider, I might well have covered a few other popular frameworks, including Theano (a 10-year-old Python deep learning and machine learning framework), Keras (a deep learning front end for Theano and TensorFlow), and DeepLearning4j (deep learning software for Java and Scala on Hadoop and Spark). If you're interested in working with machine learning and neural networks, you've never had a richer array of options. Essentially, a machine learning framework covers a variety of learning methods for classification, regression, clustering, anomaly detection, and data preparation, and it may or may not include neural network methods. A deep learning or deep neural network (DNN) framework covers a variety of neural network topologies with many hidden layers.

As stories of robot teachers (2010), AI game-show winners (2011), Big Data election victories (2012) objects communicating with one another (2013), self-driving cars (2014), robot-run hotels (2015), delivery drones (2016) have become the new normal, the outgoing Obama administration has made policy recommendations premised on a future American economy deeply affected by AI-driven automation, with 47% of US jobs estimated to be at high-risk of computerization in the next 20 years. It's perhaps therefore unsurprising that there is an abundance of words, terminology and concepts floating around the automation and artificial intelligence sphere, often achieving'buzzword' status and cited in isolation – when really they ought to be considered in concert. We can think of automation as being the consolidation of a number of such'concepts', intertwined and often related to one another: this is my take on how some of these fit together and the level of maturity that flows therefrom. We begin by plotting the number and complexity of tasks performed (X-axis) as part of each'concept' against the extent to which the behaviours exhibited are'human-like' (Y-axis). Next we demarcate a zoning of primary "operation modes": concepts relating to direct interaction with humans (mode of interaction in green); gaining and producing knowledge (thinking in purple); and, finally, interaction with other systems (machine-machine interaction in orange).

A number of fascinating projects have emerged that aim to use AI to better understand the way vibrations can give us insight into the health (or not) of machinery. A recent paper from the University of Alabama in Huntsville describes a new algorithm that aims to provide a diagnostic function for the mechanical systems that form such a big part of our commercial engine. "The ability to extract dependable and actionable information from the vibration of machines will allow businesses to keep their assets running for longer while spending far less in maintenance. Also, the investment to get there will be just software," the researcher says. A number of companies are deploying this kind of approach in the real world, with German startup KONUX making headway in the railway industry.

In America, congenital cataracts--a clouding of the eye lens at birth that can lead to blindness--are vanishingly (and thankfully) uncommon. Like tooth decay or tetanus, better screening and technologies have led to earlier diagnoses, and the problem can largely be cured with surgery. But in developing countries, a lack of widespread expertise and resources mean that hundreds of thousands of children are now blind due to this treatable disease. "Missed or mistaken diagnoses, as well as inappropriate treatment decisions, are common among rare-disease patients and are contrary to the goals of precision medicine, especially in developing countries with large populations, such as China," write a group of Chinese researchers in a study published Monday in the journal Nature Biomedical Engineering. These researchers aim to fix that preventable treatment gap by using eagle-eyed AI.

Cosmologist Stephen Hawking and Tesla CEO Elon Musk endorsed a set of principles that have been established to ensure that self-thinking machines remain safe and act in humanity's best interests. Machines are getting more intelligent every year and researchers believe they could possess human levels of intelligence in the coming decades. Once they reach this point they could then start to improve themselves and create even more powerful software, according to Oxford philosopher Nick Bostrom and several others in the field. In 2014, Musk warned that artificial intelligence has the potential to be "more dangerous than nukes" while Hawking said in December 2014 that AI could end humanity. AI could also help to cure cancer and slow down global warming.

Natural language generation, a field in artificial intelligence which automatically turns facts and statistics into coherent English and other languages, offers important advantages for finance and accounting, according to Robert Dale, a longtime expert in the technology. Gain a deeper understanding of how your business can benefit from IT and finance working cooperatively towards a unified goal. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.

Facebook scammers are making carbon copies of legitimate profiles in order to trick users into revealing financial and personal details, in a tactic known as'cloning'. By creating a new account using the exact same name, personal information and profile and cover photos included on your own profile – even going as far as copying your statuses – they can do a convincing job of imitating you online. The scheme allows them to confidently approach your friends and family members, who could unknowingly accept a friend request from the clone account and eventually share private information with it, depending on how convincing their messages appear. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

For almost three weeks, Dong Kim sat at a casino in Pittsburgh and played poker against a machine. Kim, 28, is among the best players in the world. The machine, built by two computer science researchers at Carnegie Mellon, is an artificially intelligent system that runs on a Pittsburg supercomputer. And for twenty straight days, they played no-limit Texas Hold'Em, an especially complex form of poker in which betting strategies play out over dozens of hands. About halfway through the competition, which ended this week, Kim started to feel like Libratus could see his cards.