AMONG government organisations, America's Defence Advanced Research Projects Agency (DARPA) has always been somewhat unusual. As the research arm of the Department of Defence, it is akin to a high-stakes venture capitalist, gambling large sums of money (its estimated 2004 budget is $3 billion) on risky technologies that will probably fail, but could pay off in a big way. It has had some stupendous successes, such as the internet, the Saturn rocket and micro-electro-mechanical systems (tiny machines that work at the scale of a human cell). There have also been some resounding duds, such as the Total Information Awareness project, a Big Brotherish plan to spot terrorists by combing through databases of personal information, which was swiftly abandoned. But what is arguably DARPA's most outlandish scheme yet will start rolling on March 13th, when a gaggle of strange-looking vehicles will line up in Barstow, California to make a wild run across 250 miles of scrub and desert.

Advances in deep learning have led Elon Musk and others to start preparing for the AI apocalypse. And indeed, by feeding terabytes into neural networks, computers are now able to understand voices, recognise faces and sift through data with unprecedented accuracy. And yet, advances in so-called unsupervised learning - which finds the structure or relationships in data inputs without training in the way that a child learns from experience - are almost non-existent. In recent years, Yann LeCun of Facebook, Geoffrey Hinton of Google and Yoshua Bengio from the University of Montreal have made significant advances in machine learning through their use of deep neural networks and other learning techniques. For example, Yaniv Taigman, one of my co-founders at face.com (which was acquired by Facebook in June 2012), recently published that the company achieved a 97.25 per cent accuracy rate for face recognition, just 0.25 per cent below human perception.

The cybersecurity industry has always been under constant strain from cybercriminals and malware. With increasing integration of hardware, software and services being built into every aspect of our lives, the task of keeping data secure has become even more difficult. The arsenal of tools that cybercriminals now have at their disposal has raised concerns for security companies, and turned the criminals into threat actors who can create, disseminate and penetrate a target's defenses using custom-built and never-before-seen malware. The security industry has had to adopt a new way of dealing with the unknown by leveraging the powerful capabilities of machine learning algorithms. Cybersecurity & Machine Learning Because targeted and advanced threats that seek to prey on organizations and businesses often evade traditional security mechanisms, machine learning algorithms have stepped in to fill in the gap between proactivity and detection.

The following letter was published in the Letters to the Editor of the February 2016 CACM (http://cacm.acm.org/magazines/2016/2/197433). Arguments for and against LAWS share this common foundation, but where Goose argued for a total ban on LAWS-related research, Ronald Arkin, in his "Counterpoint," favored a moratorium while research continues. Both sides accept international humanitarian law (IHL) as the definitive authority concerning whether or not LAWS represents a humane weapon. If I read them correctly, Goose's position was because LAWS would be able to kill on their own initiative they differ in kind from other technologically enhanced conventional weapons. That difference, he said, puts them outside the allowable scope of IHL and therefore ought to be banned.

A new pair of Russian robots has been developed that can track and attack humans from more than four miles away. The devices are designed for use on the Russian border, and claim to accurately detect and attack ground and aerial threats long before they reach Russian soil. Key technology at the robots' disposal include radar, HD and thermal video imaging, and multiple long-range grenade launchers. Weapons experts have also been working on a high-tech explosives system, labelled the'Alpha Device,' to work in tandem with the Flight surveillance robots. The first of the two new robots will act as a pair of scrupulous eyes for Russian border guards.

A new Twitter bot shows that not all them are bigots -some just want to make you smile. '@smilevector' was recently unleashed on Twitter and, unlike its Hitler supporting predecessor Tay, this bot manipulates faces by turning their frown upside down. Using neural networks, this algorithm can plant unsettling smiles on celebrities with what seems to be impressive accuracy. '@smilevector was unleashed on Twitter and, unlike its Hitler supporting processor Tay, this bot manipulates faces by making them smile. Created by Tom White, a lecturer at the Victoria University of Wellington School of Design in Wellington, New Zealand, this algorithm uses a generative neural net to manipulate faces.

It is every Top Gun's worst nightmare - an AI can can outmanoeuvre them in the air. Now researchers have tested their AI on a retired top gun - and left him stunned. Retired United States Air Force Colonel Gene Lee took on the AI in a simulator - and lost. An AI has beated Air Force pilots in simulated showdowns for the first time. Retired United States Air Force Colonel Gene Lee took on the AI in a simulator.

A human-sized robot is being developed in preparation for missions to the red planet. Experts will seek to improve the physical and computational abilities of the 6ft-tall (1.8 metre) machine Valkyrie. Researchers will work to give Valkyrie a much more sophisticated set of skills, enabling it to better understand and respond to its surroundings. The'superhero robot' is being designed by Nasa to help astronauts on a mission to Mars is named Valkyrie. It is 6ft-tall and weighs 275lbs.

It is the nearest thing to a human brain in silicon form. Lawrence Livermore National Laboratory (LLNL) and IBM have revealed a new deep learning supercomputer they say could boost AI systems. Based on a'neurosynaptic' computer chip called IBM TrueNorth, it can replicate the equivalent of 16 million neurons and 4 billion synapses - yet consumes just 2.5 watts, the energy equivalent of a hearing aid battery. TrueNorth can replicate the equivalent of 16 million neurons and 4 billion synapses - yet consumes just 2.5 watts, the energy equivalent of a hearing aid battery. A single TrueNorth processor consists of 5.4 billion transistors wired together to create an array of 1 million digital neurons that communicate with one another via 256 million electrical synapses.

Allowing autonomous weapons to call the shots in combat zones eases the burden for human soldiers. But it also poses a threat to our safety and security, experts have warned. At a recent meeting, researchers said they were concerned these war machines could engage in unethical behavior and become a playground for hackers. Even though we are years from deploying them for the battlefield, experts don't believe anyone will make ethical robots and hijacking will increase as systems become more automated. While the laws of war do not inherently prohibit autonomous weapons, today it would be very challenging for autonomous weapons to comply with the laws of war except under narrow circumstances.