It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.

Czech writer Karel?apek's 1920 play R.U.R. (Rossum's Universal Robots), which famously introduced the word robot to the world, begins with synthetic humans--the robots from the title--toiling in factories to produce low-cost goods. It ends with those same robots killing off the human race. Thus was born an enduring plot line in science fiction: robots spiraling out of control and turning into unstoppable killing machines. Twentieth-century literature and film would go on to bring us many more examples of robots wreaking havoc on the world, with Hollywood notably turning the theme into blockbuster franchises like The Matrix, Transformers, and The Terminator. Lately, fears of fiction turning to fact have been stoked by a confluence of developments, including important advances in artificial intelligence and robotics, along with the widespread use of combat drones and ground robots in Iraq and Afghanistan. The world's most powerful militaries are now developing ever more intelligent weapons, with varying degrees of autonomy and lethality.

The architecture includes maths capabilities that could be used by other software as part of a heterogeneous system architecture. That could include neural network software but ARM executives stressed that Bifrost is first and foremost an architecture for raster, tile-based graphics processing units (GPUs). The previous architecture – Midgard – is the one that underlies ARM's T-series Mali GPUs and has up to 16 unified shader cores and SIMD [single-instruction multiple data] instruction set architecture. Bifrost supports up to 32 unified shader cores with a scalar ISA, full hardware cache coherency and something called clause execution. The primary goal, according to Sean Ellis, GPU architect with ARM, was to achieve more performance per square millimeter of silicon and per line of "real-world" shader code.

The family of the driver killed in a US drone strike that targeted Taliban leader Mullah Akhtar Mansoor have registered a case against US officials seeking murder charges. The case, filed by the family of Mohammad Azam who was killed last week along with Mansoor in the Pakistani town of Ahmad Wal near Afghan border, said the father of four was innocent. US officials described the car's driver as a "second male combatant" but according to Pakistani security officials he was a chauffeur named Mohammad Azam who worked for the Al Habib rental company based out of Quetta, the region's main city. "US officials whose name I do not know accepted the responsibility in media for this incident, so I want justice and request legal action against those responsible for it," Mohammad Qasim, Azam's brother said in a police report, a copy of which was seen by the AFP news agency. "My brother was innocent and he was very poor who has left behind four small children and he was the lone bread earner in the family," he added.

Artificial Intelligence (AI) promises everything from self-driving cars to self-writing newspapers, but AI may be missing its greatest opportunity in healthcare, where AI-driven "conversational interfaces" hold untapped potential to influence the health and wellbeing of billions of people. Fueled by the massive popularity of messaging platforms such as WhatsApp, "conversational UI" is providing an emerging generation of chat-based digital services that may be the next thing in consumer technology. Instead of manipulating a graphical interface, users have a conversation with a chat-bot: software that is able to understand and respond to natural language inputs. The pace of technical advances combined with a shift in cultural norms is making AI conversations feel normal for increasing numbers of people. The idea of a "computer you can talk to" has captured the imagination of the computer science community, and the general public, for decades.

You are how you drive, we learned this week, when researchers showed how your car's computer can identify you based on patterns in your driving techniques. And it doesn't take much data to do so. Information collected from a car's brake pedal alone let the researchers distinguish the correct driver nine times out of 10. Patterns, of a different sort, also played a role in a map researchers have created to track where government hackers around the world are spying on journalists, activists, lawyers, and NGOs. And speaking of surveillance--whistleblower Edward Snowden also popped up in a Vice episode this week to show you how to make your phone "go black" so it's harder to surveil.

This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here. Think carefully about what you actually want to achieve with it. Most fall into the latter camp, but it seems everyone fancies themselves as containing a bit of the former (particularly if they think they're going to solve AI). To do the former well, in the international community, requires really good foundations (particularly in mathematics) followed by a PhD with a supervisor who has experience of how that community works. Doing the second well is much easier from the perspective of learning machine learning. A data generator would often be a scientist or company that is working in a particular application and wants answers. They need access to machine learning researchers or statisticians to give advice on how to answer those questions. They should try and collaborate with experts in data analytics and data science, but they should be careful, there is a lot of hype around the term'big data' at the moment. It's a difficult area to navigate. Data generators typically need an interface to consume machine learning (or statistics) effectively, if this interface is poorly chosen a lot of wasted resource can result (things get very expensive very quickly for a lot of data generators!). A data consumer is where the largest demand is right at the moment, and should probably be the starting point for someone who wants to move in the right direction. An MSc in Data Science would be a good starting point. You can also use this experience to see if you want to transit into a machine learning generator (that's basically what happened to me). What are you passionate about? That is the route in to any subject. Is it a particular approach to learning or a particular application?

"The development of a special military robot is one of the priorities of military construction in Russia," the Russian daily newspaper Komsomolskaya Pravda reported recently. The purpose of Iron Man, the newspaper continued, is to "replace the person in the battle or in emergency areas where there is a risk of explosion, fire, high background radiation, or other conditions that are harmful to humans." Experts have known that Russia has been trying in recent years to match the US and China in the development of robots, drones, and other war machines that are potentially autonomous. Today, those machines are remotely controlled. Iron Man and other recent developments illustrate how they're making progress.

The recovery of the rocket module proved once again that delivering payloads into deep orbit could be much cheaper in the near future. But this idea is still in its experimental stage and would require repeated successes to become part of normal operating procedures in 21st century space transport. SpaceX plans to use one of its four recovered first-stage rockets in a mission later this year. The rocket recovery was part of a successful mission to deliver an Asian communications satellite into so-called supersynchronous orbit, a position that puts a satellite more than 22,000 miles above the earth's surface in a way where it synchronizes with the planet's orbit in order to remain above the same area at all times. The satellite, Thaicom 8, will service communications and data transfer needs in Thailand, India and East Africa, according to nasaspaceflight.com.

Dream interpretation is the process of assigning meaning to dreams. In many ancient societies, such as those of Egypt and Greece, dreaming was considered a supernatural communication or a means of divine intervention, whose message could be unravelled by people with certain powers. In modern times, various schools of psychology and neurobiology have offered theories about the meaning and purpose of dreams. Most people currently appear to interpret dream content according to the Freudian theory of dreams in countries, as found by a study conducted in the United States, India, and South Korea.[1] People appear to believe dreams are particularly meaningful: they assign more meaning to dreams than to similar waking thoughts. For example, people report they would be more likely to cancel a trip they had planned that involved a plane flight if they dreamt of their plane crashing the night before than if they thought of their plane crashing the night before or the Department of Homeland Security issued a Federal warning.[1] However, people do not attribute equal importance to all dreams.