Jamie Metzl is a Senior Fellow for Technology and National Security at the Atlantic Council. After 4 billion years of evolution by one set of rules, our species is about to begin evolving by another. Overlapping and mutually reinforcing revolutions in genetics, information technology, artificial intelligence, big data analytics, and other fields are providing the tools that will make it possible to genetically alter our future offspring should we choose to do so. Nearly everybody wants to have cancers cured and terrible diseases eliminated. Most of us want to live longer, healthier and more robust lives. Genetic technologies will make that possible. But the very tools we will use to achieve these goals will also open the door to the selection for and ultimately manipulation of non-disease-related genetic traits -- and with them a new set of evolutionary possibilities.

After being stranded in the Mediterranean for three days, fear had overcome Alou Sango. "I thought that we would all die, because there was nothing left, the petrol had finished," he says of his journey from Libya. Like thousands before him, Sango boarded an overcrowded boat to escape the country's turmoil after being unable to return to his native Mali. But after days at sea the captain lost his way and, without a GPS position to give to the Italian authorities, the 100 or so passengers were losing hope. Their rubber dinghy was finally spotted by a Chinese vessel, which picked up the migrants and took them to Italy, where Sango, now 24, is studying through a Rome-based charity, Sant'Egidio Community.

Dreams are successions of images, ideas, emotions, and sensations that occur usually involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer. The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$. Our contributions are as follows. We present EXP-3-Transfer, a principled policy-reuse algorithm that optimally reuses a given source policy set when learning for a new MDP. We present a framework to cluster the previous MDPs to extract a source subset. The framework consists of (i) a distance $d_V$ over MDPs to measure policy-based similarity between MDPs; (ii) a cost function $g(\cdot)$ that uses $d_V$ to measure how good a particular clustering is for generating useful source tasks for EXP-3-Transfer and (iii) a provably convergent algorithm, MHAV, for finding the optimal clustering. We validate our algorithms through experiments in a surveillance domain.

Robotics scientists at the US's Stanford University have achieved a remarkable first: they have successfully sent an automated avatar – which they describe as a robo-mermaid – down to an ancient shipwreck to retrieve a vase from the sunken vessel. La Lune, the flagship of Louis XIV of France, sank 20 miles off the south coast city of Toulon in 1664. Only a few dozen of the hundreds of men on board survived. The wreck, which lies at a depth of 100 metres, had never been disturbed until the OceanOne robot craft reached it two weeks ago and recovered the grapefruit-size vase. The humanoid diving robot was piloted, using virtual reality techniques, by Oussama Khatib, professor of computer science at Stanford.

Do they miss you when you're away on business? Can they put on a VR headset without their parents' help? That last one is pretty important. The year is zipping by, but before we launch full-force into next month, there's just about enough time for a quick roundup of the best updates and launches in April. It was recently revealed that Google was testing a new app for travelers, and now we have a better idea of what it might do.

Did you know there is a 25% chance your cause of death will be due to environmental pollution? According the World Health Organization, some 12.6 million people--or nearly 1 in 4 worldwide--died in 2012 due to living or working in unhealthy conditions. In addition, environmental degradation seriously affects overall quality of life and the balance of Earth's ecosystems through loss of forests, open spaces, marine environments and biodiversity. While technological growth and industrialization historically contributed to such problems, the latest technologies--from robotics to artificial intelligence to biotechnology--will also help create healthier and greener industries benefiting both people and planet. While affordable electric and hybrid cars will help reduce pollution and use of fossil fuels, self-driving cars will make our whole transportation and logistics systems more efficient. Cars, trucks, ships, drones and jets that drive or pilot themselves and wirelessly communicate with each other can coordinate and optimize delivery of people and goods in ways requiring less energy.

Autonomous cars are a chance to reinvent the steering wheel. Because the vehicles themselves do all the driving, cars are no longer bound by such basic conventions as "keep a human facing forward at all times" and "don't try to climb over boulders like a spider." As a grand showcase for the new possibilities of autonomous cars, Honda plotted a seven-stage road trip roughly following that path of humanity's great migration from a species to the edge of the world. The auto company used miniature models for this conceptual video, but the hope is the same principles could be applied to human-sized autonomous vehicles of the future. Honda's route goes from Nairobi, Kenya to Manaus, Brazil, and new vehicles trace individual legs of that journey.

In its time the Google Doodle has celebrated mathematicians such as John Venn, George Boole and Hertha Marks Ayrton - as well codebreaker Alan Turing, the 100th anniversary of whose birth was 23 June 2012. Now it is the turn of Claude Shannon, who worked with Turing on Allied codebreaking during the Second World War - not at Bletchley Park, but in Washington, where Turing had been seconded in 1943 to bring the US up to speed with British cryptanalytic developments. Shannon was four years younger, 26 to Turing's 30. Although Shannon's war-time work was crucial to the Allied effort, he did devote some of his energies to more frivolous projects. In the 1070s, Shannon built the world's first juggling robot, using an Erector Set (the equivalent of a Mecanno set).

Even with bottled oxygen and elite training, there are underwater locations that lie well beyond our physical capabilities. But via haptic feedback technology and artificial intelligence, Stanford University's humanoid diving robot is now putting the ocean's depths within human reach. In its maiden expedition, the OceanOne droid has just scoured an untouched shipwreck off the coast of France and returned with a delicate, 17th century vase in its grip. Researchers are now eyeing future voyages to coral reefs, oil rigs and underwater disaster zones. With our deep sea diving capabilities only taking us so far, we have long sought to send robots down below to do the investigating for us.