Would you share your home with a robot or work side by side with one? People are starting to do both, which has put the relationship we have with them under the spotlight and exposed both our love and fear of the machines that are increasingly becoming a crucial part of our lives. In Japan they grow so attached to their robot dogs that they hold funerals for them when they "die". Sony, the firm that began making the popular Aibo toys in 1999, decided to stop offering repairs in 2014, meaning once they broke down they were fit only for the scrapheap. But people weren't willing to throw them in the rubbish bin, wanting instead to say goodbye to them in the same way you would to a human or pet.

Five days from now Google will publish open-source tools that will focus its machine-learning engine on music and art. But one London startup, named Jukedeck, has been working on getting machines to automatically generate original music for years. You can even generate your own ditty, composed by artificial intelligence, right now on Jukedeck's website. Jukedeck lets anyone use its machine-learning engine to generate tunes hosted on its website. The engine then produces an original piece of music.

Though research is a slow moving and rigid process, one study shows that the rate of scientific study has exploded in the last 50 years. According to the paper, humanity's scientific output now doubles every nine years. In specific areas like healthcare, the doubling rate is even faster -- as much as every 3 years currently with an expected increase to every 73 days by the early 2020s. For overwhelmed researchers navigating the growing stack of science literature -- the value isn't in having so much new information, but finding relevant insights when they need them. According to Jacobo Elosua, a co-founder of Iris AI -- a Singularity University portfolio company -- the research process is very often tedious and unfruitful.

Speaker: Evelyne Viegas Given the investment and evidence of progress in Artificial Intelligence (AI) in the last five years, some suggest that it is merely a matter of time until AI matches, complements or surpasses, human intelligence. Artificial Intelligence at Microsoft is about augmenting human abilities and experiences and having humans and machine collaborate as teams in a complementary and trustworthy fashion. In this talk I will expose the breadth of AI efforts at Microsoft, the need to build bridges across diverse communities to create new multimodal and interdisciplinary research efforts.

It seems a long time ago (1997), that IBM's Deep Blue computer beat Garry Kasparov -- then world champion -- in chess, and we started talking about artificial intelligence seriously. However, we all thought AI would be limited to logical, rational, linear models of "thinking" that a machine can be programmed to do. Computers can be taught to play chess, but would never be able to beat a human at the game Go, said many futurists, even as recently as two years ago. Now that we have AlphaGo, Google's computer program designed to beat humans at Go, do we need to rethink the "technology doesn't always beat labor" proposition? Advisers who want to take their relationship with clients to the next level certainly must.

It's hard to discuss the role of Artificial Intelligence (AI) in the workplace until you decide what AI is. Some academics tell us -- using lots of words -- that AI is computers that think, learn and ultimately act like humans while others hold that maximizing the interaction between computers and their humans -- such as in Human Computer Interaction or HCI -- qualifies as the closest thing to AI we are likely to see. Until you decide on which side of that dichotomy you fall, it's difficult to understand how, or if, AI contributes to business, and if so, how to improve its contributions. Our fascination with the idea of machines that think like humans goes back millennia, but it's only recently that it appears to potentially be in reach. And while AI research has uncovered some amazing technological capabilities, it has also run into a quagmire in its attempts to 1) agree on just what human intelligence is; and 2) the extent to which technology might be capable of replicating it.

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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.

"We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances." "When you have two competing theories that make exactly the same predictions, the simpler one is the better." One famous example of Occam's Razor in action is found in conspiracy theories surrounding the NASA moon landings. Many conspiracy theorists believe that the first Moon Landing was staged and filmed in a studio, part of an elaborate hoax. Their justification relies upon many twisted and convoluted theories, whereas the NASA argument is fairly straightforward.

An historic event unfolded in March 2016. The victory of the program AlphaGo over professional gamer Lee Sedol in the Google DeepMind Challenge demonstrated how far artificial intelligence (AI) has come: "Go's simple rules and elaborate possibilities have made it one of the most sought-after milestones in the field of AI research," writes Sam Byford of The Verge. The idea of computers learning autonomously has been around for decades. Why has machine learning gained so much ground in recent years? Increased computing power has made machine learning possible, at last.