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.

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In order to explain the differences between alternative approaches to estimating the parameters of a model, let's take a look at a concrete example: Ordinary Least Squares (OLS) Linear Regression. In Ordinary Least Squares (OLS) Linear Regression, our goal is to find the line (or hyperplane) that minimizes the vertical offsets. Or, in other words, we define the best-fitting line as the line that minimizes the sum of squared errors (SSE) or mean squared error (MSE) between our target variable (y) and our predicted output over all samples i in our dataset of size n. The closed-form solution may (should) be preferred for "smaller" datasets -- if computing (a "costly") matrix inverse is not a concern. For very large datasets, or datasets where the inverse of XTX may not exist (the matrix is non-invertible or singular, e.g., in case of perfect multicollinearity), the GD or SGD approaches are to be preferred.

Inventor Ray Kurzweil made his name as a pioneer in technology that helped machines understand human language, both written and spoken. These days he is probably best known as a prophet of The Singularity, one of the leading voices predicting that artificial intelligence will soon surpass its human creators -- resulting in either our enslavement or immortality, depending on how things shake out. Back in 2012 he was hired at Google as a director of engineering to work on natural language recognition, and today we got another hint of what he is working on. In a video from a recent Singularity conference Kurzweil says he and his team at Google are building a chatbot, and that it will be released sometime later this year. Kurzweil was answering questions from the audience, via telepresence robot naturally.