U of Alberta created the first Computing Science department in Canada in 1964. It has a long tradition of research in AI (is rated 3rd in the world in machine learning). It has also led in the development of AI for strategy games. The results can be commercialized in non-game applications as well. Among these are Checkers, Chess, Go and Poker, The evening's talks were by Jonathan Schaeffer (computer chess) and Ryan Hayward (the strategy game Hex).

When the European Union enacted the General Data Protection Regulation (GDPR) a year ago, one of the most revolutionary aspects of the regulation was the "right to be forgotten"--an often-hyped and debated right, sometimes perceived as empowering individuals to request the erasure of their information on the internet, most commonly from search engines or social networks. Darren Shou is vice president of research at Symantec. Since then, the issue of digital privacy has rarely been far from the spotlight. There is widespread debate in governments, boardrooms, and the media on how data is collected, stored, and used, and what ownership the public should have over their own information. But as we continue to grapple with this crucial issue, we've largely failed to address one of the most important aspects--how do we control our data once it's been fed into the artificial intelligence (AI) and machine-learning algorithms that are becoming omnipresent in our lives?

The recent rapid spread of a doctored video of Nancy Pelosi has frightened lawmakers in Washington. The video--edited to make her appear drunk--is just one of a number of examples in the last year of manipulated media making it into mainstream public discourse. In January, a different doctored video targeting President Donald Trump ended up airing on Seattle television. This week, an AI-generated video of Mark Zuckerberg was uploaded to Instagram.

Countering digital fraud is a lot like playing whack-a-mole: As soon as one fraudster is taken out, two more pop up where they're least expected. Fighting bad actors is particularly challenging for those in the banking industry, which lost more than $31 billion to fraud in 2018 and is projected to lose even more as cybercriminals become more sophisticated. The popularity of digital banking services has created ample opportunities for bad actors, leaving banks scrambling to protect themselves against the rising tide of fraud. Faster payments have also contributed, as banks now have less time to identify fraudulent transactions. It's nearly impossible for human analysts to examine every sign of malfeasance with banks processing millions of transactions each day, but that is exactly where learning technologies like artificial intelligence (AI) and machine learning (ML) can help.

Despite having a $12 billion budget and being located adjacent to Silicon Valley, San Francisco doesn't always take advantage of the ways in which tech can improve civic life or the work of its city employees. But there is one office that is pushing the envelope and collaborating with programmers, nonprofits, and computer scientists with the vital goal of improving its criminal justice practices. Just last month District Attorney Geroge Gascón announced that a partnership with Code for America had enabled his office to clear all old marijuana convictions made defunct with the passage of Proposition 64. And on Wednesday, he shared the news that a new collaboration with Stanford was in the works, to employ artificial intelligence as a means of mitigating implicit racial bias among his staff. If the words "artificial intelligence" combined with "criminal justice system" give you goosebumps, you're not alone.

Chatbots today pop up at websites in smartphone apps; the same technology helps robots, smart speakers, and other machines operate in a more human-like way. The idea of conversing with a computer is nothing new. As far back as the 1960s, a natural language processing program named Eliza matched typed remarks with scripted responses. The software identified key words and responded with phrases that made it seem as though the computer was responding conversationally. Since then, such conversational interfaces--also known as virtual agents--have advanced remarkably due to greater processing power, cloud computing, and ongoing improvements in artificial intelligence (AI) and machine learning.

While it might have looked like a seamless feat, every movement and placement was coded with careful consideration. Even with today's more intelligent and adaptive robots, this task remains difficult for machines with rigid hands. They tend to work only in structured environments with predefined shapes and locations, and typically can't cope with uncertainties in placement or form. In recent years, though, roboticists have come to grips with this problem by making fingers out of soft, flexible, materials like rubber. This pliability lets these soft robots pick up anything from grapes to boxes and empty water bottles, but they're still unable to handle large or heavy items.

Jin Hyun Kim (Humboldt University of Berlin, Germany, Music) - Our self and the world enhanced by music's mediating relations

They want faster, more personalised digital experiences from a brand, without their privacy being invaded or compromised. At the same time, they want a face-to-face or human experience with a company when it suits them. This requires businesses and chief marketing officers (CMOs) to engage in a difficult and fast-changing balancing act. Technology such as chatbots that answer a customer's basic queries, machine-learning and artificial intelligence (AI) are making it easier to serve consumers and, in many cases, to give them a better customer experience. But brands must also be careful not to force the pace of automation or to focus too much on potential cost-savings because they run the risk of alienating new and existing customers who want or value a human experience.

Television news coverage is typically thought of as a visual medium, yet most of the narrative we consume from television comes in the form of spoken narration. Watching a news show with the audio muted and closed captioning off reinforces that the visual elements of television act more as enrichment than primary information conveyor. This means that quantifying this spoken narrative is imperative to understanding what television news is paying attention to and how it is framing and covering those events. Using Google's Cloud Speech-to-Text API to transcribe a week of television news coverage and annotating it with Google's Natural Language API, what might we learn about how television news covers the world? In the United States, most television stations provide closed captioning for their news programming, meaning they already come with a textual human-produced transcript.