Much is made of AI augmenting human intelligence with simple automation, but might higher order human creativity go the same way? Technology is an integral part of composer Kate Simko's work. As well as writing for film and television, she founded the London Electronic Orchestra, which combines classical instruments with electronic music. Although she may start composing with paper, pencil and piano, she switches to Avid's Sibelius notation software to write a full score: "From there, you're able to take the notation and export it as Midi data or the actual sheet music," she says, referring to the Musical Instrument Digital Interface (Midi) technical standard. Software also lets Simko listen to her compositions before rehearsals start.

Few industries have been hit as hard by the technological changes of recent times as the media industry. The same trends that have improved the lives of billions -- the growth of the internet, the spread of social media, and the proliferation of smartphones -- have instead disrupted the business models of every major media company, diluting their ability to sustainably fund their core operations. Most media companies have identified a'shift to video' as a critical pathway out of this digital dilemma. Digital video content is five times more engaging for consumers and four times more valuable for advertisers than text content alone. However, despite huge investments by media companies in increased video production, these shifts to video have so far failed to deliver meaningful bottom-line results.

Affectiva today announced the launch of its Automotive AI service that lets creators of autonomous vehicles and other transportation systems track users' emotional response. The Automotive offering is Affectiva's third service for tracking the emotional response of product users and is part of a long-term strategy to build emotional profiles drawn from smart speakers, autonomous vehicles, and other platforms with a video camera or conversational interface. An MIT Media Lab spinoff, Affectiva launched a voice analysis tool last year for the makers of AI assistants and social robots, but its computing services have been available to marketers and advertisers since 2010. Affectiva can pick up emotions like joy, surprise, fear, or anger from a person's face and things like laughter and arousal levels from their voice. To develop its solution for cars, Affectiva has been working with OEMs, vehicle safety system providers like Autoliv, and robotaxi startup Renovo over the past 18 months, an Affectiva spokesperson told VentureBeat in an email.

Video game adaptations have a long history of being, well … mostly completely terrible, thanks largely to the vapid efforts of one Uwe Boll. And even the most ardent Angelina Jolie fan would presumably admit that the Tomb Raider movies were hardly the Oscar-winner's finest hour. So why would Alicia Vikander, Hollywood It girl and current art house dahling, sign up to star as Lara Croft in a reboot of the action-adventure series? Were there no Marvel superhero parts available? Critics have reacted with predictable sniffiness to Norwegian director Roar Uthaug's debut Hollywood outing, with the movie rated just 50% on the review aggregator Rotten Tomatoes.

My background is in academic research applying deep learning to a variety of problems in engineering, healthcare, fintech, etc. I've always wished for there to be an easy way for me to a) share my machine learning approaches with other individuals / experts interested in the problem domain, and b) publish result benchmarks (AUC metrics, etc.) for more transparency on state-of-the-art. The best alternatives I've found are caffe model zoo and Arxiv, both of which I haven't found to easily facilitate sharing, discussion, and experimentation. I think a buffed up "DL model zoo", with options for serving models, publishing results, and facilitating discussion could be useful. If this existed, I'm sure I would also constantly be scrolling through to read about new DL applications on interesting problems.

As Mark Hamill humorously shared the behind-the-scenes of "Star Wars: The Last Jedi" with a packed SXSW audience, two floors below on the exhibit floor Universal Robots recreated General Grievous' famed light saber battles. The battling machines were steps away from a twelve foot dancing Kuka robot and an automated coffee dispensary. Somehow the famed interactive festival known for its late night drinking, dancing and concerts had a very mechanical feel this year. Everywhere debates ensued between utopian tech visionaries and dystopia-fearing humanists. Even my panel on "Investing In The Autonomy Economy" took a very social turn when discussing the opportunities of utilizing robots for the growing aging population.

Imagine standing up to give a speech in front of a critical audience. As you do your best to wax eloquent, someone in the room uses a clicker to conspicuously count your every stumble, hesitation, um and uh; once you've finished, this person loudly announces how many of these blemishes have marred your presentation. This is exactly the tactic used by the Toastmasters public-speaking club, in which a designated "Ah Counter" is charged with tallying up the speaker's slip-ups as part of the training regimen. The goal is total eradication. The club's punitive measures may be extreme, but they reflect the folk wisdom that ums and uhs betray a speaker as weak, nervous, ignorant, and sloppy, and should be avoided at all costs, even in spontaneous conversation.

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes.

New Addition to BIAnalytix Platform Enables Real-Time Workflows That Maximize Revenue Opportunities for Cross-Media Campaign Trading DENVER -- March 20, 2018 -- Today, Decentrix introduced BIAnalytix SSP, the media industry's first cross-media-capable platform to deliver revenue maximization opportunities with modern real-time trading protocols. Built upon Artificial Intelligence for Media (AIfM), the newest addition to the company's BIAnalytix portfolio enables sophisticated cross-platform media trading at the speed of the buyer. "BIAnalytix SSP supports cross-platform and real-time trading, with realistic market pricing distilled from the data existing within a media company's order management systems and processes," said Taras Bugir, president at Decentrix. "The packaging of cross-media campaign inventory for trading represents a significant milestone in our industry. By addressing complexities that are beyond the reach of human analysis, Artificial Intelligence for Media enables media organizations -- for the first time -- to process decisions based upon current and future inventory positions across their entire delivery ecosystem."

AI is poised to drive the next wave of technological disruption across industries. Like previous technology revolutions in Web and mobile, however, there will be huge dividends for those organizations who can harness this technology for competitive advantage. I spend a lot of time working with customers, many of whom are investing significant time and effort in building AI applications for this very reason. From the outside, these applications couldn't be more diverse – fraud detection, retail recommendation engines, knowledge sharing – but I see a sweeping opportunity across the board: context. Without context (who the user is, what they are searching for, what similar users have searched for in the past, and how all these connections play together) these AI applications may never reach their full potential.