In the past years we've had a growing discussion about how to reduce social bias and increase fairness, which is good. We wouldn't want our models to be discriminatory because of discriminatory historical data. However what I've noticed is that some have taken this as an opportunity to poisen the well by suggesting active discriminatory practices in seductive language and insidious framing. "Debiasing" AI is not enough. We need to proactively use computational decision making to correct for injustice.

The company is led by Copps and Chris Rohde, who launched and sold two previous machine learning and AI companies.

Without further adieu, let's predict the 2020 winners! For each category, we predict the most likely winner along with other nominees sorted by decreasing scores. Keep in mind that these scores aren't supposed to add up to 100. Rather, they are "points" given to the nominee by the underlying Deepnet model on a scale of 0 to 100. Another way to look at this is that the model is telling us how a movie/artist with a given set of characteristics will do in a given award based on 19 years of historical data on that award AND independent of the other nominees for the same award this year.

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle and fix it.) This is being done on such a large scale, I think it is probably the main cause of fake news, and the impact is disastrous on people who take for granted what they read in the news or what they hear from the government.

It is hardly "breaking news" that technology is radically changing almost every aspect of our lives and the world around us - but we talk far less about the challenges that come along with this rapid evolution. And we certainly don't know the answer to the important question: what is the ultimate outcome of this Digital Revolution? To understand this issue, we should recognize exactly how fast things are really changing. A good metric for this purpose is the floating-point operation: a single mathematical calculation (like addition or multiplication) on two numbers that have decimal points. In 1954, IBM introduced the first mass-produced computer with dedicated floating-point arithmetic hardware. The IBM 704 was regarded as the only computer that could handle complex math at that time: it could execute up to 12,000 floating-point operations per second, or FLOPS.

It was quite by accident I bumped into former RTÉ chief digital officer Muirne Laffan on the street in Dublin recently and asked: "So, what have you been up to?" Zoom forward a few days and she gave me the answer in the form of a demo with some of the most successful people in software globally. Alongside Laffan, who now runs LaffanLABs as a platform for collaboration with content creators and start-ups, sat successful Irish tech entrepreneur Eamon Moore who runs cloud firm Hikari and who successfully exited EMIT in 2018 along with the CEO and architect of San Diego-based Solliance, Zoiner Tejada, and Ciprian Jichi, CEO of Genisoft and chief data scientist at Solliance. Under the working titled'Music Manager' (a new brand has yet to be revealed) Moore pulled together the group to build what is a software as a service (SaaS) platform that could help radio stations tune in accurately to broadcast what music lovers want to hear. The start-up will be headquartered in Ireland and is already funded. 'Music Manager' is currently a working title for the business.

AI is coming for journalism. But rather than simply being used to take jobs from writers, Reuters has now shown that it can enhance the scale and personalization of news in ways previously unimaginable. Today, it has announced a prototype for a world first: a fully automated, yet presenter-led sports news summary system. Developed in collaboration with London-based AI startup Synthesia, the new system harnesses AI in order to synthesize pre-recorded footage of a news presenter into entirely new reports. It works in a similar way to deepfake videos, although its current prototype combines with incoming data on English Premier League football matches to report on things that have actually happened.

The first director of the Energy Department's newly created Artificial Intelligence and Technology Office was sworn in yesterday.

The artificial intelligence (AI) in social media market size is poised to grow by USD 1.67 billion during the period 2019-2023, according to the latest market research report by Technavio. This press release features multimedia. The process of deploying and maintaining reliable data interfaces is difficult, owing to the complexity of the process and the huge volumes of data being generated across various end-user industries. Therefore, enterprises around the world are adopting data integration solutions. Al in social media allows real-time synthesizing of data to facilitate real-time analysis for effective decision-making.

Never before have consumers had such an outstanding lineup of content offerings from which to choose – from award-winning original programming on OTT services like Netflix, to a galaxy of live-streamed and live-broadcast entertainment and sports events. First the good news: "I want my MTV." Some of us are old enough to remember that bygone slogan that expressed viewers' craving for music videos (back when MTV was all about music videos!) and well before the multiplatform internet age. That craving for all types of content has grown exponentially in the ensuing years, creating a potential gold mine of new opportunities for savvy media companies to develop lucrative new revenue streams. As media companies look for ways to realize that new revenue potential, they're running up against some real institutional barriers when they try to get their hands on the valuable content assets they need.