In 2012, Moritz Simon Geist left a promising career as a research engineer in Germany to build robots and travel the world playing music full-time. He'd studied classical music in school, starting with the clarinet and piano, and toured with punk bands since he was a teenager in the '90s. That was when he started tinkering with equipment, building cheap solutions for complex audio problems and creating brand-new tools. Eventually, Geist's aptitude with electronics and music transformed into a new beast. Today, he's a solo artist who plays robots as music, relying on physical motion and contact microphones rather than electronics to create his beats.

Technology, says Nalbandian, is one of the three pillars that drives Animal Logic – the other two being the group's strong creative department, and its ability to make commercially viable products – and now the technology is driving Animal Logic into a whole new world: artificial intelligence and machine learning. The artificial-intelligence technology, made popular by applications such as Google's Assistant and Amazon's Alexa voice assistants, is already being adopted by the global animation and visual-effects industries to cut out some of the processor-intensive work that computers have to do, and, says Nalbandian, it's going to be adopted at Animal Logic to cut out some of the labour-intensive work that the company's humans have to do, too. In 2018 Animal Logic hired its first chief technology officer, Darin Grant, an industry veteran who was previously in charge of production technology at Steven Spielberg's legendary DreamWorks Animation, and who later went on to work at Google. Speaking from his base in Los Angeles (Animal Logic now has offices in Los Angeles and Toronto, as well as its headquarters at Fox Studios in Sydney), Grant points out that there are computer-related tasks in the animation and visual effects industry that have been moved over to machine learning, simply because the technology is more efficient. Rather than animate characters like i Peter Rabbit /i's Flopsy, Mopsy, Benjamin and Cottontail themselves, Animal Logic's artists may one day be able to teach an AI machine how to do it, leaving the artists free for even more ambitious work.

Appen just announced that it's acquiring Figure Eight in an all-cash deal that sees Appen paying $175 million upfront, with an additional payment of up to $125 million based on Figure Eight's performance this year. Both companies focus on using crowdsourced labor pools to annotate data, which in turn is used to train artificial intelligence and machine learning -- for example, Figure Eight (formerly known as CrowdFlower and Dolores Labs) says its technology has been for everything from mapping to stock photography to scanning receipts for expense reports. Appen, meanwhile, is a publicly-traded company headquartered in Sydney. CEO Mark Brayan described its technology -- and its "crowd" of more than 1 million remote workers -- as "highly complementary" to Figure Eight, which he praised for its data annotation and self-serve capabilities. "We know that to compete and to be able to deliver even higher volumes, we need a richer set of technologies," Brayan said.

Abstract: We introduce the Deep Symbolic Network (DSN) model, which aims at becoming the white-box version of Deep Neural Networks (DNN). The DSN model provides a simple, universal yet powerful structure, similar to DNN, to represent any knowledge of the world, which is transparent to humans. The conjecture behind the DSN model is that any type of real world objects sharing enough common features are mapped into human brains as a symbol. Those symbols are connected by links, representing the composition, correlation, causality, or other relationships between them, forming a deep, hierarchical symbolic network structure. Powered by such a structure, the DSN model is expected to learn like humans, because of its unique characteristics.

By now most people recognise--even if they aren't entirely comfortable with the idea--that artificial intelligence is part of our everyday existence. In a Pew Research report, 72% of Americans expressed concern about machines carrying out tasks humans can do. Even with this level of uneasiness, most of us willingly use AI-powered devices for the conveniences they offer. But, there are likely many ways we aren't even aware of that AI infiltrates our lives. So what are some of the products and services that are expected to help AI deliver $1.9 trillion in business value in 2019?

Accenture to Deliver 2017 RBS 6 Nations Insights to Fans via Machine Learning Official Technology Partner continues to innovate around fan experience, including new VR-based mixed reality experience demonstration LONDON; Jan. 23, 2017 – Accenture (NYSE: ACN), the Official Technology Partner of the RBS 6 Nations Rugby Championship for the sixth year, is bringing machine learning to international rugby. Its latest analytics dashboard will deliver improved player, match and Championship insight, which 20,000 people accessed via Twitter last year. Accenture has also developed an innovative mixed reality application for the Championship, using Virtual Reality (VR), with a twist. The one-person immersive VR experience is sharable, and broadcast as live action for others to watch. Insights from the dashboard will be fed to the Accenture Analysis Team, made up of former players and coaches.

It might be easier to state what part of our modern society artificial intelligence (AI) hasn't touched to show how important it is to our daily lives, business operations and society. Intelligence machines are influencing nearly every facet of our lives to help improve efficiencies and augment our human capabilities. AI is so intertwined in all that we do; it's hard to imagine living life without it. AI is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Put simply; artificial intelligence is when machines can learn and make decisions similarly to humans.

In November 2018, a text-generating "bot" named Tobi produced an incredible 40,000 news stories about Switzerland elections in just five minutes for Tamedia. According to a paper presented at the Computation Journalism conference in Miami, Tobi wrote on vote results of Switzerland's 2,222 municipalities in both French and German. Just three months after the incredible feat was reported by world media, robo-journalism is now being touted to change media landscape in the coming years. Now, artificial intelligence programs, which have been available for more than a decade, are being used by in news organisations to produce stories, personalise new delivery and even anchor news bulletins, reported AFP. "We've seen a greater acceptance of the potential for artificial intelligence, or robo-journalism, in newsrooms around the world," Damian Radcliffe, a University of Oregon professor who follows business models for journalism, told the news agency.