The Digital Human League, for example, recently unveiled'Digital Mike' – an artificial likeness of producer Mike Seymour. The idea, Digital Mike explains in a promo video, is'to produce a virtual human, and not only a virtual human, but one rendered in real time – puppeteered or driven in real time, rendered in real time, and not only that, at 90 frames per second, in stereo, in VR.' In a new study, researchers from Oxford University's Future of Humanity Institute, Yale University, and AI Impacts surveyed 352 machine learning experts to forecast the progress of AI in the next few decades. The idea, Digital Mike explains in a promo video, is'to produce a virtual human, and not only a virtual human, but one rendered in real time – puppeteered or driven in real time, rendered in real time, and not only that, at 90 frames per second, in stereo, in VR' A study from Oxford University's Future of Humanity Institute, Yale University, and AI Impacts released this past spring concluded that in less than 50 years, AI will beat humans at everything from language translation and truck driving to writing high-school essays.

Originally posted on The Horizons Tracker. I've written a few times recently about the initial forays of IBM's Watson into retail. For instance, at the back end of last year they launched Watson Trend to help us buy the perfect holiday gift, whilst they've also powered the recommendation engine at retailers such as North Face. Both are good examples of the use of AI to help provide more accurate predictions of the things we prefer. A good example of the work being undertaken in this area comes from a recently published paper that sees researchers develop a filtering model to do this job.

Deep neural networks (DNNs) have achieved unprecedented performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions. This has caused a recent surge of interest in methods for rendering modern neural systems more interpretable. In this work, we propose to address the interpretability problem in modern DNNs using the rich history of problem descriptions, theories and experimental methods developed by cognitive psychologists to study the human mind. To explore the potential value of these tools, we chose a well-established analysis from developmental psychology that explains how children learn word labels for objects, and applied that analysis to DNNs. Using datasets of stimuli inspired by the original cognitive psychology experiments, we find that state-of-the-art one shot learning models trained on ImageNet exhibit a similar bias to that observed in humans: they prefer to categorize objects according to shape rather than color. The magnitude of this shape bias varies greatly among architecturally identical, but differently seeded models, and even fluctuates within seeds throughout training, despite nearly equivalent classification performance. These results demonstrate the capability of tools from cognitive psychology for exposing hidden computational properties of DNNs, while concurrently providing us with a computational model for human word learning.

Wanted: 10,000 New Yorkers interested in advancing science by sharing a trove of personal information, from cellphone locations and credit-card swipes to blood samples and life-changing events. Researchers are gearing up to start recruiting participants from across the city next year for a study so sweeping it's called'The Human Project .' It aims to channel different data streams into a river of insight on health, aging, education and many other aspects of human life. Pictured are people walking inside the Oculus, the new transit station at the World Trade Center in New York. Researchers are gearing up to start recruiting 10,000 New Yorkers early next year for a study so sweeping it's called'The Human Project' 'That's what we're all about: putting the holistic picture together,' says project director Dr Paul Glimcher, a New York University neural science, economics and psychology professor.

In this Thursday, June 15, 2017, photo, people walk inside the Oculus, the new transit station at the World Trade Center Thursday, June 15, 2017, in New York. Researchers are gearing up to start recruiting 10,000 New Yorkers early next year for a study so sweeping it's called "The Human Project." They'll be asked to share a trove of personal information, from cellphone locations and credit-card swipes to blood samples and life-changing events.

Machine learning is on the rise due to the technological convergence of the growth of big data, decreasing data storage costs, increasing computing power, improved artificial intelligence algorithms and acceleration of cloud computing. Machine learning is the ability for computers to learn without explicit programming. It's analogous to the human ability to identify an octopus based on the set of data input that goes to the brain, such as eight arms, tentacles, lack of skeleton and other characteristics, without having prior knowledge of every type of cephalopod mollusk in existence. However, human decision-making is subject to numerous cognitive biases that can easily distort judgement. For example, iconoclastic author Tom Peters highlights 159 cognitive biases that impact management decision-making (Peters, Tom.

By their own admission, Jack Ma is uncomfortable with Artificial Intelligence (AI) and Elon Musk is scared. But why? Contrary to popular perception AI is old. To be precise it's 51-years old, widely acknowledged to have been born at a conference at Dartmouth College in 1956. That conference was attended by a diverse group of people. Three of them presented the Logic Theorist, the world's first true artificial intelligence programme. Two of them were Allen Newell and Herbert Simon.

For some reason, certain people just don't believe it's real. Even Scott Pruitt, the man President Trump named to head the Environmental Protection Agency, isn't steadfast about global warming and what causes it. But years of scientific study have shown the planet is getting warmer. According to Bill Nye, the answer is simple: cognitive dissonance. "People have a certain worldview; [then] they're confronted with evidence that conflicts with the worldview, so they have dissonance, conflict in their minds," Nye says.

Four months ago I attempted to synthesize Wikipedia's crazy list of cognitive biases, and after banging my head against the wall for weeks, came up with this Cognitive Bias Cheat Sheet which John Manoogian III, beautifully organized into the above poster. Since then, I've started working on a book proposal ( get on the email list!) around these topics, and wanted to start by creating an actual cheat sheet that doesn't take so long to read. There are four qualities of the universe that limit our own intelligence and the intelligence of every other person, collective, organism, machine, alien, or imaginable god. All 200-ish of our known biases are attempts to work around these conundrums! The first conundrum is that there's too much information in the universe for any individual within the universe to process.

Americans born in the United States are more murderous than undocumented immigrants. After all, that's just what the numbers say. Still, be honest: you wouldn't linger over a story with that headline. Instead, you'll see two dozen reporters flock to a single burning trash can during an Inauguration protest. The aberrant occurrence is the story you'll read and the picture you'll see.