Lifeboat Foundation

Boston Dynamics’ robots dance to “Do you love me”

Boston Dynamics, already well known for its cutting-edge robotics technology, has released a new video in which its latest machines can be seen dancing to the classic song “Do You Love Me” by the Contours.

This line-up includes the bipedal humanoid Atlas, the four-legged canine-inspired Spot, and the two-wheeled Handle. The robots’ moves appear eerily human-like as they strut their stuff – an effect known as the uncanny valley.

Genies like Adam are busy.

HIAS is an open-source Hospital Intelligent Automation System designed to control and manage an intelligent network of IoT connected devices. The network server provides locally hosted and encrypted databases, and a secure proxy to route traffic to the connected devices.

In the not so distant future you could be making money from home by controlling robots, robots that are in another country. Or there will be products, such as a self driving Tesla car, that can go out and earn money on their own.

This video takes a look at the futuristic ways people will be earning money. From telepresence jobs and future business ideas, to new space businesses, and even how people will be storing their money — moving away from cash and credit cards to using chips that are in their bodies.

Continue reading “Making Money in a Futuristic World (Jobs and Future Business Ideas)” »

Nothing to see here-just our future robot overlords dancing to retro PMJ tracks before they take over the world.

En un video, Boston Dynamics presumió el avance que ha alcanzado con sus robots al ejecutar tareas, antes limitadas a los humanos.

The head scientist for Alexa thinks the old benchmark for computing is no longer relevant for today’s AI era.

Albert Einstein once said, “You have to learn the rules of the game, and then you have to play better than anyone else.” That could well be the motto at DeepMind, as a new report reveals it has developed a program that can master complex games without even knowing the rules.

DeepMind, a subsidiary of Alphabet, has previously made groundbreaking strides using reinforcement learning to teach programs to master the Chinese board game Go and the Japanese strategy game Shogi, as well as chess and challenging Atari video games. In all those instances, computers were given the rules of the game.

But Nature reported today that DeepMind’s MuZero has accomplished the same feats—and in some instances, beat the earlier programs—without first learning the rules.

Over the past few years, artificial intelligence (AI) tools, particularly deep neural networks, have achieved remarkable results on a number of tasks. However, recent studies have found that these computational techniques have a number of limitations. In a recent paper published in Nature Machine Intelligence, researchers at Tübingen and Toronto universities explored and discussed a problem known as ‘shortcut learning’ that appears to underpin many of the shortcomings of deep neural networks identified in recent years.

“I decided to start working on this project during a science-related travel in the U.S., together with Claudio Michaelis, a dear colleague and friend of mine,” Robert Geirhos, one of the researchers who carried out the study, told TechXplore. “We first attended a deep learning conference, then visited an animal research laboratory, and finally, a human vision conference. Somewhat surprisingly, we noticed the very same pattern in very different settings: ‘shortcut learning,’ or ‘cheating,’ appeared to be a common characteristic across both artificial and biological intelligence.”

Geirhos and Michaelis believed that shortcut learning, the phenomenon they observed, could explain the discrepancy between the excellent performance and iconic failures of many deep neural networks. To investigate this idea further, they teamed up with other colleagues, including Jörn-Henrik Jacobsen, Richard Zemel, Wieland Brendel, Matthias Bethge and Felix Wichmann.

Movie ratings can determine a movie’s appeal to consumers and the size of its potential audience. Thus, they have an impact on a film’s bottom line. Typically, humans do the tedious task of manually rating a movie based on viewing the movie and making decisions on the presence of violence, drug abuse and sexual content.

Now, researchers at the USC Viterbi School of Engineering, armed with artificial intelligence tools, can rate a movie’s content in a matter of seconds, based on the movie script and before a single scene is shot. Such an approach could allow movie executives the ability to design a movie rating in advance and as desired, by making the appropriate edits on a script and before the shooting of a single scene. Beyond the potential financial impact, such instantaneous feedback would allow storytellers and decision-makers to reflect on the content they are creating for the public and the impact such content might have on viewers.

Using artificial intelligence applied to scripts, Shrikanth Narayanan, University Professor and Niki & C. L. Max Nikias Chair in Engineering, and a team of researchers from the Signal Analysis and Interpretation Lab (SAIL) at USC Viterbi, have demonstrated that linguistic cues can effectively signal behaviors on violent acts, drug abuse and sexual content (actions that are often the basis for a film’s ratings) about to be taken by a film’s characters.