Lifeboat Foundation

Twenty years ago, entertainment was dominated by a handful of producers and monolithic broadcasters, a near-impossible market to break into.

And now, over 50 years later, AI is bringing stories to life like we’ve never seen before.

Converging with the rise of virtual reality and colossal virtual worlds, AI has begun to create vastly detailed renderings of dead stars, generate complex supporting characters with intricate story arcs, and even bring your favorite stars—whether Marlon Brando or Amy Winehouse—back to the big screen and into a built environment.

Continue reading “AI Is About to Completely Change the Face of Entertainment” »

A trio of physicists from the National Autonomous University of Mexico and Tec de Monterrey has solved a 2,000-year-old optical problem—the Wasserman-Wolf problem. In their paper published in the journal Applied Optics, Rafael González-Acuña, Héctor Chaparro-Romo, and Julio Gutiérrez-Vega outline the math involved in solving the puzzle, give some examples of possible applications, and describe the efficiency of the results when tested.

Over 2,000 years ago, Greek scientist Diocles recognized a problem with optical lenses—when looking through devices equipped with them, the edges appeared fuzzier than the center. In his writings, he proposed that the effect occurs because the lenses were spherical—light striking at an angle could not be focused because of differences in refraction. Isaac Newton was reportedly stumped in his efforts to solve the problem (which became known as spherical aberration), as was Gottfried Leibniz.

In 1949, Wasserman and Wolf devised an analytical means for describing the problem, and gave it an official name—the Wasserman-Wolf problem. They suggested that the best approach to solving the problem would be to use two aspheric adjacent surfaces to correct aberrations. Since that time, researchers and engineers have come up with a variety of ways to fix the problem in specific applications—most particularly cameras and telescopes. Most such efforts have involved creating aspherical lenses to counteract refraction problems. And while they have resulted in improvement, the solutions have generally been expensive and inadequate for some applications.

Technology that translates cortical activity into speech would be transformative for people unable to communicate as a result of neurological impairment. Decoding speech from neural activity is challenging because speaking requires extremely precise and dynamic control of multiple vocal tract articulators on the order of milliseconds. Here, we designed a neural decoder that explicitly leverages the continuous kinematic and sound representations encoded in cortical activity to generate fluent and intelligible speech. A recurrent neural network first decoded direct cortical recordings into vocal tract movement representations, and then transformed those representations to acoustic speech output. Modeling the articulatory dynamics of speech significantly enhanced performance with limited data. Naïve listeners were able to accurately identify and transcribe decoded sentences. Additionally, speech decoding was not only effective for audibly produced speech, but also when participants silently mimed speech. These results advance the development of speech neuroprosthetic technology to restore spoken communication in patients with disabling neurological disorders.

Robots are about to go underground — for a competition anyways.

The Defense Advanced Research Projects Agency (DARPA), the branch of the U.S. Department of Defense dedicated to developing new emerging technologies, is holding a challenge intended to develop technology for first responders and the military to map, navigate, and search underground. But the technology developed for the competition could also be used in future NASA missions to caves and lava tubes on other planets.

Continue reading “NASA Robots to Compete in Underground Challenge in Mining Tunnels” »

This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between AI, ML and DL (deep learning.)

Use of Machine Learning (ML) in Medicine is becoming more and more important. One application example can be Cancer Detection and Analysis.

KNIME Fall Summit 2019

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Continue reading “Who is Master Chief’s new AI in Halo Infinite?” »

Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules. Images: Connor Coley, Felice Frankel.

The system, described in the August 8 issue of Science, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT.

The technology “has the promise to help people cut out all the tedious parts of molecule building,” including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen.

NASA and the Space Center Houston are seeking designs for autonomous robots that can explore the surface of the moon—and the leading one will win up to $1 million to continue research and discovery.

On Monday, the organizations announced Phase 2 of the NASA Space Robotics Challenge, focused on virtually designing autonomous robotic operations that allow the US to expand its ability to explore space and maintain its technological leadership.

SEE: Artificial intelligence: A business leader’s guide (free PDF) (TechRepublic)