Lifeboat Foundation

Isaac Asimov was one the world’s most celebrated and prolific science fiction writers, having written or edited more than 500 books over his four-decade career. The Russian-born writer was famous for penning hard science fiction in his books, such as that in I, Robot, Foundation and Nightfall. Naturally, his work contained many predictions about the future of society and technology.

We’re not living in space, but the Russian science-fiction author foresaw the rise of intelligent machines and the disruption of the digital age.

Continue reading “​In 1983, Isaac Asimov predicted the world of 2019. Here’s what he got right” »

AI researchers connect machine learning to Godel’s Incompleteness Theorem via a finding of unLearnability.

Simple artificial-intelligence problem puts researchers up against a logical paradox discovered by famed mathematician Kurt Gödel.

These dated interfaces are not equipped to handle today’s exponential rise in data, which has been ushered in by the rapid dematerialization of many physical products into computers and software.

Breakthroughs in perceptual and cognitive computing, especially machine learning algorithms, are enabling technology to process vast volumes of data, and in doing so, they are dramatically amplifying our brain’s abilities. Yet even with these powerful technologies that at times make us feel superhuman, the interfaces are still crippled with poor ergonomics.

Many interfaces are still designed around the concept that human interaction with technology is secondary, not instantaneous. This means that any time someone uses technology, they are inevitably multitasking, because they must simultaneously perform a task and operate the technology.

Continue reading “Making Superhumans Through Radical Inclusion and Cognitive Ergonomics” »

Check out the SPACe_C eMart, the ice cream truck-style food delivery service of the future that offers fresh food and educates consumers on farm-to-table eating.

Using steam to propel a spacecraft from asteroid to asteroid is now possible, thanks to a collaboration between a private space company and the University of Central Florida.

UCF planetary research scientist Phil Metzger worked with Honeybee Robotics of Pasadena, California, which developed the World Is Not Enough spacecraft prototype that extracts water from asteroids or other planetary bodies to generate steam and propel itself to its next mining target.

UCF provided the simulated asteroid material and Metzger did the computer modeling and simulation necessary before Honeybee created the prototype and tried out the idea in its facility Dec. 31. The team also partnered with Embry-Riddle Aeronautical University in Daytona Beach, Florida, to develop initial prototypes of steam-based rocket thrusters.

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Researchers make robots teach themselves how to walk through trial and error, like babies, to navigate the real world.

I find this subject very fascinating.

AI can fake their emotions but whether they can feel those emotions is another matter.

Researchers are testing a four-legged robot’s ability to find its way through the sewers under Zurich.

Researchers at the University of Waterloo, Canada, have recently developed a system for generating song lyrics that match the style of particular music artists. Their approach, outlined in a paper pre-published on arXiv, uses a variational autoencoder (VAE) with artist embeddings and a CNN classifier trained to predict artists from MEL spectrograms of their song clips.

“The motivation for this project came from my personal interest,” Olga Vechtomova, one of the researchers who carried out the study, told TechXplore. “Music is a passion of mine, and I was curious about whether a machine can generate lines that sound like the lyrics of my favourite music artists. While working on text generative models, my research group found that neural networks can generate some impressive lines of text. The natural next step for us was to explore whether a machine could learn the ‘essence’ of a specific music artist’s lyrical style, including choice of words, themes and sentence structure, to generate novel lyrics lines that sound like the artist in question.”

The system developed by Vechtomova and her colleagues is based on a neural network model called variational autoencoder (VAE), which can learn by reconstructing original lines of text. In their study, the researchers trained their model to generate any number of new, diverse and coherent lyric lines.

Continue reading “A system to generate new song lyrics that match the style of specific artists” »