Lifeboat Foundation

A glimpse at the coming AI researchers. (AI’s that do research).

A new type of artificial-intelligence-driven chemistry could revolutionise the way molecules are discovered, scientists claim.

In a new paper published today in the journal Nature, chemists from the University of Glasgow discuss how they have trained an artificially-intelligent organic chemical synthesis robot to automatically explore a very large number of chemical reactions.

Continue reading “Robot chemist discovers new molecules and reactions” »

While scientists have been learning more and more about our solar system and the way things work, many of our Sun’s mechanics still remain a mystery. In advance of the launch of the Parker Solar Probe, which will make contact with the Sun’s outer atmosphere, however, scientists are foreshadowing what the spacecraft might see with new discoveries. In a paper published this week in The Astrophysical Journal, scientists detected structures within the Sun’s corona, thanks to advanced image processing techniques and algorithms.

The question that this group of scientists, led by Craig DeForest from the Southwest Research Institute’s branch in Boulder, Colorado, was trying to answer was in regard to the source of solar wind. “In deep space, the solar wind is turbulent and gusty,” said DeForest in a release. “But how did it get that way? Did it leave the Sun smooth, and become turbulent as it crossed the solar system, or are the gusts telling us about the Sun itself?”

Continue reading “Scientists discover structure within the Sun’s atmosphere” »

Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.

“Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved,” said research team leader Shanhui Fan of Stanford University. “This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can’t imagine now.”

An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.

Continue reading “Researchers move closer to completely optical artificial neural network” »

In 1610, Galileo redesigned the telescope and discovered Jupiter’s four largest moons. Nearly 400 years later, NASA’s Hubble Space Telescope used its powerful optics to look deep into space—enabling scientists to pin down the age of the universe.

Suffice it to say that getting a better look at things produces major scientific advances.

Continue reading “Team creates high-fidelity images of Sun’s atmosphere” »

Our Fast Lightweight Autonomy program recently completed Phase 2 flight tests, demonstrating advanced algorithms designed to turn small air and ground systems into team members that can autonomously perform tasks dangerous for humans — such as pre-mission reconnaissance in a hostile urban setting or searching damaged structures for survivors following an earthquake.

A new AI algorithm can create the most advanced deepfakes yet, complete with emotions and gestures, after just a few minutes of training.

WANT a job with a successful multinational? You will face lots of competition. Two years ago Goldman Sachs received a quarter of a million applications from students and graduates. Those are not just daunting odds for jobhunters; they are a practical problem for companies. If a team of five Goldman human-resources staff, working 12 hours every day, including weekends, spent five minutes on each application, they would take nearly a year to complete the task of sifting through the pile.

Little wonder that most large firms use a computer program, or algorithm, when it comes to screening candidates seeking junior jobs. And that means applicants would benefit from knowing exactly what the algorithms are looking for.

An international team of scientists from Eindhoven University of Technology, University of Texas at Austin, and University of Derby, has developed a revolutionary method that quadratically accelerates artificial intelligence (AI) training algorithms. This gives full AI capability to inexpensive computers, and would make it possible in one to two years for supercomputers to utilize Artificial Neural Networks that quadratically exceed the possibilities of today’s artificial neural networks. The scientists presented their method on June 19 in the journal Nature Communications.

Artificial Neural Networks (or ANN) are at the very heart of the AI revolution that is shaping every aspect of society and technology. But the ANNs that we have been able to handle so far are nowhere near solving very complex problems. The very latest supercomputers would struggle with a 16 million-neuron network (just about the size of a frog brain), while it would take over a dozen days for a powerful desktop computer to train a mere 100,000-neuron network.

Scientists at the California Institute of Technology can now assess a person’s intelligence in moments with nothing more than a brain scan and an AI algorithm, university officials announced this summer.

Caltech researchers led by Ralph Adolphs, PhD, a professor of psychology, neuroscience and biology and chair of the Caltech Brain Imaging Center, said in a recent study that they, alongside colleagues at Cedars-Sinai Medical Center and the University of Salerno, were successfully able to predict IQ in hundreds of patients from fMRI scans of resting-state brain activity. The work is pending publication in the journal Philosophical Transactions of the Royal Society.

Adolphs and his team collected data from nearly 900 men and women for their research, all of whom were part of the National Institutes of Health (NIH)-driven Human Connectome Project. The researchers trained their machine learning algorithm on the complexities of the human brain by feeding the brain scans and intelligence scores of these hundreds of patients into the algorithm—something that took very little effort on the patients’ end.

Continue reading “Caltech’s new machine learning algorithm predicts IQ from fMRI” »