A story of how machines learn to think through language.
Category: robotics/AI – Page 64
Energy-efficient AI module for wearables, medical devices, and activity recognition.
Ambient Scientific has unveiled its new AI module, the Sparsh board, which operates on a coin cell battery, making it suitable for a wide array of on-device AI applications.
The module aims to offer solutions for tasks such as human activity recognition, voice control, and acoustic event detection.
This innovation is notable for its ability to function continuously for months without frequent battery replacements.
This week’s featured image from the Hubble Space Telescope showcases the spiral galaxy NGC 337, located approximately 60 million light-years away in the constellation Cetus, also known as The Whale.
The stunning image merges observations captured in two different wavelengths, revealing the galaxy’s striking features. Its golden-hued center glows with the light of older stars, while its vibrant blue edges shimmer with the energy of young, newly formed stars. Had Hubble captured NGC 337 about a decade ago, it would have witnessed an extraordinary sight among the galaxy’s hot blue stars — a dazzling supernova illuminating its outskirts.
Named SN 2014cx, the supernova is remarkable for having been discovered nearly simultaneously in two vastly different ways: by a prolific supernova hunter, Koichi Itagaki, and by the All Sky Automated Survey for SuperNovae (ASAS-SN). ASAS-SN is a worldwide network of robotic telescopes that scans the sky for sudden events like supernovae.
OKLAHOMA CITY (AP) — A body camera captured every word and bark uttered as police Sgt. Matt Gilmore and his K-9 dog, Gunner, searched for a group of suspects for nearly an hour.
Normally, the Oklahoma City police sergeant would grab his laptop and spend another 30 to 45 minutes writing up a report about the search. But this time he had artificial intelligence write the first draft.
Pulling from all the sounds and radio chatter picked up by the microphone attached to Gilmore’s body camera, the AI tool churned out a report in eight seconds.
As energy-hungry computer data centers and artificial intelligence programs place ever greater demands on the U.S. power grid, tech companies are looking to a technology that just a few years ago appeared ready to be phased out: nuclear energy.
After several decades in which investment in new nuclear facilities in the U.S. had slowed to a crawl, tech giants Microsoft and Google have recently announced investments in the technology, aimed at securing a reliable source of emissions-free power for years into the future.
Earlier this year, online retailer Amazon, which has an expansive cloud computing business, announced it had reached an agreement to purchase a nuclear energy-fueled data center in Pennsylvania and that it had plans to buy more in the future.
Amazon’s plan, by contrast, does not require either new technology or the resurrection of an older nuclear facility.
Here’s one definition of science: it’s essentially an iterative process of building models with ever-greater explanatory power.
A model is just an approximation or simplification of how we think the world works. In the past, these models could be very simple, as simple in fact as a mathematical formula. But over time, they have evolved and scientists have built increasingly sophisticated simulations of the world as new data has become available.
A computer model of the Earth’s climate can show us temperatures will rise as we continue to release greenhouse gases into the atmosphere. Models can also predict how infectious disease will spread in a population, for example.
Anyone who has dealt with ants in the kitchen knows that ants are highly social creatures; it’s rare to see one alone. Humans are social creatures too, even if some of us enjoy solitude. Ants and humans are also the only creatures in nature that consistently cooperate while transporting large loads that greatly exceed their own dimensions.
Prof. Ofer Feinerman and his team at the Weizmann Institute of Science have used this shared trait to conduct a fascinating evolutionary competition that asks the question: Who will be better at maneuvering a large load through a maze? The surprising results, published in the Proceedings of the National Academy of Sciences, shed new light on group decision making, as well as on the pros and cons of cooperation versus going it alone.
To enable a comparison between two such disparate species, the research team led by Tabea Dreyer created a real-life version of the “piano movers puzzle,” a classical computational problem from the fields of motion planning and robotics that deals with possible ways of moving an unusually shaped object—say, a piano—from point A to point B in a complex environment.
Many techniques in computational materials science require scientists to identify the right set of parameters that capture the physics of the specific material they are studying. Calculating these parameters from scratch is sometimes possible but costs a lot of time and computational power. Consequently, scientists are always eager to find more efficient ways to estimate them without doing the full calculation.
This is the case for Koopmans functionals, a promising approach to expand the power of density-functional theory so that it can be used to predict the spectral properties of materials (such as what frequencies of light a material absorbs), and not just their ground state (such as the optimal positions of the atoms in that material). The accuracy of Koopmans functionals relies on finding the right “screening parameters” for the system one is studying.
“You can interpret the screening parameters as the degree to which the rest of the electrons in a system react to the addition or removal of an electron,” explains Edward Linscott, a postdoc at the Center for Scientific Computing, Theory and Data of the Paul Scherrer Institute, and member of MARVEL.
Scientists know biological neurons are more complex than the artificial neurons employed in deep learning algorithms, but it’s an open question just how much more complex.
In a fascinating paper published recently in the journal Neuron, a team of researchers from the Hebrew University of Jerusalem tried to get us a little closer to an answer. While they expected the results would show biological neurons are more complex—they were surprised at just how much more complex they actually are.
In the study, the team found it took a five-to eight-layer neural network, or nearly 1,000 artificial neurons, to mimic the behavior of a single biological neuron from the brain’s cortex.
Showing how far AI engineering has come, a new aerospike engine burning oxygen and kerosene capable of 1,100 lb (5,000 N) of thrust has successfully been hot-fired for 11 seconds. It was designed from front to back using an advanced Large Computational Engineering Model.
Designing and developing advanced aerospace engines is generally a complicated affair taking years of modeling, testing, revision, prototyping, rinsing and repeating. With their ability to discern patterns, carry out complex analysis, create virtual prototypes, and run models thousands of times, engineering AIs are altering the aerospace industry in some surprising ways – provided, of course, they are properly programmed and trained.
Otherwise, it’s garbage in, garbage out, which has been the Golden Rule of computers since they ran on radio valves and electromechanical relays.