Summary: A newly developed AI tool can identify “deepfakes” of faces by examining the light reflection in the eyes of the images. The system is 94% accurate at detecting deepfakes.
Source: University ay Buffalo.
University at Buffalo computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes.
Technology paves way for intelligent solar cells, other highly efficient devices programmed at the macro and nano scale.
Researchers at Tufts University School of Engineering have created light-activated composite devices able to execute precise, visible movements and form complex three-dimensional shapes without the need for wires or other actuating materials or energy sources. The design combines programmable photonic crystals with an elastomeric composite that can be engineered at the macro and nano scale to respond to illumination.
Facebook has announced a new AI project called Learn From Video, which will use public Facebook videos to train its machine learning models. The company is vague about future applications, but it says such models could be used to improve captions, search functions, and much more.
P.e.a.c.e!nc. is proud to announce the conclusion as finalists in the $500k Pandemic Response Challenge sponsored by Cognizant with Landmark AI Experiment.
Space roboticist Vandi Verma, who operates the Perseverance—the most advanced astrobiology lab ever sent to another world—as it roams Mars looking for signs of ancient microbial life, said unconscious bias was also a factor in shaping aspirations. “Don’t make assumptions about what a child may be interested in because of their gender or race,” she said. “Don’t buy the Lego just for the boy.”
Digital clones are coming to an office building near you.
Hour One, the company behind photo-realistic digital clones, now has AI avatars capable of taking over receptionist roles in businesses.
AI And Robots For Law And Order — Irakli Beridze — Head, Artificial Intelligence and Robotics, UNICRI – United Nations Interregional Crime and Justice Research Institute.
Irakli Beridze is the Head of the Centre for Artificial Intelligence and Robotics at The United Nations Interregional Crime and Justice Research Institute (UNICRI).
Researchers at Tufts University School of Engineering have created light-activated composite devices able to execute precise, visible movements and form complex three-dimensional shapes without the need for wires or other actuating materials or energy sources. The design combines programmable photonic crystals with an elastomeric composite that can be engineered at the macro and nano scale to respond to illumination.
The research provides new avenues for the development of smart light-driven systems such as high-efficiency, self-aligning solar cells that automatically follow the sun’s direction and angle of light, light-actuated microfluidic valves or soft robots that move with light on demand. A “photonic sunflower,” whose petals curl towards and away from illumination and which tracks the path and angle of the light, demonstrates the technology in a paper that appears March 12th, 2021 in Nature Communications.
Color results from the absorption and reflection of light. Behind every flash of an iridescent butterfly wing or opal gemstone lie complex interactions in which natural photonic crystals embedded in the wing or stone absorb light of specific frequencies and reflect others. The angle at which the light meets the crystalline surface can affect which wavelengths are absorbed and the heat that is generated from that absorbed energy.
Most new achievements in artificial intelligence (AI) require very large neural networks. They consist of hundreds of millions of neurons arranged in several hundred layers, i.e. they have very ‘deep’ network structures. These large, deep neural networks consume a lot of energy in the computer. Those neural networks that are used in image classification (e.g. face and object recognition) are particularly energy-intensive, since they have to send very many numerical values from one neuron layer to the next with great accuracy in each time cycle.
Computer scientist Wolfgang Maass, together with his Ph.D. student Christoph Stöckl, has now found a design method for artificial neural networks that paves the way for energy-efficient high-performance AI hardware (e.g. chips for driver assistance systems, smartphones and other mobile devices). The two researchers from the Institute of Theoretical Computer Science at Graz University of Technology (TU Graz) have optimized artificial neuronal networks in computer simulations for image classification in such a way that the neurons —similar to neurons in the brain—only need to send out signals relatively rarely and those that they do are very simple. The proven classification accuracy of images with this design is nevertheless very close to the current state of the art of current image classification tools.
