Lifeboat Foundation

Researchers at Meta Reality Labs are reporting that their work on Codec Avatars 2.0 has reached a level where the avatars are approaching complete realism. The researchers created a prototype Virtual Reality headset that has a custom-built accelerator chip specifically designed to manage the AI processing capable of rendering Meta’s photorealistic Codec Avatars on standalone virtual reality headsets.

The prototype Virtual Reality avatars use very advanced machine learning techniques.

Continue reading “Meta Reality Labs Research: Codec Avatars 2.0 Approaching Complete Realism with Custom Chip” »

After nine years working at NASA Jet Propulsion Laboratory, Oliver Toupet is developing cutting-edge AI algorithms that enable the self-driving zoox vehicle to understand and make decisions based on its surroundings, and to optimize trajectories to reach its destination safely and comfortably.

Learn why he says the work he’s doing at Zoox is, in some ways, more challenging than his previous work.

Continue reading “The next frontier in robotics” »

As far as data security is concerned, there is an even greater danger than remote cyberattacks: namely tampering with hardware that can be used to read out information—such as credit card data from a card reader. Researchers in Bochum have developed a new method to detect such manipulations. They monitor the systems with radio waves that react to the slightest changes in the ambient conditions. Unlike conventional methods, they can thus protect entire systems, not just individual components—and they can do it at a lower cost. The RUB’s science magazine Rubin features a report by the team from Ruhr-Universität Bochum (RUB), the Max Planck Institute for Security and Privacy and the IT company PHYSEC.

Paul Staat and Johannes Tobisch presented their findings at the IEEE Symposium on Security and Privacy, which took place in the U.S. from 23 to 25 May 2022. Both researchers are doing their Ph.D.s at RUB and conducting research at the Max Planck Institute for Security and Privacy in Bochum in Professor Christof Paar’s team. For their research, they are cooperating with Dr. Christian Zenger from the RUB spin-off company PHYSEC.

Artificial intelligence (AI) plays an important role in many systems, from predictive text to medical diagnoses. Inspired by the human brain, many AI systems are implemented based on artificial neural networks, where electrical equivalents of biological neurons are interconnected, trained with a set of known data, such as images, and then used to recognize or classify new data points.

In traditional neural networks used for image recognition, the image of the target object is first formed on an image sensor, such as the digital camera in a smart phone. Then, the image sensor converts light into electrical signals, and ultimately into the binary data, which can then be processed, analyzed, stored and classified using computer chips. Speeding up these abilities is key to improving any number of applications, such as face recognition, automatically detecting text in photos, or helping self-driving cars recognize obstacles.

While current, consumer-grade image classification technology on a digital chip can perform billions of computations per second, making it fast enough for most applications, more sophisticated image classification such as identifying moving objects, 3D object identification, or classification of microscopic cells in the body, are pushing the computational limits of even the most powerful technology. The current speed limit of these technologies is set by the clock-based schedule of computation steps in a computer processor, where computations occur one after another on a linear schedule.

Electro-optic modulators, which control aspects of light in response to electrical signals, are essential for everything from sensing to metrology and telecommunications. Today, most research into these modulators is focused on applications that take place on chips or within fiber optic systems. But what about optical applications outside the wire and off the chip, like distance sensing in vehicles?

Current technologies to modulate light in free space are bulky, slow, static, or inefficient. Now, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with researchers at the department of Chemistry at the University of Washington, have developed a compact and tunable electro-optic modulator for free space applications that can modulate light at gigahertz speed.

“Our work is the first step toward a class of free-space electro-optic modulators that provide compact and efficient intensity modulation at gigahertz speed of free-space beams at telecom wavelengths,” said Federico Capasso, Robert L. Wallace Professor of Applied Physics and Vinton Hayes Senior Research Fellow in Electrical Engineering, senior author of the paper.

Researchers have pioneered a technique that can dramatically accelerate certain types of computer programs automatically, while ensuring program results remain accurate.

Their system boosts the speeds of programs that run in the Unix shell, a ubiquitous programming environment created 50 years ago that is still widely used today. Their method parallelizes these programs, which means that it splits program components into pieces that can be run simultaneously on multiple computer processors.

This enables programs to execute tasks like web indexing, natural language processing, or analyzing data in a fraction of their original runtime.

Regenerating legs is a first for frogs and now attention turns to whether the discovery holds clues to restoring human limbs.

This time, astronomers will see all the way to the Milky Way’s edge.

The upcoming release will add some previously unavailable information, including about the chemical compositions, ages and masses of millions of stars.

Related: 4 big Milky Way mysteries the next Gaia mission data dump may solve.

Schizophrenia is a disorder that affects how people act, think, and perceive reality. It is often very difficult to treat because it has many different causes and symptoms. In a study published last month in Cell Reports Medicine, researchers from Tokyo Medical and Dental University (TMDU) have identified an autoantibody—a protein that is produced by the immune system to attach to a specific substance from the individual’s own body, rather than to a foreign substance like a virus or bacteria—in some patients with schizophrenia. Notably, they also found that this autoantibody caused schizophrenia-like behaviors and changes in the brain when they injected it into mice.

When considering possible autoantibodies that might cause schizophrenia, the research team had a specific protein in mind. Previous research has suggested that neural cell adhesion molecule (NCAM1), which helps cells in the brain talk to one another via specialized connections known as synapses, may have a role in the development of schizophrenia.

“We decided to look for autoantibodies against NCAM1 in around 200 healthy controls and 200 patients with schizophrenia,” explains lead author of the study Hiroki Shiwaku. “We only found these autoantibodies in 12 patients, suggesting that they may be associated with the disorder in just a small subset of schizophrenia cases.”