Lifeboat Foundation

PARIS, Dec. 23, 2021 – LightOn announces the integration of one of its photonic co-processors in the Jean Zay supercomputer, one of the Top500 most powerful computers in the world. Under a pilot program with GENCI and IDRIS, the insertion of a cutting-edge analog photonic accelerator into High Performance Computers (HPC) represents a technological breakthrough and a world-premiere. The LightOn photonic co-processor will be available to selected users of the Jean Zay research community over the next few months.

LightOn’s Optical Processing Unit (OPU) uses photonics to speed up randomized algorithms at a very large scale while working in tandem with standard silicon CPU and NVIDIA latest A100 GPU technology. The technology aims to reduce the overall computing time and power consumption in an area that is deemed “essential to the future of computational science and AI for Science” according to a 2021 U.S. Department of Energy report on “Randomized Algorithms for Scientific Computing.”

INRIA (France’s Institute for Research in Computer Science and Automation) researcher Dr. Antoine Liutkus provided additional context to the integration of LightOn’s coprocessor in the Jean Zay supercomputer: “Our research is focused today on the question of large-scale learning. Integrating an OPU in one of the most powerful nodes of Jean Zay will give us the keys to carry out this research, and will allow us to go beyond a simple ” proof of concept.”

KEAR (Knowledgeable External Attention for commonsense Reasoning) —along with recent milestones in computer vision and neural text-to-speech —is part of a larger Azure AI mission to provide relevant, meaningful AI solutions and services that work better for people because they better capture how people learn and work—with improved vision, knowledge understanding, and speech capabilities. At the center of these efforts is XYZ-code, a joint representation of three cognitive attributes: monolingual text (X), audio or visual sensory signals (Y), and multilingual (Z). For more information about these efforts, read the XYZ-code blog post.

Last month, our Azure Cognitive Services team, comprising researchers and engineers with expertise in AI, achieved a groundbreaking milestone by advancing commonsense language understanding. When given a question that requires drawing on prior knowledge and five answer choices, our latest model— KEAR, Knowledgeable External Attention for commonsense Reasoning —performs better than people answering the same question, calculated as the majority vote among five individuals. KEAR reaches an accuracy of 89.4 percent on the CommonsenseQA leaderboard compared with 88.9 percent human accuracy. While the CommonsenseQA benchmark is in English, we follow a similar technique for multilingual commonsense reasoning and topped the X-CSR leaderboard.

Continue reading “Azure AI milestone: Microsoft KEAR surpasses human performance on CommonsenseQA benchmark” »

Scientists and institutions dedicate more resources each year to the discovery of novel materials to fuel the world. As natural resources diminish and the demand for higher value and advanced performance products grows, researchers have increasingly looked to nanomaterials.

Nanoparticles have already found their way into applications ranging from energy storage and conversion to quantum computing and therapeutics. But given the vast compositional and structural tunability nanochemistry enables, serial experimental approaches to identify new materials impose insurmountable limits on discovery.

Now, researchers at Northwestern University and the Toyota Research Institute (TRI) have successfully applied machine learning to guide the synthesis of new nanomaterials, eliminating barriers associated with materials discovery. The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical and automotive industries.

DeepMind’s model, with just 7 billion parameters, outperformed the 178 billion-parameter Jurassic-1 transformer on various language tasks.

With the spread of the omicron variant, not everyone can or is eager to travel for the winter break. But what if virtual touch could bring you assurance that you were not alone?

At the USC Viterbi School of Engineering, computer scientist and roboticist Heather Culbertson has been exploring various methods to simulate touch. As part of a new study, Culbertson a senior author on this study, along with researchers at Stanford, her alma mater, wanted to see if two companions (platonic or romantic), could communicate and express care and emotion remotely. People perceive a partner’s true intentions through in-person touch an estimated 57 percent of the time. When interacting with a device that simulated human touch, respondents were able to discern the touch’s intention 45 percent of the time. Thus, devices in this study appear to perform with approximately 79 percent accuracy of perceived human touch.

Our sense of touch is unique. In fact, people have a “touch language” says Culbertson, the WiSE Gabilan Assistant Professor and Assistant Professor of Computer Science and Aerospace and Mechanical Engineering at USC. Thus, she says, creating virtual touch that people can direct towards their loved ones is quite complex—not only do we have differences in our comfort with social touch and levels of “touchiness” but we also may have a distinct way of communicating different emotions such sympathy, love or sadness. The challenge for the researchers was to create an algorithm that can be flexible enough to incorporate the many dimensions of touch.

Lightelligence, a Boston-based photonics company, revealed the world’s first small form-factor, photonics-based computing device, meaning it uses light to perform compute operations. The company claims the unit is “hundreds of times faster than a typical computing unit, such as NVIDIA RTX 3080.” 350 times faster, to be exact, but that only applies to certain types of applications.

However, the PACE achieves that coveted specialization through an added field of computing — which not only makes the system faster, it makes it incredibly more efficient. While traditional semiconductor systems have the issue of excess heat that results from running current through nanometre-level features at sometimes ludicrous frequencies, the photonic system processes its workloads with zero Ohmic heating — there’s no heat produced from current resistance. Instead, it’s all about light.

Continue reading “Optical Chip Promises 350x Speedup Over RTX 3080 in Some Algorithms” »

DARPA-funded research is focused on “biobots” with potential environmental and health applications.

WASHINGTON – The National Geospatial-Intelligence Agency has selected a team of commercial and academic partners to build an artificial intelligence system with synthetic data, which will further help the agency determine how it builds machine learning algorithms moving forward.

Orbital Insight was issued a Phase II Small Business Innovation Research contract by the NGA, the company announced. Dec. 16. It will collaborate with Rendered.ai and the University of California, Berkeley, to develop a computer vision model.

As the organization charged with analyzing satellite imagery for the intelligence community, NGA has put increased emphasis on using AI for its mission. The agency sees human-machine pairing as critical for its success, with machine learning algorithms taking over the rote task of processing the torrent of satellite data to find potential intelligence and freeing up human operators to do more high level analysis and tasks.

For all that neural networks can accomplish, we still don’t really understand how they operate. Sure, we can program them to learn, but making sense of a machine’s decision-making process remains much like a fancy puzzle with a dizzying, complex pattern where plenty of integral pieces have yet to be fitted.

If a model was trying to classify an image of said puzzle, for example, it could encounter well-known, but annoying adversarial attacks, or even more run-of-the-mill data or processing issues. But a new, more subtle type of failure recently identified by MIT scientists is another cause for concern: “overinterpretation,” where algorithms make confident predictions based on details that don’t make sense to humans, like random patterns or image borders.

This could be particularly worrisome for high-stakes environments, like split-second decisions for self-driving cars, and medical diagnostics for diseases that need more immediate attention. Autonomous vehicles in particular rely heavily on systems that can accurately understand surroundings and then make quick, safe decisions. The network used specific backgrounds, edges, or particular patterns of the sky to classify traffic lights and street signs—irrespective of what else was in the image.