A multi-institution research team has developed an optical chip that can train machine learning hardware. Their research is published today in Optica. Machine learning applications have skyrocketed to $165 billion annually, according to a recent report from McKinsey. But before a machine can perform intelligence tasks such as recognizing the details of an image, it must be trained. Training of modern-day artificial intelligence (AI) systems like Tesla's autopilot costs several million dollars in electric power consumption and requires supercomputer-like infrastructure. This surging AI "appetite" leaves an ever-widening gap between computer hardware and demand for AI.

An optical chip has been developed by a multi-institution research group that has the potential to train machine learning hardware. A picture of the chip used for this work. On a yearly basis, machine learning applications skyrocketed to $165B, per a recent McKinsey report. Training for modern-day artificial intelligence (AI) systems similar to Tesla's autopilot costs millions of dollars in electrical power consumption and needs supercomputer-like infrastructure. An ever-widening gap between demand for AI and computer hardware is left by this surging AI "appetite." Photonic integrated circuits, or optical chips, have evolved as a possible solution to provide greater computing performance, as quantified by the operations executed per second per watt utilized (TOPS/W).

Researchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture startup Optelligence LLC, have developed an optical convolutional neural network accelerator capable of processing large amounts of information, on the order of petabytes, per second. Global demand for machine learning hardware is dramatically outpacing current computing power supplies. State-of-the-art electronic hardware, such as graphics processing units and tensor processing unit accelerators, help mitigate this, but are intrinsically challenged by serial data processing that requires iterative data processing and encounters delays from wiring and circuit constraints. Optical alternatives to electronic hardware could help speed up machine learning processes by simplifying the way information is processed in a non-iterative way. However, photonic-based machine learning is typically limited by the number of components that can be placed on photonic integrated circuits, limiting the interconnectivity, while free-space spatial-light-modulators are restricted to slow programming speeds.

The 70-foot-tall colossus, called a "Field Scanalyzer," is the world's biggest agricultural robot, the project's researchers say. Resembling an oversize scaffold with a box perched in its middle, it lumbers daily over 2 acres of crops including sorghum, lettuce and wheat, its cluster of electronic eyes assessing their temperature, shape and hue, the angle of each leaf. The Scanalyzer beams this data--up to 10 terabytes a day, roughly equivalent to about 2.6 million copies of Tolstoy's "War and Peace"--to computers in Illinois and Missouri. Analyzing the range and depth of data generated is possible only with machine-learning algorithms, according to data scientists at George Washington University and St. Louis University, where researchers are teaching the computers to identify connections between specific genes and plant traits the Scanalyzer observes. Deep learning, a form of AI that uses conclusions from data to further refine a system, can also help pinpoint how some varieties of a plant may subtly differ from one another in ways that plant scientists may not anticipate, researchers say.

Hawaii is ready for its midpandemic tourism boom. Starting on Aug. 1, tourists looking to visit Hawaii will be able to bypass the state's two-week quarantine requirement for arrivals by getting a negative COVID-19 test within 72 hours before landing in the state. Visitors can also have their quarantines cut short if they receive negative test results during those two weeks. The same rules will also apply to residents returning to the islands. Hawaii won't pay for the tests; travelers will have to handle that themselves before departure, though screeners will still administer temperature checks at airports.

A mushroom cloud explosion in the New Mexico desert on July 16, 1945 forever changed the nature of warfare. Science had given birth to weapons so powerful they could end humanity. To survive, the United States had to develop new strategies and policies that responsibly limited nuclear weapon proliferation and use. Warfare is again changing as modern militaries integrate autonomous and semiautonomous weapon systems into their arsenals. The United States must act swiftly to maximize the potential of these new technologies or risk losing its dominance.

Machines don't understand much of anything, especially not things such as ironic speech, but machine learning may be able to assist humanity in some way by counting the instances of linguistic and semantic constructions that indicate satire or misleading news, according to a new study by tech startup AdVerifai, in partnership with George Washington University and Amazon's AWS. A lot of nuances of writing are lost on the internet -- things such as irony. That's why satirical material such as the writing of Andy Borowitz on the website of The New Yorker magazine has to be labeled as satire, to make sure we know. Scientists in recent years have become concerned: What about writing that isn't properly understood, such as satire mistaken for the truth, or, conversely, deliberate disinformation campaigns that are disguised as innocent satire? And so began a quest to divine some form of machine learning technology that could automatically identify satire as such and distinguish it from deliberate lies.

Universities are embracing artificial intelligence solutions to assist in IT projects and academics. At George Washington University, after piloting its 24/7 chatbot service MARTHA, 89 percent of users advocated the tool be a permanent tool. "We've created a service broker that can handle decisions on where to go to look for information," Jonathan Fozard, assistant vice president for the CIO's office at George Washington University told EdTech. "As we educated it and users tested it, the Watson component was learning alongside of us. If someone types in a question about 3D printing, we know that's most likely a student who has access to 3D printers in the engineering classroom or a medical enterprise."

Texas A&M is playing a leading role in expanding the capabilities of automated vehicles and investigating how they can be safely used on rural roads. The Texas A&M Engineering Experiment Station (TEES) was recently awarded $7 million in federal grant funding from the Department of Transportation (DOT). In partnership with researchers from George Washington University and the University of California-Davis, A&M professors will be studying the specifics of how automated vehicles work on rural roadways -- something Alireza Talebpour, assistant professor in the Department of Civil and Environmental Engineering, said not much is currently known about. "Autonomous vehicle testing has pretty much only been done in urban centers," Talebpour said. "The technology is useless if it only works in big cities – the majority of roads in the United States are rural. We want to enable autonomous driving for people who don't live in big cities."

Suneel Grover is an Advisory Solutions Architect supporting digital intelligence, marketing analytics and multi-channel marketing at SAS. By providing client-facing services for SAS in the areas of predictive analytics, digital analytics, visualization and data-driven integrated marketing, Grover provides technical consulting support in industry verticals such as media, entertainment, hospitality, communications, financial services and sports. In addition to his role at SAS, Grover is an professorial lecturer at The George Washington University (GWU) in Washington DC, teaching in the Masters of Science in Business Analytics graduate program within the School of Business and Decision Science. Grover has a MBA in Marketing Research & Decision Science from The George Washington University (GWU), and a MS in Integrated Marketing Analytics from New York University (NYU).