Recent significant advances in integrating multiple Large Language Model (LLM) systems have enabled Agentic Frameworks capable of performing complex tasks autonomously, including novel scientific research. We develop and demonstrate such a framework specifically for the inverse design of photonic metamaterials. When queried with a desired optical spectrum, the Agent autonomously proposes and develops a forward deep learning model, accesses external tools via APIs for tasks like simulation and optimization, utilizes memory, and generates a final design via a deep inverse method. The framework's effectiveness is demonstrated in its ability to automate, reason, plan, and adapt. Notably, the Agentic Framework possesses internal reflection and decision flexibility, permitting highly varied and potentially novel outputs.

For a moment, I fell under the spell of Bryan Johnson. Bathed in early-morning sunlight, the 46-year-old L.A.-based tech centimillionaire and longevity celebrity didn't look much younger than his age, although he claims to have the wrinkles of a 10-year-old and organs that are several years younger than his lifespan. We were standing at the Temescal Canyon trailhead in Pacific Palisades on Jan. 13, ahead of a Johnson-sponsored "Don't Die" hike, one of many organized across the world that day and the only one hosted by him. Of the 500-plus people who had RSVP'd for the L.A. event, about 200 showed up. Some had slept in their cars to make it.

OpenAI CEO Sam Altman said language and cultural inclusivity is'very important' to his company's mission as it builds and trains powerful artificial intelligence systems. OpenAI CEO Sam Altman said language and cultural inclusivity are "very important" to his company's mission as it builds and trains powerful artificial intelligence systems. "We think this is really important," Altman told California Democratic Sen. Alex Padilla of language inclusivity in AI. "One example is that we worked with the government of Iceland, which is a language of fewer speakers than many of the languages that are well represented on the internet, to ensure that their language was included in our model," Altman said. The Senate Judiciary Subcommittee on Privacy, Technology and the Law held a hearing Tuesday during which Altman, IBM Chief Privacy & Trust Officer Christina Montgomery and New York University professor emeritus Gary Marcus delivered testimony on how best to regulate powerful artificial intelligence systems. Sam Altman, CEO and co-founder of OpenAI, speaks during a Senate Judiciary subcommittee hearing in Washington, D.C., Tuesday, May 16, 2023.

A judge in Colombia has caused a stir by admitting he used the artificial intelligence tool ChatGPT when deciding whether an autistic child's insurance should cover all of the costs of his medical treatment. He also used precedent from previous rulings to support his decision. Juan Manuel Padilla, a judge in the Caribbean city of Cartagena, concluded that the entirety child's medical expenses and transport costs should be paid by his medical plan as his parents could not afford them. While the judgment itself did not cause much fuss, the inclusion of Padilla's conversations with ChatGPT in the ruling has been more contentious. Among Padilla's inquiries with the chatbot, the legal documents show Padilla asked ChatGPT the precise legal matter at hand: "Is an autistic minor exonerated from paying fees for their therapies?"

We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natural system that produce a given set of observed measurements. Recent work has shown impressive results using deep learning, but we note that there is a trade-off between model performance and computational time. For some applications, the computational time at inference for the best performing inverse modeling method may be overly prohibitive to its use. We present a new method that leverages multiple manifolds as a mixture of backward (e.g., inverse) models in a forward-backward model architecture. These multiple backwards models all share a common forward model, and their training is mitigated by generating training examples from the forward model. The proposed method thus has two innovations: 1) the multiple Manifold Mixture Network (MMN) architecture, and 2) the training procedure involving augmenting backward model training data using the forward model. We demonstrate the advantages of our method by comparing to several baselines on four benchmark inverse problems, and we furthermore provide analysis to motivate its design.

Duke University researchers have discovered that machine learning algorithms can gain new degrees of transparency and insight into the properties of materials after teaching them known physics. According to researchers at Duke University, incorporating known physics into machine learning algorithms can help the enigmatic black boxes attain new levels of transparency and insight into the characteristics of materials. Researchers used a sophisticated machine learning algorithm in one of the first efforts of its type to identify the characteristics of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields. The algorithm was essentially forced to show its work since it first had to take into account the known physical restrictions of the metamaterial. The method not only enabled the algorithm to predict the properties of the metamaterial with high accuracy, but it also did it more quickly and with additional insights than earlier approaches.

When her phone rang in February, Mashawn Cross was skeptical of the gentle voice offering help at the end of the line. "You said you do what? And you're with who?" the 52-year-old recalled saying. Cross, who wasn't working because of her ailing back and knees, was scraping by on roughly $200 a month in aid plus whatever she could make from recycling bottles and cans. Her gas and electric bills were chewing up her checks.

IBM called for rules aimed at eliminating bias in artificial intelligence to ease concerns that the technology relies on data that bakes in past discriminatory practices and could harm women, minorities, the disabled, older Americans and others. As it seeks to define a growing debate in the U.S. and Europe over how to regulate the burgeoning industry, IBM urged industry and governments to jointly develop standards to measure and combat potential discrimination. The Armonk, New York-based company issued policy proposals Tuesday ahead of a Wednesday panel on AI to be led by Chief Executive Officer Ginni Rometty on the sidelines of the World Economic Forum in Davos. The initiative is designed to find a consensus on rules that may be stricter than what industry alone might produce, but that are less stringent than what governments might impose on their own. "It seems pretty clear to us that government regulation of artificial intelligence is the next frontier in tech policy regulation," said Chris Padilla, vice president of government and regulatory affairs at International Business Machines Corp. The 108-year-old company, once a world technology leader, has lagged behind the sector for years.

Sign up here to receive the Davos Diary, a special daily newsletter that will run from Jan. 20-24. IBM called for rules aimed at eliminating bias in artificial intelligence to ease concerns that the technology relies on data that bakes in past discriminatory practices and could harm women, minorities, the disabled, older Americans and others. As it seeks to define a growing debate in the U.S. and Europe over how to regulate the burgeoning industry, IBM urged industry and governments to jointly develop standards to measure and combat potential discrimination. The Armonk, New York-based company issued policy proposals Tuesday ahead of a Wednesday panel on AI to be led by Chief Executive Officer Ginni Rometty on the sidelines of the World Economic Forum in Davos. The initiative is designed to find a consensus on rules that may be stricter than what industry alone might produce, but that are less stringent than what governments might impose on their own. "It seems pretty clear to us that government regulation of artificial intelligence is the next frontier in tech policy regulation," said Chris Padilla, vice president of government and regulatory affairs at International Business Machines Corp.

When Waymo, the autonomous car company once known as Google's self-driving car outfit, announced it was suing Uber for trade secret theft in February, the action seemed to center on a single person: Anthony Levandowski. According to Waymo, the former Google engineer downloaded 14,000 secret documents from its system and used the contents to launch his own self-driving truck startup, Otto, in January 2016. By August, Uber had acquired Otto for an alleged $680 million, and Waymo says the ridehailing giant was in on the theft from the start. Well forget Levandowski and say hello to the litigation's newest and most unlikely star: former Uber intelligence employee Richard Jacobs. Last weekend, the US Attorney's Office pulled the very unusual move of forwarding a piece of evidence to Judge William Alsup, who is overseeing the lawsuit in the Northern District Court of California.