Azalia King was the last holdout preventing the construction of a Micron megafab. Onondaga County authorities threatened to use eminent domain to take her home away by force. Azalia King moved into an upstate New York home surrounded by sprawling cattle pastures around 1965, about the time that mass production of the world's first microchips began. Now, 60 years later, the 91-year-old is on the verge of losing her home to make way for what could become the largest chipmaking complex in the US. Local authorities threatened to exercise their power of eminent domain, or taking land for public benefit, to forcibly uproot King and proceed with construction on a $100 billion campus where US tech giant Micron plans to make memory chips for use in a variety of electronics.

Alpha School co-founder Mackenzie Price and a junior at the school Elle Kristine join'Fox & Friends' to discuss the benefits of incorporating artificial intelligence into the classroom. At a time when many American students are struggling to keep up, a private school in Texas is doing more with less, much less. At Alpha School, students spend just two hours a day in class, guided by an Artificial Intelligence (AI) tutor. But results are impressive: students are testing in the top 1 to 2% nationally. "We use an AI tutor and adaptive apps to provide a completely personalized learning experience," said Alpha co-founder MacKenzie Price during an interview on Fox & Friends.

Democrats on the House Oversight Committee fired off two dozen requests Wednesday morning pressing federal agency leaders for information about plans to install AI software throughout federal agencies amid the ongoing cuts to the government's workforce. The barrage of inquiries follow recent reporting by WIRED and The Washington Post concerning efforts by Elon Musk's so-called Department of Government Efficiency (DOGE) to automate tasks with a variety of proprietary AI tools and access sensitive data. "The American people entrust the federal government with sensitive personal information related to their health, finances, and other biographical information on the basis that this information will not be disclosed or improperly used without their consent," the requests read, "including through the use of an unapproved and unaccountable third-party AI software." The requests, first obtained by WIRED, are signed by Gerald Connolly, a Democratic congressman from Virginia. The central purpose of the requests is to press the agencies into demonstrating that any potential use of AI is legal and that steps are being taken to safeguard Americans' private data.

President Donald Trump, winner of the Battle of the Billionaires at WrestleMania 23, has maintained close ties with Linda McMahon, the former C.E.O. of World Wrestling Entertainment, for decades. During the President's first term, she served for two years as head of the Small Business Administration, stepping down in 2019 to lead America First Action, a pro-Trump super PAC. Now McMahon is Trump's nominee to run the U.S. Department of Education, although she may appear to lack conventional bona fides for the position. If McMahon is confirmed by the Senate, her odd task will be to take charge of an agency in order to euthanize it. "I told Linda, 'Linda, I hope you do a great job and put yourself out of a job,' " Trump said, on February 4th.

We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources.

OpenAI is facing multiple lawsuits over its use of several publications' and books' content to train its large language models without explicit permission or proper compensation. A judge has just dismissed one of them. New York federal judge Colleen McMahon has dismissed the lawsuit filed by Raw Story and AlterNet, which accused the company of using their materials for AI training without consent. McMahon explained that the plaintiffs failed to show that they suffered "a cognizable injury" from those actions and that the harm they had cited was "not the type of harm that has been elevated" to warrant a lawsuit. The judge also said that "the likelihood that ChatGPT would output plagiarized content from one of [their] articles seems remote."

As the modern tool of choice for question answering, large language models (LLMs) are expected to deliver answers with up-to-date knowledge. To achieve such ideal question-answering systems, locating and then editing outdated knowledge in the natural language outputs is a general target of popular knowledge editing methods. However, this target is challenging, as both identifying which tokens to edit in the reasoning steps and ensuring the coherence of the revised reasoning chain are difficult tasks. We argue that these challenges stem from the unstructured nature of natural language outputs. To address the above challenges, we propose $\textbf{Stru}$ctural $\textbf{Edit}$ing ($\textbf{StruEdit}$), an improved baseline for knowledge editing. We first prompt LLMs to produce structured outputs consisting of reasoning triplets. Then, StruEdit removes any potentially outdated knowledge and efficiently refills the structured outputs with up-to-date information in a single step. Experimental results show that StruEdit consistently delivers the highest accuracy with lowest latency compared with other knowledge editing methods.

Inside a soundproofed crate sits one of the world's worst neural networks. After being presented with an image of the number 6, it pauses for a moment before identifying the digit: zero. Peter McMahon, the physicist-engineer at Cornell University who led the development of the network, defends it with a sheepish smile, pointing out that the handwritten number looks sloppy. Logan Wright, a postdoc visiting McMahon's lab from NTT Research, assures me that the device usually gets the answer right, but acknowledges that mistakes are common. "It's just this bad," he said.

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors--one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project. "The systems we performed our demonstrations with look nothing like each other, and they seem to [be] having nothing to do with handwritten-digit recognition or vowel classification, and yet you can train them to do it."

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. Cornell researchers have successfully trained (from left to right) a computer speaker, a simple electronic circuit and a laser to perform machine-learning computations. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors – one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project.