Detecting negatives (such as non-entailment relationships, unanswerable questions, and false claims) is an important and challenging aspect of many natural language understanding tasks. Though manually collecting challenging negative examples can help models detect them, it is both costly and domain-specific. In this work, we propose Self-labeled Counterfactuals for Extrapolating to Negative Examples (SCENE), an automatic method for synthesizing training data that greatly improves models' ability to detect challenging negative examples. In contrast with standard data augmentation, which synthesizes new examples for existing labels, SCENE can synthesize negative examples zero-shot from only positive ones. Given a positive example, SCENE perturbs it with a mask infilling model, then determines whether the resulting example is negative based on a self-training heuristic. With access to only answerable training examples, SCENE can close 69.6% of the performance gap on SQuAD 2.0, a dataset where half of the evaluation examples are unanswerable, compared to a model trained on SQuAD 2.0. Our method also extends to boolean question answering and recognizing textual entailment, and improves generalization from SQuAD to ACE-whQA, an out-of-domain extractive QA benchmark.

U.S. intelligence agencies are grappling with a daunting new challenge: Making artificial intelligence safe for America's spies. An arm of the Office of the Director of National Intelligence is tapping companies and colleges to help harness rapidly developing AI technology that could provide an edge against global competitors like China. The challenge is ensuring it doesn't open a backdoor into the nation's top secrets or generate fake data. "The intelligence community wants to avail itself of the large-language models out there, but there are a lot of unknowns," said Tim McKinnon, a data scientist who manages one of the ODNI's projects, known as Bengal. "The end goal is being able to work with a model with trust."

When OpenAI's ChatGPT took the world by storm last year, it caught many power brokers in both Silicon Valley and Washington, DC, by surprise. The US government should now get advance warning of future AI breakthroughs involving large language models, the technology behind ChatGPT. The Biden administration is preparing to use the Defense Production Act to compel tech companies to inform the government when they train an AI model using a significant amount of computing power. The rule could take effect as soon as next week. The new requirement will give the US government access to key information about some of the most sensitive projects inside OpenAI, Google, Amazon, and other tech companies competing in AI.

This week, the National Science Foundation (NSF) announced it was launching a pilot program with 10 other federal agencies and 25 private sector and nonprofit organizations that could be a first step towards democratizing access to the expensive infrastructure required for cutting-edge AI research. The National Artificial Intelligence Research Resource (NAIRR) pilot aims to provide expensive computational horsepower, datasets, AI models, and other tools to academic AI researchers who otherwise often struggle to access the resources they increasingly need. Chipmaker Nvidia, one of the companies involved in the program, said that it would contribute 30 million worth of cloud computing resources and software to the pilot over two years, while Microsoft announced it would contribute 20 million of cloud computing credits in addition to other resources. OpenAI, Anthropic, and Meta, which are among the leading companies in the sector, are reportedly providing access to their AI models. The NAIRR pilot comes at a pivotal moment for AI research. As tech companies have plowed vast amounts of money into acquiring computational resources and datasets, and hiring skilled personnel, researchers in academia and the public sector have been left behind.

If there's one thing we can all agree upon, it's that the 21st century's captains of industry are trying to shoehorn AI into every corner of our world. But for all of the ways in which AI will be shoved into our faces and not prove very successful, it might actually have at least one useful purpose. Risk mitigation isn't a sexy notion but it's worth understanding how common it is for a new drug project to fail. To set the scene, consider that each drug project takes between three and five years to form a hypothesis strong enough to start tests in a laboratory. A 2022 study from Professor Duxin Sun found that 90 percent of clinical drug development fails, with each project costing more than 2 billion.

Ascento Guard is a two-wheeled, all-terrain robot equipped with cameras. Security guards are in high demand but low supply in the United States. The U.S. Bureau of Labor Statistics reports that the security guard occupation is expected to grow by 6.3% in the next decade, but many factors are discouraging people from pursuing this career. To address this challenge, a company has developed an autonomous patrol robot that can navigate any terrain and perform various security tasks. The robot, the Ascento Guard, is designed to offset the lack of security guards and provide a cost-effective and reliable solution for the security guard shortage.

The rapid online spread of deepfake pornographic images of Taylor Swift has renewed calls, including from US politicians, to criminalise the practice, in which artificial intelligence is used to synthesise fake but convincing explicit imagery. The images of the US popstar have been distributed across social media and seen by millions this week. Previously distributed on the app Telegram, one of the images of Swift hosted on X was seen 47m times before it was removed. X said in a statement: "Our teams are actively removing all identified images and taking appropriate actions against the accounts responsible for posting them." Yvette D Clarke, a Democratic congresswoman for New York, wrote on X: "What's happened to Taylor Swift is nothing new. For yrs, women have been targets of deepfakes [without] their consent. And [with] advancements in AI, creating deepfakes is easier & cheaper. This is an issue both sides of the aisle & even Swifties should be able to come together to solve."

U.S. antitrust enforcers are opening an investigation into the relationships between leading artificial intelligence startups such as ChatGPT-maker OpenAI and Anthropic and the tech giants that have invested billions of dollars into them. "We're scrutinizing whether these ties enable dominant firms to exert undue influence or gain privileged access in ways that could undermine fair competition," said Lina Khan, chair of the U.S. Federal Trade Commission, in opening remarks at a Thursday AI forum. Khan said the market inquiry would review "the investments and partnerships being formed between AI developers and major cloud service providers." The FTC said on Thursday that it has issued "compulsory orders" to five companies -- cloud providers Amazon, Google and Microsoft, and AI startups Anthropic and OpenAI -- requiring them to provide information regarding investments and partnerships. Microsoft's close and years-long relationship with OpenAI is the best known of the partnerships.

Last week, some voters in New Hampshire received an AI-generated robocall impersonating President Biden, telling them not to vote in the state's primary election. It's not clear who was responsible for the call, but two separate teams of audio experts tell WIRED it was likely created using technology from voice-cloning startup ElevenLabs. ElevenLabs markets its AI tools for uses like audiobooks and video games; it recently achieved "unicorn" status by raising 80 million at a 1.1 billion valuation in a new funding round co-led by venture firm Andreessen Horowitz. Anyone can sign up for the company's paid service and clone a voice from an audio sample. The company's safety policy says it is best to obtain someone's permission before cloning their voice, but that permissionless cloning can be OK for a variety of non-commercial purposes, including "political speech contributing to public debates." ElevenLabs did not respond to multiple requests for comment.

GM's Cruise self-driving car unit on Thursday revealed US Department of Justice and Securities and Exchange Commission probes stemming from an October collision in which one of its autonomous vehicles dragged a pedestrian who had been struck by another vehicle. Cruise reported the government investigations in a blog post in which the company also vowed to reform its culture stemming from a "failure of leadership" around the incident. The blog post did not disclose the status of the victim, who was dragged 20ft by the vehicle, nor the scope of the justice department and SEC probes. Cruise's four-page post cited "inadequate and uncoordinated internal processes, mistakes in judgment, an'us versus them' mentality with government officials, and a fundamental misunderstanding of regulatory requirements and expectations". More than 100 people knew details of the incident prior to Cruise's meetings with regulators, the report found.