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.

Stargate, the massive artificial intelligence (AI) infrastructure project recently unveiled by President Donald Trump, has begun production in Texas -- with data center construction in other states expected to be announced in the coming months. OpenAI, Softbank, Oracle and other partners' total investment of 500 million in the project will produce a large-scale network of campuses. Each campus will be designed in the roughly 1 gigawatt (GW) or greater range, a measurement of electricity that can power a minimum of 750,000 homes. During a recent press briefing on The Stargate Project attended by Fox News Digital, OpenAI announced that construction on the first site is underway in Abilene, Texas. Significant progress has been made in identifying additional locations.

The Dating Apps Reporting Project is an 18-month investigation. It was produced in partnership with the Pulitzer Center's AI Accountability Network and the Markup, now a part of CalMatters, and co-published with the Guardian and the 19th. When a young woman in Denver met up with a smiling cardiologist she matched with on the dating app Hinge, she had no way of knowing that the company behind the app had already received reports from two other women who had accused him of rape. She met the 34-year-old doctor with green eyes and thinning hair at Highland Tap & Burger, a sports bar in a trendy neighborhood. It went well enough that she accepted an invitation to go back to his apartment. As she emerged from his bathroom, he handed her a tequila soda. What transpired over the next 24 hours, according to court testimony, reads like every person's dating app nightmare. After sipping the drink, the woman started to lose control. She fell to the ground, and the man started to film her. He put her in a headlock, kissing her forehead; she struggled to free herself but managed to grab her things and leave. He followed her out the door, holding her shoes and trying to force her back inside, but she was able to call an Uber, vomiting in the car on the way home. She woke up at home, soaking wet on her bathroom floor, the key to her house still in her door. She continued vomiting for hours.

Elon Musk called on Thursday for the United States to "delete entire agencies" from the federal government as part of his push under President Donald Trump to radically cut spending and restructure its priorities. Musk offered a wide-ranging survey via a videocall to the World Governments Summit in Dubai, United Arab Emirates, of what he described as the priorities of the Trump administration interspersed with multiple references to "thermonuclear warfare" and the possible dangers of artificial intelligence. "We really have here rule of the bureaucracy as opposed to rule of the people -- democracy," Musk said, wearing a black T-shirt that read: "Tech Support." He also joked that he was the "White House's tech support," borrowing from his profile on the social platform X, which he owns. "I think we do need to delete entire agencies as opposed to leave a lot of them behind," Musk said.

Our socioeconomic system is facing an existential threat from AI. In our capitalist society, most people depend on jobs to sustain themselves. The U.S. government, in turn, relies heavily on taxing the income of individual workers for revenue. As artificial intelligence progressively eliminates job opportunities, a growing number of individuals will face severe job insecurity, leading to a corresponding decline in federal revenue. Radical action is needed now to steer away from a dystopian collapse toward better possibilities.

At least one person was killed and four others, including a nine-year-old child, were injured by a Russian missile strike in Ukraine's capital, Kyiv, the city's mayor, Vitali Klitschko, said. The strike caused damage and fires in at least four areas of the city. Regional Governor Vyacheslav Gladkov said a woman was killed by a Ukrainian drone in Russia's Belgorod region. Gladkov said the drone struck the victim's car and killed her instantly. Ukraine's military said it shot down six out of seven ballistic missiles launched by Russia in an overnight attack.

Russia may be forced to throttle back its oil output in the coming months as U.S. sanctions hamper its access to tankers to sail to Asia and Ukrainian drone attacks hobble its refineries. The United States imposed sanctions last month that targeted 180 Russian tankers while Kyiv has stepped up drone attacks to improve its bargaining position amid expectations that U.S. President Donald Trump will press Russian leader Vladimir Putin to negotiate an end to the war in Ukraine. Trump has said stopping the conflict is a priority and that he could impose new sanctions on Russia if his goals are not achieved.

Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.

Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation task is the conditional $\textit{de novo}$ generation of molecular structure given a mass spectrum. Toward a more accurate and efficient scientific discovery pipeline for small molecules, we present DiffMS, a formula-restricted encoder-decoder generative network that achieves state-of-the-art performance on this task. The encoder utilizes a transformer architecture and models mass spectra domain knowledge such as peak formulae and neutral losses, and the decoder is a discrete graph diffusion model restricted by the heavy-atom composition of a known chemical formula. To develop a robust decoder that bridges latent embeddings and molecular structures, we pretrain the diffusion decoder with fingerprint-structure pairs, which are available in virtually infinite quantities, compared to structure-spectrum pairs that number in the tens of thousands. Extensive experiments on established benchmarks show that DiffMS outperforms existing models on $\textit{de novo}$ molecule generation. We provide several ablations to demonstrate the effectiveness of our diffusion and pretraining approaches and show consistent performance scaling with increasing pretraining dataset size. DiffMS code is publicly available at https://github.com/coleygroup/DiffMS.

Traditional ethical hacking relies on skilled professionals and time-intensive command management, which limits its scalability and efficiency. To address these challenges, we introduce PenTest++, an AI-augmented system that integrates automation with generative AI (GenAI) to optimise ethical hacking workflows. Developed in a controlled virtual environment, PenTest++ streamlines critical penetration testing tasks, including reconnaissance, scanning, enumeration, exploitation, and documentation, while maintaining a modular and adaptable design. The system balances automation with human oversight, ensuring informed decision-making at key stages, and offers significant benefits such as enhanced efficiency, scalability, and adaptability. However, it also raises ethical considerations, including privacy concerns and the risks of AI-generated inaccuracies (hallucinations). This research underscores the potential of AI-driven systems like PenTest++ to complement human expertise in cybersecurity by automating routine tasks, enabling professionals to focus on strategic decision-making. By incorporating robust ethical safeguards and promoting ongoing refinement, PenTest++ demonstrates how AI can be responsibly harnessed to address operational and ethical challenges in the evolving cybersecurity landscape.