OpenAI and Microsoft failed to escape a trial over Elon Musk's claims that Sam Altman's startup betrayed its founding mission as a public charity when it took billions in funding from the software giant and made plans to operate as a for-profit business. A federal judge in Oakland, California, on Thursday rejected requests by OpenAI and Microsoft to dismiss claims by Musk and ordered the case to proceed to a jury trial set for late April. Musk helped Altman and others launch OpenAI in 2015 and went on to found his own artificial intelligence company in 2023. Musk's lawsuit continues to be baseless and a part of his ongoing pattern of harassment, and we look forward to demonstrating this at trial," OpenAI said in a statement. "We remain focused on empowering the OpenAI Foundation, which is already one of the best resourced nonprofits ever."

DHS Secretary Kristi Noem announced Charles Wall as ICE's new deputy director, replacing Madison Sheahan who stepped down to pursue a congressional run in Ohio.

Japanese internal affairs minister Yoshimasa Hayashi (center) poses for a photo with ministers from ASEAN member states in Hanoi on Thursday. HANOI - Japan and the Association of Southeast Asian Nations have agreed to work together on developing new artificial intelligence models and preparing related laws. The AI-sector cooperation was included in a joint statement adopted at a meeting of digital ministers from Japan and ASEAN member states in Hanoi on Thursday. The statement was proposed by Japanese communications minister Yoshimasa Hayashi, who attended the meeting. Japan and ASEAN aim to join hands at a time when the United States and China are boosting their presence in the AI sector.

The Department of Homeland Security said agents arrested an illegal alien after he “weaponized" his car during a chaotic attempt to evade arrest in a San Antonio parking lot.

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Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' A Ukrainian drone attack killed two workers from a state-owned pharmacy as they were transporting medicines to Polohy in a Russian-occupied area of Ukraine's Zaporizhia region, Russia's state news agency TASS reports. A Ukrainian drone strike injured three people in Russia's front-line Belgorod region, the regional task force reported, according to TASS.

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Minneapolis faces HUD investigation over alleged racial preferences in housing policy amid a much broader Minnesota fraud scandal.

Thousands of abusive videos were produced last year--that researchers know of. In 2025, new data show, the volume of child pornography online was likely larger than at any other point in history. A record 312,030 reports of confirmed child pornography were investigated last year by the Internet Watch Foundation, a U.K.-based organization that works around the globe to identify and remove such material from the web. This is concerning in and of itself. It means that the overall volume of child porn detected on the internet grew by 7 percent since 2024, when the previous record had been set.

Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distribution induced by the observed data and a balanced target distribution under which performance is evaluated. Within this framework, we study a family of oversampling procedures that augment the training data by generating synthetic samples from an estimated minority-class distribution to roughly balance the classes, among which the celebrated SMOTE algorithm is a canonical example. We show that the excess risk decomposes into the rate achievable under balanced training (as if the data had been drawn from the balanced target distribution) and an additional term, the cost of transfer, which quantifies the discrepancy between the estimated and true minority-class distributions. In particular, we show that the cost of transfer for SMOTE dominates that of bootstrapping (random oversampling) in moderately high dimensions, suggesting that we should expect bootstrapping to have better performance than SMOTE in general. We corroborate these findings with experimental evidence. More broadly, our results provide guidance for choosing among augmentation strategies for imbalanced classification.