Background: Generative artificial intelligence (AI) deployment in academic medical settings raises copyright compliance concerns. Dana-Farber Cancer Institute implemented GPT4DFCI, an internal generative AI tool utilizing OpenAI models, that is approved for enterprise use in research and operations. Given (1) the exceptionally broad adoption of the tool in our organization, (2) our research mission, and (3) the shared responsibility model required to benefit from Customer Copyright Commitment in Azure OpenAI Service products, we deemed rigorous copyright compliance testing necessary. Case Description: We conducted a structured red teaming exercise in Nov. 2024, with 42 participants from academic, industry, and government institutions. Four teams attempted to extract copyrighted content from GPT4DFCI across four domains: literary works, news articles, scientific publications, and access-restricted clinical notes. Teams successfully extracted verbatim book dedications and near-exact passages through various strategies. News article extraction failed despite jailbreak attempts. Scientific article reproduction yielded only high-level summaries. Clinical note testing revealed appropriate privacy safeguards. Discussion: The successful extraction of literary content indicates potential copyrighted material presence in training data, necessitating inference-time filtering. Differential success rates across content types suggest varying protective mechanisms. The event led to implementation of a copyright-specific meta-prompt in GPT4DFCI; this mitigation has been in production since Jan. 2025. Conclusion: Systematic red teaming revealed specific vulnerabilities in generative AI copyright compliance, leading to concrete mitigation strategies. Academic medical institutions deploying generative AI should implement continuous testing protocols to ensure legal and ethical compliance.

Relational Triple Extraction (RTE) is a fundamental task in Natural Language Processing (NLP). However, prior research has primarily focused on optimizing model performance, with limited efforts to understand the internal mechanisms driving these models. Many existing methods rely on complex preprocessing to induce specific interactions, often resulting in opaque systems that may not fully align with their theoretical foundations. To address these limitations, we propose SMARTe: a Slot-based Method for Accountable Relational Triple extraction. SMARTe introduces intrinsic interpretability through a slot attention mechanism and frames the task as a set prediction problem. Slot attention consolidates relevant information into distinct slots, ensuring all predictions can be explicitly traced to learned slot representations and the tokens contributing to each predicted relational triple. While emphasizing interpretability, SMARTe achieves performance comparable to state-of-the-art models. Evaluations on the NYT and WebNLG datasets demonstrate that adding interpretability does not compromise performance. Furthermore, we conducted qualitative assessments to showcase the explanations provided by SMARTe, using attention heatmaps that map to their respective tokens. We conclude with a discussion of our findings and propose directions for future research. Our code is available at https://github.com/Chen-XueWen/SMARTe.

-- In collaborative robotic applications, human and robot have to work together during a whole shift for executing a sequence of jobs. The performance of the human robot team can be enhanced by scheduling the right tasks to the human and the robot. The scheduling should consider the task execution constraints, the variability in the task execution by the human, and the job quality of the human. Therefore, it is necessary to dynamically schedule the assigned tasks. In this paper, we propose a two-layered architecture for task allocation and scheduling in a collaborative cell. Job quality is explicitly considered during the allocation of the tasks and over a sequence of jobs. The tasks are dynamically scheduled based on the real time monitoring of the human's activities. The effectiveness of the proposed architecture is experimentally validated. In recent years, industrial setting has been supported by a constant increase in the use of collaborative robotics (see e.g. The shift towards collaborative robotics can significantly change the quality of the job for the human. In fact, collaborative robots can take over dull, heavy or dangerous tasks making the life of the human easier.

We develop regularization methods to find flat minima while training deep neural networks. These minima generalize better than sharp minima, yielding models outperforming baselines on real-world test data (which may be distributed differently than the training data). Specifically, we propose a method of regularized optimization to reduce the spectral radius of the Hessian of the loss function. We also derive algorithms to efficiently optimize neural network models and prove that these algorithms almost surely converge. Furthermore, we demonstrate that our algorithm works effectively on applications in different domains, including healthcare. To show that our models generalize well, we introduced various methods for testing generalizability and found that our models outperform comparable baseline models on these tests.

With the rise of generative AI, industry interest in software agents is growing. Given the stochastic nature of generative AI-based agents, their effective and safe deployment in organizations requires robust governance, which can be facilitated by agentic business process management. However, given the nascence of this new-generation agent notion, it is not clear what BPM practitioners consider to be an agent, and what benefits, risks and governance challenges they associate with agent deployments. To investigate how organizations can effectively govern AI agents, we conducted a qualitative study involving semi-structured interviews with 22 BPM practitioners from diverse industries. They anticipate that agents will enhance efficiency, improve data quality, ensure better compliance, and boost scalability through automation, while also cautioning against risks such as bias, over-reliance, cybersecurity threats, job displacement, and ambiguous decision-making. To address these challenges, the study presents six key recommendations for the responsible adoption of AI agents: define clear business goals, set legal and ethical guardrails, establish human-agent collaboration, customize agent behavior, manage risks, and ensure safe integration with fallback options. Additionally, the paper outlines actions to align traditional BPM with agentic AI, including balancing human and agent roles, redefining human involvement, adapting process structures, and introducing performance metrics. These insights provide a practical foundation for integrating AI agents into business processes while preserving oversight, flexibility, and trust.

U.S. President Donald Trump said on Thursday that a phone call earlier in the day with Vladimir Putin resulted in no progress at all on efforts to end the war in Ukraine, while a Kremlin aide said the Russian president reiterated that Moscow would keep pushing to solve the conflict's "root causes." The two leaders did not discuss a recent pause in some U.S. weapons shipments to Kyiv during the nearly hourlong conversation, according to a readout provided by Putin aide Yuri Ushakov. U.S. attempts to end Russia's war in Ukraine through diplomacy have largely stalled, and Trump has faced growing calls -- including from some Republicans -- to increase pressure on Putin to negotiate in earnest. Within hours of the call's conclusion, an apparent Russian drone attack sparked a fire in an apartment building in a northern suburb of Kyiv, Ukrainian officials said, indicating little change in the trajectory of the conflict.

Elon Musk's artificial intelligence company xAI has been granted a permit to run methane gas generators at its massive datacenter in Memphis, Tennessee. The county health department approved the permit for the 15 machines late on Wednesday, a move that has sparked outcry from the local community and environmental leaders, who say the generators pollute their neighborhoods. "Our local leaders are entrusted with protecting us from corporations violating on our right to clean air, but we are witnessing their failure to do so," said KeShaun Pearson, the director of the local environmental non-profit Memphis Community Against Pollution. To supplement the facility's heavy power usage, the company brought in dozens of portable methane gas generators. In January, xAI did apply for a permit for 15 generators – even though it had been running up to 35 generators on-site, according to photographs.

Rep. Marjorie Taylor Greene, R-Ga., joins'Sunday Morning Futures' to discuss whether the government should regulate artificial intelligence, and how AI ties into President Donald Trump's spending bill. As large tech companies continue to take the lead implementing artificial intelligence (AI) into their platforms and workplaces, the latest Fox News national survey finds that while positive reviews of AI have increased, many remain skeptical about its role in society. The survey, released Thursday, finds 43% view AI technology as a good thing for society, up 5 points from April 2023. Still, nearly half of voters, 47%, think AI is bad for society -- about where it was two years ago (46% bad in April 2023). Overall, urban voters (60%), nonwhite voters (56%), voters under age 45 (53%), and men (52%) are those most likely to say AI is a good thing, while rural voters (55%), White voters (51%), voters ages 45 and over (49%), and women (55%) are likely to say it's a bad thing.

The head of the US government's Intelligence Advanced Research Projects Activity (IARPA) is leaving the unit this month to take a job with a quantum computing company, WIRED has learned. Rick Muller's pending departure from IARPA comes amid broader efforts to downsize the United States intelligence community, including the Office of the Director of National Intelligence (ODNI), which oversees IARPA. A person familiar with Muller's plans confirmed to WIRED his departure from IARPA. Born during the aftermath of the September 11, 2001 terrorist attacks, IARPA is tasked with testing AI, quantum computing, and other emerging technologies that could aid the missions of spy agencies including the Central Intelligence Agency and National Security Agency. The Trump administration reportedly has been moving to cut the workforces of intelligence agencies as part of the president's broad efforts to dismantle diversity programs and streamline government operations.

An Israeli drone attack has killed at least one person and injured three near the Lebanese capital, Beirut, the Lebanese Ministry of Public Health says, the latest violation of the ceasefire between the two countries. The air raid on Thursday hit a vehicle on a busy motorway in the Khaldeh area, about 12km (8 miles) south of Beirut. The Israeli military said it targeted "military sites and weapons depots" in the area. Bombing an area near the Lebanese capital marks another escalation by Israel, which has been carrying out near-daily bombardment in Lebanon since it reached a truce with Hezbollah in November of last year. The identities of the victims of the attack have not been released.