A California mother says her teenage son used a chatbot for drug guidance over months before dying of an overdose, raising concerns about AI safety and safeguards.

Applications of artificial intelligence or machine learning in research Modes of use Surrogate modeling for physics - based models Modeling of poorly understood phenomena Data preprocessing Large language model use Applications AI/ML as research tool Production process design, monitoring, & output prediction Part design & properties prediction Materials design & properties prediction AI/ML as research product Generative AI design tool for consumers Generic research tasks Large language models for coding Large language models for literature review Benefits of artificial intelligence or machine learning in research Reduction in accuracy/cost/speed trade - off in research, especially computer modeling Reduced computation time Replacing experimentation Reducing need for computationally intensive, physics - based models Saving research labor Exploring larger design spaces Address of previously unsolvable problems Model poorly understood relationships between variables Identify human - unidentifiable patterns or phenomena Downsides of artificial intelligence or machine learning in research Accuracy weaknesses Predict poorly outside regions of dense, high - quality training data Interpretability weaknesses Bounds of accuracy can be unclear Accuracy assessment can be difficult Long - run scientific progress concerns AI/ML cannot develop novel scientific theory AI/ML may bypass opportunities to identify empirical or theoretical novelties Resource issues Data acquisition and cleaning is time - intensive AI/ML models are computation - and energy - intensive to develop Inappropriate use issues Easy to over - trust May be inappropriately used to address problems soluble with simpler methods 8 Second, AI/ML models can be trained on input and output data for phenomena (e.g., complex production processes) which lack robust theoretical models, developing novel predictive capabilities in the absence of explicit, human - designed theory. This is somet imes referred to as "phenomenological modeling," as it attempts to model phenomena in the absence of mechanistic, explanatory understanding: [T]he first reason we choose to use AI is because we don't have a good model of what our system is. . . I get a bunch of data coming in and I have a bunch of sensor readings, you know. . . And I use the AI to map the bunch of sensor readings to the process health or process status or machine status that I have.

We thank all three reviewers for their detailed and thoughtful reviews. "How about if the slopes differ?" Per your feedback, we ran new experiments where the slopes differ. "Do the players learn from previous experience?" We do not model the player's learning but plan to in future work.

This paper studies a new variant of the stochastic multi-armed bandits problem where auxiliary information about the arm rewards is available in the form of control variates. In many applications like queuing and wireless networks, the arm rewards are functions of some exogenous variables.

You Won't Be Able to Offload Your Holiday Shopping to AI Agents Anytime Soon Chatbot developers and retail giants are battling over user data as they lay the foundation for a future in which AI agents can do all your online shopping for you. Ask OpenAI's ChatGPT about a product on Etsy, and chances are you can enter your payment details and buy it without ever leaving the app. Instant Checkout was one of the first features to emerge from a recent wave of partnerships between leading AI and ecommerce companies. The aim is to encourage people to hand off parts of the browsing and ordering experience to AI tools and usher in an era of agentic shopping. But while these so-called agents have started to become more commonplace, they are far from taking over as full-time virtual buyers. OpenAI, Google, Amazon, and other AI chatbot developers are still negotiating with major retail partners on the best way to limit costly mistakes by agents and the amount of product data and chat history that have to be exchanged to make these agents successful, according to executives at seven tech and ecommerce companies who spoke with WIRED.

Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce How I look like this at 62. I've lost 5 stone fast, 20 years off my biological age and wear size 8... without weight-loss jabs. Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

We thank all three reviewers for their detailed and thoughtful reviews. "How about if the slopes differ?" Per your feedback, we ran new experiments where the slopes differ. "Do the players learn from previous experience?" We do not model the player's learning but plan to in future work.

This paper studies a new variant of the stochastic multi-armed bandits problem where auxiliary information about the arm rewards is available in the form of control variates. In many applications like queuing and wireless networks, the arm rewards are functions of some exogenous variables.

Knowledge driven discovery of novel materials necessitates the development of the causal models for the property emergence. While in classical physical paradigm the causal relationships are deduced based on the physical principles or via experiment, rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of materials structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs). By fine-tuning ChatGPT on domain-specific literature, such as arXiv papers on ferroelectrics, and combining obtained information with data-driven causal discovery, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO). This approach enables us to hypothesize how synthesis conditions influence material properties, particularly the coercive field (E0), and guides experimental validation. The ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

President Donald Trump signed an executive order on artificial intelligence Thursday that will revoke past government policies his order says "act as barriers to American AI innovation." To maintain global leadership in AI technology, "we must develop AI systems that are free from ideological bias or engineered social agendas," Trump's order says. The new order doesn't name which existing policies are hindering AI development but sets out to track down and review "all policies, directives, regulations, orders, and other actions taken" as a result of former President Joe Biden's sweeping AI executive order of 2023, which Trump rescinded Monday. Any of those Biden-era actions must be suspended if they don't fit Trump's new directive that AI should "promote human flourishing, economic competitiveness, and national security." Last year, the Biden administration issued a policy directive that said U.S. federal agencies must show their artificial intelligence tools aren't harming the public, or stop using them. Trump's order directs the White House to revise and reissue those directives, which affect how agencies acquire AI tools and use them.