READY AND WILLING: Sam Altman, CEO of OpenAI, the creator of ChatGPT, on Sunday said he is looking forward to working with the incoming Trump administration, adding that he thinks President-elect Trump will succeed at helping to make America a world-leading force in artificial intelligence (AI) infrastructure. 'NEW CHAPTER': Louisiana Gov. Jeff Landry praised Meta's plans to build a new artificial intelligence data center in the Pelican State, calling it the "largest private capital announcement." PRESS FOR FAIRNESS: LA Times owner Dr. Patrick Soon-Shiong announced the upcoming AI feature on Wednesday in an interview with conservative commentator and newly appointed Times editorial board member Scott Jennings on "The Mike Gallagher Show," which Jennings was guest-hosting. Los Angeles Times owner Dr. Patrick Soon-Shiong explains what direction he wants to take the paper. 'NOT THAT WORRIED': Elon Musk's possible political influence under the incoming Trump administration is not a concern for OpenAI CEO Sam Altman, who dismissed claims that the X owner would use lawfare to stifle competition.

A controversial AI program used to deny elderly people health coverage is now at the center of questions about the shooting of the UnitedHealthcare CEO. Brian Thompson, 50 was gunned down Wednesday outside a Hilton in Midtown Manhattan in what police have described as a'brazen' and'targeted' attack. The killer is still on the loose and the motive is not yet known - but a former-FBI agent told Newsweek that he may have been denied health coverage. UnitedHealthcare became the largest denier of insurance plans in 2023, dismissing one in every three claims. It has now emerged that during the years before that, the company implemented AI software that had a 90 percent denial rate.

Elon Musk appears to have many obsessions. The world's richest man is evangelical about electric vehicles, space travel and Donald Trump. Another of his interests may yet have profound consequences for the UK: British politics. The billionaire is reported to be thinking of becoming the biggest donor in history with a rumoured 80m payment to Nigel's Farage's Reform UK party. Like so many who embraced Trump's bellicose brand of rightwing populism, Musk was radicalised by his frustration at lockdowns, according to Musk watchers.

Fox News correspondent Nate Foy breaks down what witnesses are saying about the drones flying over New Jersey on'Your World.' Officials in New Jersey say they're taking mystery drone sightings, now reported in 10 counties across the state, "seriously," with the suspicious aircraft recently confirmed to have been spotted near the state's largest reservoir. The reason for the drones' presence near the Round Valley Reservoir in Hunterdon County, near the Garden State's border with Pennsylvania, is unclear, according to NJ.com. Similarly unclear are any potential connections to other drones spotted in the recent onslaught of suspicious activity that's taken the state by storm, the outlet continues. The drone sighting near the reservoir wasn't the only recent one in Hunterdon County – another was reported near its 911 Center in Flemington. "There have been reports of single drones hovering over people's houses for hours at a time," Hunterdon County Commissioner John Lanza noted at a Tuesday board meeting.

This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft. This modification of the commanded reference trajectory ensures that constraints are enforced while the time shift is reduced to zero to effect the rendezvous. Our approach to TSG implementation integrates an LSTM neural network which approximates the time shift parameter as a function of a sequence of past Deputy and Chief spacecraft states. This LSTM neural network is trained offline from simulation data. We report simulation results for RD missions in the Low Earth Orbit (LEO) and on the Molniya orbit to demonstrate the effectiveness of the proposed control scheme. The proposed scheme reduces the time to compute the time shift parameter in most of the scenarios and successfully completes rendezvous missions.

Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by uncovering unknown physics. This study presents a novel unsupervised learning framework that identifies spatial subdomains with specific governing physics. It uses the partition of unity networks (POUs) to divide the space into subdomains, assigning unique nonlinear model parameters to each, which are integrated into the physics model. A vital feature of this method is a physics residual-based loss function that detects variations in physical properties without requiring labeled data. This approach enables the discovery of spatial decompositions and nonlinear parameters in partial differential equations (PDEs), optimizing the solution space by dividing it into subdomains and improving accuracy. Its effectiveness is demonstrated through applications in porous media thermal ablation and ice-sheet modeling, showcasing its potential for tackling real-world physics challenges.

Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy. This work introduces MaskLLM, a learnable pruning method that establishes Semi-structured (or ``N:M'') Sparsity in LLMs, aimed at reducing computational overhead during inference. Instead of developing a new importance criterion, MaskLLM explicitly models N:M patterns as a learnable distribution through Gumbel Softmax sampling. This approach facilitates end-to-end training on large-scale datasets and offers two notable advantages: 1) High-quality Masks - our method effectively scales to large datasets and learns accurate masks; 2) Transferability - the probabilistic modeling of mask distribution enables the transfer learning of sparsity across domains or tasks. We assessed MaskLLM using 2:4 sparsity on various LLMs, including LLaMA-2, Nemotron-4, and GPT-3, with sizes ranging from 843M to 15B parameters, and our empirical results show substantial improvements over state-of-the-art methods. For instance, leading approaches achieve a perplexity (PPL) of 10 or greater on Wikitext compared to the dense model's 5.12 PPL, but MaskLLM achieves a significantly lower 6.72 PPL solely by learning the masks with frozen weights. Furthermore, MaskLLM's learnable nature allows customized masks for lossless application of 2:4 sparsity to downstream tasks or domains. Code is available at https://github.com/NVlabs/MaskLLM.

Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement that provides necessary context for the AI Partner. In this demo, we illustrate our present capabilities at detecting and tracking nonverbal behavior in student task-oriented interactions in the classroom, and the implications for tracking common ground and engagement.

Augmenting a smooth cost function with an $\ell_1$ penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can be efficiently implemented using proximal gradient methods. However, one drawback of the $\ell_1$ penalty is bias: nonzero parameters are underestimated in magnitude, motivating techniques such as the Adaptive Lasso which endow each parameter with its own penalty coefficient. But it's not clear how these parameter-specific penalties should be set in complex models. In this article, we study the approach of treating the penalty coefficients as additional decision variables to be learned in a \textit{Maximum a Posteriori} manner, developing a proximal gradient approach to joint optimization of these together with the parameters of any differentiable cost function. Beyond reducing bias in estimates, this procedure can also encourage arbitrary sparsity structure via a prior on the penalty coefficients. We compare our method to implementations of specific sparsity structures for non-Gaussian regression on synthetic and real datasets, finding our more general method to be competitive in terms of both speed and accuracy. We then consider nonlinear models for two case studies: COVID-19 vaccination behavior and international refugee movement, highlighting the applicability of this approach to complex problems and intricate sparsity structures.

This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.