When you think of leaders in artificial intelligence, you probably picture sneaker-wearing, baby-faced Silicon Valley-types like Mark Zuckerberg and Sam Altman. But one of the the biggest funders of AI is 77-year-old Blackstone CEO and Republican megadonor Steve Schwarzman - who says his favorite app is'email.' Schwarzman, who was still using a basic flip phone at the time, became enamored with AI in 2015, when the co-founder of Alibaba told him that AI is the wave of the future and would change job functions, drug development and education. Since then, Schwarzman has invested more than 500 million dollars in the advancement of AI and has donated millions of dollars to Yale University to establish a center for AI advancements and to create the University of Oxford's Institute for Ethics in AI. Steve Schwarzman had a brief stint in the U.S. Army Reserve in 1970 before he headed to Harvard Business School In the midst of concerns over an AI takeover, Schwarzman, who is worth 37.8 billion, is now working to promote AI and reassure the public that the technology is meant to assist with daily tasks, not to replace humans.

'Gutfeld!' panelists react to Google pausing its image generation feature of its artificial intelligence (AI) tool, Gemini, after AI refuses to show images of White people. Save the energy for after the show. Can goo goo goo goo, can Google be trusted when their credibility is busted? Google's apologizing after their new AI Gemini chat bot created historically inaccurate pictures and refusing to show White people. For those unfamiliar with the software, you describe what you want to see and AI generates the images.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russian forces threatened to shoot down a French surveillance aircraft patrolling in international airspace over the Black Sea, a signal of increasingly aggressive behavior from Moscow as its invasion of Ukraine struggles to make headway, French defense officials said Thursday. "A Russian air traffic control system threatened to shoot down French aircraft in the Black Sea when we were in a free international zone where we patrol," the French defense minister, Sébastien Lecornu, said on RTL radio. A French military spokesman, Col. Pierre Gaudillière, said Lecornu was referring to an incident in mid-November that involved one of France's four giant Airborne Warning and Control System, or AWACS, surveillance aircraft that was flying over international waters in the Black Sea.

Gail Huntley recognised the gravelly voice of Joe Biden as soon as she picked up the phone. Huntley, a 73-year-old resident of New Hampshire, was planning to vote for the president in the state's upcoming primary, so she was confused that a pre-recorded message from him was urging her not to. "It's important that you save your vote for the November election," the message said. "Voting this Tuesday only enables the Republicans in their quest to elect Donald Trump again." Huntley quickly realised that that call was fake, but assumed Biden's words had been taken out of context.

Generation of science-ready data from processed data products is one of the major challenges in next-generation radio continuum surveys with the Square Kilometre Array (SKA) and its precursors, due to the expected data volume and the need to achieve a high degree of automated processing. Source extraction, characterization, and classification are the major stages involved in this process. In this work we focus on the classification of compact radio sources in the Galactic plane using both radio and infrared images as inputs. To this aim, we produced a curated dataset of ~20,000 images of compact sources of different astronomical classes, obtained from past radio and infrared surveys, and novel radio data from pilot surveys carried out with the Australian SKA Pathfinder (ASKAP). Radio spectral index information was also obtained for a subset of the data. We then trained two different classifiers on the produced dataset. The first model uses gradient-boosted decision trees and is trained on a set of pre-computed features derived from the data, which include radio-infrared colour indices and the radio spectral index. The second model is trained directly on multi-channel images, employing convolutional neural networks. Using a completely supervised procedure, we obtained a high classification accuracy (F1-score>90%) for separating Galactic objects from the extragalactic background. Individual class discrimination performances, ranging from 60% to 75%, increased by 10% when adding far-infrared and spectral index information, with extragalactic objects, PNe and HII regions identified with higher accuracies. The implemented tools and trained models were publicly released, and made available to the radioastronomical community for future application on new radio data.

Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input document. In this paper, we analyze the robustness of fine-tuning based summarization models to the knowledge conflict, which we call factual adaptiveness. We utilize pre-trained language models to construct evaluation sets and find that factual adaptiveness is not strongly correlated with factual consistency on original datasets. Furthermore, we introduce a controllable counterfactual data augmentation method where the degree of knowledge conflict within the augmented data can be adjustable. Our experimental results on two pre-trained language models (PEGASUS and BART) and two fine-tuning datasets (XSum and CNN/DailyMail) demonstrate that our method enhances factual adaptiveness while achieving factual consistency on original datasets on par with the contrastive learning baseline.

Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address these limitations, we propose ReadAgent, an LLM agent system that increases effective context length up to 20x in our experiments. Inspired by how humans interactively read long documents, we implement ReadAgent as a simple prompting system that uses the advanced language capabilities of LLMs to (1) decide what content to store together in a memory episode, (2) compress those memory episodes into short episodic memories called gist memories, and (3) take actions to look up passages in the original text if ReadAgent needs to remind itself of relevant details to complete a task. We evaluate ReadAgent against baselines using retrieval methods, using the original long contexts, and using the gist memories. These evaluations are performed on three long-document reading comprehension tasks: QuALITY, NarrativeQA, and QMSum. ReadAgent outperforms the baselines on all three tasks while extending the effective context window by 3-20x.

Understanding the link between urban planning and commuting flows is crucial for guiding urban development and policymaking. This research, bridging computer science and urban studies, addresses the challenge of integrating these fields with their distinct focuses. Traditional urban studies methods, like the gravity and radiation models, often underperform in complex scenarios due to their limited handling of multiple variables and reliance on overly simplistic and unrealistic assumptions, such as spatial isotropy. While deep learning models offer improved accuracy, their black-box nature poses a trade-off between performance and explainability -- both vital for analyzing complex societal phenomena like commuting flows. To address this, we introduce TransFlower, an explainable, transformer-based model employing flow-to-flow attention to predict urban commuting patterns. It features a geospatial encoder with an anisotropy-aware relative location encoder for nuanced flow representation. Following this, the transformer-based flow predictor enhances this by leveraging attention mechanisms to efficiently capture flow interactions. Our model outperforms existing methods by up to 30.8% Common Part of Commuters, offering insights into mobility dynamics crucial for urban planning and policy decisions.

This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unlike traditional benchmarks that evaluate general mathematical reasoning with an average accuracy, ConceptMath systematically organizes math problems under a hierarchy of math concepts, so that mathematical reasoning can be evaluated at different granularity with concept-wise accuracies. Based on our ConcepthMath, we evaluate a broad range of LLMs, and we observe existing LLMs, though achieving high average accuracies on traditional benchmarks, exhibit significant performance variations across different math concepts and may even fail catastrophically on the most basic ones. Besides, we also introduce an efficient fine-tuning strategy to enhance the weaknesses of existing LLMs. Finally, we hope ConceptMath could guide the developers to understand the fine-grained mathematical abilities of their models and facilitate the growth of foundation models.

Temporal question answering (QA) involves time constraints, with phrases such as "... in 2019" or "... before COVID". In the former, time is an explicit condition, in the latter it is implicit. State-of-the-art methods have limitations along three dimensions. First, with neural inference, time constraints are merely soft-matched, giving room to invalid or inexplicable answers. Second, questions with implicit time are poorly supported. Third, answers come from a single source: either a knowledge base (KB) or a text corpus. We propose a temporal QA system that addresses these shortcomings. First, it enforces temporal constraints for faithful answering with tangible evidence. Second, it properly handles implicit questions. Third, it operates over heterogeneous sources, covering KB, text and web tables in a unified manner. The method has three stages: (i) understanding the question and its temporal conditions, (ii) retrieving evidence from all sources, and (iii) faithfully answering the question. As implicit questions are sparse in prior benchmarks, we introduce a principled method for generating diverse questions. Experiments show superior performance over a suite of baselines.