Is this AI's worst prediction yet? Chris Sutton's guest this week, actor Éanna Hardwicke, plays Roy Keane in Saipan - a new film about the former Manchester United captain's infamous fallout with Republic of Ireland manager Mick McCarthy before the 2002 World Cup. It is in cinemas from Friday. Naturally, we asked AI who would play Sutton if a film were ever made about him. The best fit, apparently, is Hollywood heartthrob Tom Hardy - who is four inches shorter than BBC Sport football expert Sutton but is AI's top choice for the role because he is known for portraying tough, brooding characters with emotional depth. That just shows how way off the mark AI is, said Sutton. But I'm happy with Tom Hardy, even though he is not tall enough.

From sugar shortages to military contracts, World War II helped make M&Ms and Hershey's bars into symbols of American abundance. A 1940s Milky Way ad shows candy keeping pilots smiling through the war. Breakthroughs, discoveries, and DIY tips sent every weekday. Every year, Hershey manufactures 373 million of its signature milk chocolate bars . While the company doesn't release exact stats on Halloween sales, you can bet a lot of those end up in plastic Jack O'Lantern-shaped pails.

Accurate prediction of contagious disease outbreaks is vital for informed decision-making. Our study addresses the gap between machine learning algorithms and their epidemiological applications, noting that methods optimal for benchmark datasets often underperform with real-world data due to difficulties in incorporating mobility information. We adopt a two-phase approach: first, assessing the significance of mobility data through a pilot study, then evaluating the impact of Graph Convolutional Networks (GCNs) on a transformer backbone. Our findings reveal that while mobility data and GCN modules do not significantly enhance forecasting performance, the inclusion of mortality and hospitalization data markedly improves model accuracy. Additionally, a comparative analysis between GCN-derived spatial maps and lockdown orders suggests a notable correlation, highlighting the potential of spatial maps as sensitive indicators for mobility. Our research offers a novel perspective on mobility representation in predictive modeling for contagious diseases, empowering decision-makers to better prepare for future outbreaks.

Fox News senior congressional correspondent Chad Pergram joins'Fox News Live' to explain how he prepares to report on Congress for the upcoming year. Every two years, the period between the November election and when the new Congress begins is often the busiest swath of time for covering Congress. Reporters are trying to figure out who won their elections and who lost. The existing Congress is back, attempting to prevent a government shutdown and often plowing through a landscape of other major legislation. There are often leadership elections.

The increasing sizes of large language models (LLMs) result in significant computational overhead and memory usage when adapting these models to specific tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have been devised to mitigate these challenges by training a small set of parameters for the task-specific updates of the model weights. Among PEFT methods, LoRA stands out for its simplicity and efficiency, inspiring the development of a series of variants. However, LoRA and its successors disregard the knowledge that is noisy or irrelevant to the targeted task, detrimentally impacting model performance and leading to suboptimality. To address this limitation, we introduce Knowledge-aware Singular-value Adaptation (KaSA), a PEFT method that leverages singular value decomposition (SVD) with knowledge-aware singular values to dynamically activate knowledge based on its relevance to the task at hand. We conduct extensive experiments across a range of LLMs on tasks spanning natural language understanding (NLU), generation (NLG), instruction following, and commonsense reasoning. The experimental results demonstrate that KaSA consistently outperforms FFT and 14 popular PEFT baselines across 16 benchmarks and 4 synthetic datasets, underscoring our method's efficacy and adaptability. The source code of our method is available at https://github.com/juyongjiang/KaSA.

The widespread adoption of cloud-based proprietary large language models (LLMs) has introduced significant challenges, including operational dependencies, privacy concerns, and the necessity of continuous internet connectivity. In this work, we introduce an LLMOps pipeline, "LlamaDuo", for the seamless migration of knowledge and abilities from service-oriented LLMs to smaller, locally manageable models. This pipeline is crucial for ensuring service continuity in the presence of operational failures, strict privacy policies, or offline requirements. Our LlamaDuo involves fine-tuning a small language model against the service LLM using a synthetic dataset generated by the latter. If the performance of the fine-tuned model falls short of expectations, it is enhanced by further fine-tuning with additional similar data created by the service LLM. This iterative process guarantees that the smaller model can eventually match or even surpass the service LLM's capabilities in specific downstream tasks, offering a practical and scalable solution for managing AI deployments in constrained environments. Extensive experiments with leading-edge LLMs are conducted to demonstrate the effectiveness, adaptability, and affordability of LlamaDuo across various downstream tasks.

How similar are politicians to those who vote for them? This is a critical question at the heart of democratic representation and particularly relevant at times when political dissatisfaction and populism are on the rise. To answer this question we compare the online discourse of elected politicians and their constituents. We collect a two and a half years (September 2020 - February 2023) constituency-level dataset for USA and UK that includes: (i) the Twitter timelines (5.6 Million tweets) of elected political representatives (595 UK Members of Parliament and 433 USA Representatives), (ii) the Nextdoor posts (21.8 Million posts) of the constituency (98.4% USA and 91.5% UK constituencies). We find that elected politicians tend to be equally similar to their constituents in terms of content and style regardless of whether a constituency elects a right or left-wing politician. The size of the electoral victory and the level of income of a constituency shows a nuanced picture. The narrower the electoral victory, the more similar the style and the more dissimilar the content is. The lower the income of a constituency, the more similar the content is. In terms of style, poorer constituencies tend to have a more similar sentiment and more dissimilar psychological text traits (i.e. measured with LIWC categories).

Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems. Given this capability, increasing interests have been put into employing LLM agents for predicting international events, which can influence decision-making and shape policy development on an international scale. Despite such a growing interest, there is a lack of a rigorous benchmark of LLM agents' forecasting capability and reliability. To address this gap, we introduce MIRAI, a novel benchmark designed to systematically evaluate LLM agents as temporal forecasters in the context of international events. Our benchmark features an agentic environment with tools for accessing an extensive database of historical, structured events and textual news articles. We refine the GDELT event database with careful cleaning and parsing to curate a series of relational prediction tasks with varying forecasting horizons, assessing LLM agents' abilities from short-term to long-term forecasting. We further implement APIs to enable LLM agents to utilize different tools via a code-based interface. In summary, MIRAI comprehensively evaluates the agents' capabilities in three dimensions: 1) autonomously source and integrate critical information from large global databases; 2) write codes using domain-specific APIs and libraries for tool-use; and 3) jointly reason over historical knowledge from diverse formats and time to accurately predict future events. Through comprehensive benchmarking, we aim to establish a reliable framework for assessing the capabilities of LLM agents in forecasting international events, thereby contributing to the development of more accurate and trustworthy models for international relation analysis.

Devindra: because like you could just always just take a photo of something and be like, yeah, that's how we wonder if it's going to Cherlynn: be a ram hog.