Earlier last week, just around the time when the driver of Rishi Sunak's armoured Jaguar might have been thinking about typing "Bletchley Park" into the limousine's satnav, Joe Biden was in the White House putting his signature on a new executive order "on the safe, secure, and trustworthy development and use of artificial intelligence". In a mere 20,000 words, or thereabouts, the order directs an innumerable number of federal agencies and government departments that oversee "everything from housing to health to national security to create standards and regulations for the use or oversight of AI". These bodies are required to develop guidance on the responsible use of AI in areas such as criminal justice, education, healthcare, housing and labour, "with a focus on protecting Americans' civil rights and liberties". Within No 10, though, there might have been some infuriated spin doctors. After all, the main purpose of the Bletchley Park AI safety summit was to hype the prime minister's claim to "global leadership" in this matter, and here was bloody Biden announcing tangible plans actually to do something about the technology rather than just fostering lofty "declarations".

After three weeks of a punishing Israeli bombardment of Gaza, Israel is still refusing to allow international journalists in. News outlets and audiences are entirely reliant on local Palestinian reporters, who risk their lives to provide a window into the war. Meenakshi Ravi reports on how Israelis are documenting and sharing the evidence online. Tariq Nafi examines Israel's use of AI-powered surveillance in Hebron, which has entrenched the Israeli government's control over Palestinians.

Rep. Nancy Mace, R-S.C., says Congress should not be combining Israel and Ukraine wars together in aid package on'Your World.' Rep. Nancy Mace, R-S.C., is calling for solutions to the wide array of dangers posed by online content falsified using Artificial Intelligence (AI) – known as "deepfakes." "These things are only going to become more prevalent if we don't start discussing the problem and talking to AI experts on how to address deepfakes now and in the future," Mace told Fox News Digital in a Friday interview. She hopes to get those answers in next week's hearing on AI deepfakes by the House Oversight's Subcommittee on Cybersecurity, Information Technology, and Government Innovation – which Mace chairs. Mace said she hopes the expert witnesses at the Wednesday hearing will "share some of the more egregious examples" of AI deepfakes being used, like the prevalence of obscene AI generated images and video. Rep. Nancy Mace, R-S.C., is chairing a House Oversight subcommittee hearing on AI (Tom Williams/CQ-Roll Call, Inc via Getty Images) "Ninety percent of AI deepfakes are pornographic in nature," Mace said, listing off the dangers of AI-faked content.

Predictive models used for AI-based decision support are generally not designed for transparency. Although they operate in critical situations such as, e.g., medicine or defence, they are limited to only presenting a probable outcome (David Gunning, 2017; Ribeiro et al., 2016), which can lead to either misuse (based on user reliance being higher than appropriate) or disuse (due to users having less reliance than appropriate) (Alvarado-Valencia & Barrero, 2014; Buçinca et al., 2020). Due to the lack of transparency, predictions from this type of model often require an explanation. In explainable artificial intelligence (XAI), the goal is to create methods that help human users identify when to trust a prediction and when not to, such as an erroneous prediction in a medical diagnosis (Marx et al., 2023). An explanation should reveal the strengths and weaknesses of the underlying model to communicate how they will behave in the future (David Gunning, 2017; Dimanov et al., 2020). There are two main categories of explanations: local explanations, which present information about the reasons for individual predictions, and global explanations, which provide information about the general behaviour of the model (Guidotti et al., 2018b; Moradi & Samwald, 2021; Martens & Foster, 2014).

Robust and effective fruit detection and localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving fruit detection, less emphasis has been placed on the fruit localization aspect, which is a crucial yet challenging task due to limited depth accuracy from existing sensor measurements in the natural orchard environment with variable lighting conditions and foliage/branch occlusions. In this paper, we present the system design and calibration of an Active LAser-Camera Scanner (ALACS), a novel perception module for robust and high-precision fruit localization. The hardware of ALACS mainly consists of a red line laser, an RGB camera, and a linear motion slide, which are seamlessly integrated into an active scanning scheme where a dynamic-targeting laser-triangulation principle is employed. A high-fidelity extrinsic model is developed to pair the laser illumination and the RGB camera, enabling precise depth computation when the target is captured by both sensors. A random sample consensus-based robust calibration scheme is then designed to calibrate the model parameters based on collected data. Comprehensive evaluations are conducted to validate the system model and calibration scheme. The results show that the proposed calibration method can detect and remove data outliers to achieve robust parameter computation, and the calibrated ALACS system is able to achieve high-precision localization with millimeter-level accuracy.

The study of post-wildfire plant regrowth is essential for developing successful ecosystem recovery strategies. Prior research mainly examines key ecological and biogeographical factors influencing post-fire succession. This research proposes a novel approach for predicting and analyzing post-fire plant recovery. We develop a Convolutional Long Short-Term Memory Tensor Regression (ConvLSTMTR) network that predicts future Normalized Difference Vegetation Index (NDVI) based on short-term plant growth data after fire containment. The model is trained and tested on 104 major California wildfires occurring between 2013 and 2020, each with burn areas exceeding 3000 acres. The integration of ConvLSTM with tensor regression enables the calculation of an overall logistic growth rate k using predicted NDVI. Overall, our k-value predictions demonstrate impressive performance, with 50% of predictions exhibiting an absolute error of 0.12 or less, and 75% having an error of 0.24 or less. Finally, we employ Uniform Manifold Approximation and Projection (UMAP) and KNN clustering to identify recovery trends, offering insights into regions with varying rates of recovery. This study pioneers the combined use of tensor regression and ConvLSTM, and introduces the application of UMAP for clustering similar wildfires. This advances predictive ecological modeling and could inform future post-fire vegetation management strategies.

Large language models~(LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and filters out low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce AlpaGasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and the controlled human evaluation. Its 13B variant matches $>90\%$ performance of its teacher LLM (i.e., Text-Davinci-003 generating the 52k data) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes. Moreover, the experiments prove the efficacy of our method across diverse datasets, base models, and LLM filters. Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models. Our project page is available at: \url{https://lichang-chen.github.io/AlpaGasus/}

Emotion arcs capture how an individual (or a population) feels over time. They are widely used in industry and research; however, there is little work on evaluating the automatically generated arcs. This is because of the difficulty of establishing the true (gold) emotion arc. Our work, for the first time, systematically and quantitatively evaluates automatically generated emotion arcs. We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods. By running experiments on 18 diverse datasets in 9 languages, we show that despite being markedly poor at instance level emotion classification, LexO methods are highly accurate at generating emotion arcs when aggregating information from hundreds of instances. We also show, through experiments on six indigenous African languages, as well as Arabic, and Spanish, that automatic translations of English emotion lexicons can be used to generate high-quality emotion arcs in less-resource languages. This opens up avenues for work on emotions in languages from around the world; which is crucial for commerce, public policy, and health research in service of speakers often left behind. Code and resources: https://github.com/dteodore/EmotionArcs

The U.S. military is flying surveillance drones over the Gaza Strip, according to two Defense Department officials and an analysis by The New York Times. The officials said the drones were being used to aid in hostage recovery efforts, indicating that the U.S. is more involved than previously known. The aircraft are MQ-9 Reapers operated by U.S. Special Operations forces and were first spotted on Saturday on Flightradar24, a publicly accessible flight-tracking website, though Pentagon officials said that the aircraft have been active in the area since the days after the Oct. 7 surprise attack on Israel by Hamas. While Israel frequently conducts reconnaissance flights over Gaza, U.S. defense officials said it was believed to be the first time that U.S. drones have flown missions over Gaza. The flights are operating at a critical juncture.

As delegates from around the world and leaders from the tech industry gathered in the U.K. for the first ever AI Safety Summit, there appeared to be harmony between officials of historical rivals China and the U.S. On Wednesday, U.S. Secretary of Commerce Gina Raimondo and Chinese Vice Minister of Science and Technology Wu Zhaohui shared a stage at the opening plenary of the U.K. AI Safety Summit. Later that day, the U.S. and China were two of 29 countries to sign the Bletchley Declaration, which affirmed the risks AI poses and commits to international cooperation to identify and mitigate those risks, a crucial initial step to establishing regulation in the future. But simmering beneath these shows of cooperation is an undercurrent of increasing tension between the two AI superpowers. Tech supremacy has been a hallmark of the tensions between the U.S. and China in recent years. In 2017, on the heels of an impressive breakthrough in artificial intelligence by Google Deepmind, China made AI progress a priority with its New Generation AI Development Plan.