Sen. John Fetterman told CNN on Wednesday that Elon Musk was "attractive" to a voting demographic in Pennsylvania that was necessary to win the state. Pennsylvania Senator John Fetterman is warning fellow Democrats not to dismiss Elon Musk's support for former President Trump in the Keystone State. Nineteen Electoral College votes are at stake in Pennsylvania on Nov. 5 and the state is rated a toss up by Fox News' Power Rankings. With both Trump and Vice President Harris fiercely competing there, and the winner likely to take the White House, the plain-dressed and plainspoken Fetterman told the New York Post in an interview that "Musk is a concern." "Not even just that he has endorsed [Trump], but the fact that now he's becoming an active participant and showing up and doing rallies and things like that," Fetterman said, explaining that the enormously successful Tesla and SpaceX CEO is an attractive figure for the kinds of voters Harris needs to win.

DJI has filed a lawsuit against the US Department of Defense over its addition to the Pentagon list that designates it as a "Chinese military company." In its filing, shared by The Verge, the company said it's challenging the designation because it's "neither owned nor controlled by the Chinese military." It described itself as the "largest privately owned seller of consumer and commercial drones," mostly used by first responders, fire and police departments, businesses and hobbyists. The company claimed that because the Pentagon has officially proclaimed it as a national security threat, it has suffered "ongoing financial and reputational harm." It also said that it has lost business from both US and internal customers, which terminated contracts and refused to enter new ones, and it has been banned from signing contracts with multiple federal government agencies.

In what may be a first, the US Department of Justice this week charged a hacker with attempting to cause injury and death by launching distributed denial-of-service (DDoS) attacks against hospitals. Ahmed Omer and his brother Alaa are accused of carrying out a cyberattack spree that targeted hundreds of victims under the hacktivist banner Anonymous Sudan. The group's DDoS victims included Microsoft's Azure cloud services, OpenAI's ChatGPT, and Israel's missile alert system, according to prosecutors. It was the brothers' alleged attacks on hospitals, however, that drew the most serious accusations from the Justice Department, which singled out Ahmed for allegedly seeking to kill people with the crude cyberattacks that overwhelm systems, knocking them offline. If someone told you there's a tool that can--using only open source information--create a "cyber profile" of you that can locate your phone in real time or place you at the scene of a crime at any date in the past, would you believe them?

The United States government's road safety agency is again investigating Tesla's "Full Self-Driving" system, this time after getting reports of crashes in low-visibility conditions, including one that killed a pedestrian. The National Highway Traffic Safety Administration says in documents that it opened the probe on Oct. 17 with the company reporting four crashes after Teslas entered areas of low visibility, including sun glare, fog, and airborne dust. In addition to the pedestrian's death, another crash involved an injury, the agency said. Investigators will look into the ability of "Full Self-Driving" to "detect and respond appropriately to reduced roadway visibility conditions, and if so, the contributing circumstances for these crashes." The investigation covers roughly 2.4 million Teslas from the 2016 through 2024 model years.

The Israeli military, which released the video, said it shows the last moments of Mr. Sinwar, an architect of the Oct. 7 attacks, before he was killed in Rafah on Wednesday. While the room, the man's clothing and the arm injury broadly match other visual evidence of Mr. Sinwar's death, The Times could not independently verify his identity in the video. In modern warfare, militaries use drones to scope out enemy positions, and often release propaganda videos showing enemy soldiers being killed. On the battlefields in Ukraine, both sides in that conflict have released a steady stream of drone footage showing panicked soldiers moments before their deaths. But here the drone footage shows a solitary figure remarkably close-up. In a war often seen from far away, in large explosions or wide vistas of broken buildings, the moment is remarkably personal.

Reward shaping is a critical component in reinforcement learning (RL), particularly for complex tasks where sparse rewards can hinder learning. While shaping rewards have been introduced to provide additional guidance, selecting effective shaping functions remains challenging and computationally expensive. This paper introduces Online Reward Selection and Policy Optimization (ORSO), a novel approach that frames shaping reward selection as an online model selection problem. ORSO employs principled exploration strategies to automatically identify promising shaping reward functions without human intervention, balancing exploration and exploitation with provable regret guarantees. We demonstrate ORSO's effectiveness across various continuous control tasks using the Isaac Gym simulator. Compared to traditional methods that fully evaluate each shaping reward function, ORSO significantly improves sample efficiency, reduces computational time, and consistently identifies high-quality reward functions that produce policies comparable to those generated by domain experts through hand-engineered rewards.

This research is part of a systematic study of scientific time series. In the last three years, hundreds of papers and over fifty new deep-learning models have been described for time series models. These mainly focus on the key aspect of time dependence, whereas in some scientific time series, the situation is more complex with multiple locations, each location having multiple observed and target time-dependent streams and multiple exogenous (known) properties that are either constant or time-dependent. Here, we analyze the hydrology time series using the CAMELS and Caravan global datasets on catchment rainfall and runoff. Together, these have up to 6 observed streams and up to 209 static parameters defined at each of about 8000 locations. This analysis is fully open source with a Jupyter Notebook running on Google Colab for both an LSTM-based analysis and the data engineering preprocessing. Our goal is to investigate the importance of exogenous data, which we look at using eight different choices on representative hydrology tasks. Increasing the exogenous information significantly improves the data representation, with the mean square error decreasing to 60% of its initial value in the largest dataset examined. We present the initial results of studies of other deep-learning neural network architectures where the approaches that can use the full observed and exogenous observations outperform less flexible methods, including Foundation models. Using the natural annual periodic exogenous time series produces the largest impact, but the static and other periodic exogenous streams are also important. Our analysis is intended to be valuable as an educational resource and benchmark.

The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users' intentions to use secretive behavior, primarily through influencing perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of the use of LLMs or other AI technologies.

Modern large language models (LLMs) have exhibited cooperative synergy on complex task-solving, and collective decision-making (CDM) is a pivotal component in LLM-based multi-agent collaboration frameworks. Our survey on 52 recent such systems uncovers a severe lack of diversity, with a heavy reliance on dictatorial and plurality voting for CDM. Through the lens of social choice theory, we scrutinize widely-adopted CDM methods and identify their limitations. To enrich current landscape of LLM-based CDM, we present GEDI, an electoral CDM module that incorporates various ordinal preferential voting mechanisms. Our empirical case study across three benchmarks shows that the integration of certain CDM methods can markedly improve the reasoning capabilities and robustness of some leading LLMs, all without requiring intricate system designs. Additionally, we find that some CDM mechanisms generate positive synergies even with as few as three agents. The voting-based methods also demonstrate robustness against single points of failure, as well as diversity in terms of hit-rate@k and subject-wise impacts.

As Large Language Models (LLMs) become increasingly integrated into various facets of society, a significant portion of online text consequently become synthetic. This raises concerns about bias amplification, a phenomenon where models trained on synthetic data amplify the pre-existing biases over successive training iterations. Previous literature seldom discusses bias amplification as an independent issue from model collapse. In this work, we address the gap in understanding the bias amplification of LLMs with four main contributions. Firstly, we propose a theoretical framework, defining the necessary and sufficient conditions for its occurrence, and emphasizing that it occurs independently of model collapse. Using statistical simulations with weighted maximum likelihood estimation, we demonstrate the framework and show how bias amplification arises without the sampling and functional form issues that typically drive model collapse. Secondly, we conduct experiments with GPT-2 to empirically demonstrate bias amplification, specifically examining open-ended generational political bias with a benchmark we developed. We observe that GPT-2 exhibits a right-leaning bias in sentence continuation tasks and that the bias progressively increases with iterative fine-tuning on synthetic data generated by previous iterations. Thirdly, we explore three potential mitigation strategies: Overfitting, Preservation, and Accumulation. We find that both Preservation and Accumulation effectively mitigate bias amplification and model collapse. Finally, using novel mechanistic interpretation techniques, we demonstrate that in the GPT-2 experiments, bias amplification and model collapse are driven by distinct sets of neurons, which aligns with our theoretical framework.