Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed. These methods either rely on task-specific few-shot examples that require a certain level of domain knowledge, or are designed to be simple but only perform well on a few types of tasks. In this work, we attempt to introduce the concept of generalist prompting, which operates on the design principle of achieving optimal or near-optimal performance on a wide range of tasks while eliminating the need for manual selection and customization of prompts tailored to specific problems. Furthermore, we propose MeMo (Mental Models), an innovative prompting method that is simple-designed yet effectively fulfills the criteria of generalist prompting. MeMo distills the cores of various prompting methods into individual mental models and allows LLMs to autonomously select the most suitable mental models for the problem, achieving or being near to the state-of-the-art results on diverse tasks such as STEM, logical reasoning, and commonsense reasoning in zero-shot settings. We hope that the insights presented herein will stimulate further exploration of generalist prompting methods for LLMs.

China has stepped up its diplomatic and military pressure against Taiwan, alarming U.S. officials and allies in the region that Beijing is looking to take back the island by force. If projections of a Chinese military invasion to retake Taiwan are accurate, the U.S. can utilize artificial intelligence (AI) and other technology that will indicate to forces in the region that China isn't engaging in yet another provocative military exercise but is launching the invasion so many predict. According to experts, AI and machine learning (ML) can help the U.S. and its allies in the region improve the speed and efficiency of war plan development, intelligence assessments and targeting effectiveness. An MV-22 Osprey from the "Ugly Angels" of Marine Medium Tiltrotor Squadron 362 flies by the aircraft carrier USS Nimitz in the South China Sea Feb. 11, 2023. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module. Integrated during the fine-tuning of pre-trained generative language models (PLMs), QASE enables these PLMs to match SOTA extractive methods and outperform leading LLMs like GPT-4 in MRC tasks, without significant increases in computational costs.

Large language models (LLMs) have recently experienced remarkable growth in both their capabilities (OpenAI, 2023; Gemini Team et al., 2023; Touvron et al., 2023) and their applications in various fields (NLLB Team et al., 2022; Thirunavukarasu et al., 2023; Schick et al., 2023; Bubeck et al., 2023). As LLMs become increasingly complex and are deployed in safety-critical environments (Singhal et al., 2022; Li et al., 2023; Maddela et al., 2023), it is essential to thoroughly understand their robustness to different inputs. Indeed, the susceptibility of LLMs to user inputs and adversarial prompts -- prompts crafted to mislead the model or exploit its weaknesses, potentially leading to unsafe, biased, or incorrect outputs -- poses a significant challenge (Perez et al., 2022; Wei et al., 2023; Zou et al., 2023). Identifying these vulnerabilities and subsequently mitigating such risks is therefore vital to ensure the safe and reliable operation of LLMs in the real world. Current methods for identifying adversarial prompts aimed at "attacking" LLMs and eliciting undesirable outputs are limited by several factors.

Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced homotopic parallel trajectory optimization (BHPTO) approach with over-relaxed alternating direction method of multipliers (ADMM) for real-time integrated decision-making and planning. To facilitate safety interactions between the ego vehicle (EV) and surrounding vehicles, a spatiotemporal safety module exhibiting bi-convexity is developed on the basis of barrier function. Varying barrier coefficients are adopted for different time steps in a planning horizon to account for the motion uncertainties of surrounding HVs and mitigate conservative behaviors. Additionally, we exploit the discrete characteristics of driving maneuvers to initialize nominal behavior-oriented free-end homotopic trajectories based on reachability analysis, and each trajectory is locally constrained to a specific driving maneuver while sharing the same task objectives. By leveraging the bi-convexity of the safety module and the kinematics of the EV, we formulate the BHPTO as a bi-convex optimization problem. Then constraint transcription and over-relaxed ADMM are employed to streamline the optimization process, such that multiple trajectories are generated in real time with feasibility guarantees. Through a series of experiments, the proposed development demonstrates improved task accuracy, stability, and consistency in various traffic scenarios using synthetic and real-world traffic datasets.

Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, whereas video generation has remained largely limited to media entertainment. Yet video data captures important information about the physical world that is difficult to express in language. To address this gap, we discuss an under-appreciated opportunity to extend video generation to solve tasks in the real world. We observe how, akin to language, video can serve as a unified interface that can absorb internet knowledge and represent diverse tasks. Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning. We identify major impact opportunities in domains such as robotics, self-driving, and science, supported by recent work that demonstrates how such advanced capabilities in video generation are plausibly within reach. Lastly, we identify key challenges in video generation that mitigate progress. Addressing these challenges will enable video generation models to demonstrate unique value alongside language models in a wider array of AI applications.

Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results. Generally, the focus has been on historical-value-based models, which rely on past observations to predict future series. Notably, a new trend has emerged with time-index-based models, offering a more nuanced understanding of the continuous dynamics underlying time series. Unlike these two types of models that aggregate the information of spatial domains or temporal domains, in this paper, we consider multivariate time series as spatiotemporal data regularly sampled from a continuous dynamical system, which can be represented by partial differential equations (PDEs), with the spatial domain being fixed. Building on this perspective, we present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers, following the encoding-integration-decoding operations. Our extensive experimentation across seven diverse real-world LMTF datasets reveals that PDETime not only adapts effectively to the intrinsic spatiotemporal nature of the data but also sets new benchmarks, achieving state-of-the-art results

While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives. This paper delves further into current order-based methods, examining their inefficiencies in utilizing reward values and addressing misalignment issues. Building upon these findings, we propose a novel \textbf{V}alue-based \textbf{C}ali\textbf{B}ration (VCB) method to better align LLMs with human preferences. Experimental results demonstrate that VCB surpasses existing alignment methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and stability in diverse settings.

Another California community is raising concerns about plans to unleash the Waymo self-driving vehicle in its jurisdiction, following several incidents involving autonomous ride-hailing cars that resulted in injuries. San Mateo County, in the San Francisco Bay Area, has requested more information from state regulators before allowing Google-owned Waymo to operate its driverless vehicles in the county. San Mateo County made the request after Waymo submitted a letter Jan. 19 to the California Public Utilities Commission, asking the agency to approve its proposed expansion of its Automated Vehicle Passenger Services into portions of the San Francisco Peninsula, which includes San Mateo County, as well as the southwest region of Los Angeles County. The company has already been serving a portion of San Francisco, from Lands End to Bernal Heights. The autonomous car began offering rides for a limited time in November in Santa Monica, Century City, West Hollywood, Mid-City Koreatwon and downtown L.A., giving residents a chance at testing the driverless ride.

On January 28, 2022, at 6.39 a.m. EST, the Fern Hollow Bridge in Pittsburgh, Pennsylvania collapsed. Due to the timing of the failure, thankfully, fewer vehicles were on the bridge and only ten people were injured with no fatalities. Pittsburgh, also known as the City of Bridges, was getting ready for a visit from President Biden that day. Biden visited the collapse site and assured federal assistance to rebuild the bridge on the spot. This infrastructural failure, coinciding with a high-profile political visit and a push towards passing the Build Back Better infrastructure bill, attracted considerable media attention to the flailing infrastructural health in the US. As we were sifting through the social web discussions surrounding this issue, broad themes such as words of compassion for the victims and typical responses in social web political discourse such as political name-calling, conspiracy theories, and partisan mud-slinging emerged. However, apart from these expected social web reactions, we noticed a small minority of interactions that talked about anticipatory failures of other bridges in the US.