We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. We investigate two essential tasks in the NLP domain: text classification and text generation. In text classification, CB-LLM narrows the performance gap with traditional black-box models and provides clear interpretability. In text generation, we show how interpretable neurons in CB-LLM can be used for concept detection and steering text generation. Our CB-LLMs enable greater interaction between humans and LLMs across a variety of tasks -- a feature notably absent in existing LLMs. Large Language Models (LLMs) have become instrumental in advancing Natural Language Processing (NLP) tasks.

Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection methods combined with filtering techniques are commonly used for tracking, but they often lack robustness, particularly in the presence of camera motion and missed detections. Although advanced tracking methods have been proposed recently, their application in maritime scenarios is limited. To address this gap, this study proposes a vision-guided object-tracking framework for USVs, integrating state-of-the-art tracking algorithms with low-level control systems to enable precise tracking in dynamic maritime environments. We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. In addition, we evaluated the robustness of various control algorithms in conjunction with these tracking systems. The proposed framework was validated through simulations and real-world sea experiments, demonstrating its effectiveness in handling dynamic maritime conditions. The results show that SeqTrack, a Transformer-based tracker, performed best in adverse conditions, such as dust storms. Among the control algorithms evaluated, the linear quadratic regulator controller (LQR) demonstrated the most robust and smooth control, allowing for stable tracking of the USV.

"I don't kill anyone who doesn't deserve it," says Sam (Ben Whishaw), the self-described "triggerman"--hit man--in the new Netflix spy thriller Black Doves. Sam, like the series' other main character, Helen (not her real name, played by Keira Knightley), works for Black Doves' eponymous organization. They are spies, more or less, but spies for hire, and when you get right down to it, most of Sam's gigs seem to be carrying out hits for drug dealers. Sam isn't the only hit man featured in a sleek, starry TV thriller this winter. On Peacock, Eddie Redmayne plays Alex in a new adaptation of Frederick Forsyth's 1971 novel The Day of the Jackal.

Anyone in the US can now use OpenAI's artificial intelligence video generator, Sora, which the company announced on Monday would become publicly available. OpenAI first presented Sora in February, but it was only accessible to select artists, film-makers and safety testers. At multiple points on Monday, though, OpenAI's website did not allow for new sign-ups for Sora, citing heavy traffic. Sora is known as a text-to-video generator, a tool that can create AI video clips based on a user's written prompts. An example on OpenAI's website has the prompt of "a wide, serene shot of a family of woolly mammoths in an open desert".

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In 1988, we launched the Best of What's New Awards. The original list highlighted "the very things that make our lives more comfortable, more rewarding, more exciting, and more fun," to quote then-Publisher Grant A. Burnett. Now, in 2024, we continue our decades-old tradition of honoring big ideas. We even see hints of our original honorees in this year's list: Sea-Doo and Ford made both lists, 36 years apart. We're proud to bring you promising innovations--from things that make life at home easier to literal out-of-this-world explorations. This is the Best of What's New 2024. Had you asked me at the beginning of 2024 what our best gadgets list would look like, I'd have guessed it would be filled with quirky AI-driven devices like the rabbit R1 or the Humane Ai Pin. "Now with AI" is a phrase that has dominated consumer electronics in the 2020s. These devices promised unadulterated access to the power of neural networks in ways that would seamlessly integrate into our lives without relying on phones or smart fridges. Then, the devices came out. The software is slow and buggy, and the hardware is clunky. Maybe the stand-alone AI device will still have its year, and we'll look back and chuckle at these humble beginnings. In reality, 2024's big breakthrough came from Apple in the form of its long-rumored Vision Pro headset. The device has its own hurdles to clear, but after just a few minutes of using it, it was clear that it's something different, important, and honestly pretty amazing. The list also includes Sony's innovative pro-grade camera, the most accessible drone we've ever used, and a no-fun phone--no fun in a good way, of course. Credible rumors of Apple's VR bounced around the gadget blogs and tech sites for nearly a decade. It was consumer tech's sasquatch in that people claimed to have seen it, but no one knew if it even existed. Then, the Vision Pro emerged from the proverbial forest in February with a surprising design and a massive 3,500 price tag. It also came toting a new R-series chip and a dedicated OS meant for spatial computing.

Existing task-oriented AI agents often depend on explicit instructions or external rewards, limiting their ability to be driven by intrinsic motivations like humans. In this paper, we present a desire-driven autonomy framework to guide a Large Language Model-based (LLM-based) agent to simulate human-like daily activities. In contrast to previous agents, our Desire-driven Autonomous Agent (D2A) operates on the principle of intrinsic desire, allowing it to propose and select tasks that fulfill its motivational framework autonomously. Inspired by the Theory of Needs, the motivational framework incorporates an understanding of human-like desires, such as the need for social interaction, personal fulfillment, and self-care. Utilizing a desire-driven task generation mechanism, the agent evaluates its current state and takes a sequence of activities aligned with its intrinsic motivations. Through simulations, we demonstrate that our Desire-driven Autonomous Agent (D2A) generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. A comparative analysis with other LLM-based frameworks demonstrates that our approach significantly enhances the rationality of the simulated activities.

In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In this work, we--a multidisciplinary team of researchers spanning computer science and political science--present the first principled framework termed Political-LLM to advance the comprehensive understanding of integrating LLMs into computational political science. Specifically, we first introduce a fundamental taxonomy classifying the existing explorations into two perspectives: political science and computational methodologies. In particular, from the political science perspective, we highlight the role of LLMs in automating predictive and generative tasks, simulating behavior dynamics, and improving causal inference through tools like counterfactual generation; from a computational perspective, we introduce advancements in data preparation, fine-tuning, and evaluation methods for LLMs that are tailored to political contexts. We identify key challenges and future directions, emphasizing the development of domain-specific datasets, addressing issues of bias and fairness, incorporating human expertise, and redefining evaluation criteria to align with the unique requirements of computational political science. Political-LLM seeks to serve as a guidebook for researchers to foster an informed, ethical, and impactful use of Artificial Intelligence in political science. Our online resource is available at: http://political-llm.org/. Corresponding authors: Yushun Dong (yd24f@fsu.edu) is with the Department of Computer Science, Florida State University; Yue Zhao (yzhao010@usc.edu) is with the Department of Computer Science, University of Southern California; Fred Gui (pgui@lsu.edu) is with the Department of Political Science, Louisiana State University; Catherine Chen (catherinechen@lsu.edu) is with the Manship School of Mass Communication and the Department of Political Science, Louisiana State University.

In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted approach involving all the stakeholders, sustainable planning, efficient resource management, innovative solutions, and modern technologies. Like other modern technologies, social media informatics also plays its part in developing more sustainable and resilient cities and societies. Despite its limitations, social media informatics has proven very effective in various sustainable cities and society applications. In this paper, we review and analyze the role of social media informatics in sustainable cities and society by providing a detailed overview of its applications, associated challenges, and potential solutions. This work is expected to provide a baseline for future research in the domain.

Measuring the relative impact of CTs is important for prioritizing responses and allocating resources effectively, especially during crises. However, assessing the actual impact of CTs on the public poses unique challenges. It requires not only the collection of CT-specific knowledge but also diverse information from social, psychological, and cultural dimensions. Recent advancements in large language models (LLMs) suggest their potential utility in this context, not only due to their extensive knowledge from large training corpora but also because they can be harnessed for complex reasoning. In this work, we develop datasets of popular CTs with human-annotated impacts. Borrowing insights from human impact assessment processes, we then design tailored strategies to leverage LLMs for performing human-like CT impact assessments. Through rigorous experiments, we textit{discover that an impact assessment mode using multi-step reasoning to analyze more CT-related evidence critically produces accurate results; and most LLMs demonstrate strong bias, such as assigning higher impacts to CTs presented earlier in the prompt, while generating less accurate impact assessments for emotionally charged and verbose CTs.