"I was about to go to bed and then realized we had this interview," Jingna Zhang tells me. It's 9 am in Seattle, where she's currently living. The photographer and art director has been pulling all-nighters trying to keep up with demand for her social platform for artists, Cara, which recently exploded in popularity in response to widespread opposition to Meta's policies around art and artificial intelligence. More users has led to an onslaught of complications, including a hefty 96,000 bill from the social network's cloud storage provider, as well as service outages. Cara began as a side project, but its newfound prominence means that Zhang is now an accidental startup founder, keeping the hours to match.

Elon Musk has dropped his lawsuit accusing OpenAI and its co-founders Sam Altman and Greg Brockman of reneging on the startup's pledge to develop artificial intelligence for the benefit of humanity. Lawyers in the United States representing Musk, on Tuesday asked a California judge to dismiss the suit, court filings showed. No reason was provided for the application to dismiss the suit. Musk in February filed a suit claiming that ChatGPT had set "aflame" its founding agreement to put the good of humanity ahead of profit-seeking when it signed an investment deal with Microsoft. "To this day, OpenAI Inc's website continues to profess that its charter is to ensure that AGI'benefits all of humanity'," Musk claimed in the suit.

Maritime surveillance is vital to mitigate illegal activities such as drug smuggling, illegal fishing, and human trafficking. Vision-based maritime surveillance is challenging mainly due to visibility issues at night, which results in failures in re-identifying vessels and detecting suspicious activities. In this paper, we introduce a thermal, vision-based approach for maritime surveillance with object tracking, vessel re-identification, and suspicious activity detection capabilities. For vessel re-identification, we propose a novel viewpoint-independent algorithm which compares features of the sides of the vessel separately (separate side-spaces) leveraging shape information in the absence of color features. We propose techniques to adapt tracking and activity detection algorithms for the thermal domain and train them using a thermal dataset we created. This dataset will be the first publicly available benchmark dataset for thermal maritime surveillance. Our system is capable of re-identifying vessels with an 81.8% Top1 score and identifying suspicious activities with a 72.4\% frame mAP score; a new benchmark for each task in the thermal domain.

Large language models (LLMs) have exhibited remarkable performance across various tasks in natural language processing. Nevertheless, challenges still arise when these tasks demand domain-specific expertise and advanced analytical skills, such as conducting research surveys on a designated topic. In this research, we develop ResearchArena, a benchmark that measures LLM agents' ability to conduct academic surveys, an initial step of academic research process. Specifically, we deconstructs the surveying process into three stages 1) information discovery: locating relevant papers, 2) information selection: assessing papers' importance to the topic, and 3) information organization: organizing papers into meaningful structures. In particular, we establish an offline environment comprising 12.0M full-text academic papers and 7.9K survey papers, which evaluates agents' ability to locate supporting materials for composing the survey on a topic, rank the located papers based on their impact, and organize these into a hierarchical knowledge mind-map. With this benchmark, we conduct preliminary evaluations of existing techniques and find that all LLM-based methods under-performing when compared to basic keyword-based retrieval techniques, highlighting substantial opportunities for future research.

Machine unlearning is the process of updating machine learning models to remove the information of specific training data samples, in order to comply with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. In this work, we focus on membership inference attack (MIA) based evaluation, one of the most common approaches for evaluating unlearning algorithms, and address various pitfalls of existing evaluation metrics that lack reliability. Specifically, we propose a game-theoretic framework that formalizes the evaluation process as a game between unlearning algorithms and MIA adversaries, measuring the data removal efficacy of unlearning algorithms by the capability of the MIA adversaries. Through careful design of the game, we demonstrate that the natural evaluation metric induced from the game enjoys provable guarantees that the existing evaluation metrics fail to satisfy. Furthermore, we propose a practical and efficient algorithm to estimate the evaluation metric induced from the game, and demonstrate its effectiveness through both theoretical analysis and empirical experiments. This work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques.

Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ensuring alignment and thus safe use. However, accurately and efficiently unlearning knowledge from an LLM remains challenging due to the potential collateral damage caused by the fuzzy boundary between retention and forgetting, and the large computational requirements for optimization across state-of-the-art models with hundreds of billions of parameters. In this work, we present Embedding-COrrupted (ECO) Prompts, a lightweight unlearning framework for large language models to address both the challenges of knowledge entanglement and unlearning efficiency. Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget. We learn corruptions added to prompt embeddings via zeroth order optimization toward the unlearning objective offline and corrupt prompts flagged by the classifier during inference. We find that these embedding-corrupted prompts not only lead to desirable outputs that satisfy the unlearning objective but also closely approximate the output from a model that has never been trained on the data intended for forgetting. Through extensive experiments on unlearning, we demonstrate the superiority of our method in achieving promising unlearning at nearly zero side effects in general domains and domains closely related to the unlearned ones. Additionally, we highlight the scalability of our method to 100 LLMs, ranging from 0.5B to 236B parameters, incurring no additional cost as the number of parameters increases.

Recent progress in multimodal large language models has markedly enhanced the understanding of short videos (typically under one minute), and several evaluation datasets have emerged accordingly. However, these advancements fall short of meeting the demands of real-world applications such as embodied intelligence for long-term decision-making, in-depth movie reviews and discussions, and live sports commentary, all of which require comprehension of long videos spanning several hours. To address this gap, we introduce LVBench, a benchmark specifically designed for long video understanding. Our dataset comprises publicly sourced videos and encompasses a diverse set of tasks aimed at long video comprehension and information extraction. LVBench is designed to challenge multimodal models to demonstrate long-term memory and extended comprehension capabilities. Our extensive evaluations reveal that current multimodal models still underperform on these demanding long video understanding tasks. Through LVBench, we aim to spur the development of more advanced models capable of tackling the complexities of long video comprehension. Our data and code are publicly available at: https://lvbench.github.io/.

Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence. However, their tendency to hallucinate -- generate plausible but false information -- poses a significant challenge. This issue is critical, as seen in recent court cases where ChatGPT's use led to citations of non-existent legal rulings. This paper explores how Retrieval-Augmented Generation (RAG) can counter hallucinations by integrating external knowledge with prompts. We empirically evaluate RAG against standard LLMs using prompts designed to induce hallucinations. Our results show that RAG increases accuracy in some cases, but can still be misled when prompts directly contradict the model's pre-trained understanding. These findings highlight the complex nature of hallucinations and the need for more robust solutions to ensure LLM reliability in real-world applications. We offer practical recommendations for RAG deployment and discuss implications for the development of more trustworthy LLMs.

The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a list of training resources formed on the basis of the participants' requests. Participants apply their developed anonymization systems, run evaluation scripts and submit evaluation results and anonymized speech data to the organizers. Results will be presented at a workshop held in conjunction with Interspeech 2024 to which all participants are invited to present their challenge systems and to submit additional workshop papers.

The integration of Large Language Models (LLMs) in social robotics presents a unique set of ethical challenges and social impacts. This research is set out to identify ethical considerations that arise in the design and development of these two technologies in combination. Using LLMs for social robotics may provide benefits, such as enabling natural language open-domain dialogues. However, the intersection of these two technologies also gives rise to ethical concerns related to misinformation, non-verbal cues, emotional disruption, and biases. The robot's physical social embodiment adds complexity, as ethical hazards associated with LLM-based Social AI, such as hallucinations and misinformation, can be exacerbated due to the effects of physical embodiment on social perception and communication. To address these challenges, this study employs an empirical design justice-based methodology, focusing on identifying socio-technical ethical considerations through a qualitative co-design and interaction study. The purpose of the study is to identify ethical considerations relevant to the process of co-design of, and interaction with a humanoid social robot as the interface of a LLM, and to evaluate how a design justice methodology can be used in the context of designing LLMs-based social robotics. The findings reveal a mapping of ethical considerations arising in four conceptual dimensions: interaction, co-design, terms of service and relationship and evaluates how a design justice approach can be used empirically in the intersection of LLMs and social robotics.