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The IJCAI distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers. Abstract: Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a probability distribution over actions) and comes with a theoretical guarantee on the number of expansions before reaching a goal node, depending on the quality of the policy. This guarantee can be used as a loss function, which we call the LTS loss, to optimize neural networks representing the policy (LTS NN). In this work we show that the neural network can be substituted with parameterized context models originating from the online compression literature (LTS CM). We show that the LTS loss is convex under this new model, which allows for using standard convex optimization tools, and obtain convergence guarantees to the optimal parameters in an online setting for a given set of solution trajectories -- guarantees that cannot be provided for neural networks.

AI technology is quickly creeping into every industry, prompting new questions about whether online content comes from a human or a computer. The presence of advanced technology in the classroom may require conversations with students during this new school year. As artificial intelligence finds its way into more families' day-to-day routines, parents and teachers alike should be wary of how their kids are interacting with generative AI. This is according to SmartNews' head of trust and safety Arjun Narayan, who shared concerns during an interview with Fox News Digital. "As with any new technology, when it is very new, it's important to understand how you're engaging with that tech," said Narayan, who is based in Japan.

Former Chicago 911 dispatcher Keith Thornton Jr. joined "Fox & Friends First" to discuss how the crime surge is affecting law enforcement and communities nationwide. Understaffed 911 call centers across the country field non-emergency calls about stray animals or noise complaints on top of their workload of answering serious reports of medical emergencies, crimes and even death. Officials in Charleston County, South Carolina, however, are now leveraging artificial intelligence to streamline non-emergency calls in an effort to free up 911 operators to focus on getting first responders to the scene of emergency incidents as quickly as possible. "Our job is to serve the public the best way we can. So, I am not in any way demeaning anyone from the public, but someone who has their favorite cat stuck in a tree, that's an emergency for them as compared to someone's just been shot," Jim Lake, director of the Charleston County Consolidated Emergency Communications Center, told Fox News Digital in a recent phone interview.

Extended target tracking estimates the centroid and shape of the target in space and time. In various situations where extended target tracking is applicable, the presence of multiple targets can lead to interference, particularly when they maneuver behind one another in a sensor like a camera. Nonetheless, when dealing with multiple extended targets, there's a tendency for them to share similar shapes within a group, which can enhance their detectability. For instance, the coordinated movement of a cluster of aerial vehicles might cause radar misdetections during their convergence or divergence. Similarly, in the context of a self-driving car, lane markings might split or converge, resulting in inaccurate lane tracking detections. A well-known joint probabilistic data association coupled (JPDAC) filter can address this problem in only a single-point target tracking. A variation of JPDACF was developed by introducing a nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter (ST-JPDACF) to address the problem for extended targets. Using different kernel functions, we manage the dependency of measurements in space (inside a frame) and time (between frames). Kernel functions are able to be learned using a limited number of training data. This extension can be used for tracking the shape and dynamics of nonparametric dependent extended targets in clutter when targets share measurements. The proposed algorithm was compared with other well-known supervised methods in the interfering case and achieved promising results.

Jared Genser in many ways fits a certain Washington, D.C., type. He wears navy suits and keeps his hair cut short. He graduated from a top law school, joined a large firm, and made partner at 40. Eventually, he became disenchanted with big law and started his own boutique practice with offices off--where else--Dupont Circle. What distinguishes Genser from the city's other 50-something lawyers is his unusual clientele: He represents high-value political prisoners.

The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring cognitive fields like psychology. The work of Daniel Kahneman and the late Amos Tversky on biased human decision-making, including the study of the conjunction fallacy, has experienced a second revival because of this. Under the conjunction fallacy a human decision-maker will go against basic probability laws and rank as more likely a conjunction over one of its parts. It has been proven overtime through a set of experiments with the Linda Problem being the most famous one. Although this interdisciplinary effort is welcomed, we fear that AI researchers ignore the driving force behind the conjunction fallacy as captured by the Linda Problem: the fact that Linda must be stereotypically described as a woman. In this paper we revisit the Linda Problem and formulate it as a fairness problem. In doing so we introduce perception as a parameter of interest through the structural causal perception framework. Using an illustrative decision-making example, we showcase the proposed conceptual framework and its potential impact for developing fair ADM systems.

We propose Reinforcement Learning from Contrast Distillation (RLCD), a method for aligning language models to follow natural language principles without using human feedback. RLCD trains a preference model using simulated preference pairs that contain both a high-quality and low-quality example, generated using contrasting positive and negative prompts. The preference model is then used to improve a base unaligned language model via reinforcement learning. Empirically, RLCD outperforms RLAIF (Bai et al., 2022b) and context distillation (Huang et al., 2022) baselines across three diverse alignment tasks--harmlessness, helpfulness, and story outline generation--and on both 7B and 30B model scales for preference data simulation. Reinforcement Learning from Human Feedback (RLHF) has recently been used to great effect to align pretrained large language models (LLMs) to human preferences, optimizing for desirable qualities like harmlessness and helpfulness (Bai et al., 2022a) and achieving ...

Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting LLMs with external knowledge sources, the integration of knowledge bases (KBs) remains understudied and faces several challenges. In this paper, we introduce KnowledGPT, a comprehensive framework to bridge LLMs with various knowledge bases, facilitating both the retrieval and storage of knowledge. The retrieval process employs the program of thought prompting, which generates search language for KBs in code format with pre-defined functions for KB operations. Besides retrieval, KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands. With extensive experiments, we show that by integrating LLMs with KBs, KnowledGPT properly answers a broader range of questions requiring world knowledge compared with vanilla LLMs, utilizing both knowledge existing in widely-known KBs and extracted into personalized KBs.

This is part of Help! Wanted, a special series from Slate advice. In the advising biz, there are certain eternal dilemmas that bedevil letter writers and columnists alike. For this edition, we asked writer Sable Yong to field your questions about online dating. She writes the newsletter Hard Feelings and her first essay collection Die Hot With A Vengeance will be published by Harper Collins in 2024. Matched is a pop-up advice column about online dating. Have a question about navigating dating apps? Can you make a ruling once and for all: If I'm on an app like Tinder or Grindr, and it clearly states I am there for "short-term fun" or "right now," do I really need to also talk about being in an open relationship with potential partners?