We introduce 'FactCheck Editor', an advanced text editor designed to automate fact-checking and correct factual inaccuracies. Given the widespread issue of misinformation, often a result of unintentional mistakes by content creators, our tool aims to address this challenge. It supports over 90 languages and utilizes transformer models to assist humans in the labor-intensive process of fact verification. This demonstration showcases a complete workflow that detects text claims in need of verification, generates relevant search engine queries, and retrieves appropriate documents from the web. It employs Natural Language Inference (NLI) to predict the veracity of claims and uses LLMs to summarize the evidence and suggest textual revisions to correct any errors in the text. Additionally, the effectiveness of models used in claim detection and veracity assessment is evaluated across multiple languages.

While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning. Additionally, KL regularization focuses solely on regularizing the language policy, neglecting a potential source of regularization: the reward function itself. Inspired by demonstration-guided RL, we here introduce the Reward Calibration from Demonstration (RCfD), which leverages human demonstrations and a reward model to recalibrate the reward objective. Formally, given a prompt, the RCfD objective minimizes the distance between the demonstrations' and LLM's rewards rather than directly maximizing the reward function. This objective shift avoids incentivizing the LLM to exploit the reward model and promotes more natural and diverse language generation. We show the effectiveness of RCfD on three language tasks, which achieves comparable performance to carefully tuned baselines while mitigating ROO.

The Financial Times has become the latest news organization to strike a deal with OpenAI. In a joint announcement on Monday, the Financial Times and OpenAI said that maker of ChatGPT will use the Financial Times' journalism to train its AI models and collaborate on developing new AI products and features for the publication's readers. "It is right, of course, that AI platforms pay publishers for the use of their material," said Financial Times CEO John Ridding in a statement and added that the Times is "committed to human journalism." Neither company disclosed the financial terms of the agreement. Earlier this year, The Information reported that OpenAI offers publishers between 1 million and 5 million a year to license their content to train its AI models.

The Financial Times has struck a deal with ChatGPT developer OpenAI that allows its content to be used in training artificial intelligence systems. The FT will receive an undisclosed payment as part of the deal, which is the latest to be agreed between OpenAI and news publishers. Under the arrangement, ChatGPT users will receive summaries and quotes from FT journalism, as well as links to articles, in responses to prompts, where appropriate. John Ridding, the chief executive of the FT Group, said it was "right" that AI companies paid publishers for their material. The New York Times is suing OpenAI and its largest investor, Microsoft, over use of its content to train large language models, the technology that underpins chatbots like ChatGPT.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This episode we're chatting with Eleanor and Kerry on good technology and if it's even possible, that technology is political, watering down regulation, the magic of AI, the value of human creativity, how Feminism, Aboriginal, and mixed race studies can help AI development, the performative nature of tech, and more… Dr Kerry McInerney (née Mackereth) is a Research Fellow at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, where she co-leads the Global Politics of AI project on how AI is impacting international relations. She is also a Research Fellow at the AI Now Institute (a leading AI policy thinktank in New York), an AHRC/BBC New Generation Thinker (2023), one of the 100 Brilliant Women in AI Ethics (2022), and one of Computing's Rising Stars 30 (2023). Kerry is the co-editor of the collection Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines (2023, Oxford University Press), the collection The Good Robot: Why Technology Needs Feminism (2024, Bloomsbury Academic), and the co-author of the forthcoming book Reprogram: Why Big Tech is Broken and How Feminism Can Fix It (2026, Princeton University Press). Dr Eleanor Drage is a Senior Research Fellow at the University of Cambridge Centre for the Future of Intelligence, and teaches AI professionals about AI ethics on a Masters course at Cambridge.

"China will use AI to disrupt elections in the US, South Korea and India, Microsoft warns" one read. "China Is Using AI to Sow Disinformation and Stoke Discord Across Asia and the US," another claimed. The headlines were based on a report published earlier this month by Microsoft's Threat Analysis Center which outlined how a Chinese disinformation campaign was now utilizing artificial technology to inflame divisions and disrupt elections in the US and around the world. The campaign, which has already targeted Taiwan's elections, uses AI-generated audio and memes designed to grab user attention and boost engagement. But what these headlines and Microsoft itself failed to adequately convey is that the Chinese government-linked disinformation campaign, known as Spamouflage Dragon or Dragonbridge, has so far been virtually ineffective.

Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training -- often exhibiting sharp declines in capability even for minor differences. In this work, we examine robustness to a suite of these types of visual changes for RGB-D and point cloud based visual control policies. To perform these experiments on both model-free and model-based reinforcement learners, we introduce a novel Point Cloud World Model (PCWM) and point cloud based control policies. Our experiments show that policies that explicitly encode point clouds are significantly more robust than their RGB-D counterparts. Further, we find our proposed PCWM significantly outperforms prior works in terms of sample efficiency during training. Taken together, these results suggest reasoning about the 3D scene through point clouds can improve performance, reduce learning time, and increase robustness for robotic learners. Project Webpage: https://pvskand.github.io/projects/PCWM

MTL is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to STL, MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including CV, NLP, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for ZSL, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://github.com/junfish/Awesome-Multitask-Learning.

This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment. Our study involves a combination of between-subject prompt and historic consumption assessments, along with within-subject recommendation scenario evaluations. By examining conversation and survey response data from 160 active users, we find that LLMs offer strong recommendation explainability but lack overall personalization, diversity, and user trust. Our results also indicate that different personalized prompting techniques do not significantly affect user-perceived recommendation quality, but the number of movies a user has watched plays a more significant role. Furthermore, LLMs show a greater ability to recommend lesser-known or niche movies. Through qualitative analysis, we identify key conversational patterns linked to positive and negative user interaction experiences and conclude that providing personal context and examples is crucial for obtaining high-quality recommendations from LLMs.

Recent research in dialogue systems and corpora has focused on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users accomplish specific tasks, while open-domain systems aim to create engaging conversations. However, in real-world scenarios, user intents are often revealed during interactions. A recent study introduced SalesBot, which simulates dialogues transitioning from chit-chat to task-oriented scenarios to train sales agents. Unfortunately, the initial data lacked smooth transitions and coherent long-turn dialogues, resulting in poor naturalness in sales-customer interactions. To address these issues, this paper presents SalesBot 2.0, an improved dataset. It leverages commonsense knowledge from large language models (LLMs) through strategic prompting. Additionally, we introduce a novel model called SalesAgent, trained on salesperson's interactions, using chain-of-thought (CoT) reasoning. This model excels in transitioning topics, understanding user intents, and selecting appropriate strategies. Experiments using diverse user simulations validate the effectiveness of our method in controlling dialogue strategies in LLMs. Furthermore, SalesBot 2.0 enhances coherence and reduces aggression, facilitating better model learning for sales-customer interactions.