Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of interpretable predictions and limited generalization capability to other chemistry domains, such as radical and atmospheric chemistry. To address these challenges, we introduce a new reaction predictor system, RMechRP, that leverages contrastive learning in conjunction with mechanistic pathways, the most interpretable representation of chemical reactions. Specifically designed for radical reactions, RMechRP provides different levels of interpretation of chemical reactions. We develop and train multiple deep-learning models using RMechDB, a public database of radical reactions, to establish the first benchmark for predicting radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and interpretable predictions of radical reactions, and its potential for various applications in atmospheric chemistry.

The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents, providing a valuable resource for investigating conversational language models. In particular, we conduct comprehensive studies on instruction-following cooperation in multi-agent settings. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel.

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive summaries are more faithful to their sources, they may be less readable and contain redundant or unnecessary information. This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries. We define a compound reward function that learns from keywords of interest and pairwise preference labels, which we use to fine-tune a pretrained abstractive summariser via offline reinforcement learning. We carry out both automated and human evaluation on three datasets, finding that our method outperforms a comparable extractive TLS method on two of the three benchmark datasets, and participants prefer our method's summaries to those of both the extractive TLS method and the pretrained abstractive model. The method does not require expensive reference summaries and needs only a small number of preferences to align the generated summaries with human preferences.

Three years ago, our trustees sat down to discuss AI and regulation. A lot has happened since then, both on the technological development front and on the policy front, so we thought it was time to tackle the topic again. You can read more about that here.] Joining the conversation this time are: Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), and Carles Sierra (CSIC). Sabine Hauert: Regulation of AI was a very hot topic a few months ago, and interest has definitely not died down.

This week, UK prime minister Rishi Sunak is hosting a group of 100 representatives from the worlds of business and politics to discuss the potential and pitfalls of artificial intelligence. The AI Safety Summit, held at Bletchley Park, UK, begins on 1 November and aims to come up with a set of global principles with which to develop and deploy "frontier AI models" – the terminology favoured by Sunak and key figures in the AI industry for powerful models that don't yet exist, but may be built very soon. While the Bletchley Park event is the focal point, there is a wider week of fringe events being held in the UK, alongside a raft of UK government announcements on AI. Here are the latest developments. The summit got off to a bang with the announcement that 28 countries have agreed a declaration saying global action is needed to tamp down the risks of AI.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. President Biden signed what he called a "landmark" executive order (EO) on artificial intelligence, drawing mixed reviews from experts in the rapidly developing technology. "One key area the Biden AI [executive order] is focused on includes the provision of'testing data' for review by the federal government. If this provision allows the federal government a way to examine the'black box' algorithms that could lead to a biased AI algorithm, it could be helpful," Christopher Alexander, chief analytics officer of Pioneer Development Group, told Fox News Digital. "Since core algorithms are proprietary, there really is no other way to provide oversight and commercial protections," Alexander added.

Artificial intelligence image tools have a tendency to spin up disturbing clichés: Asian women are hypersexual. These stereotypes don't reflect the real world; they stem from the data that trains the technology. Grabbed from the internet, these troves can be toxic -- rife with pornography, misogyny, violence and bigotry. Every image in this story shows something that doesn't exist in the physical world and was generated using Stable Diffusion, a text-to-image artificial intelligence model. Stability AI, maker of the popular image generator Stable Diffusion XL, told The Washington Post it had made a significant investment in reducing bias in its latest model, which was released in July.

The antitrust lawsuit Epic Games and Match Group have filed against Google was supposed to go to trial on November 6, but now it looks like the video game developer might go at it alone. Google and Match, the parent company of Tinder, OkCupid and Hinge, have reached an agreement and have agreed to drop all claims against each other. According to Bloomberg and The Wall Street Journal, Google has agreed to return the $40 million Match had place in escrow to cover the service fees it would supposedly owe the Alphabet unit while the dispute is ongoing. Match also announced in its earning report that its apps will be using Google's User Choice Billing program starting on March 31, 2024. Under the program, users will have the option to choose between Google's and the developer's billing systems when purchasing an app or paying for a subscription.

Encoding legislative text in a formal representation is an important prerequisite to different tasks in the field of AI & Law. For example, rule-based expert systems focused on legislation can support laypeople in understanding how legislation applies to them and provide them with helpful context and information. However, the process of analyzing legislation and other sources to encode it in the desired formal representation can be time-consuming and represents a bottleneck in the development of such systems. Here, we investigate to what degree large language models (LLMs), such as GPT-4, are able to automatically extract structured representations from legislation. We use LLMs to create pathways from legislation, according to the JusticeBot methodology for legal decision support systems, evaluate the pathways and compare them to manually created pathways. The results are promising, with 60% of generated pathways being rated as equivalent or better than manually created ones in a blind comparison. The approach suggests a promising path to leverage the capabilities of LLMs to ease the costly development of systems based on symbolic approaches that are transparent and explainable.

Proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex task that is highly open to subjective interpretations. Existing works do not align their systems with enforceable definitions of hate speech, which can make their outputs inconsistent with the goals of regulators. This research introduces a new perspective and task for enforceable hate speech detection centred around legal definitions, and a dataset annotated on violations of eleven possible definitions by legal experts. Given the Figure 1: A visualization of our proposed method to challenge of identifying clear, legally enforceable ground hate speech to specialized legal definitions. A instances of hate speech, we augment the legal professional reads external legal resources and dataset with expert-generated samples and an makes a judgement on some hate speech input, then automatically mined challenge set. We experiment identifies offences according to our definitions and with grounding the model decision in makes a judgement on violations.