Persuasion is a key aspect of what it means to be human, and is central to business, politics, and other endeavors. Advancements in artificial intelligence (AI) have produced AI systems that are capable of persuading humans to buy products, watch videos, click on search results, and more. Even systems that are not explicitly designed to persuade may do so in practice. In the future, increasingly anthropomorphic AI systems may form ongoing relationships with users, increasing their persuasive power. This paper investigates the uncertain future of persuasive AI systems. We examine ways that AI could qualitatively alter our relationship to and views regarding persuasion by shifting the balance of persuasive power, allowing personalized persuasion to be deployed at scale, powering misinformation campaigns, and changing the way humans can shape their own discourse. We consider ways AI-driven persuasion could differ from human-driven persuasion. We warn that ubiquitous highlypersuasive AI systems could alter our information environment so significantly so as to contribute to a loss of human control of our own future. In response, we examine several potential responses to AI-driven persuasion: prohibition, identification of AI agents, truthful AI, and legal remedies. We conclude that none of these solutions will be airtight, and that individuals and governments will need to take active steps to guard against the most pernicious effects of persuasive AI.

It is widely accepted that so-called facts can be checked by searching for information on the Internet. This process requires a fact-checker to formulate a search query based on the fact and to present it to a search engine. Then, relevant and believable passages need to be identified in the search results before a decision is made. This process is carried out by sub-editors at many news and media organisations on a daily basis. Here, we ask the question as to whether it is possible to automate the first step, that of query generation. Can we automatically formulate search queries based on factual statements which are similar to those formulated by human experts? Here, we consider similarity both in terms of textual similarity and with respect to relevant documents being returned by a search engine. First, we introduce a moderate-sized evidence collection dataset which includes 390 factual statements together with associated human-generated search queries and search results. Then, we investigate generating queries using a number of rule-based and automatic text generation methods based on pre-trained large language models (LLMs). We show that these methods have different merits and propose a hybrid approach which has superior performance in practice.

The potential of sequence-to-sequence (Seq2Seq) models and prompting Recent studies have exploited advanced generative language models methods has not been fully explored [4]. Moreover, traditional evaluation to generate Natural Language Explanations (NLE) for why a certain metrics, such as BLEU [20] and Rouge [18], applied in NLE text could be hateful. We propose the Chain of Explanation (CoE) generation for hate speech, may also not be able to comprehensively Prompting method, using the heuristic words and target group, to capture the quality of the generated explanations because they generate high-quality NLE for implicit hate speech. We improved heavily rely on the word-level overlaps [3]. To fill those gaps, we the BLUE score from 44.0 to 62.3 for NLE generation by providing propose a Chain of Explanations (CoE) prompt method to generate accurate target information. We then evaluate the quality of generated high-quality NLE distinguishing the implicit hate speech from nonhateful NLE using various automatic metrics and human annotations tweets.

The question will be simple but perpetual: Person or machine? Every encounter with language, other than in the flesh, will now bring with it that small, consuming test. For some--teachers, professors, journalists--the question of humanity will be urgent and essential. For those who operate in the large bureaucratic apparatus of boilerplate--copywriters, lawyers, advertisers, political strategists--the question will be irrelevant except as a matter of efficiency. How will they use new artificial-intelligence technology to accelerate the production of language that was already mostly automatic? For everyone, the question will now hover, quotidian and cosmic, over words wherever you find them: Who's there?

Isabelle Augenstein was one of the invited speakers at this year's AAAI Conference on Artificial Intelligence. She presented some of her work relating the communication of scientific research, and how information changes as it is reported by different media. Accurate reporting of science and technology is of paramount importance. The general public relies principally on mainstream media outlets for their science news. Overhyping, exaggeration and misrepresentation of research findings erode trust in science and scientists.

Public swimming pools facing closure because of soaring energy bills have been offered a lifeline via new technology to heat the water. Mark Bjornsgaard, the chief executive of the tech startup Deep Green, has trialled the idea in Exmouth, Devon. He has put a small computer data processing centre underneath the pool and the energy from it heats the water. The idea has taken off and up to 20 public pools could be upgraded to the heat system this year. "We built a small data centre in Exmouth leisure centre. Most normal data centres waste the heat that the computers generate. We capture ours and we give it for free to the swimming pool to heat the pool," Bjornsgaard told BBC Radio 4's Today programme.

Generative AI is an artificial intelligence model that, when trained on massive datasets, can generate text, images, audio, and video by predicting the next word or pixel. The simplest input (called a prompt) to generative AI is a text description. Based on that text description, a generative pre-trained transformer (GPT) can write a paragraph, a text-to-image model such as Stable Diffusion can create a picture, MusicLM can create music, and Imagen Video can create a video. This technology will democratize all kinds of content creation. For video creation it could level the playing field more than smartphones and social video platforms have already done.

Generative AI is an artificial intelligence model that, when trained on massive datasets, can generate text, images, audio, and video by predicting the next word or pixel. The simplest input (called a prompt) to generative AI is a text description. Based on that text description, a generative pre-trained transformer (GPT) can write a paragraph, a text-to-image model such as Stable Diffusion can create a picture, MusicLM can create music, and Imagen Video can create a video. This technology will democratize all kinds of content creation. For video creation it could level the playing field more than smartphones and social video platforms have already done.

This tutorial shows how to model noise in a Digital Camera. While most methods just add Gaussian or even Uniform noise to an image, they are not characteristic of true image noise. True image noise is not additive or white, it depends on the image intensity level and has spatial correlations that are introduced through Demosaicing [1]. To gain a better understanding of how noise can be injected into an image, we should consider the full imaging pipeline, a diagram of this pipeline is shown below. In other words, as the light propagates through the camera, there are 2 noise sources added to it along the way.

Spotify has bigger plans for the technology behind its new AI DJ feature after seeing positive consumer reaction to the new feature. Launched just ahead of the company's Stream On event in L.A. last week, the AI DJ curates a personalized selection of music combined with spoken commentary delivered in a realistic-sounding, AI-generated voice. But under the hood, the feature leverages the latest in AI technologies and large language models, as well as generative voice -- all of which are layered on top of Spotify's existing investments in personalization and machine learning. These new tools don't necessarily have to be limited to a single feature, Spotify believes, which is why it's now experimenting with other applications of the technology. Though the highlight from Spotify's Stream On event was the mobile app's revamp, which now focuses on TikTok-like discovery feeds for music, podcasts, and audiobooks, the AI DJ is now a prominent part of the streaming service's new experience.