From spotty leopards to stripy zebras, nature has no shortage of distinct patterns on animals and plants. Now, the age-old question of how these patterns developed may have finally been solved. Scientists have shown that the same physical process that helps remove dirt from laundry could play a role in how tropical fish get their colourful spots and stripes. For their study, the team at the University of Colorado Boulder drew on the groundbreaking work of British computer pioneer Alan Turing, dating back more than 70 years. They believe their findings could help develop new materials and even new drugs.

When politicians, tech executives, and researchers gathered in the UK last week to discuss the risks of artificial intelligence, one prominent worry was that algorithms might someday turn against their human masters. More quietly, the group made progress on controlling the use of AI for military ends. On November 1, at the US embassy in London, US vice president Kamala Harris announced a range of AI initiatives, and her warnings about the threat AI poses to human rights and democratic values got people's attention. But she also revealed a declaration signed by 31 nations to set guardrails around military use of AI. It pledges signatories to use legal reviews and training to ensure military AI stays within international laws, develop the technology cautiously and transparently, avoid unintended biases in systems that use AI, and continue to discuss how the technology can be developed and deployed responsibly.

Cold, calculating, and robotic: lawyers of the future might really live up to their exaggerated reputations as AI takes over the legal profession. For the first time, two AIs, created by lawtech firm Luminance, have successfully negotiated a contract without any human involvement. The AIs went back and forth over the details of a real Non-Disclosure Agreement between the company and proSapient, one of Luminance's clients. The contract was finalised within minutes and the only time a human was required was to add their signature. This stunning demonstration comes just one week after Elon Musk predicted that AI would eventually create a jobless utopia where no one has to work. In a conversation with Prime Minister Rishi Sunak at the Bletchley Park AI Summit, Mr Musk said that AI would be the most disruptive force in the history of work and would ultimately remove the need for humans to have jobs.

Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim. In this paper we introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each claim is annotated with question-answer pairs supported by evidence available online, as well as textual justifications explaining how the evidence combines to produce a verdict. Through a multi-round annotation process, we avoid common pitfalls including context dependence, evidence insufficiency, and temporal leakage, and reach a substantial inter-annotator agreement of $\kappa=0.619$ on verdicts. We develop a baseline as well as an evaluation scheme for verifying claims through several question-answering steps against the open web.

Contemporary Human Computer Interaction (HCI) research relies primarily on neural network models for machine vision and speech understanding of a system user. Such models require extensively annotated training datasets for optimal performance and when building interfaces for users from a vulnerable population such as young children, GDPR introduces significant complexities in data collection, management, and processing. Motivated by the training needs of an Edge AI smart toy platform this research explores the latest advances in generative neural technologies and provides a working proof of concept of a controllable data generation pipeline for speech driven facial training data at scale. In this context, we demonstrate how StyleGAN2 can be finetuned to create a gender balanced dataset of children's faces. This dataset includes a variety of controllable factors such as facial expressions, age variations, facial poses, and even speech-driven animations with realistic lip synchronization. By combining generative text to speech models for child voice synthesis and a 3D landmark based talking heads pipeline, we can generate highly realistic, entirely synthetic, talking child video clips. These video clips can provide valuable, and controllable, synthetic training data for neural network models, bridging the gap when real data is scarce or restricted due to privacy regulations.

Since its introduction, the transformers architecture has seen great adoption in NLP applications, but it also has limitations. Although the self-attention mechanism allows for generating very rich representations of the input text, its effectiveness may be limited in specialized domains such as legal, where, for example, language models often have to process very long texts. In this paper, we explore alternatives to replace the attention-based layers with simpler token-mixing mechanisms: Hartley and Fourier transforms. Using these non-parametric techniques, we train models with long input documents from scratch in the legal domain setting. We also introduce a new hybrid Seq2Seq architecture, a no-attention-based encoder connected with an attention-based decoder, which performs quite well on existing summarization tasks with much less compute and memory requirements. We believe that similar, if not better performance, as in the case of long correlations of abstractive text summarization tasks, can be achieved by adopting these simpler infrastructures. This not only makes training models from scratch accessible to more people, but also contributes to the reduction of the carbon footprint during training.

Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real-world arguments are tightly anchored in context, existing computational methods analyze their quality in isolation, which affects their accuracy and generalizability. We propose SPARK: a novel method for scoring argument quality based on contextualization via relevant knowledge. We devise four augmentations that leverage large language models to provide feedback, infer hidden assumptions, supply a similar-quality argument, or give a counter-argument. SPARK uses a dual-encoder Transformer architecture to enable the original argument and its augmentation to be considered jointly. Our experiments in both in-domain and zero-shot setups show that SPARK consistently outperforms existing techniques across multiple metrics.

On Thursday, jurors convicted former crypto mogul Sam Bankman-Fried of defrauding his customers out of as much as $10 billion. He will likely spend the rest of his 30s--and possibly his 40s, 50s, and 60s--in prison. The judge is expected to sentence him in March. As former confidants and close friends testified against him during his monthlong trial, Bankman-Fried's parents, Joseph and Barbara, showed up day after day to support their son, whose crypto exchange FTX imploded late last year. The Stanford Law professors' hand gestures and facial expressions played prominently into journalists' recounts of the proceedings, offering the real-life version of the cutaway shot integral to any courtroom TV show.

OpenAI, the company whose ChatGPT brought AI chatbots to mainstream awareness, said Monday that it'll let you build special-purpose AI apps using its technology. And with a new app store coming that'll let you find or share these GPTs, as the company is calling these customized artificial intelligence tools, OpenAI looks like it's hoping to have something an iPhone moment. You don't need to know how to program to make a new GPT. You have to give it plain-language instructions, upload some of your own knowledge in the form of PDFs, videos or other files, then steer the bot's purpose in a direction like creating images or searching the web. "GPTs are tailored versions of ChatGPT for a specific purpose," OpenAI Chief Executive Sam Altman said at the OpenAI DevDay conference in San Francisco.

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect, and higher levels of participants' influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.