The world has seen the emergence of machines based on pretrained models, transformers, also known as generative artificial intelligences for their ability to produce various types of content, including text, images, audio, and synthetic data. Without resorting to preprogramming or special tricks, their intelligence grows as they learn from experience, and to ordinary people, they can appear human-like in conversation. This means that they can pass the Turing test, and that we are now living in one of many possible Turing futures where machines can pass for what they are not. However, the learning machines that Turing imagined would pass his imitation tests were machines inspired by the natural development of the low-energy human cortex. They would be raised like human children and naturally learn the ability to deceive an observer. These ``child machines,'' Turing hoped, would be powerful enough to have an impact on society and nature.

Generative AI models, such as the GPT and Llama series, have significant potential to assist laypeople in answering legal questions. However, little prior work focuses on the data sourcing, inference, and evaluation of these models in the context of laypersons. To this end, we propose a human-centric legal NLP pipeline, covering data sourcing, inference, and evaluation. We introduce and release a dataset, LegalQA, with real and specific legal questions spanning from employment law to criminal law, corresponding answers written by legal experts, and citations for each answer. We develop an automatic evaluation protocol for this dataset, then show that retrieval-augmented generation from only 850 citations in the train set can match or outperform internet-wide retrieval, despite containing 9 orders of magnitude less data. Finally, we propose future directions for open-sourced efforts, which fall behind closed-sourced models.

This paper introduces a system using generative AI agents to create tip sheets for investigative data reporting. Our system employs three specialized agents--an analyst, a reporter, and an editor--to collaboratively generate and refine tips from datasets. We validate this approach using real-world investigative stories, demonstrating that our agent-based system generally generates more newsworthy and accurate insights compared to a baseline model without agents, although some variability was noted between different stories. Our findings highlight the potential of generative AI to provide leads for investigative data reporting.

The dissemination of Large Language Models (LLMs), trained at scale, and endowed with powerful text-generating abilities has vastly increased the threats posed by generative AI technologies by reducing the cost of producing harmful, toxic, faked or forged content. In response, various proposals have been made to automatically discriminate artificially generated from human-written texts, typically framing the problem as a classification problem. Most approaches evaluate an input document by a well-chosen detector LLM, assuming that low-perplexity scores reliably signal machine-made content. As using one single detector can induce brittleness of performance, we instead consider several and derive a new, theoretically grounded approach to combine their respective strengths. Our experiments, using a variety of generator LLMs, suggest that our method effectively increases the robustness of detection.

Apple's newest iPhone will be released in Japan later this month missing a key feature. Apple Intelligence, which uses generative artificial intelligence to analyze text and photos, will not be activated on models of the iPhone 16 sold in several markets, including Japan, until 2025. Apple CEO Tim Cook and others from the Cupertino, California-headquartered technology company introduced the new device by video on Monday evening in the United States. "The next generation of iPhone has been designed for Apple Intelligence from the ground up," Cook said in the video. "It marks the beginning of an exciting new era."

Models like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of exploration on how to build speech interaction models based on open-source LLMs. To address this, we propose LLaMA-Omni, a novel model architecture designed for low-latency and high-quality speech interaction with LLMs. It eliminates the need for speech transcription, and can simultaneously generate text and speech responses directly from speech instructions with extremely low latency. We build our model based on the latest Llama-3.1-8B-Instruct To align the model with speech interaction scenarios, we construct a dataset named InstructS2S-200K, which includes 200K speech instructions and corresponding speech responses. Experimental results show that compared to previous speech-language models, LLaMA-Omni provides better responses in both content and style, with a response latency as low as 226ms. Additionally, training LLaMA-Omni takes less than 3 days on just 4 GPUs, paving the way for the efficient development of speech-language models in the future. Large language models (LLMs), represented by ChatGPT (OpenAI, 2022), have become powerful general-purpose task solvers, capable of assisting people in daily life through conversational interactions. However, most LLMs currently only support text-based interactions, which limits their application in scenarios where text input and output are not ideal. Recently, the emergence of GPT-4o (OpenAI, 2024) has made it possible to interact with LLMs through speech, responding to user's instruction with extremely low latency and significantly enhancing the user experience.

First, I want to apologize. My Kermit the Frog post was not entirely sincere. This particular post of mine has been viewed more than 10 million times, which is far more than I expected. But I did expect something. Social networks have never been the realm of good faith or authenticity; trolls and other engagement baiters have been able to engineer their own virality for years and years, simply by correctly predicting what large numbers of people will respond to.

What's new: Roblox has announced plans to roll out a generative AI tool that will let creators make whole 3D scenes just using text prompts. Users will also be able to modify scenes or expand their scope--say, to change a daytime scene to night or switch the desert for a forest. How it works: Once it's up and running, developers on the hugely popular online game platform will be able to simply write "Generate a race track in the desert," for example, and the AI will spin one up. Why it's a big deal: Although developers can already create similar scenes like this manually in the platform's creator studio, Roblox claims its new generative AI model will make the changes happen in a fraction of the time. It also claims that it will give developers with minimal 3D art skills the ability to craft more compelling environments.

Microsoft is currently focusing significant financial and human resources on the development of its AI assistant Copilot and its integration into Windows and Microsoft 365 applications. The company sees this as an opportunity to set itself apart from the competition of Libre Office and Google. Today, users have several alternatives for AI support in Office. This is because ChatGPT from OpenAI, the software that triggered the AI hype, is also suitable for office tasks in conjunction with Word, Excel, and others. Independent developers provide add-ons that allow you to integrate ChatGPT directly into Word so that you always have it at hand. At the same time, there are AI systems, especially from American providers, that help you create presentations online. These presentations can then be downloaded and in many cases converted into PowerPoint format PPTX.

A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, we present SciAgents, an approach that leverages three core concepts: (1) the use of large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses traditional human-driven research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the intelligent system yields material discoveries, critique and improve existing hypotheses, retrieve up-to-date data about existing research, and highlights their strengths and limitations. Our case studies demonstrate scalable capabilities to combine generative AI, ontological representations, and multi-agent modeling, harnessing a `swarm of intelligence' similar to biological systems. This provides new avenues for materials discovery and accelerates the development of advanced materials by unlocking Nature's design principles.