With the rise of artificial intelligence (A.I.) and large language models like ChatGPT, a new race for achieving artificial general intelligence (A.G.I) has started. While many speculate how and when A.I. will achieve A.G.I., there is no clear agreement on how A.G.I. can be detected in A.I. models, even when popular tools like the Turing test (and its modern variations) are used to measure their intelligence. In this work, we discuss why traditional methods like the Turing test do not suffice for measuring or detecting A.G.I. and provide a new, practical method that can be used to decide if a system (computer or any other) has reached or surpassed A.G.I. To achieve this, we make two new contributions. First, we present a clear definition for general intelligence (G.I.) and set a G.I. Threshold (G.I.T.) that can be used to distinguish between systems that achieve A.G.I. and systems that do not. Second, we present a new framework on how to construct tests that can detect if a system has achieved G.I. in a simple, comprehensive, and clear-cut fail/pass way. We call this novel framework the Turing test 2.0. We then demonstrate real-life examples of applying tests that follow our Turing test 2.0 framework on modern A.I. models.

Large Language Models (LLMs) are typically trained on data mixtures: most data come from web scrapes, while a small portion is curated from high-quality sources with dense domain-specific knowledge. In this paper, we show that when training LLMs on such data mixtures, knowledge acquisition from knowledge-dense datasets, unlike training exclusively on knowledge-dense data (arXiv:2404.05405), does not always follow a smooth scaling law but can exhibit phase transitions with respect to the mixing ratio and model size. Through controlled experiments on a synthetic biography dataset mixed with web-scraped data, we demonstrate that: (1) as we increase the model size to a critical value, the model suddenly transitions from memorizing very few to most of the biographies; (2) below a critical mixing ratio, the model memorizes almost nothing even with extensive training, but beyond this threshold, it rapidly memorizes more biographies. We attribute these phase transitions to a capacity allocation phenomenon: a model with bounded capacity must act like a knapsack problem solver to minimize the overall test loss, and the optimal allocation across datasets can change discontinuously as the model size or mixing ratio varies. We formalize this intuition in an information-theoretic framework and reveal that these phase transitions are predictable, with the critical mixing ratio following a power-law relationship with the model size. Our findings highlight a concrete case where a good mixing recipe for large models may not be optimal for small models, and vice versa.

Detecting vulnerabilities in source code remains a critical yet challenging task, especially when benign and vulnerable functions share significant similarities. In this work, we introduce VulTrial, a courtroom-inspired multi-agent framework designed to identify vulnerable code and to provide explanations. It employs four role-specific agents, which are security researcher, code author, moderator, and review board. Using GPT-4o as the base LLM, VulTrial almost doubles the efficacy of prior best-performing baselines. Additionally, we show that role-specific instruction tuning with small quantities of data significantly further boosts VulTrial's efficacy. Our extensive experiments demonstrate the efficacy of VulTrial across different LLMs, including an open-source, in-house-deployable model (LLaMA-3.1-8B), as well as the high quality of its generated explanations and its ability to uncover multiple confirmed zero-day vulnerabilities in the wild.

Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their structures remain vulnerable to malicious influences. In this paper, we address a specific threat: Byzantine attacks, wherein compromised clients inject adversarial updates to derail global convergence. We combine the concept of trust scores with trial function methodology to dynamically filter outliers. Our methods address the critical limitations of previous approaches, allowing operation even when Byzantine nodes are in the majority. Moreover, our algorithms adapt to widely used scaled methods such as Adam and RMSProp, as well as practical scenarios, including local training and partial participation. We validate the robustness of our methods by conducting extensive experiments on both public datasets and private ECG data collected from medical institutions. Furthermore, we provide a broad theoretical analysis of our algorithms and their extensions to the aforementioned practical setups. The convergence guaranties of our methods are comparable to those of classical algorithms developed without Byzantine interference.

The visually impaired population faces significant challenges in daily activities. While prior works employ vision language models for assistance, most focus on static content and cannot address real-time perception needs in complex environments. Recent VideoLLMs enable real-time vision and speech interaction, offering promising potential for assistive tasks. In this work, we conduct the first study evaluating their effectiveness in supporting daily life for visually impaired individuals. We first conducted a user survey with visually impaired participants to design the benchmark VisAssistDaily for daily life evaluation. Using VisAssistDaily, we evaluate popular VideoLLMs and find GPT-4o achieves the highest task success rate. We further conduct a user study to reveal concerns about hazard perception. To address this, we propose SafeVid, an environment-awareness dataset, and fine-tune VITA-1.5, improving risk recognition accuracy from 25.00% to 76.00%.We hope this work provides valuable insights and inspiration for future research in this field.

Harmful algal blooms are a growing threat to inland water quality and public health worldwide, creating an urgent need for e fficient, accurate, and cost-e ff ective detection methods. This research introduces a high-performing methodology that integrates multiple open-source remote sensing data with advanced artificial intelligence models. Key data sources include Copernicus Sentinel-2 optical imagery, the Copernicus Digital Elevation Model (DEM), and NOAA's High-Resolution Rapid Refresh (HRRR) climate data, all e ffi ciently retrieved using platforms like Google Earth Engine (GEE) and Microsoft Planetary Computer (MPC). The NIR and two SWIR bands from Sentinel-2, the altitude from the elevation model, the temperature and wind from NOAA as well as the longitude and latitude were the most important features. The approach combines two types of machine learning models--tree-based models and a neural network--into an ensemble for classifying algal bloom severity. While the tree models performed strongly on their own, incorporating a neural network added robustness and demonstrated how deep learning models can e ff ectively use diverse remote sensing inputs. The method leverages high-resolution satellite imagery and AI-driven analysis to monitor algal blooms dynamically, and although initially developed for a NASA competition in the U.S., it shows potential for global application. Keywords: Machine learning; Inland Water; Algal Bloom; Remote Sensing; Data Fusion; Water Quality 1. Introduction Algal blooms are becoming the greatest inland water quality threat to public health and aquatic ecosystems that can degrade water quality to a greater extent than many chemicals (Brooks et al., 2016). Human nutrient loading and climate change (warming, altered rainfall) synergistically enhance cyanobacterial blooms in aquatic ecosystems (Paerl and Paul, 2012). Excessive nutrient loads in many cases comes from agricultural, industrial and other sources (Novotny, 2011). Phenology and trends of chlorophyll-a and cyanobacterial blooms are established (Matthews, 2014).

The Industry Trump Gave the Tech Bros Everything. Why Are They Still Crashing Out? This was supposed to be their year--but a historically unpopular president and fears of an A.I. stock market crash loom large over Silicon Valley. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time.

Cloudflare CEO Matthew Prince claims the internet infrastructure company's efforts to block AI crawlers are already seeing big results. As the large language models powering generative AI tools slurp up ever more data across the web, Cloudflare cofounder and CEO Matthew Prince said at WIRED's Big Interview event in San Francisco on Thursday that the internet infrastructure company has blocked more than 400 billion AI bot requests for its customers since July 1. The action comes after the company announced a Content Independence Day in July--an initiative with prominent publishers and AI firms to block AI crawlers by default on content creators' work unless the AI companies pay for access. Since July 2024, Cloudflare has offered customers tools to block AI bots from scraping their content. Cloudflare told WIRED that the number of AI bots blocked since July 1, 2025 is 416 billion.

Sam Kirchner wants to save the world from artificial superintelligence. He's been missing for two weeks. B efore Sam Kirchner vanished, before the San Francisco Police Department began to warn that he could be armed and dangerous, before OpenAI locked down its offices over the potential threat, those who encountered him saw him as an ordinary, if ardent, activist. Phoebe Thomas Sorgen met Kirchner a few months ago at Travis Air Force Base, northeast of San Francisco, at a protest against immigration policy and U.S. military aid to Israel. Sorgen, a longtime activist whose first protests were against the Vietnam War, was going to block an entrance to the base with six other older women. Kirchner, 27 years old, was there with a couple of other members of a new group called Stop AI, and they all agreed to go along to record video on their phones in case of a confrontation with the police.

In this episode of, we talk to writer Evan Ratliff about how he created a small startup made entirely of AI employees--and what his findings reveal about the reality of an agentic future. This year, AI agents have been at the forefront of tech companies' ambitions. OpenAI's Sam Altman has often talked about a possible billion-dollar company being spun up with just one human and an army of AI agents. And so last summer, journalist Evan Ratliff decided to try to become that unicorn himself--by creating HarumoAI, a small startup that's made up of AI employees and executives. Hosts Michael Calore and Lauren Goode sit down with Evan to discuss how it's going, and the current promises and realities of AI agents. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Hey, Lauren, how are you doing? It was so fantastic that I had a hard time coming back, honestly. And I saw a lot of really beautiful art. Not a bad place to go for vacation, I have to say. I've heard this before, I confirmed it. And after seeing so much incredible art and just people doing stuff with their hands and tangible goods, I was like, I don't want to go back to the world of AI. I didn't want to go back to sitting in a coffee shop and hearing everyone pitching their AI startups and driving on the 101 and seeing the billboards. I was just like, What? No, keep me in the land of Burrata and Caravaggio. Well, Lauren, I'm sorry to tell you that you came back on the show just in time to talk about AI agents. It's something that we've talked about a lot this year and our listeners have heard about it a lot, and we're not sick of talking about it.