Netflix has used artificial intelligence in one of its TV shows for the first time, in a move the streaming company's boss said would make films and programmes cheaper and of better quality. Ted Sarandos, a co-chief executive of Netflix, said the Argentinian science fiction series El Eternauta (The Eternaut) was the first it had made that involved using generative AI footage. "We remain convinced that AI represents an incredible opportunity to help creators make films and series better, not just cheaper," he told analysts on Thursday after Netflix reported its second-quarter results. He said the series, which follows survivors of a rapid and devastating toxic snowfall, involved Netflix and visual effects (VFX) artists using AI to show a building collapsing in Buenos Aires. "Using AI-powered tools, they were able to achieve an amazing result with remarkable speed and, in fact, that VFX sequence was completed 10 times faster than it could have been completed with traditional VFX tools and workflows," he said.

Netflix says it has used visual effects created by generative artificial intelligence (AI) on screen for the first time in one of its original TV shows. The streaming giant's co-CEO Ted Sarandos said AI, which produces videos and images based on prompts, was used to create a scene of a building collapsing in the Argentine science fiction show, The Eternauts. He praised the technology as an "incredible opportunity to help creators make films and series better, not just cheaper." The use of generative AI is controversial in the entertainment industry and has sparked fears that it will replace the work of humans.

The increasing use of generative AI for resume screening is predicated on the assumption that it offers an unbiased alternative to biased human decision-making. However, this belief fails to address a critical question: are these AI systems fundamentally competent at the evaluative tasks they are meant to perform? This study investigates the question of competence through a two-part audit of eight major AI platforms. Experiment 1 confirmed complex, contextual racial and gender biases, with some models penalizing candidates merely for the presence of demographic signals. Experiment 2, which evaluated core competence, provided a critical insight: some models that appeared unbiased were, in fact, incapable of performing a substantive evaluation, relying instead on superficial keyword matching. This paper introduces the "Illusion of Neutrality" to describe this phenomenon, where an apparent lack of bias is merely a symptom of a model's inability to make meaningful judgments. This study recommends that organizations and regulators adopt a dual-validation framework, auditing AI hiring tools for both demographic bias and demonstrable competence to ensure they are both equitable and effective.

In this paper, a novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed, to enhance the robustness against both channel noise and transmission data distribution shifts. A theoretical foundation is established using stochastic differential equations (SDEs), from which a closed-form mapping between any signal-to-noise ratio (SNR) and the optimal denoising timestep is derived. Moreover, to address distribution mismatch, a mathematical scaling method is introduced to align received semantic features with the training distribution of the GAI. Built on this theoretical foundation, a latent diffusion model (LDM)-based semantic communication framework is proposed that combines a variational autoencoder for semantic features extraction, where a pretrained diffusion model is used for denoising. The proposed system is a training-free framework that supports zero-shot generalization, and achieves superior performance under low-SNR and out-of-distribution conditions, offering a scalable and robust solution for future 6G semantic communication systems. Experimental results demonstrate that the proposed semantic communication framework achieves state-of-the-art performance in both pixel-level accuracy and semantic perceptual quality, consistently outperforming baselines across a wide range of SNRs and data distributions without any fine-tuning or post-training.

While recent advancements in deep neural networks (DNNs) have substantially enhanced visual AI's capabilities, the challenge of inadequate data diversity and volume remains, particularly in construction domain. This study presents a novel image synthesis methodology tailored for construction worker detection, leveraging the generative-AI platform Midjourney. The approach entails generating a collection of 12,000 synthetic images by formulating 3000 different prompts, with an emphasis on image realism and diversity. These images, after manual labeling, serve as a dataset for DNN training. Evaluation on a real construction image dataset yielded promising results, with the model attaining average precisions (APs) of 0.937 and 0.642 at intersection-over-union (IoU) thresholds of 0.5 and 0.5 to 0.95, respectively. Notably, the model demonstrated near-perfect performance on the synthetic dataset, achieving APs of 0.994 and 0.919 at the two mentioned thresholds. These findings reveal both the potential and weakness of generative AI in addressing DNN training data scarcity.

Users of ChatGPT will be able to ask an AI agent to find restaurant reservations, go shopping for them and even draw up lists of candidates for job vacancies, as the chatbot gains the powers of a personal assistant from Thursday. ChatGPT agent, launched by Open AI everywhere apart from the EU, not only "thinks" but also acts, the US company said. The agent combines the powers of AI research tools with the ability to take control of web browsers, computer files and software such as spreadsheets and slide decks. It follows the launch of similar "agents" by Google and Anthropic as interest grows in AI models that can handle computer-based tasks by judging which software is best to use and toggling between systems to autonomously complete assignments like drafting travel itineraries or carrying out work research. "The hope is that agents are able to bring some real utility to users – to actually do things for them rather than just outputting polished text and sounding impressive," said Niamh Burns, senior media analyst at Enders Analysis.

Getting into local models takes a bit more effort than, say, navigating to ChatGPT's online interface. But the very accessibility of a tool like ChatGPT comes with a cost. "It's the classic adage: If something's free, you're the product," says Elizabeth Seger, the director of digital policy at Demos, a London-based think tank. OpenAI, which offers both paid and free tiers, trains its models on users' chats by default. It's not too difficult to opt out of this training, and it also used to be possible to remove your chat data from OpenAI's systems entirely, until a recent legal decision in the New York Times' ongoing lawsuit against OpenAI required the company to maintain all user conversations with ChatGPT.

Isa Fulford, the research lead for OpenAI's new ChatGPT agent, needed to order a bunch of cupcakes, so she asked the AI tool to do it for her. "I was very specific about what I wanted, and it was a lot of cupcakes," she says. "That one took almost an hour--but it was easier than me doing it myself, because I didn't want to do it." OpenAI has launched a new agent for ChatGPT that uses a virtual browser to complete tasks and can generate downloadable files, specifically PowerPoint presentations and Excel spreadsheets. While not a full replacement for the Microsoft suite of workplace tools, the features included in this agent from OpenAI could obviate some users' reliance on Microsoft's enterprise software.

Artificial intelligence companies are "fundamentally unprepared" for the consequences of creating systems with human-level intellectual performance, according to a leading AI safety group. The Future of Life Institute (FLI) said none of the firms on its AI safety index scored higher than a D for "existential safety planning". One of the five reviewers of the FLI's report said that, despite aiming to develop artificial general intelligence (AGI), none of the companies scrutinised had "anything like a coherent, actionable plan" to ensure the systems remained safe and controllable. AGI refers to a theoretical stage of AI development at which a system is capable of matching a human in carrying out any intellectual task. OpenAI, the developer of ChatGPT, has said its mission is to ensure AGI "benefits all of humanity".

"We want to make it really easy for people to see who is not just talking the talk, but who is also walking the walk," says Max Tegmark, president of the FLI. Read More: Some Top AI Labs Have'Very Weak' Risk Management, Study Finds SaferAI assessed top AI companies' risk management protocols (also known as responsible scaling policies) to score each company on its approach to identifying and mitigating AI risks. No AI company scored better than "weak" in SaferAI's assessment of their risk management maturity. The highest scorer was Anthropic (35%), followed by OpenAI (33%), Meta (22%), and Google DeepMind (20%). Two companies, Anthropic and Google DeepMind, received lower scores than the first time the study was carried out, in October 2024.