'Architects of AI' named Time Magazine's Person of the Year Time Magazine's Person of the Year for 2025 is not a single person. Instead, the magazine has recognised the year's most influential figure as the architects of artificial intelligence (AI). Nvidia boss Jensen Huang, Meta head Mark Zuckerberg, X owner Elon Musk and AI godmother Fei-Fei Li are among those depicted on one of the magazine's two covers. Experts say it highlights how quickly AI, and the firms behind it, are reshaping society. It comes as a boom in the technology, ushered in by OpenAI's launch of ChatGPT in late 2022, continues at pace.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. Named one of the world's top 100 voices shaping the future of AI, Tim El-Sheikh is a biomedical scientist and ex-pro athlete turned serial deeptech, AI and social entrepreneur since 2001 and is one of the pioneering, first-generation AI founders at London's Silicon Roundabout. Find more from Tim at the CEO Retort . This podcast was created and is run by Ben Byford and collaborators. The podcast, and other content was first created to extend Ben's growing interest in both the AI domain and in the associated ethics.

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A woman and her relatives look at her home, which was damaged during a night of Russian missile and drone strikes, amid Russia's attack on Ukraine, in Novi Petrivtsi, outside Kyiv, on Saturday. Microchip manufacturers Intel, Advanced Micro Devices (AMD) and Texas Instruments were accused in a series of lawsuits of failing to keep their technology out of Russian-made weapons used to kill and wound civilians in Ukraine. Those companies -- along with a company owned by Warren Buffett's Berkshire Hathaway -- demonstrated willful ignorance" as third parties resold restricted chips to Russia to power drones and missiles in violation of U.S. sanctions, according to one of the five suits, filed Wednesday in state court in Texas. The lawsuits, filed on behalf of dozens of Ukrainian civilians by Mikal Watts and prominent law firm Baker & Hostetler, cite five attacks between 2023 and 2025 that killed dozens of people. One attack allegedly involved Iranian-made drones with components associated with Intel and AMD, while the others involved Russian-made KH-101 cruise missiles and Iskander ballistic missiles.

AI has entered the classroom - but is it the solution for overworked teachers? Schools across the UK are trialling the use of deepfake teachers and even employing remote staff to deliver lessons hundreds of miles away from the classroom. It comes as the use of AI is becoming increasingly prevalent in schools. The government says AI has the power to transform education, and improve teacher workload, particularly around admin for teachers. The BBC has spoken to teachers, school leaders and unions who seem divided on what the future of the UK's classrooms should look like.

The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution's parameters as functions of time-dependent covariates. Deep neural networks provide the flexibility needed to learn complex relationships between these covariates and operational lifetime, thereby extending the capabilities of traditional regression-based models. Motivated by the analysis of a fleet of military vehicles operating in highly variable and demanding environments, as well as by the limitations observed in existing methodologies, this paper introduces WTNN, a new neural network-based modeling framework specifically designed for Weibull survival studies. The proposed architecture is specifically designed to incorporate qualitative prior knowledge regarding the most influential covariates, in a manner consistent with the shape and structure of the Weibull distribution. Through numerical experiments, we show that this approach can be reliably trained on proxy and right-censored data, and is capable of producing robust and interpretable survival predictions that can improve existing approaches.

Testing humanoid robots with users is slow, causes wear, and limits iteration and diversity. Yet screening agents must master conversational timing, prosody, backchannels, and what to attend to in faces and speech for Depression and PTSD. Most simulators omit policy learning with nonverbal dynamics; many controllers chase task accuracy while underweighting trust, pacing, and rapport. We virtualise the humanoid as a conversational agent to train without hardware burden. Our agent-centred, simulation-first pipeline turns interview data into 276 Unreal Engine MetaHuman patients with synchronised speech, gaze/face, and head-torso poses, plus PHQ-8 and PCL-C flows. A perception-fusion-policy loop decides what and when to speak, when to backchannel, and how to avoid interruptions, under a safety shield. Training uses counterfactual replay (bounded nonverbal perturbations) and an uncertainty-aware turn manager that probes to reduce diagnostic ambiguity. Results are simulation-only; the humanoid is the transfer target. In comparing three controllers, a custom TD3 (Twin Delayed DDPG) outperformed PPO and CEM, achieving near-ceiling coverage with steadier pace at comparable rewards. Decision-quality analyses show negligible turn overlap, aligned cut timing, fewer clarification prompts, and shorter waits. Performance stays stable under modality dropout and a renderer swap, and rankings hold on a held-out patient split. Contributions: (1) an agent-centred simulator that turns interviews into 276 interactive patients with bounded nonverbal counterfactuals; (2) a safe learning loop that treats timing and rapport as first-class control variables; (3) a comparative study (TD3 vs PPO/CEM) with clear gains in completeness and social timing; and (4) ablations and robustness analyses explaining the gains and enabling clinician-supervised humanoid pilots.

Medical phrase grounding (MPG) maps textual descriptions of radiological findings to corresponding image regions. These grounded reports are easier to interpret, especially for non-experts. Existing MPG systems mostly follow the referring expression comprehension (REC) paradigm and return exactly one bounding box per phrase. Real reports often violate this assumption. They contain multi-region findings, non-diagnostic text, and non-groundable phrases, such as negations or descriptions of normal anatomy. Motivated by this, we reformulate the task as generalised medical phrase grounding (GMPG), where each sentence is mapped to zero, one, or multiple scored regions. To realise this formulation, we introduce the first GMPG model: MedGrounder. We adopted a two-stage training regime: pre-training on report sentence--anatomy box alignment datasets and fine-tuning on report sentence--human annotated box datasets. Experiments on PadChest-GR and MS-CXR show that MedGrounder achieves strong zero-shot transfer and outperforms REC-style and grounded report generation baselines on multi-region and non-groundable phrases, while using far fewer human box annotations. Finally, we show that MedGrounder can be composed with existing report generators to produce grounded reports without retraining the generator.

Eufy's last-season robot vacuum-mop has a crazy price cut. Grab it before it's gone. Robot vacuums have improved in nearly every feature, and that includes the price. While you can expect to pay anywhere between $1,000 to $1,500 for a brand-new, fully-featured robot vacuum mop, you can snag last year's models for pretty incredible prices. That includes our current best robot vacuum, which is last year's Eufy X10 Pro Omni .

GPU prices could follow RAM's big rise Spotify's new playlist feature gives users more control over their recommendation algorithm Users in New Zealand will be able to write prompts for custom playlists. Spotify is attempting to give users more control over the music the streaming service recommends with a new playlist feature called Prompted Playlist. The beta feature is rolling out in New Zealand starting on December 11, and will let users write a custom prompt that Spotify can use -- alongside their listening history -- to create a playlist of new music. By tapping on Prompted Playlist, Spotify subscribers participating in the beta will be presented with a prompt field where they can type exactly what they want to hear and how they want Spotify's algorithm to respond. And while past AI features took users' individual taste into consideration, Spotify claims Prompted Playlist taps into your entire Spotify listening history, all the way back to day one.