Can ChatGPT really help you change your life - or just flatter you? Can ChatGPT really help you change your life - or just flatter you? AI as a life coach: experts share what works, what doesn't and what to look out for It's becoming more common for people to use AI chatbots for personal guidance - but this doesn't come without risks Setting goals is hard; keeping them is harder - and failure can bring about icky feelings about yourself. This year, in an effort to game the system and tilt the scales toward success, some people used AI for their 2026 resolutions. It's the latest step in an ongoing trend: in September 2025, OpenAI, the company behind ChatGPT, released findings showing that using the AI chatbot for personal guidance is very common.

PCWorld reports on a Chrome extension called "Microsoft to Microslop" that renames Microsoft references in browsers as a protest against the company's aggressive AI integration. The extension reflects widespread user frustration with Microsoft's Copilot AI, which faces extremely low adoption rates and growing privacy concerns among Windows users. Many users actively seek ways to remove AI features from Windows, highlighting significant backlash against Microsoft's AI strategy despite CEO dismissals of complaints. Microsoft CEO Satya Nadella says we shouldn't think of LLM output as "slop." You know, AI-generated content, the thing that's making the internet worse in every measurable way, and causing consumer electronics prices to skyrocket? So it would be a real shame if you installed an extension in your browser that changed "Microsoft" to "Microslop" all over the web. Yes, installing " Microsoft to Microslop " would be a naughty and entirely cynical response. Especially if you, say, used Edge's Chromium base to install it in Microsoft's own default web browser, Edge. That would just be twisting the AI-generated knife, wouldn't it?

Apple's Siri AI will be powered by Gemini OpenAI claims Translate can rewrite the output to take context and tone into account. OpenAI has debuted a dedicated ChatGPT-powered translation tool. While folks have been using the main chatbot for translation for some time, you can now find ChatGPT Translate on its own webpage, as spotted. The tool can translate text, voice inputs and images into more than 50 languages in seconds, OpenAI says. Most interestingly, ChatGPT Translate can rewrite the output to take various contexts and tones into account, much in the same way that more general text-generating AI tools can do .

Researchers risk fire, explosion or poisoning by allowing AI to design experiments, warn scientists. The use of AI models in scientific laboratories risks enabling dangerous experiments that could cause fires or explosions, researchers have warned. Such models offer a convincing illusion of understanding but are susceptible to missing basic and vital safety precautions. In tests of 19 cutting-edge AI models, every single one made potentially deadly mistakes. Serious accidents in university labs are rare but certainly not unheard of.

As the free online encyclopedia turns 25, it's facing political opposition, AI scraping, dwindling volunteers, and a public that may no longer believe in its ideals. In 2010, the FBI sent Wikipedia a letter that would be intimidating for any organization to receive. The missive demanded that the free online encyclopedia remove the FBI's logo from an entry about the agency, claiming that reproducing the emblem was illegal and punishable with fines, imprisonment, "or both." Rather than back down, a lawyer for the Wikimedia Foundation, which hosts Wikipedia, shot back a sharp refusal outlining how the FBI's interpretation of the relevant statute was incorrect and saying that Wikipedia was "prepared to argue our view in court." It worked--the FBI dropped the matter.

The news is a blow for Thinking Machines Lab. Two narratives are already emerging about what happened. Thinking Machines cofounders Barret Zoph and Luke Metz are leaving the fledgling AI lab and rejoining OpenAI, the ChatGPT-maker announced on Thursday. OpenAI's CEO of applications, Fidji Simo, shared the news in a memo to staff Thursday afternoon. The news was first reported on X by technology reporter Kylie Robison, who wrote that Zoph was fired for "unethical conduct."

Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this work, we introduce Contrastive Geometric Learning for Unified Computational Drug Design (ConGLUDe), a single contrastive geometric model that unifies structure- and ligand-based training. ConGLUDe couples a geometric protein encoder that produces whole-protein representations and implicit embeddings of predicted binding sites with a fast ligand encoder, removing the need for pre-defined pockets. By aligning ligands with both global protein representations and multiple candidate binding sites through contrastive learning, ConGLUDe supports ligand-conditioned pocket prediction in addition to virtual screening and target fishing, while being trained jointly on protein-ligand complexes and large-scale bioactivity data. Across diverse benchmarks, ConGLUDe achieves state-of-the-art zero-shot virtual screening performance in settings where no binding pocket information is provided as input, substantially outperforms existing methods on a challenging target fishing task, and demonstrates competitive ligand-conditioned pocket selection. These results highlight the advantages of unified structure-ligand training and position ConGLUDe as a step toward general-purpose foundation models for drug discovery.

The Rashomon effect -- the existence of multiple, distinct models that achieve nearly equivalent predictive performance -- has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effect, organizing them into three categories: statistical sources arising from finite samples and noise in the data-generating process; structural sources arising from non-convexity of optimization objectives and unobserved variables that create fundamental non-identifiability; and procedural sources arising from limitations of optimization algorithms and deliberate restrictions to suboptimal model classes. We synthesize insights from machine learning, statistics, and optimization literature to provide a unified framework for understanding why the multiplicity of good models arises. A key distinction emerges: statistical multiplicity diminishes with more data, structural multiplicity persists asymptotically and cannot be resolved without different data or additional assumptions, and procedural multiplicity reflects choices made by practitioners. Beyond characterizing causes, we discuss both the challenges and opportunities presented by the Rashomon effect, including implications for inference, interpretability, fairness, and decision-making under uncertainty.

Google launches Personal Intelligence for Gemini, allowing the AI to access data from Photos, YouTube, and Gmail to provide personalized recommendations and answers. PCWorld reports the feature initially requires Google AI Pro or Ultra subscriptions in the US, with broader availability planned for later. While offering enhanced convenience through data synthesis, Google warns users about potential inaccuracies and privacy considerations when enabling this opt-in feature. If you use Google apps like Gmail or Photos, Google already knows certain aspects of your life. The company is offering to make that knowledge more accessible via what it calls Personal Intelligence, which will synthesize that knowledge into Gemini.

AI's Hacking Skills Are Approaching an'Inflection Point' AI models are getting so good at finding vulnerabilities that some experts say the tech industry might need to rethink how software is built. Vlad Ionescu and Ariel Herbert-Voss, cofounders of the cybersecurity startup RunSybil, were momentarily confused when their AI tool, Sybil, alerted them to a weakness in a customer's systems last November. Sybil uses a mix of different AI models --as well as a few proprietary technical tricks--to scan computer systems for issues that hackers might exploit, like an unpatched server or a misconfigured database. In this case, Sybil flagged a problem with the customer's deployment of federated GraphQL, a language used to specify how data is accessed over the web through application programming interfaces (APIs). The issue meant that the customer was inadvertently exposing confidential information.