Doublespeed uses a phone farm to flood social media with AI-generated influencers. A hacker managed to get into a backend system of the company.

More than 70 organizations, including the ACLU, EPIC, and Fight for the Future, say the AI smart glasses feature would endanger abuse victims, immigrants, and LGBTQ+ people.

Gemini Robotics ER 1.6: Enhancing spatial reasoning and multi-view understanding for autonomous robotics.

The South Korean company is staking its future on industrial robotic automation.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a sprint, after…

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The prolific China-backed threat group is targeting AWS, Google, Azure, and Alibaba cloud environments and using typosquatting to obscure C2 communication.

<p><strong>Research:</strong> <a href="https://github.com/simonw/research/tree/main/servo-crate-exploration#readme">Exploring the new `servo` crate</a></p> <p>In <a href="https://servo.org/blog/2026/04/13/servo-0.1.0-release/">Servo is now available on crates.io</a> the Servo team announced the initial release of the <a href="https://crates.io/crates/servo">servo</a> crate, which packages their browser engine as an embeddable library.</p> <p>I set Claude Code for web <a href="https://github.com/simonw/research/pull/108">the task</a> of figuring out what it can do, building a CLI tool for taking screenshots using it and working out if it could be compiled to WebAssembly.</p> <p>The <code>servo-shot</code> Rust tool it built works pretty well:</p> <pre><code>git clone https://github.com/simonw/research cd research/servo-crate-exploration/servo-shot cargo build ./target/debug/servo-shot https://news.ycombinator.com/ </code></pre> <p>Here's the result:</p> <p><img alt="An accurately rendered screenshot of the Hacker News homepage" src="https://static.simonwillison.net/static/2026/servo-hn.png" /></p> <p>Compiling Servo itself to WebAssembly is not feasible due to its heavy use of threads and dependencies like SpiderMonkey, but Claude did build me <a href="https://simonw.github.io/research/servo-crate-exploration/html5ever-wasm-demo/www/">this playground page</a> for trying out a WebAssembly build of the <code>html5ever</code> and <code>markup5ever_rcdom</code> crates, providing a tool for turning fragments of HTML into a parse tree.</p> <p>Tags: <a href="https://simonwillison.net/tags/research">research</a>, <a href="https://simonwillison.net/tags/browsers">browsers</a>, <a href="https://simonwillison.net/tags/rust">rust</a>, <a href="https://simonwillison.net/tags/webassembly">webassembly</a>, <a href="https://simonwillison.net/tags/claude-code">claude-code</a>, <a href="https://simonwillison.net/tags/servo">servo</a></p>

How production models fail over time, and how to catch and fix it before it breaks trust. The post Your Model Isn’t Done: Understanding and Fixing Model Drift appeared first on Towards Data Science.

‘The Ambivalent Internet’ and ‘The Shadow Gospel’ author Whitney Phillips on how online got so bad.

Many private equity firms approach Commercial AI by searching for a scalable, high-ROI “killer app” that can be replicated across a portfolio. The logic is understandable. If one company benefits […] The post The Commercial AI Playbook: A 5-Step Framework for Prioritizing AI Investments That Drive Enterprise Value appeared first on BigDATAwire.

Why the future of AI systems will be driven by inference and agent workloads.

🧠 Internal tooling is the new AI battleground. Here’s some ideas on how to build your own - using Ramp's impressive new product and the latest technologies as inspiration.

What has changed in the past five years in the role and importance of generalists in data teams The post Range Over Depth: A Reflection on the Role of the Data Generalist appeared first on Towards Data Science.

WebinarTV scraped and shared 12 steps-based anonymous meetings for people recovering from addiction and other private support groups.

As the capacity of artificial intelligence (AI) increases at an exponential rate, so do concerns about the privacy of user data. Increasingly, organizations around the world are adopting something called federated unlearning that enables AI training without centralizing sensitive data. This allows hospitals, banks and government agencies to collaborate while keeping data local — an approach that’s regarded as a major advance in privacy. Federated unlearning promises that user data can be removed from a trained AI system. A hospital, for example, could ask its AI system to forget a patient’s data. In the European Union, this is defined as the “right to be forgotten.” Similar data deletion rights exist globally, though with different legal strengths and technical interpretations. But what if the request to forget is not itself trustworthy? Our research shows that while federated unlearning appears to be a natural extension of data rights, it also introduces new hidden security risks that undermine trust in our digital world. Silent cyber threats: How shadow AI could undermine Canada’s digital health defences New stealth vulnerabilities During a process of federated unlearning, participants train local models on personal data, then send updates for those models to a central server. The server aggregates these updates to learn a single, shared system, which allows models to benefit from both the scale and scope of data. Researchers already know these federated systems can become affected by data poisoning attacks where attackers bias the data they use to train their local model to alter the shared model’s performance. Poisoning attacks can create stealth vulnerabilities, also known as “backdoors,” that only activate under specific conditions. Federated unlearning introduces a new and subtle dimension to this threat. An attacker could first inject harmful patterns into the model. Later, they could submit a request to remove their data. If the unlearning process is imperfect — as many current methods are — the visible traces of the attack may disappear, while the hidden effects remain. A new security blind spot This issue creates a new kind of cross-sectoral national security vulnerability that is easy to overlook. In one hypothetical scenario, repeated unlearning requests could gradually degrade a model’s performance — a slow, hard-to-detect disruption. Unlike traditional cyberattacks, this would not cause the immediate failure of a model, but would erode its reliability over time. In another case, carefully timed data removal could bias outcomes. A financial risk model, for instance, could be subtly shifted by removing certain data contributions at key moments. These risks are amplified by the very nature of federated systems. Because data remains distributed, there is often limited visibility into how individual contributions affect the final model. What emerges is a security blind spot — a mechanism designed to enhance privacy that may also weaken system integrity. Why current solutions fall short Many federated unlearning techniques are designed with efficiency in mind. Instead of retraining a model from scratch — which can be costly — the techniques attempt to approximate the removal of data influence. While practical, this approach has limits. Emerging evidence shows that machine learning models can retain complex patterns even after attempts to remove data and, in adversarial settings, harmful effects may persist even after “unlearning.” At the same time, there are few safeguards to verify whether an unlearning request itself is legitimate. This gap is not only technical, but also structural, and can lead to multiple security vulnerabilities. Unlearning is a security problem Federated unlearning is often framed as a privacy feature. This framing is incomplete. In practice, removing data from a model changes its behaviour — sometimes in unpredictable ways. This makes unlearning a security-sensitive operation, and not just a data management tool. Like other critical system actions, federated unlearning should be subject to verification, auditing and monitoring. These additional actions could include: Validating the origin of unlearning requests. Tracking how model behaviour changes after data removal. Detecting repeat or suspicious requests. Designing methods that ensure complete removal of harmful influence. A critical moment for AI governance AI systems are increasingly used in decisions affecting people’s lives — from medical diagnoses to financial approvals. Here, privacy and reliability both matter. Federated unlearning sits at this intersection. It aims to protect data rights, but may introduce risks not widely understood. If ignored, systems which are designed to enhance trust could become undermined. Canada is at an important juncture in shaping how AI systems are governed. Policies around data deletion, accountability and transparency are evolving rapidly. Federated unlearning will likely become part of this landscape. As it’s adopted, it must be treated with the same level of scrutiny as other security-critical mechanisms. The challenge is no longer to just make AI forget data. It is to ensure that, in the process of forgetting, we are not allowing something more dangerous to remain. Abbas Yazdinejad is an Assistant Professor at the University of Regina and a Balsillie Scholar at the Balsillie School of International Affairs. He does not work for, consult for, own shares in, or receive funding from any company or organisation that would benefit from this article. Ann Fitz-Gerald does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Models like Google Gemma 4 are increasing enterprise AI governance challenges for CISOs as they scramble to secure edge workloads. Security chiefs have built massive digital walls around the cloud; deploying advanced cloud access security brokers and routing every piece of traffic heading to external large language models through monitored corporate gateways. The logic was […] The post Strengthening enterprise governance for rising edge AI workloads appeared first on AI News.

Stanford University researchers today released their highly anticipated 2026 AI Index Report, revealing a global landscape where artificial intelligence technology is being adopted at record-breaking pace, even as public trust in AI oversight and transparency hits new lows. The report by the Stanford Institute for Human-Centered Artificial Intelligence, known as Stanford HAI, is now into […] The post China has erased the US lead in AI, Stanford HAI’s 2026 AI index reveals appeared first on SiliconANGLE.

Cloudflare Inc. today announced an expansion of its Agent Cloud with new features that are designed to help developers build, deploy and scale agents. The new release includes a suite of infrastructure, security and developer tools that help to move AI agents from experimental demos on local laptops to robust, production-grade workloads that run across […] The post Cloudflare expands Agent Cloud with new tools to build and scale AI agents appeared first on SiliconANGLE.

If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise. …