AI News

The shock of seeing your body used in deepfake porn 

When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she’d made more than 10 years before, when she was in her early 20s. It did in fact return…

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Data readiness for agentic AI in financial services

Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on…

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Establishing AI and data sovereignty in the age of autonomous systems

When generative AI first moved from research labs into real-world business applications, enterprises made a tacit bargain: “Capability now, control later.” Feed your proprietary data into third-party AI models, and you will get powerful results. But your data passes through systems you do not own, under governance you do not set. The protections you rely…

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Our response to the TanStack npm supply chain attack

OpenAI details its response to the TanStack “Mini Shai-Hulud” supply chain attack, outlines protections taken to secure systems and signing certificates, and explains why macOS users must update OpenAI apps by June 12, 2026. Learn what happened, what was affected, and how OpenAI is strengthening defenses against evolving software supply chain threats.

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You can make an app for that

The tyranny of software is almost over. Since the first computer programmers wrote the first computer programs, we, the users of that software, have been forced to live in the worlds those programs create. The features are the features. The design is the design. Want something else, something better? Learn to code, I guess. Until […]

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Americans do not want AI data centers in their backyards

Over 70 percent of Americans oppose AI data center construction in their area, according to a new Gallup survey. Just 7 percent said they were “strongly” in favor of new data centers. According to Gallup, data centers are so strongly disliked that Americans would prefer to live near a nuclear power plant than a data […]

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Fastino Labs Open-Sources GLiGuard: A 300M Parameter Safety Moderation Model That Matches or Exceeds Accuracy of Models 23–90x Its Size

Fastino Labs has released GLiGuard, a 300M parameter open-source safety moderation model that evaluates four safety tasks — prompt safety, jailbreak strategy detection, harm category classification, and refusal detection — in a single forward pass. Built on an encoder architecture rather than the decoder-only design used by most guardrail models, GLiGuard achieves up to 16x higher throughput and 16.6x lower latency than current state-of-the-art models, while matching or exceeding the accuracy of models 23 to 90 times its size across nine safety benchmarks. Model weights are available under the Apache 2.0 license on Hugging Face.

The post Fastino Labs Open-Sources GLiGuard: A 300M Parameter Safety Moderation Model That Matches or Exceeds Accuracy of Models 23–90x Its Size appeared first on MarkTechPost.

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How to Build a Dynamic Zero-Trust Network Simulation with Graph-Based Micro-Segmentation, Adaptive Policy Engine, and Insider Threat Detection

In this tutorial, we build a realistic Zero-Trust network simulation by modeling a micro-segmented environment as a directed graph and forcing every request to earn access through continuous verification. We implement a dynamic policy engine that blends ABAC-style permissions with device posture, MFA, path reachability, zone sensitivity, and live risk signals such as anomaly and […]

The post How to Build a Dynamic Zero-Trust Network Simulation with Graph-Based Micro-Segmentation, Adaptive Policy Engine, and Insider Threat Detection appeared first on MarkTechPost.

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Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models

Nous Research releases Token Superposition Training (TST), a two-phase pre-training method that cuts wall-clock training time by up to 2.5x at matched FLOPs by averaging contiguous token embeddings into bags during Phase 1 and reverting to standard next-token prediction in Phase 2 — without changing the model architecture, tokenizer, optimizer, or inference-time behavior. Validated at 270M, 600M, 3B dense, and 10B-A1B MoE scales.

The post Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models appeared first on MarkTechPost.

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AI chatbots are giving out people’s real phone numbers

People report that their personal contact info was surfaced by Google AI—and there’s apparently no easy way to prevent it.  A Redditor recently wrote that he was “desperate for help”: for about a month, he said, his phone had been inundated by calls from “strangers” who were “looking for a lawyer, a product designer, a…

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