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Adversarial Clothing Designed to Fool Facial Recognition Systems

There are many companies manufacturing adversarial clothing designed to confuse facial recognition systems. It’s a cool idea, but I worry that it’s mostly security theater: “Our patterns play with that chaos, confuse algorithms and make it way harder to pin you down,” he said. Bell, however, said “none of these products are tried and tested, and a lot of these surveillance technologies can deal with a little resistance … [but] even if the designs don’t necessarily work perfectly, fashion is also a visible sign of resistance. “This is consumers collectively coming together to make a visible statement.”...

2026-08-06 原文 →
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Facial Recognition at Madison Square Garden

Last month, the story broke (alternate link ) that Madison Square Garden uses facial recognition software on everyone entering the facility, and—among other groups—flags activists that oppose using facial recognition. Turns out that the system was shut off for Taylor Swift’s wedding. Evan Greer—one of the people that MSG alerts on— comments : Ironically, Swift herself has reportedly used facial recognition at her own concerts to identify stalkers. This “privacy for me, surveillance for thee” attitude feels like a perfect encapsulation of the future we’re already living in: one where wealthy elites can afford privacy, while the rest of us are forced to live in a corporate surveillance panopticon...

2026-07-31 原文 →
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What a HIPAA-Compliant AI Voice Agent Actually Costs

A HIPAA-compliant AI voice agent for healthcare typically costs $40,000-$150,000 to build, depending on call complexity and EHR integration, plus $2,000-$15,000/month to operate. The build cost isn't dominated by the speech model — it's dominated by the compliance and data-retention layer wrapped around it. Most cost estimates for "AI voice agents" quietly assume a sales or support use case, where a wrong transcription costs you an annoyed customer. In healthcare, a wrong transcription in a medication name or a dropped consent statement is a liability. That difference reshapes the budget. Where the money actually goes 1. Speech recognition (10-20% of build cost) This is the smallest line item, despite being the part founders worry about most. You have three options: Managed API with a BAA (e.g., enterprise-tier Deepgram, Azure Speech, Google Healthcare API) — fastest to ship, but you're paying per-minute and locked into the vendor's accuracy on medical terminology. Fine-tuned open-weight model — better accuracy on clinical vocabulary and accents, but adds MLOps overhead. Self-hosted model — highest control over data residency, needed if your contracts or state law prohibit sending PHI to a third party. If your patient population speaks Gulf Arabic or another dialect underserved by mainstream ASR, budget separately for this — see our breakdown on Arabic speech recognition costs for how accent and dialect coverage move accuracy and price independently of the base model choice. 2. Compliance infrastructure (30-40% of build cost) This is where healthcare voice AI diverges hardest from a generic voice bot: Business Associate Agreements with every vendor in the call path (ASR, LLM, telephony, storage) Encryption at rest and in transit, with key management you can audit Role-based access control on transcripts and recordings Immutable audit logs of who accessed what patient data and when The U.S. Department of Health and Human Services publishes the actual HIPAA Security R

2026-07-25 原文 →