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QUASA MEDIA: Daily Insights on Web3, AI, Crypto & Freelance

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The "Mentally Retarded" AI: How Training on Junk Data Creates Irreversibly Dumb LLMs

The "Mentally Retarded" AI: How Training on Junk Data Creates Irreversibly Dumb LLMs

In a provocative experiment that has sparked debates across AI research circles, scientists from three prominent U.S. universities—Shuo Xing from Stanford University, Junyuan Hong from the University of California, Berkeley, and Yifan Wang from Carnegie Mellon University—deliberately sabotaged a large language model (LLM) by training it on low-quality "junk data." The result? An AI that exhibits profound intellectual deficits, akin to what the researchers describe as "mental retardation" in human terms.

|Creator Economy| 1897
AI Browsers Are Sneaking Past Paywalls: How Do They Do It?

AI Browsers Are Sneaking Past Paywalls: How Do They Do It?

In an era where quality journalism increasingly relies on subscriptions to fund investigative reporting and in-depth analysis, a new breed of AI-powered tools is quietly upending the paywall model. "Agentic" AI browsers, such as OpenAI's Atlas and Perplexity's Comet, are demonstrating a remarkable ability to access and summarize content behind paywalls that block traditional users—and even conventional AI chatbots. This isn't just a quirky loophole; it's a symptom of evolving technology that blurs the lines between human browsing and automated scraping, raising profound questions for publishers, ethics, and the future of online information.

|Technology| 4423