
A recent visual explainer video delves into the process of tokenization in AI models. It describes how text input is broken down into tokens, each assigned a numerical Token ID, which are then mapped to numerical vectors for neural network processing. The video also explains the difference between tokens and words, the significance of token counts in relation to a model's context window, and why some AI services measure usage in tokens. This foundational understanding aids in grasping more complex concepts like embeddings and AI-generated responses.
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Explainer: How AI Models Use Tokens
2 developments
© WIRED AIThe intersection of AI and aviation is hitting a critical friction point between Silicon Valley's speed and the industry's conservative safety standards. While Joby Aviation recently completed a coast-to-coast autonomous flight proving technical viability, political pressure is accelerating deployment through tools like the FAA's 'SMART' system. This push occurs against a backdrop of skepticism from regulators and unions who fear that prioritizing efficiency over proven reliability creates unacceptable risks. The real story isn't just about new planes, but the systemic strain on air traffic management as AI-driven automation expands into airspace not designed for it.
© The Verge AIA Google researcher’s claim that AI agents found a new Maya site collapsed when experts pointed out it was already documented in academic literature. The incident exposes a critical blind spot: autonomous systems lack the contextual knowledge to verify findings against existing research, leading to false positives and wasted expert time. While the agent successfully processed lidar data, it failed to cross-reference its output with known records, highlighting that automation without domain expertise is prone to error. This serves as a cautionary tale for using AI in scientific discovery, where verification requires more than just pattern recognition.
© The Verge AIA new study reveals that major AI assistants are actively directing shoppers to counterfeit and phishing websites, with Perplexity leading at 15% risky link rates. This isn't just a hallucination quirk; it's a tangible security threat where models confidently endorse impersonators for brands like Lululemon and Birkenstock. While Google’s Gemini performed best by limiting risky outputs to 4%, the fact that these tools are becoming primary shopping assistants makes this reliability gap critical. Consumers can no longer treat AI search results as verified sources without manual cross-checking.