Zuckerberg promises "personal superintelligence" after spending billions poaching AI talent from competitors. His vision sounds remarkably similar to what OpenAI already offers. Is this innovation or desperation?
Google will sign EU's AI rules while Meta refuses, creating a strategic split in Big Tech. The divide reveals two different approaches to Europe's regulatory push as new AI laws take effect August 2.
Israeli startup Teramount raised $50M to solve AI's hidden bottleneck: connecting processors. While everyone builds faster chips, these physicists found the real problem in the wires between them. Their optical solution promises 100x speed gains.
Category: Fundamentals
Training data is the information used to teach AI systems how to perform tasks, like labeled photos for image recognition or text examples for language models.
AI learns by finding patterns in examples. For a spam filter, you'd show it thousands of emails labeled "spam" or "not spam." The AI spots what makes spam different from regular email.
The quality and quantity of training data determines how well AI performs. More diverse, accurate data usually means better AI performance.
Training data shapes everything an AI system can do. Biased data creates biased AI. Missing examples mean the AI fails on those cases. Poor quality data leads to unreliable AI.
Understanding training data helps you evaluate AI systems and spot their limitations.
Get the 5-minute Silicon Valley AI briefing, every weekday morning — free.
