"A" for "Average"
An analysis of AI's perceived quality in familiar vs unfamiliar languages, arguing LLMs produce average results by design.
An analysis of AI's perceived quality in familiar vs unfamiliar languages, arguing LLMs produce average results by design.
Explains the causes of bias in AI systems, focusing on training data and proxy variables, and offers practical steps for developers to mitigate it.
Analyzes public reactions to AI bias claims, contrasting them with responses to traditional software bugs, using a viral example.
Explores privacy risks, bias, dependence, misinformation, and manipulation dangers associated with conversational AI and chatbots.