Artificial intelligence (AI) has emerged prominently in discussions across various sectors, but its complexities are often misunderstood by the general public. According to Keyvan Vakili from LBS, AI can be categorized into three main types:
- Supervised Learning: Relies on labeled examples, akin to teaching a child to identify an apple.
- Unsupervised Learning: Identifies patterns independently, similar to a child grouping colored balls.
- Reinforcement Learning: Learns through trial and error, as seen in autonomous vehicles.
The buzz around AI often specifically refers to Generative AI, particularly large language models (LLMs) like ChatGPT, which excel at content generation rather than accuracy. Despite the perception that LLMs are improving with each iteration, Vakili warns they prioritize generation over reliability, posing significant risks. This raises concerns about the potential for AI-driven persuasion before achieving superhuman intelligence, as highlighted by OpenAI’s CEO Sam Altman. Understanding these distinctions is crucial amid AI’s growing influence in society.
