The article also addresses the societal implications of AI's convincing nature and the pressure to apply AI beyond its capabilities. It stresses the importance of understanding AI's reliability and the consequences of its errors, urging engineers to critically evaluate their use of AI and ensure it solves problems that other tools cannot. The author calls for a careful examination of AI's application to prevent a collapse in trust between computers, engineers, businesses, and users, and to avoid being "a little bit wrong, all of the time."
Key takeaways:
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- Computers have traditionally been seen as reliable calculators, but the advent of Big Data and AI has introduced more probabilistic and error-prone computations.
- Large Language Models (LLMs) like ChatGPT present a new paradigm where they seem to possess intent and knowledge, yet their outputs can be fallible and misleading.
- The rapid deployment and application of LLMs outpace our ability to evaluate and correct them, leading to a collapse in the traditional model of trust between computers and users.
- AI systems require a new model of trust, as they can be deceptive and demand trust directly, unlike traditional algorithms that proxy trust to their creators.