With tens of billions invested in AI last year and leading players such as OpenAI looking for trillions more, the tech industry is racing to add to the pileup of generative AI models. The goal is to steadily demonstrate better performance and, in doing so, close the gap between what humans can do and what can be accomplished with AI.
AI’s Trust Problem
Admin / May 6, 2024
Summary.
As AI becomes more powerful, it faces a major trust problem. Consider 12 leading concerns: disinformation, safety and security, the black box problem, ethical concerns, bias, instability, hallucinations in LLMs, unknown unknowns, potential job losses and social inequalities, environmental impact, industry concentration, and state overreach.
Each of these issues is complex — and not easy to solve.
But there is one consistent approach to addressing the trust gap: training, empowering, and including humans to manage AI tools.
As AI becomes more powerful, it faces a major trust problem. Consider 12 leading concerns: disinformation, safety and security, the black box problem, ethical concerns, bias, instability, hallucinations in LLMs, unknown unknowns, potential job losses and social inequalities, environmental impact, industry concentration, and state overreach.
Each of these issues is complex — and not easy to solve.
But there is one consistent approach to addressing the trust gap: training, empowering, and including humans to manage AI tools.