Abram Demski

Abram Demski, an AI Alignment researcher and writer, clarifies concepts of AI Alignment focusing on optimization, reference, endorsement, and legitimacy. The podcast explores the implications of agency as a natural phenomenon for AI risk analysis and delves into naturalistic representation theorems, denotation vs. connotation in language, and conditional endorsement and legitimacy. It also discusses the distinction between selection and control processes and their impact on trust and inner alignment.

In this engaging discussion, Abram Demski, an AI researcher, shares insights about the timelines for achieving artificial general intelligence (AGI) and the implications for existential risk. He largely agrees with the perspectives outlined in the AI 2027 report. The conversation delves into the limitations of current AI models, the nature of creativity in machines versus humans, and the complexities of navigating public discourse on technological advancements. They explore the challenges of predicting AGI timelines, advocating for comprehensive approaches to mitigate risks.

Abram Demski discusses the evolving definitions of AGI, acknowledging modern transformers as AGI. He emphasizes the importance of clear terminology in AI risk assessment. They explore generative pre-training in AI technology leading to human-level performance in modern transformers, discussing the capabilities and limitations of AI-generated responses.