Babbage from The Economist (subscriber edition)
AGI, part two: how to make artificial intelligence more like the human kind
Sep 11, 2024
In this enlightening discussion, Abby Bertics, a tech researcher at the Geometric Intelligence Lab with a knack for science writing, explores the quest for artificial general intelligence. The conversation unveils the limitations of current large language models and proposes innovative approaches to integrate reasoning and knowledge. Bertics highlights the need for AI to truly grasp concepts, not just pattern-match. The ethical implications of creating superintelligent machines are examined, raising questions about the balance between potential benefits and inherent risks in this advancing field.
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Quick takeaways
- AGI remains a challenging concept requiring clarity on intelligence and consciousness, highlighting the difficulty in defining human-like capabilities for AI.
- Current LLMs fall short of true general intelligence due to their reliance on statistical patterns, necessitating innovative approaches like multimodal models for better reasoning.
Deep dives
The Challenge of Defining AGI
The concept of artificial general intelligence (AGI) is notoriously challenging to define, often portrayed as a key goal for AI researchers. Defining AGI raises complex questions, such as whether it refers to an AI that matches or exceeds human intelligence or one that possesses consciousness akin to humans. The ambiguity is further compounded by the fact that even cognitive scientists struggle to articulate what intelligence and consciousness are in humans. Ultimately, AGI serves as a placeholder for a future technology capable of performing a variety of tasks at a human level, yet the road to achieving this remains unclear.
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