Description

Join us on The Deep Dig as we delve into Khayyam’s thought-provoking essay, “The 10% Delusion.” This episode challenges the trillion-dollar AI industry’s foundational assumptions, questioning if current AI models, based on the influential 2017 “Attention is All You Need” paper, are more ghosts than minds. We explore the critical flaws in AI’s development, the economic implications, and the potential risks of continuing down this path without reevaluation.

Category/Topics/Subjects:

• Artificial Intelligence
• AGI (Artificial General Intelligence)
• Technological Critique
• Data Science and AI Models
• Economic and Societal Impacts of AI
• Philosophy of Technology

Best Quotes:

1. “Silicon Valley took this key 2017 paper and ran with it in completely the wrong direction, building ghosts, not minds.”
2. “We’re building these incredibly optimized, fragile, hothouse flowers and expecting them to survive in the wild.”
3. “Your AI will never truly understand falling until it has knees that can bleed.”
4. “We’re building ghosts, and making them bigger doesn’t make them real.”
5. “The ghosts we’re building won’t become real by making them larger, faster, or more expensive.”

Three Major Areas of Critical Thinking:

1. Mechanism vs. Sufficiency:

• Khayyam argues that the AI industry mistook a powerful mechanism (attention) for a sufficient solution to achieve AGI. This section explores the difference between possessing an effective tool and erroneously assuming it can achieve the end goal of genuine intelligence.

2. The 10% Economy and Data Limitations:

• The essay introduces the concept of the “10% Economy,” where AI models are trained on only 10% of human communication (text), ignoring the remaining 90% of experiential and contextual elements. This critical thinking area examines the limitations imposed by training data and how this affects AI’s understanding of the real world.

3. Economic and Societal Risks:

• The episode highlights the broader implications of AI’s foundational flaws, including potential economic misallocations, privacy concerns due to extensive data collection, and the erosion of public trust in technology. This section urges listeners to consider the consequences of an AI industry driven by market hype rather than genuine scientific progress.

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