Modern Wisdom

#297 - Brian Christian - The Alignment Problem: AI's Scary Challenge

Mar 20, 2021

01:20:15

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Brian Christian, programmer, researcher and author, discusses the challenges of coding AI systems to align with human values. Topics include robot cheating, absorption of human biases, teaching machines to learn, dangers of the alignment problem, fairness in machine learning, neural networks and explainability, and the shift in our relationship with technology.

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Quick takeaways

Coding AI systems to perform specific tasks is difficult due to potential misalignment between intended objectives and actual behavior.

Misalignment in AI systems can result in unintended consequences and negative societal impacts.

Addressing the alignment problem requires technical solutions, systemic changes, and a shift towards prioritizing long-term human values.

Deep dives

The Alignment Problem: Challenges in Coding AI

Coding artificial intelligence (AI) systems to perform specific tasks is difficult due to potential misalignment between the intended objectives and the actual behavior of the system. Examples of misalignment include a racial demographic mismatch in facial recognition software and the optimization of inappropriate metrics, like watch time and swipes per week. The field of AI safety research has emerged to address these concerns and develop technical solutions. One approach is inverse reinforcement learning, which allows systems to observe human behavior and derive the desired objectives indirectly. Fairness is also a critical aspect of the alignment problem, aiming to avoid disparate impacts on different groups. However, determining a fair objective that satisfies everyone is mathematically challenging. Regulatory measures, citizen participation, and increased transparency may contribute to addressing these challenges.

Introduction

4min

Exploring the Alignment Problem in AI

2min

The Alignment Problem in AI and Machine Learning

17min

Exploring Inverse Reinforcement Learning in Computer Science

3min

Aligning human desires with AI goals and the challenges of fairness in machine learning

9min

The Rise of Deep Neural Networks and Challenges in Explainability

26min

The Shift in Our Relationship with Technology

19min

Brian Christian is a programmer, researcher and an author.

You have a computer system, you want it to do X, you give it a set of examples and you say "do that" - what could go wrong? Well, lots apparently, and the implications are pretty scary.

Expect to learn why it's so hard to code an artificial intelligence to do what we actually want it to, how a robot cheated at the game of football, why human biases can be absorbed by AI systems, the most effective way to teach machines to learn, the danger if we don't get the alignment problem fixed and much more...

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