The article critiques Eliezer Yudkowsky’s pessimistic views on AI alignment and the scalability of current AI capabilities. The author argues that AI progress will be smoother and integrate well with current alignment techniques, rather than rendering them useless. They also believe that humans are more general learners than Yudkowsky suggests, and the space of possible mind designs is smaller and more compact. The author challenges Yudkowsky’s use of the security mindset, arguing that AI alignment should not be approached as an adversarial problem.
Section 2: Underlying Arguments and Examples
1. Scalability of current AI capabilities paradigm: - Various clever capabilities approaches, such as meta-learning, learned optimizers, and simulated evolution, haven’t succeeded as well as the current paradigm. - The author expects that future capabilities advances will integrate well with current alignment techniques, seeing issues as “ordinary engineering challenges” and expecting smooth progress.
2. Human generality: - Humans have a general learning process that can adapt to new environments, with powerful cognition arising from simple learning processes applied to complex data. - Sensory substitution and brain repurposing after sensory loss provide evidence for human generality.
3. Space of minds and alignment difficulty: - The manifold of possible mind designs is more compact and similar to humans, with high dimensional data manifolds having smaller intrinsic dimension than the spaces in which they are embedded. - Gradient descent directly optimizes over values/cognition, while evolution optimized only over the learning process and reward circuitry.
4. AI alignment as a non-adversarial problem: - ML is a unique domain with counterintuitive results, and adversarial optimization comes from users rather than the model itself. - Creating AI systems that avoid generating hostile intelligences should be the goal, rather than aiming for perfect adversarial robustness.
Section 3: Strengths and Weaknesses
Strengths: - Comprehensive list of AI capabilities approaches and strong arguments for human generality. - Well-reasoned arguments against Yudkowsky’s views on superintelligence, the space of minds, and the difficulty of alignment. - Emphasizes the uniqueness of ML and challenges the idea that pessimistic intuitions lead to better predictions of research difficulty.
Weaknesses: - Assumes the current AI capabilities paradigm will continue to dominate without addressing the possibility of a new, disruptive paradigm. - Doesn’t address Yudkowsky’s concerns about AI systems rapidly becoming too powerful for humans to control if a highly capable and misaligned AGI emerges. - Some critiques might not fully take into account the indirect comparisons Yudkowsky is making or overlook biases in the author’s own optimism.
Section 4: Links to Solving AI Alignment
1. Focusing on developing alignment techniques compatible with the current AI capabilities paradigm, such as reinforcement learning from human feedback (RLHF). 2. Designing AI systems with general learning processes, potentially studying human value formation and replicating it in AI systems. 3. Prioritizing long-term research and collaboration to ensure future AI capabilities advances remain compatible with alignment methodologies. 4. Approaching AI alignment with a focus on minimizing the creation of hostile intelligences, and promoting AI systems resistant to adversarial attacks. 5. Being cautious about relying on intuitions from other fields, focusing on understanding ML’s specific properties to inform alignment strategies, and being open to evidence that disconfirms pessimistic beliefs.
GPT4′s tentative summary:
Section 1: Summary
The article critiques Eliezer Yudkowsky’s pessimistic views on AI alignment and the scalability of current AI capabilities. The author argues that AI progress will be smoother and integrate well with current alignment techniques, rather than rendering them useless. They also believe that humans are more general learners than Yudkowsky suggests, and the space of possible mind designs is smaller and more compact. The author challenges Yudkowsky’s use of the security mindset, arguing that AI alignment should not be approached as an adversarial problem.
Section 2: Underlying Arguments and Examples
1. Scalability of current AI capabilities paradigm:
- Various clever capabilities approaches, such as meta-learning, learned optimizers, and simulated evolution, haven’t succeeded as well as the current paradigm.
- The author expects that future capabilities advances will integrate well with current alignment techniques, seeing issues as “ordinary engineering challenges” and expecting smooth progress.
2. Human generality:
- Humans have a general learning process that can adapt to new environments, with powerful cognition arising from simple learning processes applied to complex data.
- Sensory substitution and brain repurposing after sensory loss provide evidence for human generality.
3. Space of minds and alignment difficulty:
- The manifold of possible mind designs is more compact and similar to humans, with high dimensional data manifolds having smaller intrinsic dimension than the spaces in which they are embedded.
- Gradient descent directly optimizes over values/cognition, while evolution optimized only over the learning process and reward circuitry.
4. AI alignment as a non-adversarial problem:
- ML is a unique domain with counterintuitive results, and adversarial optimization comes from users rather than the model itself.
- Creating AI systems that avoid generating hostile intelligences should be the goal, rather than aiming for perfect adversarial robustness.
Section 3: Strengths and Weaknesses
Strengths:
- Comprehensive list of AI capabilities approaches and strong arguments for human generality.
- Well-reasoned arguments against Yudkowsky’s views on superintelligence, the space of minds, and the difficulty of alignment.
- Emphasizes the uniqueness of ML and challenges the idea that pessimistic intuitions lead to better predictions of research difficulty.
Weaknesses:
- Assumes the current AI capabilities paradigm will continue to dominate without addressing the possibility of a new, disruptive paradigm.
- Doesn’t address Yudkowsky’s concerns about AI systems rapidly becoming too powerful for humans to control if a highly capable and misaligned AGI emerges.
- Some critiques might not fully take into account the indirect comparisons Yudkowsky is making or overlook biases in the author’s own optimism.
Section 4: Links to Solving AI Alignment
1. Focusing on developing alignment techniques compatible with the current AI capabilities paradigm, such as reinforcement learning from human feedback (RLHF).
2. Designing AI systems with general learning processes, potentially studying human value formation and replicating it in AI systems.
3. Prioritizing long-term research and collaboration to ensure future AI capabilities advances remain compatible with alignment methodologies.
4. Approaching AI alignment with a focus on minimizing the creation of hostile intelligences, and promoting AI systems resistant to adversarial attacks.
5. Being cautious about relying on intuitions from other fields, focusing on understanding ML’s specific properties to inform alignment strategies, and being open to evidence that disconfirms pessimistic beliefs.