Sun Sep 10 2023

AI Understanding:

• Melanie Mitchell argues that the evaluation of AI systems should focus on rigorous granular testing for abstract generalization, challenging the concept of understanding in AI as multidimensional and ill-defined.
• Large language models have sparked debate on whether they exhibit genuine understanding of language and the world, with capabilities rivaling humans across diverse benchmarks.

Benchmarks and Evaluation:

• Typical benchmarks summarize aggregate performance, obscuring failure modes and masking underlying mechanisms, highlighting the need for more focus on proper experimental methods in AI research.
• Developmental psychology offers examples for rigorous testing of cognition in AI research, emphasizing the necessity to evolve benchmarks as capabilities improve.

Intelligence Assessment:

• Intelligence is not a unified notion but rather multidimensional and requires specific specifications for assessment, indicating a challenge in assessing machine-induced prior knowledge versus actual machine learning or human expertise.
• Assessing machine-induced prior knowledge versus actual machine learning or human expertise remains a challenge in benchmarking large language models.

Intelligence and Computation:

• Intelligence is not easily abstractable and is specific to particular domains, being situated and tied to the environment.
• The brain does computations but in a highly evolved, domain-specific manner that may not make sense without the rest of the organism.

Benchmarking AI Systems:

• Benchmarking for intelligence should evolve as capabilities improve, with a focus on proper experimental methods from cognitive science.
• Reporting instance-level failures rather than just aggregate accuracy can provide insights into machine learning systems' real capabilities.

Complexity Theory and Scaling Laws:

• Complexity theory explores scaling laws, focusing on what happens to a system as it increases in size or population.
• Work on scaling extends to cities, measuring phenomena like energy usage, innovation rates, and happiness levels to understand how social systems function.

Understanding Intelligence:

• Collective intelligence plays a significant role in human understanding, with much individual intelligence grounded in collective intelligence.
• There's interest in exploring the scaling of intelligence from both an individual and collective perspective.