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Research#llm📝 BlogAnalyzed: Dec 25, 2025 16:37

Are We Testing AI’s Intelligence the Wrong Way?

Published:Dec 4, 2025 23:30
1 min read
IEEE Spectrum

Analysis

This article highlights a critical perspective on how we evaluate AI intelligence. Melanie Mitchell argues that current methods may be inadequate, suggesting that AI systems should be studied more like nonverbal minds, drawing inspiration from developmental and comparative psychology. The concept of "alien intelligences" is used to bridge the gap between AI and biological minds like babies and animals, emphasizing the need for better experimental methods to measure machine cognition. The article points to a potential shift in how AI research is conducted, focusing on understanding rather than simply achieving high scores on specific tasks. This approach could lead to more robust and generalizable AI systems.
Reference

“I’m quoting from a paper by [the neural network pioneer] Terrence Sejnowski where he talks about ChatGPT as being like a space alien that can communicate with us and seems intelligent.”

PermalinkIEEE Spectrum
Research#llm📝 BlogAnalyzed: Jan 3, 2026 07:12

Prof. Melanie Mitchell 2.0 - AI Benchmarks are Broken!

Published:Sep 10, 2023 18:28
1 min read
ML Street Talk Pod

Analysis

The article summarizes Prof. Melanie Mitchell's critique of current AI benchmarks. She argues that the concept of 'understanding' in AI is poorly defined and that current benchmarks, which often rely on task performance, are insufficient. She emphasizes the need for more rigorous testing methods from cognitive science, focusing on generalization and the limitations of large language models. The core argument is that current AI, despite impressive performance on some tasks, lacks common sense and a grounded understanding of the world, suggesting a fundamentally different form of intelligence than human intelligence.
Reference

“Prof. Mitchell argues intelligence is situated, domain-specific and grounded in physical experience and evolution.”

PermalinkML Street Talk Pod
Research#AI Challenges📝 BlogAnalyzed: Jan 3, 2026 07:16

Why AI is harder than we think

Published:Jul 25, 2021 15:40
1 min read
ML Street Talk Pod

Analysis

The article discusses the cyclical nature of AI development, highlighting periods of optimism followed by disappointment. It attributes this to a limited understanding of intelligence, as explained by Professor Melanie Mitchell. The piece focuses on the challenges in realizing long-promised AI technologies like self-driving cars and conversational companions.
Reference

“Professor Melanie Mitchell thinks one reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself.”

PermalinkML Street Talk Pod
Research#llm📝 BlogAnalyzed: Dec 29, 2025 07:54

Complexity and Intelligence with Melanie Mitchell - #464

Published:Mar 15, 2021 17:46
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Melanie Mitchell, a prominent researcher in artificial intelligence. The discussion centers on complex systems, the nature of intelligence, and Mitchell's work on enabling AI systems to perform analogies. The episode explores social learning in the context of AI, potential frameworks for analogy understanding in machines, and the current state of AI development. The conversation touches upon benchmarks for analogy and whether social learning can aid in achieving human-like intelligence in AI. The article highlights the key topics covered in the podcast, offering a glimpse into the challenges and advancements in the field.
Reference

“We explore examples of social learning, and how it applies to AI contextually, and defining intelligence.”

PermalinkPractical AI
Research#llm📝 BlogAnalyzed: Dec 29, 2025 17:42

Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI

Published:Dec 28, 2019 18:42
1 min read
Lex Fridman Podcast

Analysis

This article summarizes a podcast episode featuring Melanie Mitchell, a computer science professor, discussing AI. The conversation covers various aspects of AI, including the definition of AI, the distinction between weak and strong AI, and the motivations behind AI development. Mitchell's expertise in areas like adaptive complex systems and cognitive architecture, particularly her work on analogy-making, is highlighted. The article also provides links to the podcast and Mitchell's book, "Artificial Intelligence: A Guide for Thinking Humans."
Reference

“This conversation is part of the Artificial Intelligence podcast.”

PermalinkLex Fridman Podcast