Modularity Memo: Reddit Reacts, AI Book Recs, Thoughts on Building Better AI

Ideas to Ponder

We've spent the last couple of weeks putting pen to paper with some of our thoughts about why we're doing what we're doing in chasing self-evolving AI. These are all relatively bite-size essays that we hope will help provide some clarity about the importance of moving beyond LLM-based AI systems.

• Why Trust, Reliability, and Interpretability Matter in AI (Seriously)
• 5 Things Made Possible by Continuous Learning in AI
• The Power of Niche Expert Modules in AI

Agree or disagree? We want to hear from you! We appreciate any and all feedback and questions about what we're building. That's actually a good segue into something exciting that happened on Reddit last week...

Reddit Reacts

Our self-evolving modular AI's success over Anthropic's Claude at complex tasks (demo here) recently got a lot of attention on r/agi on Reddit. It was great to see so much enthusiasm from the community—we received a lot of really interesting questions and notes, especially on the topics of hallucination-mitigation, safety, self-evolving mechanics, and benchmarking. We addressed all of this feedback in the comments.

Check out the post and the comments here >

We're going to be sharing more on our blog in response to some of the community's questions in the coming weeks, so stay tuned for that.

Our CEO's AI Book Recommendations

AI can do a pretty good job of summarizing the content of an article, essay, or book, but we think it's worth taking the time to read books, slow down, and contemplate big ideas.

Our CEO Alexey Lee recently shared some of the AI-related reading that's gotten his wheels turning lately:

• AI Engineering by Chip Huyen: The first part lays a strong foundation for the mechanics of AI, even for tech managers (not just engineers)
• A Brief History of Intelligence by Max Bennett: To understand how the brain works and where foundational AI may go beyond LLMs
• Genius Makers by Cade Metz (tech reporter for New York Times): A short history of modern AI and the movers behind it

Tokens of Note

• 77% of AI researchers say a new architecture is needed to improve trustworthiness in AI systems. AAAI Survey >
• LLMs’ “simulated reasoning” abilities are a “brittle mirage” creating “a false aura of dependability”. Simply put: We can't trust current AI systems in mission-critical contexts.

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Onward,
the humanity.ai team