from series, words and their mappings.

inspirations behind words and their mappings

what am I trying to do

As I work on my latest series, I wanted to share some of the resources and inspirations that have deeply influenced my perspective on AI and its potential as a tool for augmenting human intelligence. These pieces shaped my understanding of how LLMs really work, what insights can be gained from them, and how to use them more creatively.

Using Artificial Intelligence to Augment Human Intelligence by Shan Carter and Michael Nielsen

This paper was initially published on distill.pub, a platform dedicated to creating visually rich and accessible explanations of machine learning research. The ideas presented in this work fundamentally shaped how I think about AI. Carter and Nielsen outline how AI can act as an augmentation tool, amplifying human intelligence rather than just automating tasks. Their vision aligns with the concept of AI as a "bicycle for the mind"—a tool that enhances our ability to understand, create, and solve complex problems.

Mechanistic Interpretability research, especially the Transformer Circuits Thread

Another significant source of inspiration is mechanistic interpretability research, particularly the work from Anthropic’s Transformer Circuits thread. This research builds on the original Circuits thread from distill.pub, exploring how large language models represent and process information at a structural level. Understanding the mechanisms inside neural networks is crucial for ensuring AI systems are both safe and reliable. This work dives into the internal organization of language models, providing a lens through which we can better interpret and control complex AI behaviors.

Interfaces for AI: Insights from Amelia Wattenberger and Linus Lee

The design of AI interfaces is another area that fascinates me, especially as it relates to the true capabilities of LLMs. Recent explorations by Amelia Wattenberger and Linus Lee have rekindled my curiosity in this area. I am a huge fan of their work!

In LLMs as a Tool for Thought, Wattenberger explores how large language models (LLMs) can go beyond traditional applications like chatbots to become tools for thought, acting as extensions of the mind.

In Synthesizer for Thought, Lee presents the idea of AI as a synthesizer for human thought, emphasizing its role in blending, expanding, and transforming ideas rather than simply generating text. Lee sees AI as a tool that not only answers questions but actively combines fragments of thought into coherent, novel insights, much like a musical synthesizer layers and manipulates sounds to create new harmonies.

I highly recommend reading their other relevant work as well.

At its best, AI has the power to expand our intellectual horizons and enable new ways of thinking. By combining mechanistic insights with well-designed interfaces, we can create AI tools that not only enhance human intelligence but do so in ways that are safe, interpretable, and user-centered.