References & Resources

A set of links with more information about the things that I talk about in the lecture I did on 2/12/2025.

Technical Deep Dives

• [1] BERT Embeddings Explained
• [2] arXiv:1504.01255 - Deep Learning Research
• [3] arXiv:1706.03762 - Attention Is All You Need (Transformers)
• [4] arXiv:2410.23884 - Recent AI Research
• [5] YouTube: AI Technical Overview
• [6] Nature: Deep learning (LeCun, Bengio & Hinton)
• [7] YouTube: The Lighthill Debate (1973)
• [8] YouTube: Jeff Dean on Important AI Trends
• [9] Hugging Face: Continuous batching from first principles

AI Perspecives

• [10] AI as Normal Technology
• [11] AI as Cultural and Social Technology
• [12] The danger of Stochastic Parrots

Industry Analysis & Commentary

• [13] AI 2027 - Future Predictions
• [14] WSJ: MIT AI Research
• [15] Navigating AI Chemistry Hype
• [16] Business Insider: AI Frontier Winners and Losers

OpenAI Resources

• [17] OpenAI Tokenizer Tool
• [18] OpenAI Embeddings API Reference

Critical Perspectives

• [19] Previous Transformations (WSJ)
• [20] The Verge: LLMs and Intelligence Problems

My blogs that I used in the talk

• [21] Synthetic Data for Language Models
• [22] AI Reality Checks
• [23] Depend on Them
• [24] Utility and Bust Scale in Future AI Provision
• [25] Vector Databases, Large Language Models and Case-Based Reasoning

Expert Commentary

• [26] Terence Tao on AI (Mastodon)
• [27] GPT-5 and the Arc of Progress
• [28] LinkedIn: AI and GPT-5 Discussion