galaxy.comarticleUnderstanding AI, Part I: The Machines That We Taught
Galaxy Research's explainer traces AI's intellectual history from the 1956 Dartmouth Workshop and the Logic Theorist through the symbolic-AI era (expert systems like Mycin and XCON, the Cyc project) to the sub-symbolic/connectionist revival (Perceptron, Hopfield networks, backpropagation, LeCun's CNN zip-code reader) that culminated in deep learning and the 2017 Transformer architecture. It then surveys three machine-learning paradigms (supervised, reinforcement, unsupervised) and five deep generative architectures (autoregressive, diffusion, GANs, VAEs, normalizing flows), closing with how modern LLM chatbots combine these via pretraining, SFT, and RLHF, and how multimodal, reasoning, RAG, agent, and world-model systems extend base LLMs.
Organisations named here
- Galaxy Digital Inc.
- NVIDIA
- OpenAI
- Defense Advanced Research Projects Agency
- Dartmouth College
- World Labs
- U.S. Postal Service
- Alan Turing
- Alfred North Whitehead
- Atlas
- Baruch Spinoza
- Bertrand Russell
- DARPA
- David Hume
- David Rumelhart
- Edward Feigenbaum
- Fei-Fei Li
- Frank Rosenblatt
- Galaxy Digital
- Geoffrey Hinton
- George Berkeley
- Gottfried Wilhelm Leibniz
- Greg Brockman
- Jensen Huang
- John Hopfield
- John Locke
- Kimi
- Marvin Minsky
- Principia Mathematica
- René Descartes
- Seymour Papert
- Yann LeCun
Technologies named here
- ChatGPT
- Claude
- Gemini
- Kimi
- GPT-6 Astra
- Logic Theorist
- Mycin
- XCON
- Cyc
- Perceptron
- Atlas
- Transformer
Techniques named here
- Symbolic AI
- Sub-symbolic AI
- Supervised learning
- Reinforcement learning
- Unsupervised learning
- Deep learning
- Backpropagation
- Transformer architecture
- Autoregressive models
- Diffusion models
- Generative Adversarial Networks
- Variational Autoencoders
- Normalizing flows
- Three-stage LLM training
- Multimodal models
- Reasoning models
- Retrieval-augmented generation
- Agents
- Embodied and world models
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