An interactive journey through the science, history, and future of artificial intelligence — from Turing's dream to today's frontier models.
Alan Turing publishes "Computing Machinery and Intelligence," proposing the imitation game as a measure of machine intelligence.
The term "Artificial Intelligence" is coined at a summer workshop, marking the birth of AI as an academic discipline.
Joseph Weizenbaum creates ELIZA, the first chatbot — simulating a psychotherapist and surprising users with its apparent understanding.
IBM's Deep Blue defeats world chess champion Garry Kasparov, a landmark moment for AI in strategic reasoning.
AlexNet wins ImageNet by a huge margin, igniting the modern deep learning era with convolutional neural networks.
Google publishes "Attention Is All You Need," introducing the Transformer architecture that powers today's large language models.
ChatGPT, DALL-E 2, and Stable Diffusion bring generative AI to the mainstream, reaching 100M users in two months.
AI systems gain reasoning capabilities and tool use, moving toward autonomous agents that can plan, act, and learn.
From drug discovery to medical imaging, AI accelerates diagnosis and enables personalized treatment plans at unprecedented scale.
AlphaFold · Med-PaLMSelf-driving systems fuse computer vision, LiDAR, and reinforcement learning to navigate complex real-world environments safely.
Waymo · Tesla FSDGenerative models produce images, music, code, and video — augmenting human creativity and redefining artistic collaboration.
Midjourney · Sora · ClaudeAlgorithmic trading, fraud detection, and risk modeling leverage AI to process vast datasets in milliseconds for better decisions.
High-Freq Trading · RegTechAI models predict weather patterns, optimize energy grids, and accelerate materials discovery for clean energy solutions.
GraphCast · FusionFoundation models for robots enable general-purpose manipulation, locomotion, and human collaboration in factories and homes.
RT-2 · Figure 01Machine learning is the science of getting computers to learn from data without being explicitly programmed. It encompasses supervised learning (labeled data), unsupervised learning (finding hidden patterns), and semi-supervised approaches.
Deep learning uses artificial neural networks with many layers to learn hierarchical representations of data. Inspired by the brain's structure, these networks excel at pattern recognition in images, speech, and text.
NLP enables machines to understand, interpret, and generate human language. From sentiment analysis to machine translation, today's transformer-based models achieve near-human performance on many language tasks.
Computer vision teaches machines to interpret visual information from the world — images, video, and 3D scenes. Applications span facial recognition, autonomous driving, medical imaging, and augmented reality.
RL trains agents to make decisions by rewarding desired behaviors. From mastering Go to optimizing data center cooling, RL excels at sequential decision-making in complex environments.
Generative AI creates new content — text, images, audio, video, and code — by learning patterns from training data. Powered by transformers and diffusion models, it's the fastest-adopted technology in history.