Artificial Intelligence - Technology

Early AI (1950s-1980s)

Alan Turing proposed the Turing Test in 1950. The term "artificial intelligence" was coined at the Dartmouth Conference in 1956. Early optimism gave way to "AI winters" - periods of reduced funding when promised breakthroughs failed to materialize. Expert systems in the 1980s encoded human knowledge as rules but were brittle and couldn't learn.

Machine Learning

Rather than hand-coding rules, machine learning systems learn patterns from data. The algorithm adjusts internal parameters to minimize errors on training examples. By the 2000s, ML was handling spam filters, credit scoring, and recommendation systems. The key insight: given enough data and compute, statistical pattern recognition can outperform human-designed rules.

Deep Learning Breakthrough (2012)

In 2012, AlexNet - a deep convolutional neural network by Geoffrey Hinton's team - won the ImageNet competition by a massive margin, slashing the error rate from 26% to 15%. This started the deep learning revolution. The key enablers were large labeled datasets (ImageNet: 1.2 million images), GPUs for parallel computation, and algorithmic improvements. The same approach now powers image recognition, voice assistants, and autonomous vehicles.

AlphaGo and Reinforcement Learning

DeepMind's AlphaGo defeated world champion Lee Sedol at Go in 2016 - a game with more board positions than atoms in the observable universe, thought to require human intuition. The system learned by playing millions of games against itself. AlphaZero (2017) mastered Chess, Go, and Shogi to superhuman level in 24 hours from scratch, with no human game knowledge beyond the rules.

Transformers and Large Language Models

The 2017 paper "Attention Is All You Need" introduced the Transformer architecture. GPT-3 (2020, 175 billion parameters) showed that scaling these models produced emergent capabilities nobody predicted. ChatGPT launched in November 2022 and reached 100 million users in two months - the fastest adoption of any technology in history. These models can write code, pass bar exams, and generate images from text.

AI in Science

AlphaFold2 (2020) solved the 50-year protein folding problem, predicting the 3D structure of proteins from their amino acid sequence. It has since published structures for over 200 million proteins, potentially accelerating drug discovery by decades. AI is also being used to discover new antibiotics, design fusion reactor configurations, and identify gravitational waves in noisy data.

Risks and Ethics

AI systems inherit biases from training data. Facial recognition has higher error rates for darker skin tones. Algorithmic hiring systems can discriminate. Deepfakes can fabricate videos of real people. Autonomous weapons raise profound ethical questions. The longer-term question of whether AI systems will eventually surpass human intelligence in all domains - and what that means - is actively debated by leading researchers.

Nuclear and AI Connections

AI is increasingly used in nuclear research: simulating fusion plasma behavior, analyzing reactor sensor data for anomaly detection, and automating inspection of nuclear facilities. Conversely, nuclear deterrence theory is being challenged by the possibility that AI-enabled cyberattacks could disable early warning systems, creating pressure for rapid automated response - a dangerous combination.