Deep Learning
A subset of machine learning that uses neural networks with many layers (hence 'deep') to learn complex patterns in data, powering breakthroughs in vision, language, and generative AI.
Deep learning became the dominant AI paradigm after AlexNet's 2012 ImageNet win. The field has since produced transformers, diffusion models, and LLMs with trillions of parameters. Deep learning requires massive compute and data — training frontier models costs $78-191 million. The technique excels at unstructured data (text, images, audio) but requires significant compute resources. Over 85% of AI research papers published today involve deep learning architectures.
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Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
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