Artificial Intelligence
The Science of Intelligent Machines
Artificial Intelligence (AI) is the science and engineering of creating machines capable of intelligent behavior. From language models like GPT to self-driving cars, AI powers the technologies that are reshaping our world.
🧠 Core AI Concepts
As entropy accelerates across systems—climate, computation, cognition—certain architectures emerge not as tools, but as substrates for resilience and synthesis. The field of Artificial Intelligence, seeded by Alan Turing's 1950 question, "Can machines think?", now manifests in scalable systems that learn, adapt, and act.
Neural Networks
Inspired by the brain, neural networks mimic biological neurons to recognize patterns. From McCulloch & Pitts (1943) to Hinton's deep belief nets, this is the nervous system of machine perception.
Machine Learning
Coined by Arthur Samuel in the 1950s, machine learning enables systems to learn from data. Algorithms find statistical regularities to predict, classify, and adapt—powering everything from spam filters to recommendation engines.
Natural Language Processing (NLP)
Thanks to models like BERT (Devlin et al., 2018) and GPT-3 (Brown et al., 2020), machines now understand and generate language. NLP bridges semantics, syntax, and knowledge—bringing language into the loop of computation.
Deep Learning
Popularized by LeCun, Bengio, and Hinton, deep learning uses multi-layered neural networks to understand visual, audio, and textual data. It powers speech recognition, medical imaging, and generative art.
Reinforcement Learning
Used by DeepMind's AlphaGo and OpenAI's robotics systems, RL allows agents to learn via reward and trial. Inspired by behavioral psychology, it merges exploration, feedback, and policy optimization.
Transformers
Introduced in "Attention Is All You Need" (Vaswani et al., 2017), transformers broke the bottlenecks of sequence modeling. Now foundational to models like GPT, T5, and RoBERTa, they underpin modern generative AI.
📚 AI Dictionary
Fundamentals
- Algorithm: Step-by-step procedure to solve problems or make decisions.
- Data: The raw input used to train, test, and validate AI systems.
- Model: A trained system capable of making predictions or decisions.
- Training: Process of adjusting model weights to learn patterns.
- Testing: Assessing model performance on unseen data.
Neural Networks
- Backpropagation: Learning algorithm for neural networks.
- Epoch: One full pass over the training dataset.
- Latent Space: Abstract representation of learned features.
- Overfitting: When a model memorizes instead of generalizes.
- Feature: An attribute or property used by models to learn patterns.
Learning Types
- Supervised Learning: Models trained on labeled input-output pairs.
- Unsupervised Learning: Models that find structure in unlabeled data.
- Reinforcement Learning: Training by rewards from environment interaction.
- Zero-Shot Learning: AI solving tasks it hasn't seen before.
- Fine-Tuning: Customizing a pretrained model on new data.
Advanced Concepts
- GAN: Generative Adversarial Network — two AIs competing to generate data.
- Transformer: Model architecture behind GPT, BERT, and similar systems.
- Prompt Engineering: Crafting text to steer AI behavior.
- Explainability: Ability to interpret how and why a model made its decisions.
- Bias: Skew in model outcomes due to training data or design.
Applications
- Inference: Using a trained model to predict outcomes.
- Prediction: The model's output based on new input data.
- Scalability: AI's capacity to handle more data or complexity efficiently.
- Human-AI Collaboration: Synergy between AI systems and human decision-making.
- AI Safety: Ensuring AI systems align with human values and don't cause harm.
Data & Processing
- Corpus: Large dataset of text for language model training.
- Signal Processing: Analyzing and manipulating signals for AI applications.
- Data Augmentation: Creating additional training data through transformations.
- Cross-Validation: Technique to assess model performance and prevent overfitting.
- Ensemble Methods: Combining multiple models for better predictions.
📄 Foundational Papers in AI
🏗️ Architecture Papers
- Attention Is All You Need (Vaswani et al., 2017) — Introduced the Transformer model architecture.
- BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2018) — Revolutionized NLP.
- Language Models are Few-Shot Learners (Brown et al., 2020) — Introduced GPT-3.
🧠 Neural Networks
- Learning Representations by Back-Propagating Errors (Rumelhart et al., 1986) — Popularized backpropagation.
- A Fast Learning Algorithm for Deep Belief Nets (Hinton et al., 2006) — Deep learning revival.
- Deep Learning (LeCun, Bengio, & Hinton, 2015) — Comprehensive overview.
🎯 Reinforcement Learning
- Human-level Control through Deep Reinforcement Learning (Mnih et al., 2015) — Deep Q-networks.
- Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013) — Deep Q-learning.
🎨 Generative AI
- Generative Adversarial Nets (Goodfellow et al., 2014) — Introduced GANs.
- DALL-E: Creating Images from Text (Ramesh et al., 2021) — Text-to-image generation.
- Codex: AI for Code Generation (Chen et al., 2021) — Powers GitHub Copilot.
🔬 Foundational Research
- Computing Machinery and Intelligence (Alan Turing, 1950) — Introduces the Turing Test.
- A Logical Calculus of the Ideas Immanent in Nervous Activity (McCulloch & Pitts, 1943) — Earliest neural networks.
- Programs with Common Sense (John McCarthy, 1958) — Defined the term "AI".
📈 Scaling & Emergence
- Emergent Abilities of Large Language Models (Wei et al., 2022) — Capabilities emerge with scale.
- Scaling Laws for Neural Language Models (Kaplan et al., 2020) — Model size vs performance.
- The Unreasonable Effectiveness of Data (Halevy, Norvig, & Pereira, 2009) — Data over algorithms.