Patterns and Data

AI performance is fundamentally limited by data quality. 'Garbage in, garbage out' applies — sophisticated models cannot compensate for poor data.

Large-scale labeling uses crowdsourcing (Amazon Mechanical Turk), active learning, and semi-automated approaches. Label quality directly impacts model quality.

Like code versioning (Git), data versioning tracks changes to datasets. Tools like DVC ensure reproducibility of AI experiments.

Correlation ≠ causation. Causal inference methods (A/B testing, instrumental variables, do-calculus) determine actual cause-effect relationships.

GANs and VAEs generate new data by learning the underlying distribution. They can create realistic images, augment datasets, and model complex distributions.

Combining text, images, audio, and structured data improves AI understanding. Multimodal models learn richer representations than single-modality ones.

Data drift occurs when real-world data changes from training data. Continuous monitoring detects drift and triggers model retraining.

Techniques like federated learning, homomorphic encryption, and secure multi-party computation allow AI training on sensitive data without exposing it.

More users generate more data, which trains better models, which attract more users. This data flywheel creates competitive moats for AI companies.

Standard benchmarks (ImageNet, GLUE, SQuAD) enable comparing AI models. However, over-optimization on benchmarks can miss real-world performance.

Data governance establishes policies for data quality, access, security, and compliance. Organizations need clear governance for responsible AI development.

ML relies on probability: Bayes' theorem for updating beliefs, maximum likelihood for parameter estimation, and distributions for modeling uncertainty.

Data underpins all AI. From probability to privacy, embeddings to drift monitoring, mastering data is essential for building effective AI systems.

High-dimensional data (many features) is hard to process. PCA and t-SNE reduce dimensions while preserving important patterns for visualization and analysis.

Embeddings map data (words, images) to vector spaces where similar items are near each other. Word2Vec showed 'king - man + woman ≈ queen.'

Graph data represents relationships — social networks, molecular structures, knowledge graphs. Graph neural networks process these relationship patterns.

Text requires special preprocessing: tokenization, stop word removal, stemming, and encoding. Modern NLP uses subword tokenization like BPE.

Images are 3D tensors (height × width × channels). Normalization, augmentation, and resizing prepare images for neural network processing.

Time series analysis uses autocorrelation, seasonality decomposition, and recurrent architectures. LSTMs and transformers handle temporal dependencies.

Bayesian methods update probability estimates as new data arrives. They quantify uncertainty in predictions, which is crucial for decision-making.