data

And its implication on how we assess learning. an overview.

and moral responsibilities of corporation.

There has been this notion of “garbage in, garbage out” in CS which states that bad data, inputs will then produce an output that is of equal quality.

large language models, often implemented as autoregressive transformers models. GPTs and friends Most variants of LLMs are decoder-only (Radford et al., 2019) Have “capabilities” to understand natural language.

Detects pattern within data and use it to make useful prediction. Generally AI \subset ML \subset DL Some main exploration: Transformers Large language models NLP CNN Logistic regression Optimization gradient descent hyperparameter tuning ensemble learning Recommender systems Reinforcement learning Q-learning Policy Gradient Monte-Carlo Tree Search Generative Models GAN VAE Autoencoder sparse autoencoder sparse crosscoders Supervised Q-learning Low-rank adapters Fields mechanistic interpretability Related: linear algebra.

extends Boyer-Moore finding algorithm

Engine A search engine is essentially query processing. It is a form of information retrieval that helps one to answer questions The search results are generally presented in a line of results, often referred to as search engine results pages (SERPs).

See also: Edward Tuffe’s The Visual Display of Quantitative Information - ISBN-13: 978-1930824133 Question How do we represent information flow when we interact with different ...

Book: Database Management System ISBN-13:978-0072465631, or pdf Midterm Thurs Oct.24 2024 (during lecture time) Due at 2200, late penalty of 20% per 24h, max 5 days ...

See also slides Node as information and edges as relationship between data Directed acyclic graph (DAG) Application: Merkle DAG undirected. (\mathcal{N}, \mathcal{E}) \mathcal{N}: set of vertices \mathcal{E}: set of undirected edges: \mathcal{E} \subseteq \mathcal{N} \times \mathcal{N} path is a sequence of nodes and edges connect two nodes.