Artificial Intelligence

Artificial Intelligence#

AI, ML, DL, RL, NLP, LLM & Prompting, CV & Robotics (also Data Mining & Pattern Recognition, Cybernetics & Informatics,…)

Threat of AI#

  • [ y ] 04-02-2026 Chris Williamson. “Why AI CEOs Are Building Bunkers - Tristan Harris”.

  • [ y ] 03-19-2026 Senator Bernie Sanders. “Bernie vs. Claude”.

  • [ y ] 03-04-2026 Senator Bernie Sanders. “AI Expert Tells Bernie: The Humans will be Discarded”.

  • [ y ] 03-26-2026 The Diary Of A CEO. “AI Whistleblower: We Are Being Gaslit By AI Companies, They’re Hiding The Truth! - Karen Hao”.

  • [ y ] 11-27-2025 The Diary Of A CEO. “AI Expert: Here Is What The World Looks Like In 2 Years! Tristan Harris”.

  • [ y ] 09-04-2025 The Diary Of A CEO. “The AI Safety Expert: These Are The Only 5 Jobs That Will Remain In 2030! - Dr. Roman Yampolskiy”.

Resources#

Andy Stapleton

  • [ y ] 04-20-2026 Andy Stapleton. “Claude Cowork for Research: The System Most Academics Miss”.

  • [ y ] 04-11-2026 Andy Stapleton. “This AI Might Be a PERFECT Research Companion (Recall 2026 Review)”.

  • [ y ] 04-08-2026 Andy Stapleton. “This Changes Academic AI Forever… And No One’s Talking About It”

  • [ y ] 04-01-2026 Andy Stapleton. “Unbelievable! 3 Tools That Bypass AI Detection in Seconds”.

  • [ y ] 03-11-2026 Andy Stapleton. “The Best Free AI Tools for Research (2026)”.

  • [ y ] 02-09-2026 Andy Stapleton. “NotebookLM’s Latest Features Are Insane”.

  • [ y ] 02-02-2026 Andy Stapleton. “The Best AI Tools for Academia in 2026 - Stop Searching, Start Using!”.

  • [ y ] 07-23-2025 Andy Stapleton. “I Can Spot AI Writing Instantly - Bypass ChatGPT Detectors for FREE”.

Eli the Computer Guy

  • [ y ] 01-25-2024 Eli the Computer Guy. “Machine Learning with OpenAI API and Relational Database (OpenAI, Python, SQLite)”.

  • [ y ] ------2021 Eli the Computer Guy. “‘Easy’ Computer Vision with Azure and AWS”.

freeCodeCamp

  • [ y ] 03-31-2026 freeCodeCamp.org. “AI-Assisted Coding Tutorial – OpenClaw, GitHub Copilot, Claude Code, CodeRabbit, Gemini CLI”.

  • [ y ] 03-26-2026 freeCodeCamp.org. “AI Foundations for Absolute Beginners”.

  • [ y ] 03-23-2026 freeCodeCamp.org. “Claude Code Essentials”.

  • [ y ] 02-25-2026 freeCodeCamp.org. “Python Essentials for AI Agents – Tutorial”.

  • [ y ] 09-22-2025 freeCodeCamp.org. “How to Build Advanced AI Agents – Course for Beginners (LiveKit, Exa, LangChain)”.

  • [ y ] 09-03-2025 freeCodeCamp.org. “Guide to Agentic AI – Build a Python Coding Agent with Gemini”.

  • [ y ] 11-05-2024 freeCodeCamp.org. “AI Foundations Course – Python, Machine Learning, Deep Learning, Data Science”.

  • [ y ] 12-04-2023 freeCodeCamp.org. “MLOps Course - Build Machine Learning Production Grade Projects”.

  • [ y ] 04-19-2023 freeCodeCamp.org. “ChatGPT Course - Use The OpenAI API to Code 5 Projects”.

  • [ y ] 06-06-2023 freeCodeCamp.org. “Deep Learning for Computer Vision with Python and TensorFlow - Complete Course”.

  • [ y ] 06-07-2021 freeCodeCamp.org. “OpenCV Python Course - Learn Computer Vision and AI”.

  • [ y ] ------2021 freeCodeCamp.org. “Advanced Computer Vision with Python - Full Course”.

  • [ y ] ??-??-2020 freeCodeCamp.org. “Deep Learning with PyTorch Live Course - Tensors, Gradient Descent & Linear Regression (Part 1 of 6)”.

  • [ y ] ??-??-2020 freeCodeCamp.org. “Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard”.

  • [ y ] 07-16-2019 freeCodeCamp.org. “Deep Reinforcement Learning in Python Tutorial - A Course on How to Implement Deep Learning Papers”.

Jason Morton

  • [ y ] ------2015 Jason Morton. “An Algebraic Perspective on Deep Learning, Part 1”.

  • [ y ] ------2015 Jason Morton. “An Algebraic Perspective on Deep Learning, Part 2”.

  • [ y ] ------2015 Jason Morton. “An Algebraic Perspective on Deep Learning, Part 3”.

MIT OpenCourseWare

  • [ y ] 05-16-2019 MIT OpenCourseWare. “9. Four Ways to Solve Least Squares Problems”. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.

My Lesson

  • [ y ] 08-09-2021 “Mathematics for Machine Learning Tutorial (3 Complete Courses in 1 video)”.

Stanford Online

  • [ y ] 12-16-2023 Stanford Online. “Stanford Seminar - Foundations of Spatial Perception for Robotics”.

Steve Bunton

  • [ y ] 05-29-2024 “Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]”.

  • [ y ] 01-05-2024. “A Neural Network Primer”.

  • [ y ] 12-29-2023 “A Machine Learning Primer: How to Build an ML Model”.

  • [ y ] 01-27-2020 “Principal Component Analysis (PCA)”.

More

  • [ y ] 03-31-2024 Artem Kirsanov. “The Most Important Algorithm in Machine Learning”.

  • [ y ] 07-04-2021 Adian Liusie. “Intuitively Understanding the Cross Entropy Loss”.

  • [ y ] 08-11-2022 Asianometry. “Running Neural Networks on Meshes of Light”.

  • [ y ] 03-05-2026 Dan Martell. “You’re not behind (yet): How to learn AI in 18 minutes”.

  • [ y ] 03-16-2020 Digital Learning Hub - Imperial College London. “Mathematics for Machine Learning - Linear Algebra”.

  • [ y ] 08-27-2025 EO. “Stanford’s Practical Guide to 10x Your AI Productivity | Jeremy Utley”.

  • [ y ] 06-01-2023 Jousef Murad LITE. “Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley | The Science Circle”.

  • [ y ] 11-24-2020 Samson Zhang. “Building a neural network FROM SCRATCH (no Tensorflow/Pytorch, just numpy & math)”.

  • [ y ] 05-09-2022 Visually Explained. “The Kernel Trick in Support Vector Machine (SVM)”.

Texts#

  • ???? Abu-Mostafa, Yaser S. Learning from Data. [ Home ]

  • 2020 Aggarwal, Charu C. Linear Algebra and Optimization for Machine Learning: A Textbook. Springer.

  • 2018 Aggarwal, Charu C. Neural Networks and Deep Learning: A Textbook. Springer.

  • 2018 Aggarwal, Charu C. Machine Learning for Text. Springer.

  • 2017 Aggarwal, Charu C. Outlier Analysis 2e. Springer.

  • 2016 Aggarwal, Charu C. Recommender Systems: The Textbook. Springer.

  • 2015 Aggarwal, Charu C. Data Mining: The Textbook. Springer.

  • 2018 Bengfort, Benjamin; Rebecca Bilbro; & Tony Ojeda. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. O’Reilly.

  • 2009 Bird, Steven; Ewan Klein; & Edward Loper. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly. [ Home ]

  • 2024 Bishop, Christopher & Hugh Bishop. Deep Learning: Foundations and Concepts. Springer. [ Home ]

  • 2006 Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer. [ book ]

  • 2017 Buduma, Nikhil. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms. O’Reilly. [ GitHub ]

  • 2015 Buffalo, Vince. Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools. O’Reilly. [ GitHub ]

  • 2020 Calin, Ovidiu. (2020). Deep Learning Architectures: A Mathematical Approach. Springer Series in the Data Sciences.

  • 2019 Charniak, Eugene. Introduction to Deep Learning. MIT Press. [ book ]

  • 2018 Chio, Clarence & David Freeman. Machine Learning and Security: Protecting Systems with Data and Algorithms. O’Reilly. [ GitHub ]

  • 2021 Chollet, François. Deep Learning with Python. 2e. Manning. [ GitHub ]

  • 2017 Chollet, Francois. Deep Learning with Python. 1e. Manning. [ GitHub ]

  • 1944 Curry, Haskell B. “The Method of Steepest Descent for Non-Linear Minimization Problems”. [ paper ]

  • 2017 Daume III, Hal. A Course in Machine Learning. [ Home ]

  • 2020 Deisenroth, Marc Peter, A. Aldo Faisal, & Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press. [ Home ]

  • 2018 Deng, Li & Yang Liu. Deep Learning in Natural Language Processing. Springer.

  • 2019 Eisenstein, Jacob. Introduction to Natural Language Processing. MIT Press Adaptive Computation and Machine Learning Series.

  • 2013 El Emam, Khaled & Luk Arbuckle. Anonymizing Health Data: Case Studies and Methods to Get You Started. O’Reilly.

  • 1994 Fausett, Laurene. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice Hall.

  • 2012 Flach, Peter. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press. [ Home ]

  • 2022 Ford, Colby T. Genomics in the Azure Cloud: Scaling Your Bioinformatics Workloads Using Enterprise-Grade Solutions. O’Reilly.

  • 2020 Forsyth, David. Applied Machine Learning. Springer.

  • 2002 Forsyth, D. & J. Ponce. Computer Vision: A Modern Approach.

  • ???? Geron, Aurelion. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 2e. O’Reilly. [ GitHub ]

  • 2021 Glassner, Andrew. Deep Learning: A Visual Approach. No Starch Press.

  • 2020 Gomez-Perez, Jose Manuel; Ronald Denaux; & Andres Garcia-Silva. A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP. Springer.

  • 2016 Goodfellow, Ian; Yoshua Bengio; & Aaron Courville. Deep Learning. MIT Press. [ Home ]

  • 2019 Graesser, Laura & Wah Loon Keng. Foundations of Deep Reinforcement Learning: Theory and Practice in Python. Addison-Wesley Professional. [ GitHub ]

  • 2011 Han, Jiawei, Micheline Kamber, & Jian Pei. Data Mining: Concepts and Techniques. 3e. [ Home ]

  • 2020 Hapke, Hannes & Catherine Nelson. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow. O’Reilly.

  • 2015 Hastie, Trevor, Robert Tibshirani, & Martin Wainwright. Statistical Learning with Sparsity: The Lasso and Generalizations [ Home ]

  • 2009 Hastie, Trevor, Robert Tibshirani, & Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2e. Springer. [ Home ]

  • 2021 Holley, Kerrie L. & Siupo Becker. AI-First Healthcare: AI Applications in the Business and Clinical Management of Health. O’Reilly.

  • 2020 Howard, Jeremy & Sylvain Gugger. Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD. O’Reilly. [ Home ][ GitHub ]

  • 2010 Huang, Chu-Ren et al. (eds.) Ontology and the Lexicon: A Natural Language Processing Perspective. Cambridge University Press Studies in Natural Language Processing.

  • 2022 Huyen, Chip. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. O’Reilly.

  • 2021 James, Gareth et al. An Introduction to Statistical Learning with Applications in R, 2e. Springer. [ Home ]

  • 2022 Jurafsky, Dan & James H. Martin. Speech and Language Processing. 3e. [ Home ]

  • 2019 Kamath, Uday, John Liu, & James Whitaker. Deep Learning for NLP and Speech Recognition. Springer.

  • 2021 Kneusel, Ronald T. Math for Deep Learning: What You Need to Know to Understand Neural Networks. No Starch Press.

  • 2019 Koul, Anirudh, Siddha Ganju, & Meher Kasam. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI and Computer Vision Projects Using Python, Keras, and TensorFlow. O’Reilly. [ GitHub ]

  • 2021 Lakshmanan, Valliappa; Martin Gorner; & Ryan Gillard. Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images. O’Reilly. [ GitHub ]

  • 2020 Lakshmanan, Valliappa; Sara Robinson; & Michael Munn. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps. O’Reilly. [ GitHub ]

  • 2019 Lane, Hobson; Cole Howard; & Hannes Max Hapke. Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python. O’Reilly.

  • 2020 Lanham, Michael. Practical AI on the Google Cloud Platform: Utilizing Google’s State-of-the-Art AI Cloud Services. O’Reilly. [ GitHub ]

  • 2016 Leskovec, Jure, Anand Rajaraman, & Jeff Ullman. Mining of Massive Datasets. 3e. Stanford University Press. [ Home ]

  • 2020 Liu, Zhiyuan, Yankai Lin, & Maosong Sun. Representation Learning for Natural Language Processing. Springer.

  • 2003 MacKay, David J. Information Theory, Inference, and Learning Algorithms. Cambridge University Press.

  • 1999 Manning, Christopher D. & Hinrich Schutze. Foundations of Statistical Natural Language Processing. MIT Press.

  • 2023 Matloff, Norman. The Art of Machine Learning: Algorithms + Data + R. No Starch Press.

  • 1969 Minsky, Marvin & Seymour Papert. Perceptrons: An Introduction to Computational Geometry. [ Wikipedia ]

  • 1997 Mitchell, Tom M. Machine Learning.

  • 2018 Mohri, Mehryar; Afshin Rostamizadeh; & Ameet Talwalkar. Foundations of Machine Learning. MIT Press. [ Home ]

  • 2020 Moroney, Laurence. AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence. O’Reilly. [ GitHub ]

  • 2016 Muller, Andreas C. & Sarah Guido. Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly. [ GitHub ]

  • 2012 Murphy, Kevin Patrick. Machine Learning: A Probabilistic Perspective. MIT Press. [ Home ]

  • 2022 Nelson, Hala. Essential Math for AI: Next-Level Mathematics for Developing Efficient and Successful AI Systems. O’Reilly.

  • 2022 Nield, Thomas. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics. O’Reilly.

  • 2020 Nielsen, Aileen. Practical Fairness: Achieving Fair and Secure Data Models. O’Reilly.

  • 2018 Osinga, Douwe. _Deep Learning Cookbook: Practical Recipes to Get Started Quickly. O’Reilly. [ GitHub ]

  • 2021 Patel, Ankur A. & Ajay Uppili Arasanipalai. Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand. O’Reilly. [ GitHub ]

  • 2017 Poibeau, Thierry. Machine Translation. MIT Press Essential Knowledge Series.

  • 2019 Pointer, Ian. Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Algorithms. O’Reilly. [ GitHub ]

  • ???? Prince, Simon J. D. Computer Vision: Models, Learning, and Inference. Cambridge University Press. [ Home ]

  • 2020 Raaijmakers, Stephan. Deep Learning for Natural Language Processing. Manning.

  • 2019 Ramsundar, Bharath et al. Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, & More. O’Reilly. [ GitHub ]

  • 2019 Rao, Delip & Brian McMahon. Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning. O’Reilly.

  • 2020 Russell, Stuart & Peter Norvig. Artificial Intelligence: A Modern Approach 4e. Pearson. [ Home ][ Wikipedia ]

  • 2014 Shalev-Shwartz, Shai & Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. [ Home ]

  • 2018 Sutton, Richard S. & Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press. [ Home ]

  • 2011 Szeliski, Rick. Computer Vision: Algorithms and Applications. Springer. [ Home ]

  • 2017 Tan, Pang-Ning et al. Introduction to Data Mining. 2e. Pearson. [ Home ][ R code ]

  • 2023 Tang, Yuan. Distributed Machine Learning Patterns. Manning.

  • 2020 Thomas, Alex. Natural Language Processing with Spark NLP: Learning to Understand Text at Scale. O’Reilly.

  • 2020 Thomas, Rob & Paul Zikopoulos. The AI Ladder: Accelerate Your Journey to AI. O’Reilly.

  • 2021 Tok, Wee Hyong; Amit Bahree; & Senja Filipi. Practical Weak Supervision: Doing More with Less Data. O’Reilly.

  • 2020 Treveil, Mark et al. Introducing MLOps: How to Scale Machine Learning in the Enterprise. O’Reilly.

  • 2011 Trotter, Fred & David Uhlman. Hacking Healthcare: A Guide to Standards, Workflows, and Meaningful Use. O’Reilly.

  • 1998 Trucco, E. & A. Verri. Introductory Techniques for 3D Computer Vision.

  • 2021 Tung, KC. TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models. O’Reilly.

  • 2022 Tunstall, Lewis; Leandro von Werra; & Thomas Wolf. Natural Language Processing with Transformers: Building Language Applications with HuggingFace. O’Reilly.

  • 2020 Vajjala, Sowmya et al. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. O’Reilly.

  • 2020 Van der Auwera, Geraldine A. & Brian D. O’Connor. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O’Reilly.

  • 2020 Vasiliev, Yuli. Natural Language Processing with Python and spaCy. No Starch Press.

  • 2020 Vaughan, Daniel. Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise. O’Reilly. [ GitHub ]

  • 2018 Vershynin, Roman. High-Dimensional Probability: An Introduction with Applications in Data Science. Cambridge University Press.

  • 2019 Wainwright, Martin J. High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge University Press.

  • 2019 Warr, katy. Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery. O’Reilly. [ Home ][ GitHub ]

  • 2019 Weidman, Seth. Deep Learning from Scratch: Building with Python from First Principles. O’Reilly. [ GitHub ]

  • 2013 Wiener, Norbert. Cybernetics: or, Control and Communication in the Animal and the Machine. 2e.

  • 1988 Wiener, Norbert. The Human Use of Human Beings: Cybernetics and Society.

  • 1966 Wiener, Norbert. God and Golem, Inc.: A Comment on Certain Points where Cybernetics Impinges on Religion, 7e. MIT Press.

  • 2020 Winder, Phil. Reinforcement Learning: Industrial Applications of Intelligent Agents. O’Reilly. [ Home ]

  • 2016 Witten et al. Data Mining: Practical Machine Learning Tools and Techniques. 4e. Morgan Kaufmann. [ Home ]

  • 2021 Youens-Clark, Ken. Mastering Python for Bioinformatics: How to Write Flexible, Documented, Tested Python Code for Research Computing. O’Reilly. [ GitHub ]

  • 2020 Zhang, Xian-Da. A Matrix Algebra Approach to Artificial Intelligence. Springer.

  • 2018 Zheng, Alice & Amanda Casari. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O’Reilly. [ GitHub ]

Figures#

  • [ w ] 1920-1992 Asimov, Isaac

  • [ w ] 1973----- Bostrom, Nick

  • [ w ] 1890-1938 Čapek, Karel

  • [ w ] 1917-2008 Clarke, Arthur C.

  • [ w ] 1900-1982 Curry, Haskell Brooks

  • [ w ] 1452-1519 Da Vinci, Leonardo

  • [ w ] 1709-1782 De Vaucanson, Jacques

  • [ w ] 1966----- Goertzel, Ben

  • [ w ] 1850-1925 Heaviside, Oliver

  • [ w ] 1904-1985 Hebb, Donald

  • [ w ] 1947----- Hinton, Geoffrey

  • [ w ] 1933----- Hopfield, John

  • [ w ] 1967----- Hutter, Marcus

  • [ w ] 1928-1999 Kubrick, Stanley

  • [ w ] 1948----- Kurzweil, Ray

  • [ w ] 1960----- LeCun, Yann

  • [ w ] 1950-2023 Lenat, Douglas

  • [ w ] 1927-2011 McCarthy, John

  • [ w ] 1898-1969 McCulloch, Warren

  • [ w ] 1927-2016 Minsky, Marvin

  • [ w ] 1948----- Moravec, Hans

  • [ w ] 1927-1992 Newell, Allen

  • [ w ] 1976----- Ng, Andrew

  • [ w ] 1956----- Norvig, Peter

  • [ w ] 1928-2016 Papert, Seymour

  • [ w ] 1923-1969 Pitts, Walter

  • [ w ] 1928-1971 Rosenblatt, Frank

  • [ w ] 1962----- Russell, Stuart

  • [ w ] 1932----- Searle, John

  • [ w ] 1916-2001 Simon, Herbert

  • [ w ] 1856-1943 Tesla, Nikola

  • [ w ] 1852-1936 Torres Quevado, Leonardo

  • [ w ] 1912-1954 Turing, Alan

  • [ w ] 1947----- Werbos, Paul

  • [ w ] 1929-2025 Widrow, Bernard

  • [ w ] 1894-1964 Wiener, Norbert

  • [ w ] 1956----- Winfield, Alan

  • [ w ] 1979----- Yudkowsky, Eliezer

1890                          1920                1940                                              1990
+---------+---------+---------+---------+-------- Čapek (1938)
          +---------+---------+---------+---------+---------+---------+---------+---------+-- Curry (1982)
                           ---+---------+---------+---------+---------+---------+---------+---------+---------+-------- Clarke (2008)
                              +---------+---------+---------+---------+---------+---------+---------+-- Asimov (1992)
                                                                                   -------+---------+---------+---------+---------+----- Bostrom (present)

Terms#

  • [ w ] Activation Function

  • [ w ] Activity Recognition

  • [ w ] Actuator

  • [ w ] Adaptive Linear Neuron (ADALINE)

  • [ w ] Agentic AI

  • [ w ] AI Alignment

  • [ w ] AI Completeness

  • [ w ] AI History

  • [ w ] AI Progress

  • [ w ] AI Timeline

  • [ w ] AI Winter

  • [ w ] AI-Assisted Software Development

  • [ w ] AIXI

  • [ w ] Alphanumeric Character

  • [ w ] Android

  • [ w ] Anomaly Detection

  • [ w ] Artificial Consciousness (AC)

  • [ w ] Artificial Intelligence (AI) [ SEP ][ i ]

  • [ w ] Artificial General Intelligence (AGI)

  • [ w ] Artificial Life

  • [ w ] Artificial Neural Network (ANN) [ history ]

  • [ w ] Artificial Neuron

  • [ w ] ASCII

  • [ w ] Associative Rule Learning

  • [ w ] Automated Reasoning

  • [ w ] Automaton

  • [ w ] Backpropagation

  • [ w ] Bard

  • [ w ] Batch Normalization

  • [ w ] Bayes’ Theorem

  • [ w ] Bayesian Network

  • [ w ] Binary Classification

  • [ w ] Bio-Inspired Robotics

  • [ w ] Bioinformatics

  • [ w ] Biomimetics

  • [ w ] Bionics

  • [ w ] Body Schema

  • [ w ] Carbon Nanotube

  • [ w ] Chain-of-Thought (CoT)

  • [ w ] Character

  • [ w ] Character Encoding

  • [ w ] Chatbot [ list ]

  • [ w ] Chinese Room

  • [ w ] Classification

  • [ w ] Closed-Circuit Television (Video Surveillance)

  • [ w ] Cluster Analysis

  • [ w ] Computational Graph

  • [ w ] Computational Learning Theory

  • [ w ] Computer Vision

  • [ w ] Connectionism

  • [ w ] Constitutional AI

  • [ w ] Context Awareness

  • [ w ] Context Engineering

  • [ w ] Context Window

  • [ w ] Conversational AI

  • [ w ] Conversational User Interface (CUI)

  • [ w ] Convolutional Neural Network (CNN)

  • [ w ] Corpus

  • [ w ] Cross-Validation

  • [ w ] Curse of Dimensionality

  • [ w ] Cybernetics

  • [ w ] Cyc

  • [ w ] Data Mining

  • [ w ] Decision Tree

  • [ w ] Decision Tree Learning

  • [ w ] Deep Learning (DL)

  • [ w ] DeepMind (Google)

  • [ w ] Delta Rule

  • [ w ] Diffusion Model

  • [ w ] Digesting Duck

  • [ w ] Digital Image Processing

  • [ w ] Dimensionality Reduction

  • [ w ] Document

  • [ w ] Drone

  • [ w ] End Effector

  • [ w ] Ensemble Learning

  • [ w ] Exclusive Disjunction (XOR)

  • [ w ] Expert System

  • [ w ] Explanation-Based Learning (EBL)

  • [ w ] F Score

  • [ w ] Face Detection

  • [ w ] Feature

  • [ w ] Feature

  • [ w ] Feature Engineering

  • [ w ] Feature Extraction

  • [ w ] Feature Learning

  • [ w ] Feature Selection

  • [ w ] Feedforward Neural Network (Multi Layer Perceptron (MLP))

  • [ w ] Few-Shot Learning

  • [ w ] Fine-Tuning

  • [ w ] Gated Recurrent Unit (GRU)

  • [ w ] General Game Playing (GGP)

  • [ w ] Generalization Error

  • [ w ] Generative AI

  • [ w ] Generative Pre-Trained Transformer (GPT)

  • [ w ] Gödel Machine

  • [ w ] GPT-3

  • [ w ] Gradient Boosting

  • [ w ] Gradient Descent

  • [ w ] Grammar Induction/Inference

  • [ w ] Hallucination

  • [ w ] Hard Problem of Consciousness

  • [ w ] Health Informatics

  • [ w ] Hebbian Learning

  • [ w ] Hopfield Network

  • [ w ] Human-in-the-Loop (HITL)

  • [ w ] Humanoid Robot

  • [ w ] Hyperbolic Functions

  • [ w ] Hyperparameter

  • [ w ] Image Retrieval

  • [ w ] In-Context Learning

  • [ w ] Inductive Bias

  • [ w ] Industrial Robot

  • [ w ] Inference Engine

  • [ w ] Informatics

  • [ w ] Information Extraction

  • [ w ] Intelligence

  • [ w ] Intelligent Agent

  • [ w ] K Nearest Neighbors (KNN)

  • [ w ] Kinematics

  • [ w ] Knowledge Base (KB)

  • [ w ] Knowledge Engineering [ i ]

  • [ w ] Knowledge Extraction

  • [ w ] Knowledge Representation and Reasoning (KRR)

  • [ w ] Labeled Data

  • [ w ] LaMDBA (Language Model for Dialogue Applications)

  • [ w ] LangChain

  • [ w ] Language Model

  • [ w ] Large Language Model (LLM)

  • [ w ] Lemma

  • [ w ] Lemmatization

  • [ w ] Leonardo’s Robot

  • [ w ] Lexeme

  • [ w ] Lexical Analysis

  • [ w ] Linear Actuator

  • [ w ] Linear Regression

  • [ w ] Linear Separability

  • [ w ] Logic-based AI [ SEP ]

  • [ w ] Logistic Function

  • [ w ] Logistic Regression

  • [ w ] Long Short-Term Memory (LSTM)

  • [ w ] Loss Function

  • [ w ] Machine Learning (ML)

  • [ w ] Machine Perception

  • [ w ] Machine Translation

  • [ w ] Markov Logic Network

  • [ w ] Mobile Robot

  • [ w ] Model Selection

  • [ w ] Model Specification

  • [ w ] Morphological Parsing

  • [ w ] Moving Object Detection

  • [ w ] Multiclass Classification

  • [ w ] Multilabel Classification

  • [ w ] Multi Layer ADALINE (MADALINE)

  • [ w ] Multi Layer Perceptron (MLP) (Feedforward Neural Network)

  • [ w ] Multimodal Learning

  • [ w ] Multitask Learning (MTL)

  • [ w ] Multitask Optimization

  • [ w ] N-Gram

  • [ w ] Named Entity

  • [ w ] Named-Entity Recognition (NER)

  • [ w ] Nanorobotics

  • [ w ] Natural Language Interface

  • [ w ] Natural Language Generation (NLG)

  • [ w ] Natural Language Processing (NLP) [ Investopedia ]

  • [ w ] Natural Language Understanding (NLU)

  • [ w ] Neural Network (NN)

  • [ w ] Neural Turing Machine (NTM)

  • [ w ] Neuroinformatics

  • [ w ] Object Categorization from image search

  • [ w ] Object Detection

  • [ w ] Object Recognition

  • [ w ] One-Shot Learning

  • [ w ] OpenAI

  • [ w ] Ontology

  • [ w ] Overfitting

  • [ w ] Parsing

  • [ w ] Part of Speech

  • [ w ] Part-of-Speech (PoS) Tagging

  • [ w ] Pedestrian Detection

  • [ w ] Perceptron

  • [ w ] Philosophy of Artificial Intelligence

  • [ w ] Physics-Informed Neural Network (PINN)

  • [ w ] Plain Text

  • [ w ] Principal Component Analysis (PCA)

  • [ w ] Prompt

  • [ w ] Prompt Engineering

  • [ w ] Prompt Injection

  • [ w ] Proprioception

  • [ w ] Pull Prompting

  • [ w ] Punctuation

  • [ w ] Push Prompting

  • [ w ] Random Forest

  • [ w ] Rectifier

  • [ w ] Recurrent Neural Network (RNN)

  • [ w ] Regression

  • [ w ] Regularization

  • [ w ] Reinforcement Learning (RL)

  • [ w ] Reinforcement Learning from Human Feedback (RLHF)

  • [ w ] Representation Learning

  • [ w ] Retrieval-Augmented Generation (RAG)

  • [ w ] Robonaut

  • [ w ] Robot History

  • [ w ] Robot Locomotion

  • [ w ] Robotic Process Automation (RPA)

  • [ w ] Robotic Sensing

  • [ w ] Robotic Sensors

  • [ w ] Robotics

  • [ w ] Rossum’s Universal Robots (R.U.R.)

  • [ w ] Self-Surpervised Learning

  • [ w ] Semi-Supervised Learning

  • [ w ] Sensory-Motor Map

  • [ w ] Sentence Segmentation or Sentence Boundary Disambiguation (SBD)

  • [ w ] Sentiment Analysis

  • [ w ] Shadow Hand

  • [ w ] Sigmoid Function

  • [ w ] Soar

  • [ w ] Spatial Contextual Awareness

  • [ w ] Spec-Driven Development (SDD)

  • [ w ] Statistical Learning

  • [ w ] Stem

  • [ w ] Stemming

  • [ w ] Stochastic Gradient Descent (SGD)

  • [ w ] Stop Word

  • [ w ] String

  • [ w ] Strong AI

  • [ w ] Superintelligence

  • [ w ] Supervised Learning

  • [ w ] Support-Vector Machine (SVM)

  • [ w ] SWOT Analysis

  • [ w ] Symbolic AI

  • [ w ] Symbol Grounding Problem

  • [ w ] SynthID

  • [ w ] Systems Theory

  • [ w ] Tag

  • [ w ] Tag Cloud

  • [ w ] Term Frequency Inverse Document Frequency (TFIDF)

  • [ w ] Test Data

  • [ w ] Text

  • [ w ] Text Mining

  • [ w ] Text Segmentation

  • [ w ] Text-to-Image Model

  • [ w ] Text-to-Video Model

  • [ w ] Three Laws of Robotics

  • [ w ] Token

  • [ w ] Tokenization

  • [ w ] Tone Analysis

  • [ w ] Topic Model

  • [ w ] Training Data

  • [ w ] Transfer Learning

  • [ w ] Transformer

  • [ w ] Turing Test [ SEP ][ i ]

  • [ w ] Underfitting

  • [ w ] Unicode

  • [ w ] Unimate

  • [ w ] Unlabeled Data

  • [ w ] Unmanned Aerial Vehicle (UAV)

  • [ w ] Unsupervised Learning

  • [ w ] Validation Data

  • [ w ] Viable System Theory

  • [ w ] Vibe Coding

  • [ w ] Video Surveillance (Closed-Circuit Television)

  • [ w ] Video Tracking

  • [ w ] Virtual Assistant

  • [ w ] Weak AI [ i ]

  • [ w ] Weight

  • [ w ] Whole Brain Emulation

  • [ w ] Word

  • [ w ] Word Cloud

  • [ w ] Word Embedding

  • [ w ] Word Segmentation

  • [ w ] Zero-Shot Learning

Contents