Artificial Intelligence (AI)

Last updated Apr 8, 2023 Edit Source

The expression artificial intelligence is an umbrella term encompassing a suite of technologies that can perform complex tasks when acting in conditions of uncertainty, including visual perception, speech recognition, natural language processing, reasoning, learning from data, and a range of optimisation problems.

• https://en.wikipedia.org/wiki/Artificial_intelligence
• AtHomeWithAI | Deepmind
• https://github.com/owainlewis/awesome-artificial-intelligence
• https://github.com/amusi/awesome-ai-awesomeness
• https://github.com/JosPolfliet/awesome-ai-usecases
• Stop Calling Everything AI, Machine-Learning Pioneer
• Artificial Intelligence—The Revolution Hasn’t Happened Yet
• Making sense of artificial intelligence (Google)
• People + AI guidebook (Google)
• General AI Challenge
• AI Index (Stanford)
• AI Playbook
• What’s the Difference Between AI, ML, and Deep Learning?
• The AI takeover is coming. Let’s embrace it
• What worries me about AI
• Machine Learning Confronts the Elephant in the Room
• AI Experiments
• Where will AGI come from? (Karpathy)
• AlphaGo Zero
  • AlphaGo Zero Explained In One Diagram
• More Is Different for AI
  • More Is Different for AI - Scaling Up, Emergence, and Paperclip Maximizers (w/ Jacob Steinhardt)
• Explosion in number of papers (tweet by A. Karpathy)
• Can society adjust at the speed of artificial intelligence?

See AI/AI-ML-DL for scientific discovery

See AI4ES/AI4ES

See AI4G/AI4good

See AI/FairAI

• Neural Information Processing Systems Conference (NeurIPS)
  • Proceedings
  • Videos
• International Conference on Machine Learning (ICML)
• International Conference for Learning Representations (ICLR)
• AI & Deep Learning Conference (NVIDIA)
• AAAI Conference on Artificial Intelligence
• World summit AI

• #BOOK AI Transformation Playbook (Andrew Ng, 2018)
• #BOOK Artificial Intelligence - Foundations of Computational Agents (Poole 2017, Cambridge)
• #BOOK The Future of Machine Intelligence (Beyer 2016, O’REILLY)
• #BOOK Artificial Intelligence - A Modern Approach (Russell & Norvig, 2010)
  • https://github.com/aimacode
  • Javascript visualization (and implementation) of algorithms
• #BOOK The quest for AI - A history of ideas and achievements (Nilson 2010, Cambridge)

• #COURSE Introduction to Artificial Intelligence (CS 188, Berkeley)
• #COURSE Intro to AI (CS188 , UC Berkeley)
  • http://ai.berkeley.edu/lecture_videos.html
• #COURSE Introduction to Artificial Intelligence with Python (CS50, Harvard U)
• #COURSE Introduction to Artificial Intelligence (ULiege)
• #COURSE Artificial General Intelligence (MIT 6.S099)
• #COURSE Artificial General (MINES Saint-Etienne)
• #COURSE Elements of AI (Reaktor and the U of Helsinki)
• #COURSE Introduction to Artificial Intelligence (Coursera - UVA Darden )
• #COURSE Artificial Intelligence (edX - Columbia U)
• #COURSE Artificial Intelligence Nanodegree (Udacity)
• #COURSE Self-Driving Car Engineer (Udacity)
  • https://github.com/udacity/self-driving-car
  • https://github.com/udacity/self-driving-car-sim
  • https://github.com/jessicayung/self-driving-car-nd
  • https://github.com/Everhusk/Self-Driving-Car-Engineering

• #TALK Lex Fridman Podcast
• #TALK Building machines that see, learn, and think like people (Tenenbaum)
• #TALK The Rise of Artificial Intelligence through Deep Learning (Bengio)
• #TALK Creating human-level AI (Bengio)
• #TALK A DARPA Perspective on Artificial Intelligence
• #TALK AI, Deep Learning, and Machine Learning: A Primer
• #TALK Symbolic, Statistical and Causal Artificial Intelligence, MLSS 2020
• #TALK Francois Chollet - Intelligence and Generalisation (Interview/podcast)

• #PAPER Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network (Krenn 2022)
  • #CODE https://github.com/artificial-scientist-lab/FutureOfAIviaAI
  • Graph with exponential growth in AI papers
• #PAPER Reflexion: an autonomous agent with dynamic memory and self-reflection (Shinn 2023)
  • See AI/Foundation models
  • Paper explained - GPT 4 Can Improve Itself

See AI/Math and Statistics/Math and Statistics

See AI/DS and DataEng/Data engineering and computer science

See AI/DS and DataEng/Data Science

See AI/ML

See AI/Computer Vision/Computer vision

See AI/NLP

See AI/Deep learning/DL

See AI/Causality

See AI/Problem Solving and Search

See AI/Automated planning

See AI/Evolutionary computation

See AI/XAI and AI/Deep learning/Explainability methods for NNs

See AI/Neuro-symbolic AI

See AI/Foundation models