Applied Data Sciences

Robotics: Creating machines that can perform complex tasks autonomously or semi-autonomously.

Autonomous Vehicles: AI-driven vehicles that operate without human input. They use AI to process sensory input, make decisions, and navigate safely in diverse conditions.

AI in Healthcare: AI for medical diagnosis, treatment planning, patient monitoring, and healthcare administration. 

AI in Finance: AI for algorithmic trading, fraud detection, risk management, and personalized financial planning. 

AI in Retail and E-commerce: AI for personalized shopping experiences, inventory management, price optimization, and customer service enhancements. 

AI in Manufacturing: AI for predictive maintenance, quality control, supply chain management, and production optimization. 

AI in Education: AI for personalized learning, automated grading, and learning analytics. 

AI in Marketing: AI for targeted advertising, customer segmentation, market analysis, and content personalization. 

AI in Agriculture: AI for crop monitoring, predictive analysis for farming, automated equipment, and optimizing agricultural supply chains. 

AI in Smart Cities: AI for optimizing urban services such as traffic management, energy utilization, waste management, and smart infrastructure maintenance. 

AI in Cybersecurity: AI for detecting and counteracting cyber threats, analyzing security risks, and improving overall cybersecurity measures. 

AI in Entertainment: AI for content recommendation, game development, music composition, and personalized streaming services. 

AI in Legal and Compliance: AI for legal document analysis, compliance monitoring, and automating routine legal tasks. 

AI in Environmental Science: AI for climate modeling, environmental monitoring, and analyzing ecological data.
 

AI Organizations 

OpenAI

• Focus: Advanced AI research, large-scale machine learning models.
• Website: OpenAI

DeepMind Technologies Limited

• Focus: Neural networks, deep learning, AI for games and healthcare.
• Website: DeepMind

Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL)

• Focus: Robotics, human-computer interaction, data science.
• Website: MIT CSAIL

Stanford University Artificial Intelligence Lab (SAIL)

• Focus: Robotics, deep learning, NLP, computer vision.
• Website: Stanford AI Lab

Allen Institute for Artificial Intelligence (AI2)

• Focus: NLP, machine reading, knowledge extraction.
• Website: Allen AI

Partnership on AI to Benefit People and Society

• Focus: AI best practices, societal impacts of AI.
• Website: Partnership on AI

AI Now Institute

• Focus: AI's impact on society, rights and liberties.
• Website: AI Now Institute

Google AI (Google)

• Focus: Machine learning, quantum AI, AI for social good.
• Website: Google AI

Meta AI (Facebook, Inc.)

• Focus: Self-supervised learning, AI ethics, robotics.
• Website: Facebook AI

IBM Research - Artificial Intelligence (IBM)

• Focus: AI for business, blockchain, cloud computing.
• Website: IBM AI Research

Microsoft Research - AI (Microsoft)

• Focus: AI in healthcare, conversational AI, AI ethics.
• Website: Microsoft AI

Baidu Research - Institute of Deep Learning (Baidu, Inc.)

• Focus: Speech recognition, NLP, autonomous driving.
• Website: Baidu Research

Berkeley Artificial Intelligence Research Lab (BAIR)

• Focus: AI for creativity, human-compatible AI.
• Website: BAIR

The Institute for Ethical AI & Machine Learning

• Focus: Ethical frameworks, AI policy, and governance.
• Website: Ethical AI

Educational Resources and Online Courses

Google AI Education

• Google's Machine Learning Crash Course: An introduction to machine learning concepts using TensorFlow.

Coursera

• Machine Learning by Andrew Ng: A foundational course by Stanford University, covering algorithms, machine learning theory, and practical applications.
• Deep Learning Specialization by Andrew Ng: Deep learning, neural networks, and machine learning projects.

edX

• Introduction to Artificial Intelligence by Harvard University: Python, Mathematical Optimization, Algorithms, and Machine Learning.
• Data Science MicroMasters Program by UC San Diego: Probability and statistics, machine learning, and big data analysis.

Udacity

• AI Programming with Python Nanodegree: Python, NumPy, Pandas, Matplotlib, PyTorch, and deep learning.
• Deep Learning Nanodegree: Learning on neural networks, convolutional networks, recurrent networks, GANs, and deployment.

MIT OpenCourseWare

• Introduction to Computer Science and Programming Using Python: A beginner-friendly course that introduces the fundamentals of computer science.
• Artificial Intelligence with Python: AI concepts, techniques, and algorithms.

fast.ai

• Practical Deep Learning for Coders: Deep learning for software developers.

DataCamp

• Machine Learning with Python Track: Machine learning fundamentals, supervised and unsupervised learning in Python.

Kaggle

• Kaggle Learn: Python, data visualization, machine learning, and deep learning.

Books

Introductory Texts

• Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig.
• Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark.
• Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell.
• Superintelligence: Paths, Dangers, Strategies by Nick Bostrom.
• AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee.

Advanced Texts

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
• Pattern Recognition and Machine Learning by Christopher Bishop.
• Machine Learning: A Probabilistic Perspective by Kevin Murphy.
• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
• The Hundred-Page Machine Learning Book by Andriy Burkov.
• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos.

Specialized Topics

• Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
• Natural Language Processing in Python by Steven Bird, Ewan Klein, and Edward Loper.
• Deep Learning for Computer Vision by Rajalingappaa Shanmugamani.
• Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster.
• Interpretable Machine Learning by Christoph Molnar - Focus on explainable AI.
• The Singularity is Near: When Humans Transcend Biology by Ray Kurzweil.
• Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

Journals

Leading Academic Journals

• IEEE Transactions on Pattern Analysis and Machine Intelligence
• Expert Systems with Applications
• Journal of Machine Learning Research
• IEEE Transactions on Neural Networks and Learning Systems
• Pattern Recognition
• IEEE Transactions on Fuzzy Systems
• Information Sciences
• International Journal of Computer Vision
• IEEE International Conference on Robotics and Automation
• Fuzzy Sets and Systems
• International Journal of Robotics Research
• Neurocomputing
• Pattern Recognition Letters
• Journal of Memory and Language
• Neural Networks

Specialized Journals

• Journal of Robotics and Autonomous Systems
• Journal of Natural Language Engineering
• Data Mining and Knowledge Discovery
• IEEE Computational Intelligence Magazine
• IEEE Transactions on Cognitive Communications and Networking

Did you pick up a Large Language Model Card in one of the AI Literacy Interest Group or Research Data and Digital Scholarship events?

Learn more about the details mentioned in the card, presented here:

• HuggingFace Open LLM Leaderboard
• Reducing the Carbon Footprint of Generative AI by Boris Gamazaychikov
• Model Table at LifeArchitect.ai

Core Concepts of AI

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Deep Learning (DL)

Types of Machine Learning

• Supervised Learning 
• Unsupervised Learning 
• Reinforcement Learning

Neural Networks and Architectures

• Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)

Data Science and Analytics

• Predictive Analytics
• Data Mining
• Feature Engineering
• Dimensionality Reduction
• Principal Component Analysis (PCA)

AI Techniques and Algorithms

• Bayesian Networks
• Decision Trees
• Random Forests
• Support Vector Machines (SVMs)
• Gradient Descent
• Backpropagation
• Cross-Validation
• Hyperparameter Tuning
• Ensemble Learning
• Optimization Techniques

AI in Perception and Understanding

• Natural Language Processing (NLP)
• Computer Vision
• Speech Recognition
• Semantic Analysis
• Sentiment Analysis
• Object Detection
• Facial Recognition

AI Ethics and Governance

• Algorithmic Bias
• Ethical AI
• AI Governance
• AI Ethics and Law

Advanced and Emerging Topics in AI

• Transfer Learning
• Explainable AI (XAI)
• Federated Learning
• Quantum Computing in AI
• Edge AI
• Internet of Things (IoT) and AI
• Robotics
• Autonomous Systems
• AI in Cybersecurity

AI Model Management

• Overfitting and Underfitting
• Model Generalization