A list of resources, links, and courses. Last updated 1/3/2016.
As we look at the resources available to learn about AI, it’s helpful to divide them into two categories.
The first category we will call Everybody. The second category is Engineers.
Everybody will include both developers and non-technical or business people. These resources are for anybody who has an interest in the general social, philosophical, history, and business impact of AI.
Engineers are programmers, architects, data scientists, and so forth, working in a hands-on or technical leadership role in building AI or machine learning systems.
Until now, we have only heard of and read of the technology side of artificial intelligence. Well, mostly. What we will see become increasingly important over the next couple of years, is the business of artificial intelligence.
If you’re a business person and want to stay focused on the non-technical side of things, meaning you aren’t actually going to be building these platforms yourself, then you will benefit most from the materials listed under Everybody below.
At some point in the future, I’ll add additional resources for kids.
Engineers are often tempted to focus on technology at the expense of the business and societal impact, but AI is different. It will have such a profound influence on every area of our lives that, I believe, anyone building AI systems has a responsibility to understand as much as possible about the big picture.
Recommendations are always welcome if you have something to add. Ping me on Twitter at @conintellect.
AI Resources for Everybody
There is a growing body of material aimed at the non-technical person interested in AI. If you read only one book and take only one online course from this list, I’d go with the Superintelligence book and the Udacity AI course.
Often overlooked as a place to start learning, Wikipedia is a great source of articles and links on some of the most important topics within Artificial Intelligence and Machine Learning.
Here are some of the top ones you should read:
Artificial intelligence - Wikipedia, the free encyclopedia
Artificial intelligence ( AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior.
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
Artificial neural network - Wikipedia, the free encyclopedia
In machine learning and cognitive science, artificial neural networks ( ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.
Natural language processing
Natural language processing ( NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human-computer interaction.
Author: Nick Bostrom Link on Amazon
Just released in late 2014, Superintelligence has quickly become the go-to book for business leaders and politicians wishing to learn more about AI and the risks it brings. A must-read.
The Age of Spiritual Machines
Author: Ray Kurzweil Link on Amazon
One of the earliest internet-era books about technology advancements, Ray Kurzweil paints a picture of the future and how much of it will be completely changed by AI and human / computer interaction. Kurzweil has both fans and detractors, but it’s difficult to ignore him.
The Singularity is Near: When Humans Transcend Biology
Author: Ray Kurzweil Link on Amazon
Not centered on AI per se, this book is still worth a read to understand the concept of the Singularity and how AI, advances in biological engineering, and other computing technology will leave a permanent imprint on human history and our next stage of evolution, assuming we make it that far.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
Authors: Erik Brynjolfsson, Andrew McAfee Link on Amazon
Two of the top researchers in AI and machine learning technology examine the societal impact of machine intelligence. A great follow-up read to Bostrom’s Superintelligence, as they go deeper into the business changes that are likely to result from learning-based automation on a massive scale.
Artificial Intelligence Illuminated
Author: Ben Coppin Link on Amazon
This is a textbook and gives about the deepest, non-technical overview I’ve seen about the various technologies and techniques that support modern AI research. This is a good book for Product Managers and other executives to read in order to stay relevant in product discussions with engineers.
Other Book Lists
There are other book lists that come out on a regular basis, so I’ll add them here when I find good examples.
If you’re just getting into AI, regardless of whether you are an Engineer or an Everybody, this is the first online course you should take. It’s free and has two of the top experts in the field. It’s mostly non-technical but covers some of the deeper topics that you’ll need to be aware of if you’re going to go anywhere in the field.
Executive Data Science Courses
This a specialization course that is offered to executives. It’s not free but it is very affordable and focuses mostly on Data Science. Data Science isn’t exactly AI, of course, but much of the foundational knowledge is the same and it will provide a solid educational background that will help you understand machine learning and intelligence more more quickly.
Other Resources & Links
OpenAI is a non-profit research organization, chaired by Elon Musk (Tesla, SpaceX, PayPal) and Sam Altman (President of Y Combinator). OpenAI has raised over $1B in funding to date and is dedicated to ensuring that AI technology is developed in such a way that supports, instead of threatens, human life and civilized society.
Nvidia is playing an important role in the data center world with their high-performance hardware and chips, specifically GPUs.
The Association for the Advancement of Artificial Intelligence is one of the oldest non-profit scientific organizations dedicated to AI research. They hold a number of excellent events and symposiums around the year.
The Machine Intelligence Research Institute (MIRI) is another non-profit scientific society focused on ensuring that AI has a positive impact. Their blog is quite informative.
AI Resources for Engineers
As an engineer looking to go deep on AI, I’d start with some of these course. In particular, start with Andrew Ng’s Machine Learning course. He reviews a lot of Linear Algebra at the beginning and you’ll need it.
Coursera Machine Learning Course with Andrew Ng - THE Machine Learning course online.
Data Science and Analytics in Context - a series of three online courses that covers Machine Learning, Algorithms, Big Data, IoT, and statistical thinking.
Artificial Intelligence for Robotics - taught by the leader of Google’s autonomous driving team.
Neural Networks for Machine Learning - a great way to dive more into Neural Networks
Computational Neuroscience - learning more about the the human brain works will help understand how computers can learn too.
Programming Languages and Frameworks
If you are new to Computer Science or programming, you’ll want to start there before going any further. There are many options here but if you’re looking for a good, general-purpose programming language to learn that has strong support in the machine intelligence, you can’t go wrong with the Python language.
Once you get familiar with Python, there are some great libraries that can help you work more on machine learning projects.
NumPy - the fundamental library for scientific computing. In particular, contains built-in libraries for Linear Algebra.
scikit-learn - machine learning library for Python.
DEAP - distributed evolutionary algorithms in Python
TensorFlow - In November 2015, Google open-sourced one of their primary machine learning packages. It’s a fantastic set of tools and the website provides excellent tutorials as well. Highly recommended.
Torch is another open-source machine learning library.
Artificial Intelligence: A Modern Approach (3rd Edition)
Author: Stuart Russell, Peter Norvig
Stuart Russell and Peter Norvig are legendary in the field of machine intelligence. This book is huge and it’s not light reading. Still considered one of the most important textbooks in learning AI.
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
Author: Peter Norvig
Peter Norvig is also legendary in the Lisp world. I’ve heard many programmers, even non-Lisp programmers, say that this is the best programming book they’ve ever read.
Data Science from Scratch
Author: Joel Grus
Following on the Python recommendation above, this book dives into a lot of the more practical aspects in programming machine learning in the Python language.
MIT also has a great list of AI programming books.
Here is another good list of free AI programming books.
Neural Networks and Deep Learning - an excellent site in the form of an online book that dives deep into working with Neural Networks.
DeepLearning - reading list, links to papers, software, datasets, conferences, and more.
Open Compute Project - open sourced hardware and server specs, started by Facebook.
NVIDIA has posted a great set of tutorials around the core concepts of machine learning