The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014, is a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society. It considers the science, engineering, and deployment of AI-enabled computing systems. As its core activity, the Standing Committee that oversees the One Hundred Year Study forms a Study Panel every five years to assess the current state of AI.
The Study Panel reviews AI’s progress in the years following the immediately prior report, envisions the potential advances that lie ahead, and describes the technical and societal challenges and opportunities these advances raise, including in such arenas as ethics, economics, and the design of systems compatible with human cognition.
By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. Of course this problem is not limited to the field of AI. Jacques Monod wrote: “A curious aspect of the theory of evolution is that everybody thinks he understands it” (Monod 1975). My father, a physicist, complained about people making up their own theories of physics; he wanted to know why people did not make up their own theories of chemistry.
(Answer: They do.) Nonetheless the problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard; as indeed it is. But the embarrassment does not stem from the difficulty.
Mackworth 2017. Please create links to this site rather than redistributing parts. There are many online resources including AIspace, with interactive tools of many algorithms, AIPython.org, with Python implementations of many of the algorithms, slides for teaching in class, and online learning resources.
Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning by Andrew Ng
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
Prioritize the most promising directions for an AI project Diagnose errors in a machine learning system Build ML in complex settings, such as mismatched training/test sets Set up an ML project to compare to and/or surpass human- level performance Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
The Quest for Artificial Intelligence: A History of Ideas and Achievements by Nils J. Nilsson
Artificial intelligence (AI) may lack an agreed-upon definition, but someone writing about its history must have some kind of definition in mind. According to that definition, lots of things – humans, animals, and some machines – are intelligent. Machines, such as “smart cameras,” and many animals are at the primitive end of the extended continuum along which entities with various degrees of intelligence are arrayed.
At the other end are humans, who are able to reason, achieve goals, understand and generate language, perceive and respond to sensory inputs, prove mathematical theorems, play challenging games, synthesize and summarize information, create art and music, and even write histories.
Reinforcement Learning: An Introduction, 2nd edition by Richard S. Sutton and Andrew G. Barto
Reinforcement learning is learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may a↵ect not only the immediate The relationships to psychology and neuroscience are summarized in this book.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition
The field of Statistics is constantly challenged by the problems that science and industry brings to its door. In the early days, these problems often came from agricultural and industrial experiments and were relatively small in scope. With the advent of computers and the information age, statistical problems have exploded both in size and complexity.
Challenges in the areas of data storage, organization and searching have led to the new field of “data mining”; statistical and computational problems in biology and medicine have created “bioinformatics.” Vast amounts of data are being generated in many fields, and the statistician’s job is to make sense of it all: to extract important patterns and trends, and understand.
A Brief Introduction to Neural Networks by David Kriesel, manuscript
How to teach a computer? You can either write a fixed program – or you can enable the computer to learn on its own. Living beings do not have any programmer writing a program for developing their skills, which then only has to be executed. They learn by themselves – without the previous knowledge from external impressions – and thus can solve problems better than any computer today.
What qualities are needed to achieve such a behavior for devices like computers? Can such cognition be adapted from biology? History, development, decline and resurgence of a wide approach to solve problems.
Advanced Machine Learning with Python by John Hearty
Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more – all whilst working with real-world applications that include image, music, text, and financial data.
Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach.
Recent research is giving us ways to define the behaviors of future artificial intelligence (AI) systems, before they are built, by mathematical equations. We can use these equations to describe various broad types of unintended and harmful AI behaviors, and to propose AI design techniques that avoid those behaviors. That is the primary subject of this book.
Because AI will affect everyone’s future, the book is written to be accessible to readers at different levels. Mathematical explanations are provided for those who want details, but it is also possible to skip over the math and follow the general arguments via text and illustrations. The introductory and final sections of the mathematical chapters (2−4 and 6−9) avoid mathematical notation.
The Essential AI Handbook for Leaders by Peltarion
Every positive leap for mankind has been fueled by intelligence – technical revolutions as well as achievements in sustainability, business or democracy. Now with the advent of artificial intelligence, we give ourselves the opportunity to massively expand our intelligence.
This makes AI a monumental asset for positive change – for individuals, organizations and humanity. Explaining or grasping the speed and breadth of this revolution we find ourselves standing on the brink of is difficult, and AI itself is complicated.