What is the difference between artificial intelligence and machine intelligence? I have read the term "machine intelligence" in a few places, e.g. https://web.archive.org/web/20170219022131/https://research.google.com/pubs/MachineIntelligence.html:

Research at Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms.

What is the difference between artificial intelligence and machine intelligence? 
I'm not sure if machine intelligence is a rebranding of artificial intelligence, machine learning, or if it means something else.
 A: Nice, provocative, definition is given by Yonatan Zunger in his Asking the Right Questions About AI article posted on Medium.com:

... I’m going to use the terms “artificial intelligence”
  (AI) and “machine learning” (ML) more or less interchangeably. There’s
  a stupid reason these terms mean almost the same thing: it’s that
  “artificial intelligence” has historically been defined as “whatever
  computers can’t do yet.” For years, people argued that it would take
  true artificial intelligence to play chess, or simulate conversations, or recognize images; every time one of those things
  actually happened, the goalposts got moved. The phrase “artificial
  intelligence” was just too frightening: it cut too close, perhaps, to
  the way we define ourselves, and what makes us different as humans. So
  at some point, professionals started using the term “machine learning”
  to avoid the entire conversation, and it stuck.

A: Artificial intelligence (AI) in their highly cited book by that name has been described by Stuart J. Russell and Peter Norvig as intelligent agent design consisting of the following:

The unifying theme of the book is the concept of an intelligent agent.
  In this view, the problem of AI is to describe and build agents that
  receive precepts from the environment and perform actions. Each such
  agent is implemented by a function that maps percepts to actions, and
  we cover different ways to represent these functions, such as
  production systems, reactive agents, logical planners, neural
  networks, and decision-theoretic systems. We explain the role of
  learning as extending the reach of the designer into unknown
  environments, and show how it constrains agent design, favoring
  explicit knowledge representation and reasoning. We treat robotics and
  vision not as independently defined problems, but as occurring in the
  service of goal achievement. We stress the importance of the task
  environment characteristics in determining the appropriate agent
  design.

In contrast to AI the term machine intelligence appears in a more sub-specialized or mechanical computational context, for example a natural language translation machine, a Turning machine, and more generally Raymond
Kurzweil's (1990) Age of Intelligent Machines treats AI in the context of computer science and intellectual history in general.
I suppose one could claim that not every artifice is silicon based, that some could be constructed from biological neurons grown in a Petri dish, in which case we could construct an artificial intelligence that is not especially a machine intelligence. In other words, not every artifice is a machine.
A: Difference between AI & ML
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system .AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.”Therefore It is a intelligence where we want to add all the capabilities to machine that human contain.

Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”

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