Some basic concepts

Artificial Intelligence is the study of how to create intelligent agents. In practice, it is how to program a computer to behave and perform a task as an intelligent agent (say, a person) would. This does not have to involve learning or induction at all, it can just be a way to 'build a better mousetrap'. For example, AI applications have included programs to monitor and control ongoing processes (e.g., increase aspect A if it seems too low). Notice that AI can include darn-near anything that a machine does, so long as it doesn't do it 'stupidly'.
In practice, however, most tasks that require intelligence require an ability to induce new knowledge from experiences. Thus, a large area within AI is machine learning. A computer program is said to learn some task from experience if its performance at the task improves with experience, according to some performance measure. Machine learning involves the study of algorithms that can extract information automatically (i.e., without on-line human guidance). It is certainly the case that some of these procedures include ideas derived directly from, or inspired by, classical statistics, but they don't have to be. Similarly to AI, machine learning is very broad and can include almost everything, so long as there is some inductive component to it. An example of a machine learning algorithm might be a Kalman filter.
Data mining is an area that has taken much of its inspiration and techniques from machine learning (and some, also, from statistics), but is put to different ends. Data mining is carried out by a person, in a specific situation, on a particular data set, with a goal in mind. Typically, this person wants to leverage the power of the various pattern recognition techniques that have been developed in machine learning. Quite often, the data set is massive, complicated, and/or may have special problems (such as there are more variables than observations). Usually, the goal is either to discover / generate some preliminary insights in an area where there really was little knowledge beforehand, or to be able to predict future observations accurately. Moreover, data mining procedures could be either 'unsupervised' (we don't know the answer--discovery) or 'supervised' (we know the answer--prediction). Note that the goal is generally not to develop a more sophisticated understanding of the underlying data generating process. Common data mining techniques would include cluster analyses, classification, regression trees, and neural networks

Quotes

Some of my favourite quotes

• Do not dwell in the past, do not dream of the future, concentrate the mind on the present moment. Buddha
• He who has a why to live can bear almost any how. Friedrich Nietzsche
• The ego is not master in its own house. Sigmund Freud
• You have to die a few times before you can really live. Charles Bukowski
• It is often safer to be in chains than to be free. Franz Kafka
• To live without Hope is to Cease to live. Fyodor Dostoevsky
• The master has failed more times the beginner has ever tried. Stephen McCranie
• Winners never quit and quitters never win. Vince Lombardi
• Push yourself because no one is going to do it for you. Unknown