bayesian filters

Artificial Intelligence is one of those subjects that is not well defined. To some, it is really 'learning systems', to others it includes any kind of modern algorithm - including Bayesian Belief networks, genetic algorithms and artficial life systems. In the broadest sense, AI is used in daily life. Train scheduling uses Genetic Algorithms to opimise the manner in which signalling is performed. Neural Networks are used to classify highly complex, high dimensional systems with a very large number of inputs. In systems like these, Bayes becomes intractable. They are more useful for contained problems: I know they are used for fault diagnostics on cars. It is far from a bogus field and its application has allowed progress in areas which are grinding to a halt under the weight of their own data. Non-AI techniques have been tried and have failed due to their inability to model the subtleties of the systems adequately. Systematics is a good example of this, where the number of experts of biological taxonomy is on the decline but the number of measurements is on the increase.

You can reduce any piece of AI down to a mathematical function however, for some systems (Dynamic Neural Networks, for example) the function shape is so complex that it ceases to be useful.

If you're talking about strong AI, where machines learn to think and act like humans, then this is a very difficult subject to argue. Most arguments boil down to the fact that a human is a poor judge of what is alive and what is not. Furthermore, classifying intelligence is more than just a dictionary entry, it's actually a thorny problem that hinges around subjectivity of the debators.

Finally, if you're going to discuss a topic, it's not wise to expand the topic to make your argument heard. By saying that Bayesian Filters is part of AI is redefining the problem, not responding to the statement of your manager. Some people still think that Expert Systems (if-then-else rules) are AI but they are clearly not!