How many AI people does it take to change a lightbulb?

At least 81:

The problem space group (5):

The logical formalism group (16):

The statistical group (1):

One to point out that, in the real world, a lightbulb is never “on” or “off”, but usually something in between.

The planning group (4):

The robotics group (10):

One to build a vision system to recognize the dead bulb.
One to build a vision system to locate a new bulb.
One to figure out how to grasp the lightbulb without breaking it.
One to figure out how to make a universal joint that will permit the hand to rotate 360+ degrees.
One to figure out how to make the universal joint go the other way.
One to figure out the arm solutions that will get the arm to the socket.
One to organize the construction teams.
One to hack the planning system.
One to get Westinghouse to sponsor the research.
One to indicate about how the robot mimics human motor behavior in lightbulb changing.

The knowledge engineering group (6):

One to study electricians’ changing lightbulbs.
One to arrange for the purchase of the lisp machines.
One to assure the customer that this is a hard problem and that great accomplishments in theory will come from his support of this effort.
The same can negotiate the project budget.
One to study related research.
One to indicate about how it is a description of human lightbulb changing behavior.
One to call the lisp hackers.

The Lisp hackers (14):

The Connectionist Group (6):

One to claim that lightbulb changing can only be achieved through massive parallelism.
One to build a backpropagation network to direct the robot arm.
One to assign initial random weights to the connections in the network.
One to train the network by showing it how to change a lightbulb (training shall consist of 500,000 repeated epochs).
One to tell the media that the network learns “just like a human does”.
One to compare the performance of the resulting system with that of traditional symbolic approaches (optional).

The Natural Language Group (5):

One to collect sample utterances from the lightbulb domain.
One to build an English understanding program for the lightbulb-changing robot.
One to build a speech recognition system.
One to tell lightbulb jokes to the robot in between bulb-changing tasks.
One to build a language generation component so that the robot can make up its own lightbulb jokes.

The Learning Group (4):

One to collect twenty lightbulbs
One to collect twenty “near misses”
One to write a concept learning program that learns to identify lightbulbs
One to show that the program found a local maximum in the space of lightbulb descriptions

The Game-Playing Group (5):

One to design a two-player game tree with the robot as one player and the lightbulb as the other
One to write a minimax search algorithm that assumes optimal play on the part of the lightbulb
One to build special-purpose hardware to enable 24-ply search
One to enter the robot in a human lightbulb-changing tournament
One to state categorically that lightbulb changing is “no longer considered AI”

The Psychological group (5):