Update pigeonholing.md

_ai/includes/pigeonholing.md

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 \figure{\includejpg{\diagramsDir/TooManyPigeons}{60%}}{Decompartmentalization of the model into parts can be seen as pigeonholing the separate tasks that are required.}{too-many-pigeons}
 
-\notes{To deal with the complexity of systems design, a common approach is to break complex systems down into a series of tasks. An approach we can think of as "pigeonholing". Classically, a sub-task could be thought of as a particular stage in machining (by analogy to productionlines in factories) or a sub-routine call in computing. Machine learning allows any complex sub-task, that was difficult to decompose by classical methods, to be reconstituted by acquiring data. In particular, when we think of emulating a human, we can ask many humans to perform the sub-task many times and fit machine learning models to reconstruct the performance, or to *emulate* the human in the performance of the task. For example, the decomposition of a complex process such as driving a car into apparently obvious sub-tasks (following the road, identifying pedestrians, etc).}
+\notes{To deal with the complexity of systems design, a common approach is to break complex systems down into a series of tasks. An approach we can think of as "pigeonholing". Classically, a sub-task could be thought of as a particular stage in machining (by analogy to division of labour and production lines in factories) or a sub-routine call in computing. Machine learning allows any complex sub-task, that was difficult to decompose by classical methods, to be reconstituted by acquiring data. In particular, when we think of emulating a human, we can ask many humans to perform the sub-task many times and fit machine learning models to reconstruct the performance, or to *emulate* the human in the performance of the task. For example, the decomposition of a complex process such as driving a car into apparently obvious sub-tasks (following the road, identifying pedestrians, etc).}
 
 \notes{The practitioner's approach to deploying artificial intelligence systems is to build up systems of machine learning components. To build a machine learning system, we decompose the task into parts, each of which we can emulate with ML methods. These parts are typically independently constructed and verified. For example, in a driverless car we can decompose the tasks into components such as "pedestrian detection" and "road line detection". Each of these components can be constructed with, for example, a classification algorithm. Nowadays, people will often deploy a deep neural network, but for many tasks a random forest algorithm may be sufficient. We can then superimpose a logic on top. For example, "Follow the road line unless you detect a pedestrian in the road".}