For those of you old enough, or unfortunate enough, to have used early versions of the Microsoft office suite, you will probably remember the Mr Clippy office assistant. This feature, first introduced in Office 97, popped up uninvited from the bottom right-hand side of your computer screen every time you typed the word 'Dear' at the beginning of a document, with the prompt "it looks like you are writing a letter, would you like help with that?".

Mr Clippy, turned on by default in early versions of Office, was almost universally derided by users of the software and could go down in history as one of machine learning's first big fails.

So, why was the cheery Mr Clippy so hated? Clearly the folks at Microsoft, at the forefront of consumer software development, were not stupid, and the idea that an automated assistant could help with day to day office tasks is not necessarily a bad idea. Indeed, later incarnations of automated assistants, the best ones at least, operate seamlessly in the background and provide a demonstrable increase in work efficiency. Consider predictive text. There are many examples, some very funny, of where predictive text has gone spectacularly wrong, but in the majority of cases where it doesn't fail, it goes unnoticed. It just becomes part of our normal work flow.

At this point, we need a distinction between error and failure. Mr Clippy failed because it was obtrusive and poorly designed, not necessarily because it was in error; that is, it could make the right suggestion, but chances are you already know that you are writing a letter. Predictive text has a high error rate, that is, it often gets the prediction wrong, but it does not fail largely because of the way it is designed to fail: unobtrusively.

The design of any system that has a tightly coupled human interface, to use systems engineering speak, is difficult. Human behavior, like the natural world in general, is not something we can always predict. Expression recognition systems, natural language processing, and gesture recognition technology, amongst other things, all open up new ways of human-machine interaction, and this has important applications for the machine learning specialist.

Whenever we are designing a system that requires human input, we need to anticipate the possible ways, not just the intended ways, a human will interact with the system. In essence, what we are trying to do with these systems is to instil in them some understanding of the broad panorama of human experience.

In the first years of the web, search engines used a simple system based on the number of times search terms appeared in articles. Web developers soon began gaming the system by increasing the number of key search terms. Clearly, this would lead to a keyword arms race and result in a very boring web. The page rank system measuring the number of quality inbound links was designed to provide a more accurate search result. Now, of course, modern search engines use more sophisticated and secret algorithms.

What is also important for ML designers is the ever increasing amount of data that is being generated. This presents several challenges, most notably its sheer vastness. However, the power of algorithms in extracting knowledge and insights that would not have been possible with smaller data sets is massive. So, many human interactions are now digitized, and we are only just beginning to understand and explore the many ways in which this data can be used.

As a curious example, consider the study The expression of emotion in 20^th century books (Acerbi et al, 2013). Though strictly more of a data analysis study, rather than machine learning, it is illustrative for several reasons. Its purpose was to chart the emotional content, in terms of a mood score, of text extracted from books of the 20^th century. With access to a large volume of digitized text through the project Gutenberg digital library, WordNet (http://wordnet.princeton.edu/wordnet/), and Google's Ngram database (books.google.com/ngrams), the authors of this study were able to map cultural change over the 20^th century as reflected in the literature of the time. They did this by mapping trends in the usage of the mood words.

For this study, the authors labeled each word (a 1gram) and associated it with a mood score and the year it was published. We can see that emotion words, such as joy, sadness, fear, and so forth, can be scored according to the positive or negative mood they evoke. The mood score was obtained from WordNet (wordnet.princeton.edu). WordNet assigns an affect score to each mood word. Finally, the authors simply counted the occurrences of each mood word:

Here, ci is the count of a particular mood word, n is the total count of mood words (not all words, just words with a mood score), and C_the is the count of the word the in the text. This normalizes the sum to take into account that some years more books were written (or digitized). Also, since many later books tend to contain more technical language, the word the was used to normalize rather than get the total word count. This gives a more accurate representation of emotion over a long time period in prose text. Finally, the score is normalized according to a normal distribution, M_z, by subtracting the mean and dividing by the standard deviation.

This figure is taken from The expression of Emotions in 20^th Century Books, (Alberto Acerbi, Vasileios Lampos, Phillip Garnett, R. Alexander Bentley) PLOS.

Here we can see one of the graphs generated by this study. It shows the joy-sadness score for books written in this period, and clearly shows a negative trend associated with the period of World War II.

This study is interesting for several reasons. Firstly, it is an example of data-driven science, where previously considered soft sciences, such as sociology and anthropology, are given a solid empirical footing. Despite some pretty impressive results, this study was relatively easy to implement. This is mainly because most of the hard work had already been done by WordNet and Google. This highlights how using data resources that are freely available on the Internet, and software tools such as the Python's data and machine learning packages, anyone with the data skills and motivation can build on this work.