In the late 18th Century, an automaton chess master known as the ‘Mechanical Turk’ toured Europe and the US. Designed in 1770 by the inventor Wolfgang von Kempelen, the machine appeared to be able to defeat any human player.
It later turned out the Turk was in fact a mechanical illusion. A puppet dressed in oriental garb, it concealed under its fez and robes a human chess master. The American poet Edgar Allen Poe was so convinced of the Turk’s fraudulence that he wrote an essay to draw attention to the hoax.
A predetermined mechanism beating a human mind at chess was impossible, Poe claimed, for “no one move in chess necessarily follows upon any one other. From no particular disposition of the men at one period of a game can we predicate their disposition at a different period.”
Today, artificial intelligence allows computers to make just such predictions, so it might be fair to assume that such naive illusions are behind us. After all, computers now exist that can beat any human at chess.
But a similar illusion characterises the artificial intelligence industry. On Amazon Mechanical Turk, an online platform owned and operated by Amazon since 2005, human activity is supposed to take the appearance of mechanical activity. The premise of Amazon Mechanical Turk is simple. The site hosts contractors, often large tech companies, which outsource short data tasks to a crowd of workers.
The workers fulfil the tasks that machine learning algorithms are not yet able to complete. Because the work is supposed to appear as if artificial intelligence is doing it, the former Amazon CEO, Jeff Bezos, referred to the platform as “artificial artificial intelligence”. The contractors tend to interact only with the platform, which hosts the tasks and sources the workers. Having little to no direct contact with the workers, contractors experience the process as if it were entirely fulfilled by computers.
Machine learning, the most common branch of AI training, relies on large data sets to train models which are then used to make predictions. Integrated into this process are algorithms that analyse data to extract patterns and make further predictions, which then use those predictions to generate further algorithms.
The richer the data these technologies are exposed to, the more comprehensive their training and the more sophisticated their capacities become, enhancing their performance in tasks as varied as image categorisation, text classification and speech recognition.
