Recaptcha if you can

An army of billions is teaching computers to ‘think’.

Members of the Adam Investment team have been busy training robots over recent years. In fact, we have all been involved in training them, and no one has had any clue we have been doing it! 

We have all encountered the frustration of having to prove to a website that we are not a robot as a layer of security, to prevent a malicious programme or ‘bot’ infecting a particular website. Only after this time-consuming challenge are we permitted to access the website.

These ‘CAPTCHA’ programmes are tests which are hard for computers to perform but which humans find easy: the ‘T’ standing for Alan Turing, given his work in this field.

Whilst it is true that this helps security, the test serves a further purpose – helping to improve Google’s Artificial Intelligence capabilities.

The first ‘CAPTCHA’ tests asking you to enter letters, words or phrases served a secondary purpose of helping to digitise books. Many billions of manual entries from users helped robots to digitise books and articles across languages dating back several centuries in a fraction of the time that manual entries would have taken.

Building on this, and not one to turn down a challenge, Google’s AI programme recently demonstrated its prowess by going head-to-head with leading historians to guess missing words from ancient Grecian inscriptions. Trained with over three million words from existing artefacts, the AI was able deliver 50% greater accuracy than humans as well as completing the first fifty inscriptions in a handful of seconds whereas its human counterparts took two hours.

Having seemingly mastered the nuances of the written word, Google then challenged us with recognising objects in a photo taken from their Google Street View database. Users are presented with a series of scenarios whereby they are required to select all boxes containing traffic lights, bridges, shop fronts, and buses to name a few. With billions of users clicking away, Google’s Artificial Intelligence can learn what these objects are. Why is this useful? It has helped refine and produce more accurate Google Image search results for one; but perhaps more interestingly, it is also training the intelligence behind driverless cars.

“The AI was able to deliver 50% greater accuracy than humans”

As users, we have trained them to identify objects such as street signs and pedestrian crossings, and in doing so are playing a small yet vital part in the development of driverless car technology. Whilst most of us may well be happy that we are contributing to the digitalisation of books and historic archives and the development of artificial intelligence, as ever it is not without its critics. Google has been criticised and has faced legal action from those who feel they have been used unwittingly and without compensation for the role they have played here, whilst Google have reaped the benefits from this free labour.

As we look ahead to the innovation expected to come over the next decade, it is also comforting to see that some things never change: even driverless cars will still have to put up with disgruntled backseat drivers along the way.

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