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machine learning

Reducing Misinformation: spotting fake news

This presentation at the London Text Analytics Meetup (January 2018) alarmed me a little. The title "Reducing Misinformation" sounds a bit like the heavy-handed slogan painted on the sides of Thames Valley Police Force cars some years ago: “Reducing Crime, Disorder and Fear”. Somehow an admirable goal had been mixed with a rather oppressive approach. It smacked a little of late-night security squads taking out suspects.

Sherlock Holmes and machine learning

Some people would claim there is an uncanny parallel between the methods used by Sherlock Holmes and machine learning. In both cases an apparently insoluble problem is suddenly resolved using nothing but careful assessment of the available evidence. In both cases we are startled that it was possible to find a solution when we, the readers, had no idea of it. So I was intrigued when Phil Gooch presented an analysis of a Sherlock Holmes story to discover the most important details. Could machine learning, like Holmes, solve a crime mystery? Phil’s presentation, at the excellent London Text Analytics Meetup Group, lived up to expectations, even if he didn’t quite demonstrate a machine solving the mystery. Instead, and quite an achievement in its own right, he coded the analysis in front of us while he was presenting. I’ve been to presentations where there is a live demo, but I’ve not seen coding on the screen (and taking suggestions for alterations) as the main part of the presentation. All credit to Phil, then, for coolness!

What can machines discover from scholarly content?

Just as you thought that everything was known about the academic user journey, a workshop comes along (the WDSM Workshop on Scholarly Web Mining, SWM 2017, held in Cambridge, February 10 2017) that presents a whole new set of tools and investigations to consider.

It was a rather frantic event, squeezing no fewer than 11 presentations into a half-day session, even if the event took place in the sumptuous and rather grand surroundings of the Council Chamber in the Cambridge Guildhall. Trying to summarise all 11 presentations would be a challenge; were there any common areas of inquiry?

World domination through machine learning: a review of The Master Algorithm

Pedro Domingos likes big ideas. He sets out to describe to us how computers can write their own programs. For example, there is the well-established case of handwriting recognition. This is a form of machine learning in which the computer is provided with sufficient examples (and a training set) to enable the machine to learn to do something. If you show the machine the number “9” written enough ways, the machine eventually becomes as good or even better than a human at recognising a handwritten “9”.

Unfortunately, he alternates between very sensible and clear description like this, and sweeping optimistic generalisations. Mr Domingos is in no doubt who the new masters of the world are going to be. In his potted description of commerce, he describes the how “the progression from computers to the Internet to machine learning was inevitable ... once the inevitable happens and learning algorithms become the middlemen, power becomes concentrated in them.” In fact, there is no future for any company without using machine learning: “a company without machine learning can’t keep up with one that uses it ... businesses embrace it because they have no choice.” That’s a very stern conclusion!