It is very likely that at least some of the people who suggested using an algorithm to predict A-level results thought that they were being scientific and rational. They imagined that their algorithm would be neutral, remove bias and do an overall better job than the teachers, who are too close to the students to remain clear-headed.

The irony is that it is exactly this thinking that is unscientific, irrational and biased. Let me explain why, starting with a metric from football called ‘expected goals’. Expected goals are calculated by feeding a lot of data (shot location, whether it was made with foot or head, etc.) about historical chances in to a statistical model. …

Maths is often seen as hard.

Not ‘hard’ as in ‘difficult’ (it can be that too, of course) but ‘hard’ as in delivering hard truths, undeniable facts and fool-proof reasoning.

For me, maths isn’t like that. My 20 years of experience as an applied mathematician — modelling everything from gambling and football to racial segregation and epidemics — has taught me that maths has a softer side.

Maths can be used to think about whether you should give up (or stick with) a romantic relationship. It helps you deal with feelings of insecurity that arise when you compare yourself to others. It provides ways of coping with the vast flood of information from social media and to decide how long your kids should be allowed to spend on their phones. …

I was totally amazed how many entries their were for the ‘Friends oF Tracking’ Liverpool analytics challenge. The brief was:

1, Use one or more of the tools we have learnt so far (pitch control, speed and acceleration, passing networks, pass maps etc.) to analyse the data.

2, Feel free to combine with other data available from other sources on Liverpool.

3, Produce an output (short report/video) that can be communicated either to a coach, a video analyst or players.

4, Write technical details in a separate appendix.

5, Post a link in comments below and/or to Twitter using #FoT.

One of the questions that I am regularly asked by friends and family about the current crisis is: what numbers should we pay most attention to? With all the information out there, what should we concentrate on?

Before I start I need to make a disclaimer. In this crisis, we should depend primarily on the experts: the government scientists and researchers directly involved in the policy and the science. The Imperial College reports and the John Hopkins Covid-19 map are valuable and reliable resources. These should be your primary sources for information. I am reasonably competent in epidemic modelling. I have taught the it to undergraduates at University, I have attended lots of academic seminars on the subject and have written peer-reviewed articles on SIR and other models. But I am not directly involved in modelling the current crisis, and I am very sceptical about the vast numbers of ‘machine learning Corona’, ‘physics models of deaths’ and ‘amateur SIR models’ that have appeared on Medium and elsewhere. …

Dan Katz, licensed psychologist and psychotherapist, explains why Thomas Erikson’s success with his book Surrounded by Idiots is one of the biggest pseudoscience scandals in recent history. This version was translated in to English and edited by David Sumpter, professor of mathematics at Uppsala University.

Over the last few years, hundreds of thousands of Swedes have spent an estimated total of more than ten million euros on a book which many of them believed contained a scientific account of human psychology, written by an expert in the area. The book’s success has led many companies and other organizations to order personality tests, from a growing number of suppliers eager to exploit the new market, and apply them on their employees. Surrounded by Idiots has had a major impact on how Swedish people talk to each other about psychology and discuss the behaviour of those around them. …

One of the biggest challenges, as machine learning and AI is increasingly used to make decisions about everything from credit risk to employee recruitment, is how to evaluate its fairness. Do algorithms make judgements that are sexist, racist or otherwise discriminatory? And how do those using them mitigate against bias?

When asked what they think, many AI practitioners will say the right thing. But what drives their decisions when they actually start building a system?

This was the question that motivated Johanna Fyrvald, who wrote her Masters thesis with me as supervisor last term. Through three interviews with academics and four interviews with practitioners (CTO’s and Heads of AI) at Swedish companies she aimed to find out how they think about fairness in Artificial Intelligence. …

‘Zlatan Ibrahimović! I want to go and give you a man hug!’

These were the words of commentator Stan Collymore after he witnessed the gigantic Swede rotate his body vertically through 180 degrees, meet the ball in a bicycle kick and lob it over Joe Hart’s head from over 25 metres. Seconds before, Collymore was calmly describing the game as a “worthwhile exercise” as England and Sweden looked forward to the World Cup qualifiers. Suddenly he exclaimed, “O my God, an insane goal! I’ve just seen the most insane goal I’ve ever seen on a football pitch!” …

The argument against superintelligence or general-AI in the near future is really taking off, with recent books by Melanie Mitchell and Gary Marcus, putting much needed common sense back in to the debate. Gary’s Twitter feed, in particular, is a great starting point for learning how claims about AI have got out of hand.

The general-AI question has interested me for some time and in my book, Outnumbered, I approached the problem using my own background in mathematical biology. I took a starting point I think we can all accept: at present AI can’t do all human-level tasks. I then asked whether it could compete with other animals? …