The algorithms are well understood -- natural language processing, speech analytics, computer vision, and biometrics -- but we're only starting to come to grips with applications and with data, bias, and ethical implications. Emotion AI has many applications: consumer and market research, conversational interfaces, contact center operations, policy-making and finance, education, and, notably, healthcare uses that include suicide prevention. Potential but not-yet-fully-realized applications include emotionally intelligent design, design that aims to humanize technology. Actually, I wouldn't call the emotion AI potential of any application fully realized. That thought was the starting point for an exploratory conversation I had with Andy, who is professor of digital life at Bangor University in Wales and who'll be speaking at the up-coming Emotion AI Conference, taking place online on May 5th.

Innovative machine learning may seem light years away from first class test cricket, but it was the introduction of machine learning which enabled experts at Bangor University to reveal to the England & Wales Cricket Board (ECB) the factors which can lead to developing county or international world-class cricketers. Having gathered detailed and multifaceted information on over 1000 factors believed to be important predictors within a player's journey to elite or super elite cricket; whether they become a County or international player, experts at Bangor University's Institute for Psychology of Elite Performance worked with computer scientists at the University to number-crunch the vast data-set. The results revealed that a crucial combination of only 18 factors can determine talent development. "Anecdotal and scientific evidence suggests that it takes 10,000 hours of deliberate practice to reach expertise. Unfortunately, what this evidence can't tell us is exactly what type of practice do we need to undertake and when? As a result, there could be hundreds or even thousands of aspiring cricketers practicing the wrong stuff at the wrong time. "Until now, science has not been able to answer these questions.