According to a new report "An increasing number of businesses are investing in advanced technologies that can help them forecast the future of their workforce and gain a competitive advantage". It's true, almost every day we see more and more bollocks being written by supposedly intelligent people who believe that by using something called 'Big Data', machines can already be relied on to make better decisions than humans, and that soon computers will equal or even surpass us in intelligence.
Such people are to be pitied rather than despised, obsessed with 'science' they are simply not intelligent enough to distinguish factual information from the far fetched fantasies of science fiction writers. Having already put a very successful career in Information Technology behind me (I had to retire early due to health problems,) I have always maintained that machines will only be capable of behaving intelligently if we radically redefine what we mean by 'intelligence'.
Personally I am quite sure there is a little more to our thought processes than the ability to parse vast amounts of data extremely quickly and filter / match certain keywords. Language is how we communicate not only information but ideas, emotions and stories. And machines have no ability to infer meanings from words. You can feed a million words into a computer, along with definitions. And when you enter that word and ask for a definition, a simple program will display the answer almost instantly, without the machine having the slightest idea what any of it means.
Many analysts and business consultants and hi - tech corporations however continue to believe that, with enough data, algorithms embedded in currently fashionable People Analytics (PA) applications can predict all aspects of employee behavior: from productivity, to engagement, to interactions and emotional states. Predictive analytics powered by algorithms are designed to help managers make decisions that favourably impact the bottom line. The global market for this technology is expected to grow from US$3.9 billion in 2016 to US$14.9 billion by 2023.
Despite all the usual promises and all the geek mythology, predictive algorithms are as mystical as the oracles and auguries of the ancient world. One of the fatal flaws of predictive algorithms, the one that has made such nonsense of the predictions of climate change soothsayers, is their reliance on "inductive reasoning". This is when we draw conclusions based on our knowledge of a small sample, and assume that those conclusions apply across the board. It is the methodology that predicted the Remain campaign would win Britain's EU referendum and that Hillary Clinton would anihilate Trump in the US Presidential election.
Where inductive reasoning falls down is it 'thinks' like a machine. To put it in human terms, a manager might observe that all employees with an MBA are highly motivated. According to inductive reasoning it therefore follows that all workers with an MBA are highly motivated. The conclusion is flawed because it assumes a consistent pattern where there are many unpredictable factors in play.
Experience to date informs us the pattern exists, so there can be no reason to suspect it will be broken. In other words, inductive reasoning can only be inductively justified: it works because it has worked before. Therefore, there is no logical reason to consider that the next person our company hires who has an MBA degree will not be highly motivated. That is how machines think. A human manager, in looking for a highly motivated candidate to fill a position would not make assumptions based on the kind of qualification candidates hold, but would frame certain questions in the interview to explore that aspect of a candidate's suitability.
And until machines can handle unpredictability we should stop indulging fantasists by talking about Artificial Intelligence and refer more realistically to data processing.