Automation has already touched every industry on the planet: whether it’s sorting the rotten apples out of a harvest or customers scanning their own shopping at a supermarket checkout, machines are deeply entwined in our lives, whether we notice them or not.

It’s tempting, especially for those of us whose skill set falls in what might broadly be called ‘the creative industries’, to think that we are immune from the onslaught of smart machines: who wants a robot to present their conference? Or design their logo? Or write a restaurant review (‘I started with a delicious WD40 before moving onto the main course…’)? But the reality is that nobody is going to fail to feel the touch of artificial intelligence in their lives, and that means in their jobs.

The usual technology rules apply: what starts off as a niche idea in a lab on top of the cost mountain today, rolls quickly down, getting cheaper and gathering applications along the way. By the time it hits the village at the bottom, it’s unstoppable, and everyone gets hit by the avalanche.

The Japanese seem to be the culture with the greatest capacity for enthusiastic adoption of robot technology – such as a humanoid robot plastic priest Pepper has put itself up for hire as a Buddhist priest for funerals.

Which is slightly reminiscent of Douglas Adams’s Electric Monk (who believes in all the things you don’t have time to believe in yourself).

The Artificial intelligence snowball has been rolling down the mountainside for some time now, and is beginning to bounce off the chalets of the companies that can stand the cost – and want the edge that the technology promises.

Narrative Science based in Chicago, takes data and turns it into natural language. What does this mean? An early iteration of their software generated newspaper reports of Little League games based on the data fed into it: ‘Friona fell 10-8 to Boys Ranch in five innings on Monday at Friona despite racking up seven hits and eight runs. Friona was led by a flawless day at the dish by Hunter Sundre, who went 2-2 against Boys Ranch pitching…’

Feed in data from a stock market, and get out a clear picture of what’s going on, and you can make informed, human decisions about which stocks to back while your rivals are still pouring over a spreadsheet. Feed in data from an Ebola outbreak and you know where to direct resources. So what does this mean for our line of work – presentation, crisis media management, live events? The mantra of the AI industry is that the technology won’t be stealing your job, but it will change it.

Does this mean we will soon be settling down to watch a robot deliver a keynote speech at a conference? Unlikely. Instead of worrying that technology is going to make your job obsolete, think instead about how it will be made different. The potential is for data itself to change. Computers won’t be calling the shots, but they may decide what information to put in front of the human who makes the final decision.

At live events, technology has been most evident on media screens and audience polls. But let’s consider just one data-driven part of the events industry: feedback is always the vital part of an event that the organisers are keen to capture. If there were an easier, more natural way of gathering this data – an eavesdropping robot that understands natural speech springs to mind – this could have an impact on how the next conference is put together. Another application, purely from the presenter’s point of view, is that there is often a lot of audience feedback, some of it written, some of it spoken, and some of it on social media. Part of the presenter’s job is to sort out relevant information from a lot of noise – and do it under pressure, whilst providing a balanced representation of reality. AI would be able to pull out the main points being raised across more channels and from more sources than the human brain can manage, and deliver it with relevant facts or statistics to the presenter via a tablet or earpiece. This is just one way that AI could lift some of the weight without the audience necessarily being any the wiser!

We can all think of the benefits, but what are the risks? Once you’ve discounted the Terminator scenario, in which machines realise that all the world’s problems really are human-shaped, the downsides of AI are the same as with any other technology: using it wrong, and blaming the machine for not doing what it was supposed to do. The greatest danger is in our own biases and beliefs: consider the Dunning-Kruger effect which is a known and demonstrable cognitive bias in which a person with low ability or knowledge mistakenly believes they have a much higher ability or knowledge than they do in reality. You know you’re feeling the Dunning-Kruger effect when you start a task with confidence – and then find it’s harder than you thought. You might also have been frustrated by meeting someone who knows nothing of your job but assumes it to be easy.

Couple that with cognitive dissonance which is our ability to hold two contradictory beliefs at the same time – for example, that medicine works, we can split an atom and land people on the moon, but that 97% of scientists are wrong about climate change. Combining these two – a belief that we’re smarter than we think we are, plus a belief that is contrary to all known data – and you have a potentially toxic mix of determinedly disregarding all the benefits that AI can provide.

This isn’t to say we should do everything the machines tell us to do; it does mean that we may be more confident than we ought to be about our ability to sort good advice from bad, and the great gift that computers may give to us may be a greater understanding of our own fallibility. The more computers talk like us, the more we are going to imbue them with human qualities. The danger down the line is that the sort of solid, dependable data on which we should base our decisions, becomes no more valuable than opinion. We may ‘feel’ we are right. And we may turn out to be very, very wrong.

This article appears on Nadine Dereza’s website as well as PS Programmes.

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