People have always had a fascination with robots – from Da Vinci’s automatons in the 15th century right the way through to Channel 4’s biggest drama hit in 20 years, Humans, which is back for a second season.

The power and capability of robotics and computing is increasing at pace – Google Deepmind’s AlphaGo programme finally beat Lee Sedol at the game ‘Go’ earlier this year, something programmes have aimed for since IBM’s Deep Blue beat chess champion Garry Kasparov in 1995. Our fascination at this progress is certainly tinged with fear. While it may make for excellent TV, we are not comfortable with the idea of machines coming for our jobs, our partners and world domination.

The robot apocalypse is still (hopefully) a while off, so rather than hiding in a bunker and waiting for the end, we should instead look to the world of chess as an example of how to make the most of technology. Facing disarray after Kasparov’s defeat, the new discipline of ‘freestyle’ chess has emerged – with teams of humans and machines competing together to bring chess to new heights.

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The combination of man AND more intelligent machines, working together in perfect harmony, might give us that utopian future science fiction writers promised. By automating the calculations and drudgery we can have more time to spend doing the things we enjoy, or the things that we are uniquely good at, that computers can’t – being creative.

You might be forgiven for thinking that, like call centre workers (as discussed in Sally Abernethy’s article), the data-heavy role of a researcher is ripe for being taken by a computer. At Ipsos, we have been experimenting with the latest technology to explore how we can get to deeper, better insights around human behaviour, motivations or beliefs, and identifying where machines can do things better and where we humans can still add value.

Computers are ideal for collecting and creating more data sets. In a recent study for Orange we married together the use of heart rate monitors and passive mobile monitoring to enhance the more traditional approach of direct questions to participants, to explore the emotional impact of the UEFA Euro 2016 football tournament on fans. This allowed us to access new data sets and become smarter in how we understand people’s natural behaviours, from within.

In a project for a major broadcaster, we were able to harness passive data collection. This replaced self-completed diaries, which in addition to being cumbersome and time consuming for the participants, are not always accurate. We can now use machine learning-based text analytics of verbatims and social media data to predict the success of new shows.

We have also used natural language processing to prompt customers, in real time, to give further, more specific feedback when responding to open-ended survey questions. The prompts are tailored based on previous responses (e.g. “You mentioned our staff, what about them impressed you?”) to replicate the flow of a natural conversation. This simple but effective technology nudges the customer to develop or recall their thoughts and makes their survey experience more interactive, as well as producing a richer level of detail for our text analytics tools to mine.

What many of these techniques have in common is that they increase the volume of data that can be collected, and the speed at which it can be analysed. While these are undoubtedly positive things, there is still something missing – something that only humans can do. We can take all this data and put it in the wider context of the business problem at hand and offer actionable solutions. Arguably, it also takes the direct questioning, and intuition of a human, to uncover the core motivations, desires and perspectives of the participant on the issues being explored.

There will always be a need for human thinking around data, although algorithms can highlight patterns in data faster than ever. Robots are not taking over, but they can certainly improve our everyday jobs, as the research world is already discovering.