‘What separates humans from machines is the fact that we are creative’
Data science has been revolutionising the insights industry. If we were to believe the media, artificial intelligence (AI) and machine learning (ML) applications are already fully integrated in countless industries. But what is it really? Can technology take that next giant leap and actually drive qualitative research? Will it ever be able to predict human behaviour and account for biases? In reality, this technology has a long way to go. We put these intriguing questions to a panel of experts. Spoiler alert: humans will still be needed, as AI is still rather restricted to humble text analysis.
Let us first introduce our four experts by asking them for their own definitions of AI.
Definitions
Bart Langton, research director at Ipsos New Zealand, talked about big data, AI, machine learning and behavioural science at ESOMAR Asia Pacific 2017. His definition of AI is “any kind of automated task that looks at patterns within data. This data can be unstructured and more qualitative in nature, or more structured, like big data.”
Jonathan Williams, founder of the AI driven platform Discover.ai, believes that right now AI is less about machine intelligence and more about machine learning (teaching machines based on example, not programming based on rules). “Most of us are combining machine learning with programmatic technology to create smarter tools that enable agile insight. In short, the definition for us as an insights industry should be something like: AI = smarter machines, smarter insights.”
Kyle Findlay leads the Kantar Innovation Global Data Science Team based in Cape Town and gave a presentation at ESOMAR Big Data World 2017 in New York, where he won ‘best paper’ for his insight in big data, artificial intelligence, data science and deep learning. He thinks the term AI is very amorphous. “It basically means ‘machine doing smart things’, and what is considered smart is in the eye of the beholder and also evolves constantly. At this stage, I’d consider something that uses machine learning to make a prediction as AI, especially if it continues to learn and evolve from its experience.”
Jonathan Mall is CEO and founder of Neuro Flash, which uses patterns and huge banks of words to predict human behaviour and account for biases. He says “AI is the ability of a machine to exhibit ‘intelligent’ human behaviour. For example, the Neuro Flash AI accurately predicts human associations to imitate how consumers perceive brands and content.”
Accelerating human expertise
Challenging as it is to delineate what AI and data science comprise, we encounter even bigger hurdles when connecting these technologies to market research, and the qualitative variety in particular.
According to Williams, much of the advances in AI in the insights industry to date has been about automation and using smarter machines to take people out of the loop. But, “there is another, more important role for AI: accelerating human expertise. It’s about recognising the best of what makes us human and developing smarter machines that work with us to accelerate and maximise those strengths.”
Findlay believes that as AI improves, machines are able to do more and more things that used to be the preserve of humans, and do them at scale. “Historically, qualitative has been the preserve of the more human side of research – the side that recognises empathy, emotion and the complexities of the human experience, which it tries to distil down into relevant insights. Conversely, quantitative research was the preserve of cold, hard structured data, which has always had its limits when it comes to human understanding. The trade-off used to be: a loss of the complexity in favour of scalable, structured data versus less scalable but more complex insight through qualitative. AI bridges this gap. More and more, we are able to account for the complex vagaries of the human condition at scale, essentially empowering quantitative qual research.”
Blurred lines
AI as merely an upgraded text analysis tool is selling the technology short, most believe. Williams explains where this notion stems from. “Text analysis is where there is a lot of action because there is a lot of unstructured data out there in text form. But in fact, to the machine learning algorithms, text is really no different to any other sources of data, like analysing a picture or facial recognition.
Indeed, Mall can see AI advancing in many areas. “Using competing network architectures or deep learning approaches, we are starting to solve very complex problems. Even long-term strategic planning, short- and long-term memory and curiosity are already effectively modelled. And we analyse thousands of consumer verbatims to quickly find content that contains relevant associations.”
For truly creative AI work, Langton concedes that the more complex machine learning is developing, and that faster computers and cloud computing will accelerate things, especially with the likes of Apple and Google moving in. “We’re slowly but surely competing with them. They use all their information and data to predict human behaviour. And that’s what we do as well. The lines between what tech companies, consultancies and traditional research companies do, are blurring.”
Paradigm
No matter the developments, Williams is not worried about the future of human researchers. “Qualitative research is about people, their motivations and behaviours, and the brand solutions we are trying to help build are creative. Experts in the qualitative field will naturally be resistant to the idea that machines can do what they can, and in this I think they are absolutely right.” Give the same insight experts the technology that accelerates their own expertise, and it frees them to explore more sources of insight more quickly to get to deeper insights, and Williams thinks AI technologies will flourish. “We’ve seen it happen already and the potential for the future is huge. So, expect more acceleration.”
“To the extent that we still engage in interviews,” explains Findlay “they will probably end up feeling more like a one-on-one conversation between a therapist and a patient than the rote, staccato interviews of today. These conversations might not even be with real interviewers but rather chatbots, robots and assistants. Beyond this, personality, motivations, etc., will likely be derived indirectly from observational signals and used to customise experiences, as we see with programmatic advertising and personalised offerings. Such understanding might be put into a qual-inspired framework to make it understandable and impactful.”
Democratizing
What will be automated first, predicts Mall, are annoying tasks, like response coding. Here, AI agencies will be able to outcompete on price and speed. But with cultural and behavioural tracking becoming a common thing in online and offline content, he also believes that the future holds many exciting ways of integrating AI in the research process.
Langton predicts huge applications, especially in handling more dynamic conversations humans have with each other, facial recognition, and decoding volumes of ethnographic video and derive meaning from those. “It might be able to uncover more meaning or ‘truth’ behind what people actually mean, based on their facial expressions and tonality. So that’s probably where qualitative will go with AI in the future. It’s just that I’m not sure how far away that future is and how fast things will move.”
Williams sees a lot of apprehension, fear and scepticism around the change that AI can bring. But he says: “If we all work together for a future that embraces acceleration, as well as automation, it will be a positive benefit to the whole industry. It’s a really exciting time to be in the industry.” Mall believes it’s time to stop seeing qualitative and quantitative as opposites. “In the end, both rely on data to draw conclusions and advance our understanding. AI may be one way of enabling people to see that combining data in meaningful ways will benefit everyone.”
This article is an edited excerpt of the original “What is data science after all, and how human is it?”, published in the 2019 Global Market Research report. Read the full content by accessing the report, here.
