This article was originally published on the Trybes Agency blog as ‘The Simplest Way to Explain Hybrid Intelligence (Machine Learning + Human Understanding) for Consumer Insights‘.
You can read Part 1 in the series here.
Part 1 (read here)
- The Challenge: introducing machine learning for consumer insights to a non-expert audience
- The Solution: make it simple and relevant for everybody while going beyond buzzwords
- How Machines Learn: the simplest way to explain Machine Learning
- The Limits of Machines and the Limits of Humans
- The Future is Hybrid: machine learning + human understanding
- Conclusion: the benefits of using hybrid intelligence to get actionable consumer insights
The Future is Hybrid
Machine Learning + human understanding
So, our solution was to create a hybrid intelligence, a part-human, part-machine solution. Our hybrid intelligence approach couples AI-driven research methodologies with social sciences.
How exactly does hybrid intelligence work?
Let me give you an example of a full data science project in market research.
A few months ago, the tourism board of Sardinia reached out to us to conduct market research in order to design a winning marketing strategy.
Sardinia is an Italian island with Maldives-like beaches and ancient, unique culture. Considered by us Italians one of the most incredible places on Earth. If you haven’t been there yet, you should really consider spending some holidays there.
So, here are the steps we took, and so you can see which step is human, which is machine-led, and which is both.
1) Understanding the business problem
Sardinia wanted to have a deep understanding of who the traveller is and why she wants to travel to Sardinia as opposed to Puglia or North Africa.
The thing is: a deep understanding of a user involves a lot more than their demographic data.
Think about it: can we define this person by saying that she is 35, female from Nancy, living in Paris?
No, we can’t. Sardinia won’t benefit much from information like she is 35, female from Nancy, living in Paris.
We have to go deeper.
[human or machine?] — human
2) Data Exploration and Strategy
In this step, we try to understand what matters for French travellers interested in Sardinia as a destination.
Once we have understood what matters, we now have to understand where to gather the data.
To get this done, we have to ask ourselves questions like:
- Where do French travellers gather to make their travel decisions?
- Where are the conversational territories where they gather to talk about Sardinia?
- Where do they find information?
- What else has an impact on the perception of Sardinia?
[human or machine?] — human
3) Data Mining
Here we gather hundreds of thousands of data from multiple sources, such as:
- Web servers
- Online repositories
In simple, non-technical words, here we gather a bunch of data from multiple sources. In this travel case study, this includes information on hotels, historical records on reviews on travel agencies like Booking.com (over 342 thousand reviews) and Airbnb.com (almost 20 thousand reviews), online forums and communities, and over 200 other online destinations.
[human or machine?] — machine. It would take us years to do that manually 😉
4) Data Preparation/Wrangling
- Data Cleaning: since the data gathered is obtained from numerous sources, which are of varying kinds, the data is prone to be noisy, to have inconsistent data types, misspelt attributes, missing and duplicate values. Thus, we apply Data Cleaning techniques to remove noise and correct inconsistencies in these data.
- Data Integration: In this step, we merge the data from multiple sources into a coherent data store, and we store them in a database (sometimes available to the client for further inquiries).
[human or machine?] — part human, part machine. (the machine cannot possibly identify the data we don’t want. But on the other hand, we need the machine to automate the cleaning as we can’t remove the data manually for millions of records).
5) Data Understanding
Here we need to simply understand what the data is telling us by finding patterns, correlations, and insights in the data.
In the case study, since the majority of the data was qualitative and we couldn’t possibly look into each review or conversation (only Booking.com had over 340.000 reviews!!!), we applied the branch of Machine Learning called “Unsupervised Learning”. That allowed us to find patterns based only on input data, as explained earlier.
Our goal here was to allow machines to dig into the data, group data items that have some measure of similarity based on characteristic values.
This makes such a large qualitative dataset manageable for our team of researchers, psychologists, anthropologists, analysts, and ethnographers. And it surfaces parts for us to dig further and give context to the data.
[human or machine?] — part human, part machine
6) Data Visualisation and communication
Here we communicate our findings followed by actionable insights (which means insights that can be implemented in the business and marketing strategy, we talked about it here in the Consumer Insights Roadmap — you can download the whole roadmap at this link)… simply and effectively to the stakeholders.
I want to stress here the importance of this step. Communicating insights effectively is so crucial for a company that this step generated in some cases the need of a data translator: being relevant and speaking the business language with the stakeholders seems to be quite rare among researchers (according to CEOs and CMOs).
I think that being an entrepreneur and having a marketing background helped me a lot in the past to communicate consumer insights that are aimed at business results. I think that all research agencies and suppliers should try their best to study companies’ strategies and revenues, in order to make their information valuable.
[human or machine?] — mainly human, with a bit of machine in the automated data viz tools.
Benefits of using hybrid intelligence to get actionable consumer insights
So, what are the benefits of hybrid intelligence for marketing a travel product or destination?
With the process explained above, we were able to identify opportunities and threats for Sardinia in the market, for example, which price segment was less satisfied (thanks to machine learning).
We were also able to manage qualitatively (thanks to human understanding) a large amount of data (machine learning powered it), and this resulted in the topicgraphic analysis of the French traveller.
Do you remember the French traveller at the beginning?
Here is what topicgraphic data looks like.
As opposed to saying she is 35, female from Nancy, living in Paris, we are now able to define:
- How long her trip is
- How she gets there
- The cities and towns she wants to visit
- The locations she prefers (the beach, not the countryside)
- The activities she wants to do
- The type of accommodation, restaurants, and tours she wants to book
- The problems and concerns she has
- The services/disservices that are important to her
If you have any questions or you want to dive into this case, contact us. I’m happy to share the consumer insights obtained there.
Other cases studies we worked on
We used the hybrid intelligence model to analyse the most different segments,
from mothers to the Burning Man ecosystem,
from healthy living lovers to Italian Americans,
from digital nomads to beauty products users,
from fitness enthusiasts to binge-watchers.
Our Burning Man ecosystem research was the first quant and qual ever conducted on Burning Man and Transformational Festivals.
Our goal was to be able to answer questions like “who is BM?” Is it the official channel, or is it all the tribes, communities, official and unofficial events that gather around the BM?
And even more complex questions “What does the BM and the transformational festivals mean for millions of people?”, “Why do the tech leaders go to such an event”, or “What is BM impact on society?”
These are examples of how we were able to leverage machines as tools to look for signals in large amounts of data for us to dig deeper into and find answers to complex questions about anything.
But, we’re not the only ones. There are several cases of humans-machines hybrids out there.
Peter Thiel, for example, suggests that we should play complementarity as opposed to substitution.
In Zero to One, he explains how the human/machine solution allowed PayPal to defeat online credit card fraud.
They were losing $10 million per month. While humans couldn’t possibly process each transaction to check if it was a fraud, they tried to automate fraud detection with a designed software.
But, as turns out, thieves were smarter than machines, changing tactics and fooling them. So how could they stop this race machine-humans?
They created a hybrid human-machine that would flag the most suspicious transactions on a well-designed user interface, and human operators would make the final judgement.
They saved Paypal by fixing a major problem, but they didn’t stop there. This results caught the attention of the FBI and inspired Peter to create big data analytics company Palantir.
Another fascinating case is the one of Netflix, that is majestically revealed by Alexis Madrigal in The Atlantic article.
Everybody knows how Netflix wins over by turning up with the right content to the right person. That’s why we binge, right?
This implies, yes, tracking the behaviour of millions of users (which is a machine’s job), but first, it means understanding the content itself so that they can propose you similar content.
But how could they understand the content and classify it?
It turns out that a trained team, under the strategic supervision of VP of product Todd Yellin, watched all the movies and TV shows to understand and classify by micro-tagging all the content.
They decoded, deconstructed, all the movies and shows by micro-tagging characteristics as specific as, for instance, how socially acceptable are the protagonists, how romantic is the movie from 1 to 5, where is the location, and when is set, who are directors, actors and whether it has a happy, sad or ambiguous end.
So next time you think the Netflix recommendation algorithm is so great, remember it was a hybrid intelligence that powered it.
In the TED talk, I’ve listed a few other cases, such as:
Amazon cut the time it takes to prepare a product for shipment from 1 hour to 15 minutes by using collaborative robots as opposed to human-only or machine-only solutions.
Mercedes E-Class factory achieved dramatic levels of performance by de-automating the large scale robots and using instead smaller scale robots collaborating with people.
The Japanese startup Ory Lab launched a cafe where robot waiters are controlled from home by paralyzed people. This was my favourite… I mean, just imagine: there are millions of people whose lives will be forever changed by this kind of virtual presence workforce!
There were books, articles and resources that have been taken off from the initial TED draft, for — again — time reasons. They have been incredibly inspiring for me and great lessons in our own hybrid intelligence path, so I’d like to share them with you. Please let me know if you come across any other, I’d love to co-create knowledge on this topic.
Academically speaking, there is the International Journal of Hybrid Intelligence (IJHI) that focuses on the role of the hybrid intelligence paradigm in the modern context of rapidly evolving technologies, and Ece Kamar’s Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence (Microsoft Research).
In this article, Jo Stichbury explores how the hybrid model of computer-human intelligence offers a way to build on our mutual strengths and deliver efficiency. In this other, Ryan Nakashima reveals AI’s dirty little secret (spoiler: it’s powered by people).
I also loved the panel Artificial Intelligence: Augmenting Not Replacing People. Here is an extract of the synopsis:
Artificial intelligence (AI) technologies are rapidly maturing into tools that are impacting our everyday lives. However, contrary to popular conception, most of these tools will not be autonomous, stand-alone systems, but rather will manifest as human assistants and augmentations. While autonomous driving is featured in the headlines, the short-term impact of advances in this field will be increased safety, comfort, and convenience, with the driver still at the wheel. New technologies in healthcare will not replace doctors, but will leverage their skill and judgement by providing super-human augmentations for eyes, hands, and intellect. As more robots move onto the manufacturing floor, they are most likely to function as ever-smarter programmable tools, and will still require human coworkers to teach them new tasks and to do those elements that are simply too hard to automate.
According to Professor Robert Dale, Natural-language generation co-authoring gives the best of both worlds — human and machines.
- Human authors bring their insights and nuance and their subtle understanding of audience
- Machines can do the grunt work that would otherwise take a human author endless amounts of time, if it’s feasible at all, delivering detailed and accurate descriptive narratives about the information that would otherwise be left buried in data.
And what about Pinterest’s ‘artificial’ artificial intelligence?
In this article Adrian Bridgwater explains how, in order to build what it calls ‘artificial’ artificial intelligence, Pinterest’s ‘Discovery Science’ team built a library that plugs into multiple crowdsourcing services, including CrowdFlower and Mturk.
Maesen Churchill, software engineer at Pinterest, talks with Adrian about the things humans tend to do better, such as evaluating content, and how they’ve automated human evaluation for the purposes of analyzing the relevance of search results and to filter out certain types of content. This ‘artificial’ artificial intelligence, might be why Pinterest quality of content is way better than the other platforms’ content.
On the other side of hybrid approach, a very interesting POV on augmenting humans with tech is David Eagleman’s on Data sensing: essentially, we can use technology to develop new sensory perceptions to supplement or complement our current capabilities.
And while Karthik Rajan argued that intuition is still in the hands of us humans, and machines self-learn only for problems where the goals are clear and quantifiable (he talks about the epic Lee Sedol vs AlphaGo here), Giorgia Lupi was launching the Data Humanism Manifesto that forever changed the world of data vis and, I’d say, the whole big data industry. She, in fact, showed us the way to humanise data, and that resulted in shaping some of our careers (including mine, Giorgia! ❤)
I mean, WOW, right? I personally feel excited just listing these pieces of knowledge here. I could go on and on. For hours, maybe days. :)))
Fear is not a solution, but you can leverage it to create impact.
Fear is never a solution, even though it sells and it’s easier to leverage (see: cli-fi, Black Mirror, pretty much all the narrative around AI and data. Etcetera etcetera: look around you, fear is everywhere).
In my case, I used it as an excuse to make a point in front of a broad audience that would probably not care much about consumer insights technicalities. But amazingly, people care enough about their future that they picked up my provocation and started talking about it and acting differently.
What about you? Are you using machines as tools to take your skills to the next level, or are you playing machines? Are you making yourself indispensable or easy to substitute?
The truth is, we are not machines. Machines are not humans.
I believe there are great benefits to those that find creative and strategic ways to redesign their work with tech. We should capitalise on the best attributes of humans and robots, instead of fearing the advent of machines taking over.
I’m very proud that at Trybes we took this path. I’m willing to fight for the importance of human understanding in the data analysis/market research industry.
Also, I’m a big fan of making complexity accessible, ’cause another big cause of fear comes from not understanding. The TED talk constituted an incredible opportunity to explain some of the most buzzed words in our industry, while giving us the chance to matter in the actual world by showing the way to hybrid intelligence processes.
I hope this article can be of inspiration to marketers and researchers that are interested in applying hybrid intelligence, machine learning and human understanding to consumer insights. After reading this, you should feel more confident in talking about these topics and forming your own opinion, beyond buzzwords.
Please let me know what your takes are on this, and let’s keep the hybrid conversation going. This is literally just the beginning.