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‘.
This is what I learnt breaking down data analysis that involves machine learning and social sciences for a TED talk.
For more context, you can watch my TEDx talk about this 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
Part 2 (read here)
- The Future is Hybrid: machine learning + human understanding
- Conclusion: the benefits of using hybrid intelligence to get actionable consumer insights
Introducing machine learning for consumer insights to a non-expert audience
When they asked me to give a TED talk I almost panicked.
I mean, it’s amazing, don’t get me wrong, I love TED talks. I love them because they make accessible complex topics out of my expertise and create inspiration across industries and information silos.
But, it’s a huge challenge I realised.
Debunking your own work and knowledge for people that have never even heard of it while being accurate and scientific about it is extremely hard. In 10 minutes or so? Almost impossible. :O
To make it even harder is the question: would people care? Very few people outside of market research talk about market research. People outside the industry don’t even know what consumer insights are.
So no, they wouldn’t — I thought.
But, we live in a time of amazing opportunities and great challenges and
- Most of us have heard of how machines are learning to do our jobs. Being through sci-fi stories, news or simply overhearing a chat about technology.
- Some have already faced this kind of disruption and have had to learn how to upgrade themselves and their companies to be competitive in the market.
- Some have even seen their own job becoming obsolete.
Make it simple and relevant for everybody while going beyond buzzwords
So yes, there’s a reason why AI and ML are buzzwords. In the era of artificial intelligence, machine learning and automation, we increasingly ask ourselves if machines will end up replacing us.
That is the core of the curiosity, passion and fear involved in all the conversations about AI and ML. The real fuel.
So, once I got that after hundreds of conversations back and forth between our CTO, some industry experts and the “normal people” (with zero knowledge on tech and implications of AI/ML) plus the TEDx team, I felt I’d unlocked the superpower of TED. This story IS relevant even for people outside of the industry. Yay!
Now, the second part of the challenge: what value am I providing to the audience?
I need to create inspiration, I need to get them to learn something that they can apply to their own job, industry and path, I said to myself.
I need to provide them with a solution.
So, what if there was a solution to our fear of machines taking over? What if the future was a man-machine hybrid?
How can machine learning augment human minds and give birth to a hybrid intelligence, as opposed to just artificial intelligence?
The next step was to make it simple and tangible. So we sat down with my partner, expert of hybrids systems, and explained step by step how a data analysis complex process actually works — how we leverage machines AND humans power for marketing.
We took a philosophical issue (quoting the Humanism cultural movement) to the very simplest way to explain tech and data analysis: Hybrid Intelligence (Machine Learning + Human Understanding).
How Machines Learn
The simplest way to explain Machine Learning
So what is this Machine Learning?
ML is basically machines imitating and adapting human-like behaviour.
Let’s start with a quiz.
How did you come to 81???
That’s exactly the kind of behaviour that we, humans, are trying to teach machines today. We’re trying to teach them to “learn from experience”.
More specifically, machine learning algorithms use computational methods to
- Learn information directly from the data
- Find natural patterns within the data
- Get insights
- Adaptively improve their performance as the number of samples available for learning increases
In the talk, I couldn’t go into the types of Machine Learning techniques for time reasons, but I would like to briefly mention them here. Just so you have a tiny, super simple digest of machine learning techniques.
You can find Machine Learning in two forms:
- Supervised Learning
- Unsupervised Learning
1) Supervised Learning
Supervised Learning is when you train the machine with both input data and output data. The machine learns by finding patterns and is able to predict by itself.
All Supervised Learning techniques area form of either Classification or Regression.
- Classification is used for predicting discrete responses. An everyday example you came across is whether an email is spam or genuine.
- Regression is used for predicting continuous responses. Some common examples: trends in stock market prices, weather forecast, etc.
2) Unsupervised Learning
Unsupervised Learning finds patterns based only on input data. This machine learning technique is useful when you’re not quite sure what to look for. It is in fact often used for Exploratory Analysis of raw data.
Most Unsupervised Learning techniques are a form of Cluster Analysis.
In Cluster Analysis, you group data items that have some measure of similarity based on characteristic values. At the end what you will have is a set of different groups.
(example from Blaze of Inspiration — where we tracked millions of posts from communities, groups, tribes, influencers, brands, organizations, events, official and unofficial channels that constitute the ecosystem of Burning Man and transformational festivals)
Real-life examples of machine learning
If you’re a user of Spotify or Amazon, you have directly experienced the results of machine learning. Both companies use machine learning algorithms to recommend products based on your listening and purchase history. And as you feed them more data, the recommendations get even better.
The Limits of Machines and the Limits of Humans
Now, all this sounds amazing.
But, is that all? Can machines actually replace us — given the great results achieved in tech? Will machine learning make our job obsolete? Will the industry where we work be completely automated and run by AI, making us humans redundant?
To answer this, let me start from my industry, by giving some examples of AI/ML applied to consumer insights.
Simon Chadwick in this article brought great answers to the widespread feeling that marketing and business insights going forward will be a technology-driven industry and that the human element will become increasingly unimportant.
Simon argued — backing up with useful case studies — that the best tool for understanding the human being is the human brain and given the growing excitement around AI and ML, these forms of tech-led processes will not replace the ability of the human brain, human collaboration, human curiosity and the capacity to accept ambiguity and contradiction as pointers to insight.
I totally agree with Simon. Another case that I can think of on top of my mind is NLP vs NLU.
NLP (Natural Language Processing — a part of computer science and artificial intelligence which deals with human languages) is making our job much more accurate by giving us the chance to scale research and understanding. It’s getting better and better, even though it carries — still — limitations if compared to human language processing.
But the gap human-machine is huge when we face NLU.
NLU (Natural Language Understanding) is much harder than NLP, and of course extremely easy for humans, even for non-developed humans, see: babies. It concerns things like text planning, sentence planning, text realisation.
So, why is NLU so hard for computers?
1) Lexical Ambiguity
Also called semantic ambiguity: the presence of two or more possible meanings within a single word. I.e.:
- She is looking for a match (Is she looking for a match or a partner?)
- The fisherman went to the bank (A bank where he withdraws money or a bank where he parked his boat?)
2) Syntactic Ambiguity
Also called structural ambiguity or grammatical ambiguity: the presence of two or more possible meanings with a single sentence or a sequence of words.
- The chicken is ready to eat (is the chicken ready to eat something or is the chicken ready for us to eat?)
- I saw the man with the binoculars (Did I see the man by using my binocular or did I see a man holding a binocular?)
3) Referential Ambiguity
This ambiguity arises when we refer to something using pronouns.
- The boy told his father the theft. He was very upset (was the boy upset or his father?)
But let’s step outside of the market research/consumer insights industry.
Perhaps one of the most famous examples of machine learning was when a supercomputer from Google research made headlines. When it was fed 10 million thumbnails from Youtube videos, the computer was able to learn how to identify a cat with 75% accuracy.
That seems impressive, right?
Until you remember that a 3-year-old can do this with 100% accuracy.
This further shows the differences between humans and machines, when machines can beat the smartest mathematicians at some tasks and can’t beat a 3-year-old at others.
My point is: humans and computers are fundamentally good at different things.
We can’t make sense of an enormous amount of data, but machines can do that efficiently.
We can form plans and make decisions in complex situations whereas a machine can’t make a basic judgement that would be simple for a 3-year-old.
We experienced this first hand analysing consumer insights: we faced the limits of both humans and machines.
Let me explain to you why, exactly. Our work at Trybes Agency requires us to gain a deep understanding of people.
We look for answers to questions like “What makes people join a topic, a brand, or a movement?”. For that, we need to understand their cultures and beliefs, why they make specific choices, what influences them, and how their motivations and perception supports the way they see their world.
No human-only or machine-only solution can gain the level of understanding we aim to achieve on people.
To do this, we have to look through millions and sometimes billions of data points. A huge portion of which is qualitative.