Increasingly books are appearing that express concern about technology vs humanity. You are not a sum of data or an algorithm. You are not math, engineering, science. You are art (read Big Magic), you are joy, you are soul, you are small data, you are intuition, you are quirky, messy, and you are amazing.
Hard science
Hard science is not good at explaining us. Because in science, when human behaviour enters the equation, things go nonlinear. That’s why physics is easy, and sociology is hard. The essence of being human is that one does not seek perfection. Not a day goes by without hearing about how irrational or inefficient we are when compared with machines. Next to our sleek silicon-powered computer counterparts, our brains are sluggish and burdened by emotions. In engineering circles, this is referred to as the human factor. The solution to the human problem seems straightforward. If we want to remain useful – and employed – we should cede territory to the algorithms all around us – even become subservient to them.
Humanities
Funding for humanities research has declined precipitously. In 2011, it amounted to less than half of a per cent of the funds for science and engineering research and development. The humanities – disciplines that explore culture, such as literature, history, philosophy, art, psychology, and anthropology – no longer meet “society’s needs.” But those are exactly the disciplines that robots and AI find the hardest to do. That is why I always advise young people to study arts, humanities, anthropology or languages.
Reductionists
Leadership in companies have lost touch with the humanity of their customers and their constituents, and, as a result, they mistake numerical representations and models for real life. They are reductionists without the sensitivity to recognise the most exciting and essential patterns. A good CEO should be able to read both a novel and a spreadsheet.
Narrative data
We need to go back to sensemaking, intuition and what the author calls cultural intelligence. We need to know how their cassoulet smells after an hour in the oven; we need to know that the sand and dirt blowing in their deserts hurts their eyes in the morning; we need to know that their poetry never uses the first-person singular and that they have always considered their mountains a safe haven when under attack. Most important to remember: if we want to say something meaningful about another culture, we have to let go – just a little bit – of the biases and assumptions that form the scaffolding of our own culture.
Sensemaking
We need sensemaking. Sensemaking is a method of practical wisdom grounded in the humanities. Algorithmic thinking can go wide – processing trillions of terabytes of data per second – but only sensemaking can go deep. Sensemaking has five principles:
- Culture – not individuals
- Thick data – not just thin data
- The savannah – not the zoo
- Creativity – not manufacturing
- The North star – not the GPS
Culture – Not individuals
Understanding social context. Understanding why people act the way they do. The assumptions, the history, the art, the food (lunch is a two-hour feast in some cultures and a ten-minute sandwich in others), the unspoken rules In realms where quantitative analysis reigns supreme – corporations and financial firms come to mind, as do, increasingly, education and health institutions – these notions of shared worlds and background practices are radical. The concept of “culture – not individuals” serves as an essential corrective to the widely held belief that human behaviour is based on individual choices, preferences, and logical structures.
Thick data – Not just thin data
If sensemaking is interested in cultures, not individuals, it follows that sensemaking data has an entirely different texture. What about the tactile and visceral pieces of data that communicate so much about French life – a loaf of fresh bread or a glass of Bordeaux? Take the wink as an example: the computer might classify it as a twitch of the eye lasting for a millisecond, but we all know that a wink can mean so much more. An apple weighing .09 pound and a gram of honey is thin data. A Rosh Hashanah meal with apples dipped in honey, by contrast, is thick data. Your mood is thick data.
Coffee
You know when a cup of coffee is just a touch too cold; you know how it feels outside just before a thunderstorm; you know something is wrong when you look in your partner’s eyes. It is what AI researchers continually attempt to copy and inevitably get wrong. We are dominated by thick data. When we are attuned to this kind of data, we can sense the subtle but constantly evolving changes of the worlds all around us.
Texture
Layers and layers of abstraction almost always surround leaders and strategic thinkers. Without this texture of experience, the data shoved before these executives’ eyes lose any truth. Context and colour are absent; all that remains are abstract representations of the world rather than the world itself. Simply put, the imaginations and intuitions of top leaders are starving. They live on a diet of desiccated facts and figures – thin data stripped of all its organic life.
The savannah – Not the zoo
This is the basis of a philosophical method called “phenomenology,” or the study of human experiences. The methodology of studying human experience is not interested in what is extraordinary but what is ordinary and common for all (or most) of us. For example, what is a glass of wine? What is it like to be a man or woman? In human or existential time, one second might feel longer than an hour.
Creativity – Not manufacturing
The American philosopher and logician Charles Sanders Peirce became famous for defining the three kinds of reasoning we use to solve problems – deduction, induction, and abduction – each one appropriate for different levels of certainty. We prefer hard data we can believe because doubt is an uneasy and dissatisfied state, But doubt is the only state of being that will open us up to new understanding. This is the real story of creativity.
Creativity is at the heart of the sensemaking process. How do humans actually experience creativity? How do we talk about creativity in everyday language? We say: I got an idea. It came to me. It dawned on me. We don’t say: I made an idea or I took an idea. This seemingly minor semantic observation is actually quite telling. Creative insights do not come “from us.” Instead, they travel “through us” from the social sphere in which we live. Famed psychologist Wolfgang Köhler once described the “three Bs” of creativity: the bus, the bath, and the bed. Heidegger calls this act of revealing or bringing to light phainesthai. You see, in ancient Greek, phainesthai uses the “middle voice”; this is a voice in the Greek language that is neither entirely active nor entirely passive. Grace or open are accurate words for how we actually experience creativity. What do creative geniuses like writer George Saunders mean when they say “open”? This receptive state requires remaining unattached to preconceptions, expectations, and biases. This is no small order. Ancient Buddhists in Japan found that young monks had such difficulty staying open that they created a whole philosophical discipline around it. It is called the “beginner’s mind”.
The North star – Not the GPS
Our world is actually no more complex than it has ever been, nor is it more incomprehensible. Today’s world feels overwhelmingly complex because we are obsessed with organising it as an assembly of facts. We are so fixated on staring at the oracle of the GPS that we have lost all sensitivity to the stars shining right above our heads.
Big data doesn’t care about explaining why
Sensemaking teaches us two essential things about leadership in an era of big data. Sensemaking can guide us in selecting an appropriate context for data collection. The mere task of collecting data is meaningless in the abstract. We need a counterweight to the organisation s where quantitative analysis reigns supreme – corporations and financial firms come to mind, as do, increasingly, education and health institutions. Sensemaking is the difference between a holistic understanding of a world versus a more atomised understanding of rows of numbers in a spreadsheet.
We are not what silicon valley think we are
Silicon Valley is now an ideology, a mindset that values knowledge from the hard sciences above all other forms of knowing. Its well-worn mantras have now seeped into mainstream discourse: everything from the “sharing economy” to “leapfrogging” to “fail forward” to the “lean start-up.”Despite the different phrases, the ideology remains the same. The promise is that technology will solve it – whatever it is. Should we all take for granted the role of technology in our lives, or are there times and situations where we want a more thoughtful engagement with the technology we use? The dangers of technology lie not in what it can or cannot do for us but in how it shapes our thinking. The critique here is of Silicon Valley’s quiet, creeping costs on our intellectual life. The humanities, or our tradition of describing the rich reality of our world – its history, politics, philosophy, and art – are being denigrated by every assumption at play in Silicon Valley.
We are human
What does it mean to be human: what does it mean to be us? We are human because of the way we exist within different social contexts. In a sensemaking process, we are not trying to find out what people “think” about things. We are interested in uncovering the structures that govern different realities. When we get our understanding of humanity wrong, we get everything wrong.
We are relationships
Companies are not a listing on the stock exchange. The reality inside any company is different. What assumptions are widely held? Why do people do things? Does the world reward those who challenge the orthodoxy? Is it a world where people are curious about the products they sell? How do their workstreams flow from one department to another? What is the heart and the soul of the company?
We are meaning
Products are a chain of meaning. The smell of a new car. The memories. The past experiences. Sense, texture, nuance. A dense web of worlds with overlapping meanings and skills.
We are learning
In his book Mind Over Machine, Hubert Dreyfus, the preeminent interpreter of Heidegger and professor of philosophy at the University of California, Berkeley, outlines a phenomenology of skill that directly challenges the computational theory of mind. This is how human intelligence works, and it is a marvel of sophistication. Dreyfus breaks down how such a progression happens. Here is my summary of his five-stage process:
Stage 1: Novice. Context-free.
Stage 2: Advanced Beginner. Recognise a pattern based on prior experience.
Stage 3: Competence. Adopt a hierarchical procedure for decision making that helps prioritise the most relevant elements of the situation.
Stage 4: Proficiency. fluid, and “involved” behaviour that is not characterised by rational application of rules. Instead, it is characterised by the recognition of patterns that emerge out of the accumulation of past experience. The proficient learner sees the situation in its totality,
Stage 5: Expertise. When someone acquires expertise, their level of involvement with their practice becomes so involved that little rational thought goes into the process. When the extremely skilled are absorbed in their craft, the effect is uncanny. Witnesses to this level of mastery often describe an almost mystical experience – they get the impression that the activity flows not from the master but rather through him. In all of these examples, mastery is characterised by an intuitive flow – an involvement in a world – rather than a self-aware, computational process.
Computers can never experience flow. Flow is what makes us human.
We are knowledge
There are four types of knowledge
- Objective knowledge.
- Subjective knowledge. After objective knowledge, there is subjective knowledge: the world of personal opinions and feelings.
- Shared Knowledge. Shared human experience. This is not universal knowledge; it is necessarily situational. And it is not inner knowledge but rather a shared codex. Understanding moods, a form of thick data, was an essential element in this analysis. Moods are bigger than we are: they can take over a room, a city, or a country.
- Sensory knowledge. A type of knowledge coming from the body. A kind of stream of consciousness, inextricably linked to perception. Feeling knowledge. Intuition. Read “Metaskills“.
Sensemaking does not prioritise any one knowledge as more valid than another. A computer can´t “feel” knowledge or has intuition.
We are empathy
The first level of empathy – is below the threshold of our awareness. This is the kind of empathy we rarely ever talk about. We adjust to each other like Alice Munro’s “spores”; we become more and more enmeshed in our immediate surroundings. There are always particular styles and codes that anyone entering the organisation is socialised into. The second level of empathy is often triggered when we notice something is amiss. If we want to engage in a process of understanding, we move to the third level of empathy, or analytical empathy.
Beyond big data
Behind data are signs and symbols, texture, the meaning and context of words, codes, rituals, perceptions, and social models (explain trust or reciprocity in a data model), relationships, ecosystems and the conclusion is that big data is a feeble and pale reflection of real life. Your family, your friends, your company and your customers are people. It is time we start embracing that again. Soft data. Or small data. Or just humanity.
