Hello World: Being Human in the Age of Algorithms

by Hannah Fry

Prioritization: making an ordered list

Classification: picking a category

Association: finding links

Filtering: isolating what’s important

Rule-based algorithms

Machine-learning algorithms

as a machine-learning algorithm, which comes under the broader umbrella of artificial intelligence or AI. You give the machine data, a goal and feedback when it’s on the right track – and leave it to work out the best way of achieving the end.

‘When people are unaware they are being manipulated, they tend to believe they have adopted their new thinking voluntarily,’

Psychologists generally agree that we have two ways of thinking. System 1 is automatic, instinctive, but prone to mistakes. (This is the system responsible for the answer of 10p jumping to mind in the puzzle above.) System 2 is slow, analytic, considered, but often quite lazy.

Weber’s Law states that the smallest change in a stimulus that can be perceived, the so-called ‘Just Noticeable Difference’, is proportional to the initial stimulus.

This is all Bayes’ theorem does: offers a systematic way to update your belief in a hypothesis on the basis of the evidence.30 It accepts that you can’t ever be completely certain about the theory you’re considering, but allows you to make a best guess from the information available.

By

now, we know that humans are really good at understanding subtleties, at analysing context, applying experience and distinguishing patterns. We’re really bad at paying attention, at precision, at consistency and at being fully aware of our surroundings. We have, in short, precisely the opposite set of skills to algorithms.

Douglas Hofstadter, years before he encountered EMI: A ‘program’ which could produce music . . . would have to wander around the world on its own, fighting its way through the maze of life and feeling every moment of it. It would have to understand the joy and loneliness of a chilly night wind, the longing for a cherished hand, the inaccessibility of a distant town, the

heartbreak and regeneration after a human death. It would have to have known resignation and world-weariness, grief and despair, determination and victory, piety and awe. It would have had to commingle such opposites as hope and fear, anguish and jubilation, serenity and suspense. Part and parcel of it would have to be a sense of grace, humour, rhythm, a sense of the unexpected – and of course an exquisite awareness of the magic of fresh creation. Therein, and only therein, lie the sources of meaning in music.

This is the future I’m hoping for. One where the arrogant, dictatorial algorithms that fill many of these pages are a thing of the past. One where we stop seeing machines as objective masters and start treating them as we would any other source of power. By questioning their decisions; scrutinizing their motives; acknowledging our emotions; demanding to know who stands to benefit; holding them accountable for their mistakes; and refusing to become complacent. I think this is the key to a future where the net overall effect of algorithms is a positive force for society. And it’s only right that it’s a job that rests squarely on our shoulders. Because one thing is for sure. In the age of the algorithm,

humans have never been more important.