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a ZX Spectrum, a little 8-bit computer
‘Hello world’ is a reminder of a moment of dialogue between human and machine. Of an instant where the boundary between controller and controlled is virtually imperceptible. It marks the start of a partnership – a shared journey of possibilities, where one cannot exist without the other.
Jones Beach on Long Island, New York,
they hang extraordinarily low, sometimes leaving as little as 9 feet of clearance from the tarmac.
strange design. In the 1920s, Robert Moses, a powerful New York urban planner,
Too low for the 12-foot buses to pass under.1 Racist bridges
Sometimes it’s deliberately and maliciously factored into their design, but at other times it’s a result of thoughtless omissions:
Sometimes it’s an unintended consequence,
They have learned our likes and dislikes; they tell us what to watch, what to read and who to date. And all the while, they have the hidden power to slowly and subtly change the rules about what it means to be human.
Forming an opinion on an algorithm means understanding the relationship between human and machine.
the future doesn’t just happen. We create it.
the IBM engineers made the brilliant decision to design Deep Blue to appear more uncertain than it was.
it can be useful to think of the real-world tasks they perform in four main categories:10 Prioritization: making an ordered list
Association: finding links
Amazon’s recommendation engine uses a similar idea, connecting your interests to those of past customers.
Filtering: isolating what’s important
separate the signal from the noise.
speech recognition algorithms, like those running inside Siri, Alexa and Cortana, first need to filter out your voice from the background noise before they can get to work on deciphering what you’re saying.
that’s what algorithms can do. Now, how do they manage to do it?
Rule-based algorithms
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.
Rule-based algorithms will only work for the problems for which humans know how to write instructions.
Machine-learning algorithms, by contrast, have recently proved to be remarkably good at tackling problems where writing a list of instructions won’t work.
if you let a machine figure out the solution for itself, the route it takes to get there often won’t make a lot of sense to a human observer.
How can we control something we don’t understand? What if the capabilities of sentient, super-intelligent machines transcend those of their makers? How will we ensure that an AI we don’t understand and can’t control isn’t working against us?
For the time being, worrying about evil AI is a bit like worrying about overcrowding on Mars.
if we’re handing over all that power – are they deserving of our trust?
Back in 2015 scientists set out to examine how search engines like Google have the power to alter our view of the world.18 They wanted to find out if we have healthy limits in the faith we place in their results, or if we would happily follow them over the edge of a metaphorical cliff.
participants spent most of their time reading the websites flagged up at the top of the first page
the degree to which the ordering influenced the volunteers’ opinions shocked
‘When people are unaware they are being manipulated, they tend to believe they have adopted their new thinking voluntarily,’
All around us, algorithms provide a kind of convenient source of authority.
someone wrote this garbage spreadsheet; second, others naïvely trusted it. The ‘algorithm’ was in fact just shoddy human work wrapped up in code. So why were the people who worked for the state so eager to defend something so terrible?
the only way to objectively judge whether an algorithm is trustworthy is by getting to the bottom of how it works.
algorithms are a lot like magical illusions. At first they appear to be nothing short of actual wizardry, but as soon as you know how the trick is done, the mystery evaporates. Often there’s something laughably simple (or worryingly reckless) hiding behind the façade.
When to over-rule
If the system had been acting entirely autonomously, without a human like Petrov to act as the final arbiter, history would undoubtedly have played out rather differently. Russia would almost certainly have launched what it believed to be retaliatory action and triggered a full-blown nuclear war in the process. If there’s anything we can learn from this story, it’s that the human element does seem to be a critical part of the process:
1954, Paul Meehl, a professor of clinical psychology at the University of Minnesota, annoyed an entire generation of humans when he published Clinical versus Statistical Prediction,
mathematical algorithms, no matter how simple, will almost always make better predictions than people.
giving a human a veto over the algorithm would just add more error.
the best flying team has three components: a pilot, a computer and a dog. The computer is there to fly the plane, the pilot is there to feed the dog. And the dog is there to bite the human if it tries to touch the computer.
there’s a paradox in our relationship with machines. While we have a tendency to over-trust anything we don’t understand, as soon as we know an algorithm can make mistakes, we also have a rather annoying habit of over-reacting and dismissing it completely, reverting instead to our own flawed judgement.
algorithm aversion. People are less tolerant of an algorithm’s mistakes than of their own – even if t...
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This tendency of ours to view things in black and white – seeing algorithms as either omnipotent masters or a useless pile of junk – presents quite a problem in our high-tech age.
‘What scares me about this,’ he said, ‘is that you know more about my customers in three months than I know in 30 years.’
Target had a pregnancy predictor running behind the scenes – as most retailers probably do now. The only difference is that it will mix in the pregnancy-related coupons with other more generic items so that the customers don’t notice they’ve been targeted.
As soon as you unsuspectingly log into your favourite website, the broker will get a ping to alert them to the fact that you’re there. Virtually instantaneously, the broker will respond by placing a little tiny flag – known as a cookie – on your computer. This cookie* acts like a signal to all kinds of other websites around the internet, saying that you are someone who should be served up an advert for Fry’s Caribbean cruises. Whether you want them or not, wherever you go on the internet, those adverts will follow you.
Google, Facebook, Instagram and Twitter
don’t make money by having users, so their business models are based on the idea of micro-targeting. They are gigantic engines for delivering adverts, making money by having their millions of users actively engaged on their websites, clicking around, reading sponsored posts, watching sponsored videos, looking at sponsored photos. In whatever corner of the internet you use, hiding in the background, these algorithms are trading on information you didn’t know they had and never willingly offered.
Eventually, they had the full names of virtually everyone in the dataset, and full access to a month’s worth of complete browsing history for millions of Germans as a result. Among those 3 million people were several high-profile individuals. They included a politician who had been searching for medication online. A police officer who had copied and pasted a sensitive case document into Google Translate, all the details of which then appeared in the URL and were visible to the researchers.
