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in the very worst manifestation it was actually kind of a weaponized mathematical algorithm. I was working in online advertising. Most of the people working online advertising represented it as a way of giving people opportunities. That’s true for most technologists, most educated people, most white people. On the other side of the spectrum you have poor people, who are being preyed upon, by the same kinds algorithms.

people need to stop trusting mathematics and they need to stop trusting black box algorithms. They need to start thinking to themselves. You know: Who owns this algorithm? What is their goal and is it aligned with mine? If they’re trying to profit off of me, probably the answer is no.

So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?”

Angela Merkel has called on major internet platforms to divulge the secrets of their algorithms, arguing that their lack of transparency endangers debating culture. The German chancellor said internet users had a right to know how and on what basis the information they received via search engines was channelled to them. Speaking to a media conference in Munich, Merkel said: “I’m of the opinion that algorithms must be made more transparent, so that one can inform oneself as an interested citizen about questions like ‘what influences my behaviour on the internet and that of others?’. “Algorithms, when they are not transparent, can lead to a distortion of our perception, they can shrink our expanse of information.”

And with the Islamic State’s prolific use of social media, terrorism experts and government agencies continually search for clues in posts and Twitter messages that appear to promote the militants’ cause.A physicist may not seem like an obvious person to study such activity. But for months, Neil Johnson, a physicist at the University of Miami, led a team that created a mathematical model to sift order from the chaotic pro-terrorism online universe.

In Israel, for instance, we have one of the largest laboratories for A.I. surveillances in the world — it’s called the Occupied Territories. In fact, one of the reasons Israel is such a leader in A.I. surveillance is because of the Israeli-Palestinian conflict.Explain this a bit further.Part of why the occupation is so successful is because of A.I. surveillance technology and big data algorithms.

To what extent can we and should we aspire to create machines that can outthink us? For example, Netflix has an algorithm that can predict what movies you will like based on the ones you've already seen and rated. Suppose a dating site were to develop a similar algorithm—maybe even a more sophisticated one—and predict with some accuracy which partner would be the best match for you. Whose advice would you trust more? The advice of the smart dating app or the advice of your parents or your friends?

The question, it seems to me, is should we use new genetic technologies only to cure disease and repair injury, or also to make ourselves better-than-well. Should we aspire to become the masters of our natures to protect our children and improve their life prospects?AdvertisementAdvertisement This goes back to the role of accident. Is the unpredictability of the child an important precondition of the unconditional love of parents for children? My worry is that if we go beyond health, we run the risk of turning parenthood into an extension of the consumer society.

A former journalist, equipped with an algorithm and the largest collection of murder records in the country, finds patterns in crime.

What attributes make for a successful trader? Is it comprehensive knowledge of an industry? The ability to read the markets? Luck?Or might it be something subtler and seemingly unrelated — namely, an awareness of one’s own heartbeat?

It’s never been so easy to pretend to know so much without actually knowing anything. We pick topical, relevant bits from Facebook, Twitter or emailed news alerts, and then regurgitate them.

Who decides what we know, what opinions we see, what ideas we are repurposing as our own observations? Algorithms, apparently, as Google, Facebook, Twitter and the rest of the social media postindustrial complex rely on these complicated mathematical tools to determine what we are actually reading and seeing and buying.Continue reading the main story Continue reading the main story AdvertisementWe have outsourced our opinions to this loop of data that will allow us to hold steady at a dinner party, though while you and I are ostensibly talking about “The Grand Budapest Hotel,” what we are actually doing, since neither of us has seen it, is comparing social media feeds.

As machine learning becomes more powerful, the field’s researchers increasingly find themselves unable to account for what their algorithms know — or how they know it.

The thought experiment is meant to show how an optimization algorithm, even if designed with no malicious intent, could ultimately destroy the world.