There’s one scene in Nymphomaniac that I can watch on repeat, for hours on end.

This is the one.

For your viewing pleasure, Giffy provided this nice little gif.

My girlfriend introduced me Taqanu founder Balazs Nemethi, because, well first, she knows that I’m best kept around fellow entrepreneurs and Balazs is great fun, and second, their bank-for-refugees startup is a great fit for our magazine Yakuzuzu. This post is excerpt from the interview that followed.

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YZ: How was life in Norway? What did you learn there?

Norwegians are great at keeping a healthy work-life balance. They would work hard for eight hours, but then they would go home and hang out in front of the fireplace for the evening. This is something I try to take with me from those days: I try to keep one day a week when I do absolutely no work at all. It’s great for new ideas.

I’m not so great at this, sometimes I get stuck in work-mode for ways too long, but then my friends intervene: if I’m not responding for a few days, they would start writing me texts like “hey, it’s Tuesday night, stop working!”

YZ: How did you stumble upon the Taqanu idea?

The point with finding any project in Norway was to be distracted from reality. I was on the market for something with the promise of keeping me excited during the evenings: a project that’s bigger than you are.

It took some corners to arrive at Taqanu. I was interested in blockchain and banking, and explored ideas in that area. Plus, I was in Norway and I’m Hungarian, and then, in the Summer of 2015 Hungary built the wall to keep refugees out. All the people I’ve met in Norway asked about this one thing: how is it with Hungary and the wall? I tried to explain that it’s not like everyone in Hungary would want to keep the refugees out, most of us are in fact nice people.

These chats around the topic helped a lot to discover the utility in a digital fingerprint, and so the idea was born. I started to work on Taqanu.

It all started as project to offer banking services for refugees. Now it’s been developing into a global solution for decentralised identification and an answer to the lack of access to financial services for the estimated two billion people without financial opportunities.

YZ: What’s the next step after the idea is born? How did you get started?

I went to blockchain meetups in Oslo to pitch the idea. I had great experience there, the success started to knock on the door pretty early.

Taqanu was one of the few selected startups in the first Nexuslab programme. In cooperation with the London startup accelerator Startupbootcamp Fintech, Nexuslab gave us mentorship and coaching sessions for three months. There was only a small glitch: the final month of the programme was held in Zurich. I didn’t think twice, quit my architect job in Norway, and started to pack my bags.

The programme was great, Taqanu was taking shape, and I was talking to people who previously seemed to be way out of my league. I was invited to be a panelist on the “Proof of Identity for Refugees and Beyond” discussion on the European Identity & Cloud Conference in Munich, talking about blockchain based supranational identity infrastructure with people like Kim Cameron, Chief Architect of Identity at Microsoft or Mia Harbitz, advisor at the World Bank Group.

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The story continues on Yakuzuzu.

“Well, Mr. Frankel, who started this program, began to suffer from the computer disease that anybody who works with computers now knows about. It’s a very serious disease and it interferes completely with the work. The trouble with computers is you play with them. They are so wonderful. You have these switches – if it’s an even number you do this, if it’s an odd number you do that – and pretty soon you can do more and more elaborate things if you are clever enough, on one machine.

After a while the whole system broke down. Frankel wasn’t paying any attention; he wasn’t supervising anybody. The system was going very, very slowly – while he was sitting in a room figuring out how to make one tabulator automatically print arc-tangent X, and then it would start and it would print columns and then bitsi, bitsi, bitsi, and calculate the arc-tangent automatically by integrating as it went along and make a whole table in one operation.

Absolutely useless. We had tables of arc-tangents. But if you’ve ever worked with computers, you understand the disease – the delight in being able to see how much you can do. But he got the disease for the first time, the poor fellow who invented the thing.”

— Richard Feynman in the book “Surely You’re Joking, Mr. Feynman!: Adventures of a Curious Character”

“It seemed very fresh to me—I mean that part where you say how the First Industrial Revolution devalued muscle work, then the second one devalued routine mental work. I was fascinated.” […]

“Do you suppose there’ll be a Third Industrial Revolution?”
Paul paused in his office doorway. “A third one? What would that be like?”
“I don’t know exactly. The first and second ones must have been sort of inconceivable at one time.”

“To the people who were going to be replaced by machines, maybe. A third one, eh? In a way, I guess the third one’s been going on for some time, if you mean thinking machines. That would be the third revolution, I guess—machines that devaluate human thinking. […]”

“Uh-huh,” said Katharine thoughtfully. She rattled a pencil between her teeth. “First the muscle work, then the routine work, then, maybe, the real brainwork.”

“I hope I’m not around long enough to see that final step.”

(From Kurt Vonnegut’s Player Piano. I enjoy reading it very, very much right now.)

A friend asked me yesterday when artificial intelligence is going to take over the world and kill all humans. It’s a rather casual question at a Summer barbecue party, a question that I predict to come up more and more frequently in the next years.

AI was uninteresting for a long time. Yes, it was hot for a while in the 90s, but then it has not-worked for so long that it acquired a bad reputation. Even when it did work it was doing only computer stuff like playing silly games or fly airplanes: none of them feels really human or seems to be that hard in the first place.

Now however, artificial intelligence is capable of mind-blowing things. It understands what you say to them. It responds. It translates what you see or hear, on the fly.

Have you ever seen kids say “Siri, do you love me?” It’s not uncommon at all. (And, apparently, you don’t know what sexy means until you’ve heard a guy with a slight Indian accent slowly enunciate “I want to have sex with you” to his texting app.)

Next-generation AI is all around us. It drives our cars, watches over our home and family, translates texts on our phone.

The problem with humans and our inventions is that we have a tendency to mess things up at first try. The more power we give to semi-perfect artificial intelligence, the more damage it can cause: Tesla’s autopilot made the decision to drive under a trailer last week, killing the driver in the accident. And, Google’s Nest thermostat seems to have an appetite to freeze people’s houses every now and then.

On the big picture, AI does make our life safer. The real problem is that when it goes wrong, we don’t even understand what has just happened. In normal accidents we tend to know what was going on: the thermostat broke. The engine stalled. The driver fell asleep.

When it comes to AI, most of the time we have no idea what was going on in the computer’s “brain”.

This isn’t a human move

Whenever your phone’s camera uses face recognition to identify the areas to set the focus on, it uses simple algorithms that search for a face’s core features like skin colours or the a position of the eyes.

Modern AI can go many steps further: it can also recognise things like whether you have a hat on, whether you’re smiling, or if it really isn’t you but a dog. Or an orange. Modern AI can actually tell what it sees on a picture.

Unless it can’t. In an experiment on the University of Wyoming, researchers were able to fool cutting-edge deep neural networks using simple, random-generated images. For example, artificial intelligence looked at this first picture and said, with a over 99 percent certainty: it’s a centipede.

What’s interesting here is not that researchers can bring up a state-of-the-art image recognition algorithm and trick it into being wrong. What’s interesting is that most of the time no one can tell where exactly did it go off track.

When you show a picture to a kid and they say something funny, we can understand how their brain worked: it’s not a cat, it’s a lion. When it comes to deep neural networks, even if they are right, we don’t even know why exactly they are right. We don’t share the context with them.

[End of the outtake from my article in Yakuzuzu Issue 7. To continue, read “AI might actually kill us all” there.]

“It is impossible for a man to begin to learn what he has a conceit that he already knows.” — Epictetus

It’s so simple and so smart: you can’t learn something you think you already know.

And another share-worthy quote from a James Altucher blog post:

“The floors are empty. That’s the Citigroup building over there. That’s probably some ad agency there. That’s a bank or a law firm over there. All empty desks, empty floors, empty buildings.

“The middle class has been hollowed out. There’s no need for paper shufflers anymore. No need for middle management. It’s either outsourced to China or technology takes care of it. Millions of people in middle management, in middle class jobs will be fired or replaced by cheap labor and technology and there’s nothing anybody can do about it.