‘Our minds can be hijacked’: the tech insiders who fear a smartphone dystopia | Technology | The Guardian

Justin Rosenstein had tweaked his laptop’s operating system to block Reddit, banned himself from Snapchat, which he compares to heroin, and imposed limits on his use of Facebook. But even that wasn’t enough. In August, the 34-year-old tech executive took a more radical step to restrict his use of social media and other addictive technologies.

Rosenstein purchased a new iPhone and instructed his assistant to set up a parental-control feature to prevent him from downloading any apps.

He was particularly aware of the allure of Facebook “likes”, which he describes as “bright dings of pseudo-pleasure” that can be as hollow as they are seductive. And Rosenstein should know: he was the Facebook engineer who created the “like” button in the first place.

A decade after he stayed up all night coding a prototype of what was then called an “awesome” button, Rosenstein belongs to a small but growing band of Silicon Valley heretics who complain about the rise of the so-called “attention economy”: an internet shaped around the demands of an advertising economy.

These refuseniks are rarely founders or chief executives, who have little incentive to deviate from the mantra that their companies are making the world a better place. Instead, they tend to have worked a rung or two down the corporate ladder: designers, engineers and product managers who, like Rosenstein, several years ago put in place the building blocks of a digital world from which they are now trying to disentangle themselves. “It is very common,” Rosenstein says, “for humans to develop things with the best of intentions and for them to have unintended, negative consequences.”

‘Our minds can be hijacked’: the tech insiders who fear a smartphone dystopia | Technology | The Guardian

Will Democracy Survive Big Data and Artificial Intelligence? – Scientific American

The digital revolution is in full swing. How will it change our world? The amount of data we produce doubles every year. In other words: in 2016 we produced as much data as in the entire history of humankind through 2015. Every minute we produce hundreds of thousands of Google searches and Facebook posts. These contain information that reveals how we think and feel. Soon, the things around us, possibly even our clothing, also will be connected with the Internet. It is estimated that in 10 years’ time there will be 150 billion networked measuring sensors, 20 times more than people on Earth. Then, the amount of data will double every 12 hours. Many companies are already trying to turn this Big Data into Big Money.

Everything will become intelligent; soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet?

Will Democracy Survive Big Data and Artificial Intelligence? – Scientific American

The Dark Secret at the Heart of AI – MIT Technology Review

Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.

Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.

The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.

The Dark Secret at the Heart of AI – MIT Technology Review

If I Only Had a Brain: How AI ‘Thinks’ – The Daily Beast

Artificial intelligence has gotten pretty darn smart—at least, at certain tasks. AI has defeated world champions in chess, Go, and now poker. But can artificial intelligence actually think?

The answer is complicated, largely because intelligence is complicated. One can be book-smart, street-smart, emotionally gifted, wise, rational, or experienced; it’s rare and difficult to be intelligent in all of these ways. Intelligence has many sources and our brains don’t respond to them all the same way. Thus, the quest to develop artificial intelligence begets numerous challenges, not the least of which is what we don’t understand about human intelligence.

Still, the human brain is our best lead when it comes to creating AI. Human brains consist of billions of connected neurons that transmit information to one another and areas designated to functions such as memory, language, and thought. The human brain is dynamic, and just as we build muscle, we can enhance our cognitive abilities—we can learn. So can AI, thanks to the development of artificial neural networks (ANN), a type of machine learning algorithm in which nodes simulate neurons that compute and distribute information. AI such as AlphaGo, the program that beat the world champion at Go last year, uses ANNs not only to compute statistical probabilities and outcomes of various moves, but to adjust strategy based on what the other player does.

Facebook, Amazon, Netflix, Microsoft, and Google all employ deep learning, which expands on traditional ANNs by adding layers to the information input/output. More layers allow for more representations of and links between data. This resembles human thinking—when we process input, we do so in something akin to layers.

If I Only Had a Brain: How AI ‘Thinks’ – The Daily Beast