By: John H. Richardson
IN AN ORDINARY hospital room in Los Angeles, a young woman named Lauren Dickerson waits for her chance to make history.
She’s 25 years old, a teacher’s assistant in a middle school, with warm eyes and computer cables emerging like futuristic dreadlocks from the bandages wrapped around her head. Three days earlier, a neurosurgeon drilled 11 holes through her skull, slid 11 wires the size of spaghetti into her brain, and connected the wires to a bank of computers. Now she’s caged in by bed rails, with plastic tubes snaking up her arm and medical monitors tracking her vital signs. She tries not to move.
The room is packed. As a film crew prepares to document the day’s events, two separate teams of specialists get ready to work—medical experts from an elite neuroscience center at the University of Southern California and scientists from a technology company called Kernel. The medical team is looking for a way to treat Dickerson’s seizures, which an elaborate regimen of epilepsy drugs controlled well enough until last year, when their effects began to dull. They’re going to use the wires to search Dickerson’s brain for the source of her seizures. The scientists from Kernel are there for a different reason: They work for Bryan Johnson, a 40-year-old tech entrepreneur who sold his business for $800 million and decided to pursue an insanely ambitious dream—he wants to take control of evolution and create a better human. He intends to do this by building a “neuroprosthesis,” a device that will allow us to learn faster, remember more, “coevolve” with artificial intelligence, unlock the secrets of telepathy, and maybe even connect into group minds. He’d also like to find a way to download skills such as martial arts, Matrix-style. And he wants to sell this invention at mass-market prices so it’s not an elite product for the rich.
Right now all he has is an algorithm on a hard drive. When he describes the neuroprosthesis to reporters and conference audiences, he often uses the media-friendly expression “a chip in the brain,” but he knows he’ll never sell a mass-market product that depends on drilling holes in people’s skulls. Instead, the algorithm will eventually connect to the brain through some variation of noninvasive interfaces being developed by scientists around the world, from tiny sensors that could be injected into the brain to genetically engineered neurons that can exchange data wirelessly with a hatlike receiver. All of these proposed interfaces are either pipe dreams or years in the future, so in the meantime he’s using the wires attached to Dickerson’s hippocampus to focus on an even bigger challenge: what you say to the brain once you’re connected to it.
That’s what the algorithm does. The wires embedded in Dickerson’s head will record the electrical signals that Dickerson’s neurons send to one another during a series of simple memory tests. The signals will then be uploaded onto a hard drive, where the algorithm will translate them into a digital code that can be analyzed and enhanced—or rewritten—with the goal of improving her memory. The algorithm will then translate the code back into electrical signals to be sent up into the brain. If it helps her spark a few images from the memories she was having when the data was gathered, the researchers will know the algorithm is working. Then they’ll try to do the same thing with memories that take place over a period of time, something nobody’s ever done before. If those two tests work, they’ll be on their way to deciphering the patterns and processes that create memories.
Although other scientists are using similar techniques on simpler problems, Johnson is the only person trying to make a commercial neurological product that would enhance memory. In a few minutes, he’s going to conduct his first human test. For a commercial memory prosthesis, it will be the first human test. “It’s a historic day,” Johnson says. “I’m insanely excited about it.”
For the record, just in case this improbable experiment actually works, the date is January 30, 2017.
"Here’s the problem with artificial intelligence today," says David Cox. Yes, it has gotten astonishingly good, from near-perfect facial recognition to driverless cars and world-champion Go-playing machines. And it’s true that some AI applications don’t even have to be programmed anymore: they’re based on architectures that allow them to learn from experience.
Yet there is still something clumsy and brute-force about it, says Cox, a neuroscientist at Harvard. “To build a dog detector, you need to show the program thousands of things that are dogs and thousands that aren’t dogs,” he says. “My daughter only had to see one dog”—and has happily pointed out puppies ever since. And the knowledge that today’s AI does manage to extract from all that data can be oddly fragile. Add some artful static to an image—noise that a human wouldn’t even notice—and the computer might just mistake a dog for a dumpster. That’s not good if people are using facial recognition for, say, security on smartphones (see “Is AI Riding a One-Trick Pony?”).
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.
But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will. That’s one reason Nvidia’s car is still experimental.
Now that you're here, we can begin.
For most people, I suspect the phrase, “exponential growth,” probably doesn’t generate much excitement. But once you’ve read this post, that will probably change. That is, if you’re anything like me or Ray Kurzweil, Peter Diamandis and others (to be clear, I am not placing myself among those incredibly accomplished individuals).
Humans think linearly. If it took 5 years to build half of a bridge, we naturally think it'll take another 5 years to complete it.
That's essentially how linear growth works. By “linear growth” I mean something that grows or advances by the same amount each step, regardless of the total underlying amount of progress or quantity.
"The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly -- and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do.
Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you're not late."
Google’s decision to reorganize itself around A.I. was the first major manifestation of what has become an industrywide machine-learning delirium. Over the past four years, six companies in particular — Google, Facebook, Apple, Amazon, Microsoft and the Chinese firm Baidu — have touched off an arms race for A.I. talent, particularly within universities. Corporate promises of resources and freedom have thinned out top academic departments. It has become widely known in Silicon Valley that Mark Zuckerberg, chief executive of Facebook, personally oversees, with phone calls and video-chat blandishments, his company’s overtures to the most desirable graduate students. Starting salaries of seven figures are not unheard-of. Attendance at the field’s most important academic conference has nearly quadrupled. What is at stake is not just one more piecemeal innovation but control over what very well could represent an entirely new computational platform: pervasive, ambient artificial intelligence.
For those of you that haven't heard of the incredibly popular website WaitButWhy.com or Tim Urban, this post is for you. Calling it a "blog" is a disservice to the site, its content and its creators. Tim Urban's posts are lengthy and well-researched, yet they remain approachable and dotted with humor - even with what would otherwise be dry, existential topics. Simply put, Wait But Why embodies the quality-over-quantity principle.
Wait But Why and Tim Urban tackle everything from handling life's troubles to artificial intelligence to cryonics. Below is an excerpt and link to Tim Urban's piece on artificial intelligence, which remains one his most popular posts to date. Read on and head to WaitButWhy.com, you'll thank us later.