His innovation was to teach a computer to spot trends in unsolved murders, using publicly available information that no one, including anyone in law enforcement, had used before. This makes him, in a manner of speaking, the Billy Beane of murder. His work shines light on a question that’s gone unanswered for too long: Why, exactly, aren’t the police getting any better at solving murder? And how can we even dream of reversing any upticks in the homicide rate while so many killers remain out on the streets?
“Today, we buy a lot of stuff made in China by Chinese people. Tomorrow, we’ll buy stuff made in America — by Chinese robots.”
“Many people have long speculated that there has to be a basic design principle from which intelligence originates and the brain evolves, like how the double helix of DNA and genetic codes are universal for every organism,” Dr. Tsien said.
“We present evidence that the brain may operate on an amazingly simple mathematical logic.”
The authors believe that future designs of their technology could be used to automatically trigger drug release in humans when required.
The algorithm can be trained to track brain states that underlie ADHD or schizophrenia or otherwise be modified to suit your needs, explains study author Sachar Arnon to New Scientist. For example, if EEG detects signs of a burgeoning depressive episode, it could trigger DNA robots to expose anti-depressants briefly to counteract symptoms before they become full-blown. This way, the brain isn’t perpetually bathed in mind-altering drugs even when they’re not needed.
It’s a futuristic idea, and lots of things still need to be ironed out.
Artificial neural networks are famously based on biological ones. So not only do Lin and Tegmark’s ideas explain why deep learning machines work so well, they also explain why human brains can make sense of the universe. Evolution has somehow settled on a brain structure that is ideally suited to teasing apart the complexity of the universe.
This work opens the way for significant progress in artificial intelligence. Now that we finally understand why deep neural networks work so well, mathematicians can get to work exploring the specific mathematical properties that allow them to perform so well. “Strengthening the analytic understanding of deep learning may suggest ways of improving it,” say Lin and Tegmark.
Deep learning has taken giant strides in recent years. With this improved understanding, the rate of advancement is bound to accelerate.
“On Wednesday, researchers at the Joint Quantum Institute at the University of Maryland unveiled a first-of-its-kind fully programmable and reconfigurable quantum computer. The five-qubit machine, which is described in the journal Nature, represents a dramatic step toward general-purpose quantum computing—and, with it, an upending of what we can even consider to be computable.”
When used with awareness and attention, our tools foster embodied cognition—they become extensions of our bodies or our minds. But if we stop paying attention, those tools can come to dominate our lives and we become “functional cyborgs,” or fyborgs, to use Alexander Chislenko’s evocative blend. We necessarily extend ourselves technologically with eyeglasses or canes or hearing aids, but we frequently go far beyond that to use our latest tools—particularly smartphones and similar devices—to mediate all or most of our experiences.
“[…] the future of computing seems to be about a set of platform and device-independent services. Specifically, voice-based interactions, driven by large installations of cloud-based servers running deep learning-based algorithms are what’s hot these days. This kind of computing model doesn’t necessarily need the kind of local horsepower that traditional computing devices have had. Indeed, these types of services can be accessed by the simplest of devices, with little more than an audio input, an audio output, and a wireless connection.”
What’s needed for AI’s wide adoption is an understanding of how to build interfaces that put the power of these systems in the hands of their human users. What’s needed is a new hybrid design discipline, one whose practitioners understand AI systems well enough to know what affordances they offer for interaction and understand humans well enough to know how they might use, misuse, and abuse these affordances.
I just read this great article in the Atlantic.
80 MILLION Americans have an IQ of 90 or below. What is your first gut reaction about those people when you hear that?
Throughout human history, the most valuable substance on Earth has been… the human brain. Even the dimmest of humans can be taught to do tasks that we still have trouble getting machines to do.
But that is changing rapidly. And just like jobs that require “muscle” (agriculture, manufacturing) have mostly disappeared, jobs that require structured thought (finance, law) are starting to disappear as well.
So that piece of wetware in your skull is going to be scrutinized further and further, as its economic value plummets and your worth as a cog in the GDP falls with it.
Source: The War on Stupid People