Kutz believes machine scientists are bringing the field to the cusp of what he calls “GoPro physics,” where researchers will simply point a camera at an event and get back an equation capturing the essence of what’s going on.
While AI has yet to attain human-like cognition, artificial neural networks that replicate language processing — a system thought to be a critical component behind higher cognition — are starting to look surprisingly similar to what we see taking place in the brain.
The researchers developed a tool called Geneva (short for Genetic Evasion), which automatically learns how to circumvent censorship. Tested in China, India and Kazakhstan, Geneva found dozens of ways to circumvent censorship by exploiting gaps in censors’ logic and finding bugs that the researchers say would have been virtually impossible for humans to find manually.
The brain-inspired chip, based on OxRAM technology, has the capability of self-learning and has been demonstrated to have the ability to compose music.
“Ignoring AI now means the United States will lag behind more forward-thinking countries that invest in AI today. While the United States waits “50 to 100 years” for AI to become a reality of life, other countries will be doing the hard work, laying the necessary infrastructure, and gaining from machine learning, and the human learning that goes along with it.”
“Future versions of these systems will be armed with non-lethal weapons that could shut down the engines of the targeted boat, and even lethal weapons that could be remotely operated by humans from afar,” says military analyst Peter Singer. “Israel, for instance, has a version that’s armed with a machine gun.”
“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.”
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.
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.