Open Ended ...

BRT has talked often about AI as this is the one tech that's open ended with the real possibility of it getting out of control given just how powerful hardware has become along with the rise of the net, a potential feast for the asking for any enterprising AI that may be built by us unsuspecting rubes. With that being said, some rather disquieting events regarding said tech has come to pass as we speak.

Researches at Facebook shut down an artificial intelligence (AI) program after it created its own language, Digital Journal reports.

The system developed code words to make communication more efficient and researchers took it offline when they realized it was no longer using English.

The incident, after it was revealed in early July, puts in perspective Elon Musk’s warnings about AI.

“AI is the rare case where I think we need to be proactive in regulation instead of reactive,” Musk said at the meet of U.S. National Governors Association. “Because I think by the time we are reactive in AI regulation, it’ll be too late.”

Example 1

Arkin and his team learned by reviewing biological research results that squirrels gather acorns and store them in specific locations. The animal then patrols the hidden caches, routinely going back and forth to check on them. When another squirrel shows up, hoping to raid the hiding spots, the hoarding squirrel changes its behavior. Instead of checking on the true locations, it visits empty cache sites, trying to deceive the predator.

Arkin and his Ph.D. student Jaeeun Shim implemented the same strategy into a robotic model and demonstration. The deceptive behaviors worked. The deceiving robot lured the "predator" robot to the false locations, delaying the discovery of the protected resources.

Example 2

Dmitry Malioutov can’t say much about what he built.

As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM’s corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. “We couldn’t explain the model to them because they didn’t have the training in machine learning.”

In fact, it may not have helped even if they were machine learning experts. That’s because the model was an artificial neural network, a program that takes in a given type of data—in this case, the insurance company’s customer records—and finds patterns in them. These networks have been in practical use for over half a century, but lately they’ve seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.

HIDDEN MEANINGS: In neural networks, data is passed from layer to layer, undergoing simple transformations at each step. Between the input and output layers are hidden layers, groups of nodes and connections that often bear no human-interpretable patterns or obvious connections to either input or output. “Deep” networks are those with many hidden layers. Michael Nielsen / NeuralNetworksandDeepLearning.com

As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work. And that means no one can predict when they might fail.

A graphic primer on AI

Something to think about don't you think?