Related to our various AI discussions, I noticed this news: Robot masters new skills through trial and error — ScienceDaily.
Researchers at the University of California, Berkeley, have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence.
They demonstrated their technique, a type of reinforcement learning, by having a robot complete various tasks — putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more — without pre-programmed details about its surroundings.
…In the world of artificial intelligence, deep learning programs create “neural nets” in which layers of artificial neurons process overlapping raw sensory data, whether it be sound waves or image pixels. This helps the robot recognize patterns and categories among the data it is receiving. People who use Siri on their iPhones, Google’s speech-to-text program or Google Street View might already have benefited from the significant advances deep learning has provided in speech and vision recognition.
If the robot learning concerns you, if you’re concerned that it, or more likely one of its successors, might bootstrap itself into Ultron or Skynet, then consider this part:
The algorithm controlling BRETT’s learning included a reward function that provided a score based upon how well the robot was doing with the task.
BRETT takes in the scene, including the position of its own arms and hands, as viewed by the camera. The algorithm provides real-time feedback via the score based upon the robot’s movements. Movements that bring the robot closer to completing the task will score higher than those that do not. The score feeds back through the neural net, so the robot can learn which movements are better for the task at hand.
Obviously, what direction the robot learns in, and what it will do with what it’s learned, will be heavily influenced by its reward system, in other words, by its programming. (Just as what direction we learn in is heavily influenced by the gene propagating reward system evolution programmed into us.)