Category: Uncategorized

"NEURAL NETWORKS have made great strides in allowing us to build computer programs that can process images, speech, text, and even draw pictures." https://lnkd.in/dQpQxhq View in LinkedIn

GÖDEL MACHINE. "We believe that among the most promising approaches for learning to memorize are our recurrent neural networks, policy gradients, and the Optimal Ordered Problem Solver. An ambitious long-term goal is to implement a full-fledged Gödel machine for a real learning robot." https://lnkd.in/d8GTmxc View in LinkedIn

INDIVIDUALIZED LEARNING. "Robot learning in realistic environments requires novel algorithms for learning to identify important events in the stream of sensory inputs, and to temporarily memorize them in adaptive, dynamic, internal states until the memories can help to compute proper control actions." https://lnkd.in/d8GTmxc View in LinkedIn

TRIAL-AND-ERROR LEARNING. 'Developed by Pieter Abbeel and his team at UC Berkeley's Robot Learning Lab, the neural network that allows Darwin to learn is not programmed to perform any specific functions, like walking or climbing stairs. The team is using what's called "reinforcement learning" to try and make the robots adapt to situations as a human child would. Like a child's brain, reinforcement technology invokes the trial-and-error process." http://www.cnbc.com/2015/12/04/a-baby-step-on-way-to-robots-learning-every-human-thing.html View in LinkedIn

SILICO SALTATIONS. "Darwin's baby steps speak to what many researchers believe will be the greatest leap in robotics — a kind of general machine learning that allows robots to adapt to new situations rather than respond to narrow programming." http://www.cnbc.com/2015/12/04/a-baby-step-on-way-to-robots-learning-every-human-thing.html View in LinkedIn

AUTOSUFFICIENCY. "Will robots soon be able to teach themselves ... everything? There's a robot in California teaching itself to walk. Its name is Darwin, and like a toddler, it teeters back and forth in a UC Berkeley lab, trying and falling, and then trying again before getting it right. But it's not actually Darwin doing all this. It's a neural network designed to mimic the human brain." http://www.cnbc.com/2015/12/04/a-baby-step-on-way-to-robots-learning-every-human-thing.html View in LinkedIn

MACHINE LEARNING. "In our lab, instead of manually "programming" our robots, we take a machine learning approach where we use variety of data and learning methods to train our robots. Our robots learn from watching (3D) images on the Internet, from observing people via RGB-D cameras, from observing users playing video games, and from humans giving feedback to the robot." http://pr.cs.cornell.edu/ View in LinkedIn

POTENT PROSPECT. "The fact that robots of the future will be capable of shared and distributed learning has profound implications and is scaring some, while exciting others." http://www.lifehacker.com.au/2015/12/how-do-robots-see-the-world/ View in LinkedIn

ENGINE. "We introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks." https://lnkd.in/d9dr5SN View in LinkedIn

DISTRIBUTED LEARNING. ‘'Equally important is that robots which share experiences may learn together. For example, one thousand robots may each observe a different cat, share that data with one another via the internet and together learn to classify all cats. This is an example of distributed learning." http://www.lifehacker.com.au/2015/12/how-do-robots-see-the-world/ View in LinkedIn