Month: September 2022

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

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

"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

ADDITIVE LEARNING. "The significance of having robots learn is that they can easily share their learning. One robot will not have to learn from scratch like a newborn animal. A new robot can be given the experiences of other robots and can build upon those." https://lnkd.in/dv6mpRG View in LinkedIn

YOU TUBE TUTORIALS. "The research we’re talking about here is from a paper titled, “Robot Learning Manipulation Action Plans by ‘Watching’ Unconstrained Videos from the World Wide Web.” The paper is really about visual processing: watching a human interacting with objects in a video, and then figuring out what that human is doing and how they’re doing it, with a final step of replicating those actions using the manipulation capabilities of a robot." http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/robots-learning-to-cook-by-watching-youtube-videos View in LinkedIn

MOTHER TREES. “We know that big old trees—we call them mother trees—will communicate with seedlings that are their kin or their kids and make room for those kids compared to seedlings that are strangers, and they are doing this through their mycorrhizal networks.” https://lnkd.in/dJsUQxb View in LinkedIn

TREE NON-SELF. “Indeed, abrupt change of plants' behavior towards its detached ramets suggests that integrity of the physical attachment is essential, and that plants do not remember their past relationships. Interesting as it is, self-nonself recognition does not pass the memory model test—and thus it should be left out from the plant intelligence discussion.” https://lnkd.in/dfsCv77 View in LinkedIn

FASTER LEARNING. "Reinforcement learning is one of the more prominent machine learning technologies, because of its unsupervised learning structure and its ability to produce continual learning, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from other agents in the system, in order to increase the speed of learning." https://lnkd.in/d76E6Cv 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