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WORKER BOTS. "The scientists at UC Berkeley hope to move closer to a world where robots are autonomous, nimbly performing many functions typically done by humans. In the future, robots may be able to provide care for the elderly, conduct rescue efforts, clean up in disaster areas and even deliver mail." (2015!) http://www.cnbc.com/2015/12/04/a-baby-step-on-way-to-robots-learning-every-human-thing.html View in LinkedIn

UNFAIR ADVANTAGE. "Japanese researchers introduced a robot that can play rocks-paper-scissors against humans and wins 100 percent of the time. And this robot wins by watching. Its perfect track record is the result of a built-in high-speed camera and extremely fast reflexes that allow it to read microscopic physical cues—wrist angle, finger movement, head tilt—and (essentially) cheat its way to victory." https://lnkd.in/dZT3Xg7 View in LinkedIn

NAUTILUS ”is another self-learning supercomputer. This unit was fed millions of newspaper articles starting from 1945, by basing its search on two criteria: the nature of the publication and location. Using this wealth of information about past events, the computer was asked to come up with suggestions on what would happen in the “future.” And these turned out to be surprisingly accurate guesses. How accurate? Well, for example, it had located Bin Laden." http://www.learning-mind.com/five-creepiest-advances-in-artificial-intelligence/ 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

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

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

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