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A Wikipedia for #robots allowing them to #share knowledge and experience worldwide | #algorithms

A Wikipedia for #robots allowing them to #share knowledge and experience worldwide | #algorithms | Cyborgs_Transhumanism | Scoop.it

European scientists from six institutes and two universities have developed an online platform where robots can learn new skills from each other worldwide — a kind of “Wikipedia for robots.” The objective is to help develop robots better at helping elders with caring and household tasks. “The problem right now is that robots are often developed specifically for one task”, says René van de Molengraft, TU/e researcher and RoboEarth project leader.

 

“RoboEarth simply lets robots learn new tasks and situations from each other. All their knowledge and experience are shared worldwide on a central, online database.” In addition, some computing and “thinking” tasks can be carried out by the system’s “cloud engine,” he said, “so the robot doesn’t need to have as much computing or battery power on‑board.”

 

For example, a robot can image a hospital room and upload the resulting map to RoboEarth. Another robot, which doesn’t know the room, can use that map on RoboEarth to locate a glass of water immediately, without having to search for it endlessly. In the same way a task like opening a box of pills can be shared on RoboEarth, so other robots can also do it without having to be programmed for that specific type of box.

 

RoboEarth is based on four years of research by a team of scientists from six European research institutes (TU/e, Philips, ETH Zürich, TU München and the universities of Zaragoza and Stuttgart).

 

 

Robots learn from each other on 'Wiki for robots'


Via Dr. Stefan Gruenwald
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Rescooped by luiy from Human Nature ,Brain and Cognitive Sciences &Singularity
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AIXI : To create a super-intelligent machine, start with an equation I #algorithms #agents

AIXI : To create a super-intelligent machine, start with an equation I #algorithms #agents | Cyborgs_Transhumanism | Scoop.it
Intelligence is a very difficult concept and, until recently, no one has succeeded in giving it a satisfactory formal definition.

Via Spaceweaver, Mlik Sahib
luiy's insight:

Universal artificial intelligence


This scientific field is called universal artificial intelligence, with AIXI being the resulting super-intelligent agent.

 

The goal of AIXI is to maximise its reward over its lifetime – that's the planning part.

In summary, every interaction cycle consists of observation, learning, prediction, planning, decision, action and reward, followed by the next cycle.

If you're interested in exploring further, AIXI integrates numerous philosophical, computational and statistical principles:

 

Ockham's razor (simplicity) principle for model selectionEpicurus principle of multiple explanations as a justification of model averagingBayes rule for updating beliefsTuring machines as universal description languageKolmogorov complexity to quantify simplicitySolomonoff's universal prior andBellman equations for sequential decision making.



Read more at: http://phys.org/news/2013-11-super-intelligent-machine-equation.html#jCp

 

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Spaceweaver's curator insight, December 2, 2013 8:52 AM

interesting! See the link to the book.

Mlik Sahib's curator insight, December 2, 2013 7:29 PM

Imagine a robot walking around in the environment. Initially it has little or no knowledge about the world, but acquires information from the world from its sensors and constructs an approximate model of how the world works.

It does that using very powerful general theories on how to learn a model from data from arbitrarily complex situations. This theory is rooted in algorithmic information theory, where the basic idea is to search for the simplest model which describes your data.

The model is not perfect but is continuously updated. New observations allow AIXI to improve its world model, which over time gets better and better. This is the learning component.

AIXI now uses this model for approximately predicting the future and bases its decisions on these tentative forecasts. AIXI contemplates possible future behaviour: "If I do this action, followed by that action, etc, this or that will (un)likely happen, which could be good or bad. And if I do this other action sequence, it may be better or worse."



Read more at: http://phys.org/news/2013-11-super-intelligent-machine-equation.html#jCp