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The Singularity Ray Kurzweil Inventor and Futurist
Other Views on the Singularity Dr. Peter Norvig Google Director of Research
Emulating Intelligence via brain scanning Blue brain project (bottom-up or reverse engineering) An entire neocortical column lights up with electrical activity. Modeled on a two-week-old rodent brain, this 0.5 mm by 2 mm slice is the basic computational unit of the brain and contains about 10,000 neurons. This microcircuit is repeated millions of times across the rat cortex and many times more in the brain of a human. Courtesy of Alain Herzog/EPFL
Artificial General Intelligence and its Potential Role in the Singularity Narrow AI Artificial Intelligence (AI), Artificial General Intelligence (AGI) or Strong AI or Human Level Intelligence Holy Grail of AI: creating machinesthat have similar minds to ours.
Sensorimotor Stage Circular reaction, object permanence, goal directed behavior, A not B search error Knowledge that objects exist, independent of our perception of them Piaget suggests that until 18 months of age appearances and disappearance of an object are not taken as the same object. Out of sight, out of mind
Embodiment and AI The natural synergy between advanced AI and gaming/virtual worlds has been avidly discussed for at least a decade, see (john E.laird 2000)
Some AI theorists believe that robotic embodiment is necessary for the achievement of powerful AGI (Rodney Brooks for instance is a prominent champion of this perspective)
Others believe embodiment is entirely unnecessary, and one can create a human-level AGI exclusively using other mechanisms, e.g. textual conversation, interaction in formal logic, etc.
Novamente believes that embodiment is extremely convenient for AGI though perhaps not strictly necessary; and that virtual-world embodiment is an important, pragmatic and scalable approach to pursue alongside physical-robot embodiment.
Why simulated worlds? Cheaper than robotics Controllability of the environment with physics Masses of potential AGI teachers (like Wikipedia or Google implicit knowledge) Linguistic and embodied perceptual/active experiences (different from chat robots) Different form of avatars (Virtual animals, shopkeepers etc) Commercial vehicle for the popularization and monetization of early-stage AGI’s It's easier for a distributed team to work on project
Virtual Environment Problems SL does not lend itself well with detailed perceptual and motoric interaction. It is better for its users base. Integration of virtual world platforms with robotics simulation platforms such as Player/Gazebo would be one route. SL Lack of integrity with other tools Virtual worlds needs better richness of physics and sensory motions(What must a world be, that an intelligent system may grow and learn within it?) Object recognition and integration into learning. At present time objects are labeled with metadata indicating their type
Example solution: Integration of a robot simulator with a virtual world engine + Player / Gazebo: 3D robot control + simulation framework RealXTend/OpenSim: open-source virtual world It seems feasible to replace OpenSim’s physics engine with appropriate components of Player/Gazebo, and make coordinated OpenSim client modifications
Work done in simulated worlds Object permanence / Object recognition in AGISIM False belief test in Second Life Current SL stage with Novamente cognition engine, virtual pet (fetch game)
Novamente AI Sim Hiding an object and seeing if the AI remembers that it still exists (what Piaget called object permanence)
Fetch games Using imitation-reinforcement-correction methodology to teach avatars various behaviors (verbal and non verbal communication)
Leonardo, the creation of MIT scientists Passed the test of false Belief
What is OpenCog The Open Cognition Framework (OpenCog) provides research scientists and software developers with a common platform to build and share artificial intelligence programs. http://www.opencog.org/
What do they offer to Kinesthetic learners and how can they be improved in the future. How virtual worlds can help to develop Intelligence for Human and Artificial Agents? Learning style in Virtual Worlds
Road Map Look at the different type of learning and choose kinesthetic learning style Fleming and Mills’ Vark model for kinesthetic learners Virtual worlds vs. other 3D environments, why a Second Life type environment should bring something to kinesthetic learners? What Virtual Worlds could bring to kinesthetic learners? What else could be done to improve kinesthetic learning in a virtual world?
Fleming's VARK model visual learners auditory learners reading/writing-preference learners kinesthetic learners or tactile learners Kinesthetic learning is when someone learns things from doing or being part of them. They make up about 15% of the population and struggle to pick things up by reading/ listening to things
Naïve Physics Naive physics (Hayes, 1985) refers to the theories about the physical world that human beings implicitly develop and utilize during their lives. What goes up must come down. A dropped object falls straight down. A vacuum sucks things towards it. Centrifugal force throws rotating things outwards. An object is either at rest or moving, in an absolute sense.
Current Virtual Worlds lack fluids, powders, pastes, fabrics … they don’t completely implement “naïve physics”
One likely solution: bead physics Spherical beads with specially designed adhesion properties can emulate fluids, fabrics, pastes, strings, rubber bands, etc. Bead physics can be added to virtual world physics engines
What Must a World Be That an AGI Can Develop In It?
Ethical views: Friendly AI Goal Invariance Under Radical Self-Modification How to architect an AGI system so as to maximize the odds that, as it radically self-modifies and self-improves, it will not lose track of its originally programmed/taught goal system? Eliezer Yudkowsky