Steps Toward an AGI Roadmap

1. Roadmaps: • A Ten Year Roadmap to Machines with Common Sense (Push Singh, Marvin Minsky, 2002) • Euron (EU Robotics) Research Roadmap (2004) • Neuro-IT Roadmap (EU, A. Knoll, M de Kamps, 2006) • Challanges: Word games of increasing complexity: • 20Q is the simplest, only object description. • Yes/No game to understand situation. • Logical entailment competitions. • Collaborative project: concept description, Wordnet editor Steps Toward an AGI Roadmap Włodek Duch (Google: W. Duch) AGI, Memphis, 1-2 March 2007

2. A Ten Year Roadmap to Machines with Common Sense (Push Singh, Marvin Minsky, 2002) Large society of agents – is collaborative project possible? In Second Life? Steps Toward an AGI Roadmap AGI, Memphis, 1-2 March 2007

3. Progress evaluation • How to measure progress? Depends on the area. • Variants of Turing test, Loebner competition, 20Q and other word games – methodology exists. • Machine Intelligence Quotient (MIQ) can be systematically measured in human-machine cooperative control tasks, ex. using Intelligence Task Graph (ITG) as a modeling and analysis tool (Park, Kim, Lim 2001). • HCI indicators of efficiency of various AI tools, ex. tutoring tools. • Agent-Based Modeling and Behavior Representation (AMBR) Model Comparison (2005), compared humans/CA performance in a simplified air traffic controller environment. • 2007 AAAI Workshop “Evaluating Architectures for Intelligence” proposed several ideas: in-city driving environment as a testbed for evaluating cognitive architectures, measuring incrementality and adaptivity components of general intelligent behavior.

4. Cognitive age • “Cognitive age” based on a set of problems that children at a given age are able to solve, in several groups: e.g. vision and auditory perception, understanding language, common-sense reasoning, abstract reasoning, probing general knowledge about the world, learning, problem solving, imagination, creativity. • Solving all problems from a given age group will qualify cognitive system to pass to the next grade. • Some systems will show advanced age in selected areas, and not in the others – CA are very young in vision but quite advanced in mathematical reasoning, at least comparing to typical population. • General world knowledge may be probed using a Q/A system. Compare CA answers with answers of a 5-year old child. • Common sense knowledge bases are quite limited, except for CyC, but it seems to be quite difficult to use. Common-sense ontologies are missing, representation of concepts in dictionaries is minimal.

5. Query Semantic memory Applications, eg. 20 questions game Humanized interface Store Part of speech tagger & phrase extractor verification On line dictionaries Active search and dialogues with users Parser Manual

6. Realistic goals? Different applications may require different knowledge representation. Start from the simplest knowledge representation for semantic memory. Find where such representation is sufficient, understand limitations. Drawing on such semantic memory an avatar may formulate and may answer many questions that would require exponentially large number of templates in AIML or other such language. • Adding intelligence to avatars involves two major tasks: • building semantic memory model; • provide interface for natural communication. • Goal: • create 3D human head model, with speech synthesis & recognition, use it to interact with Web pages & local programs: a Humanized InTerface (HIT). Control HIT actions using the knowledge from its semantic memory.

7. Word games Word games were popular before computer games. They are essential to the development of analytical thinking. Until recently computers could not play such games. The 20 question game may be the next great challenge for AI, because it is more realistic than the unrestricted Turing test; a World Championship could involve human and software players. Finding most informative questions requires knowledge and creativity. Performance of various models of semantic memory and episodic memory may be tested in this game in a realistic, difficult application. Asking questions to understand precisely what the user has in mind is critical for search engines and many other applications. Creating large-scale semantic memory is a great challenge: ontologies, dictionaries (Wordnet), encyclopedias, MindNet (Microsoft), collaborative projects like Concept Net (MIT) …

8. HIT – larger view … Learning Affective computing T-T-S synthesis Brain models Behavioralmodels Speech recognition HIT projects Cognitive Architectures Talking heads AI Robotics Cognitive science Graphics Lingu-bots A-Minds VR avatars Knowledgemodeling Info-retrieval WorkingMemory EpisodicMemory Semantic memory

9. Web/text/databases interface Text to speech NLP functions Natural input modules Talking head Behavior control Cognitive functions Control of devices Affectivefunctions Specialized agents DREAM architecture DREAM is concentrated on the cognitive functions + real time control, we plan to adopt software from the HIT project for perception, NLP, and other functions.