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Machine Learning, Artificial General Intelligence, and Robots with Human Minds

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Talk for the 2017 University of Huddersfield 'Engage' research festival

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Machine Learning, Artificial General Intelligence, and Robots with Human Minds

  1. 1. Machine Learning, Artificial General Intelligence, and Robots with Human Minds David Peebles Reader in Cognitive Science Department of Psychology April 25, 2017
  2. 2. Cognitive science
  3. 3. Talk outline Structure Discuss different types of artificial intelligence (AI) Human-level artificial general intelligence (AGI) Human cognitive architecture as a basis for AGI Introduce a new cognitive robotics project Key aims Allay your fears of a possible AI invasion and/or enslavement by super-intelligent AI systems in the foreseeable future Enthuse you about the prospect of human-level AI To entertain you with amusing and informative videos
  4. 4. Recent advances in artificial intelligence
  5. 5. Some recent headlines. . . Artificial intelligence: We’re like children playing with a bomb. Sentient machines are a greater threat to humanity than climate change... (Observer. June, 2016) Will democracy survive big data and artificial intelligence? (Scientific American. Feb, 2017) The AI threat isn’t SkyNet, it’s the end of the middle class (Wired Business. Feb, 2017) Stephen Hawking, Elon Musk, and Bill Gates warn about artificial intelligence (Observer. Aug, 2015) DESTROYING OURSELVES: Artificial intelligence poses THREAT to humanity – shock report (Express. Jan, 2017)
  6. 6. Are intelligent machines really upon us? It depends what you mean by “machine intelligence” Three types of AI: Artificial Narrow Intelligence (ANI). Machine intelligence that equals or exceeds human intelligence or efficiency, but only in one specific area. Artificial General Intelligence (AGI). Machines with general human intelligence, capable of sustaining long-term goals and intent, which can successfully perform any task that a human might do. Artificial Super-intelligence (ASI). Machine intelligence that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills (Bostrom, 2014).
  7. 7. The importance of distinguishing AI The three types of AI are often conflated or confused in the press and public mind. All of the recent developments are in Narrow AI due to: Faster hardware (GPUs) Huge amounts of data New statistical machine learning methods (e.g., deep learning in neural networks) Main machine learning methods Supervised learning (classification and regression) Unsupervised learning (clustering) Reinforcement learning (optimisation via reward signal)
  8. 8. Narrow AI Benefits Rapid conversion of unstructured information into useful and actionable knowledge. Narrow AI will provide tools to automate and optimise this process in science, medicine, business etc. Costs Elimination of jobs (mainly white-collar knowledge work). Concentrations of knowledge, power and resources may exacerbate inequality. ‘Black box’ systems where humans don’t know why AI systems are making decisions Limitations Require extensive training Each system is specific to the data set it’s trained on. One slight change requires retraining
  9. 9. Artificial General Intelligence “Machines with general human intelligence, capable of sustaining long-term goals and intent, which can successfully perform any task that a human might do”. More interesting for cognitive science Much more complex and harder to achieve than narrow AI Some recent examples from Hollywood. . . Ex Machina (2014) Robot and Frank (2012) Prometheus (2012)
  10. 10. Criteria for advanced autonomous agents US NSF $16.5M Smart and Autonomous Systems (2016) Specified five desired capabilities: Cognisant. Exhibit high-level cognition and awareness beyond primitive actions. Taskable. Users can specify desired behaviours and outcomes in a natural and concise (possibly vague) manner Reflective. Can adapt behaviour and learn new behaviour from experience, observation, and interaction with others. Ethical. Adhere to a system of societal and legal rules. Do not violate accepted ethical norms. Knowledge-rich. Semantic, probabilistic and commonsense reasoning and meta-reasoning about uncertain, dynamic environments. Able to acquire and understand knowledge.
  11. 11. Advanced autonomous agents Long-term autonomy (able to operate unaided over time) Flexible assistants and agents in applications such as manufacturing, military, agriculture, health, space etc. Robots that can be trained for new task using natural language rather than having to be re-programmed. Robots that can perform complex task planning without human intervention NASA Mars Exploration Rover NASA Valkyrie robot
  12. 12. A key human skill – knowledge transfer Excel spreadsheet SPSS spreadsheet
  13. 13. Cognitive architectures Originated in 1950s but active research programme in 1980s. Cognitive science – differs from mainstream “narrow” AI and traditional “divide and conquer” approach of experimental cognitive psychology. Theories of the core, immutable structures and processes of the human cognitive system. Integration of multiple components of cognition. Computer architecture Perception Action Task Environment Learning Procedural Perceptual Learning Learning Declarative Selection Action Short−term memory Long−term memory Procedural Long−term memory Declarative Human cognitive architecture
  14. 14. Example 1 – Interactive task learning Rosie (Univ. Michigan). Soar agent able to learn new tasks specified in natural language and interactive demonstration. Complex skill integrating natural language processing, vision, logical reasoning, search and motor control. V1: Learns several different games and puzzles through natural language descriptions of the legal actions and goals of the task. V2. Learns new goals from single examples, either by being shown a visual example of the goal or from executing a series of actions to achieve the goal. Agent’s hypotheses are refined and corrected by language input from human instructor. Video: Simple task learning Video: Tower of Hanoi puzzle
  15. 15. Example 2 – Mind reading and mental simulation Cognitive architecture installed on a MDS (Mobile / Dexterous / Social) robot Aim: AA that can construct accurate mental representations of other agents’ mental states and perspectives in order to reason about their beliefs and intentions. In order to: Coordinate actions with teammates effectively Anticipate teammates’ (potentially ambiguous) actions Understand shared team goals of the team Communicate more naturally. Current tasks include gaze following, hide and seek, task interruption and resumption, theory of mind. Agent creates mental model of teammates’ beliefs and (explicitly stated) goals and predicts what they will do next. Video: Gaze and gesture interpretation
  16. 16. The ‘robot with a human mind’ project NAO robot Hadeel Jazzaa Lee McCluskey
  17. 17. A flexible research robot Video: NAO robot Video: Robot football
  18. 18. The ‘robot with a human mind’ project NAO robot NAOqi architecture Visual Module ACT−R Buffers Environment Pattern Matching Execution Production Module Motor Problem State Declarative Memory Procedural Memory Control State ACT-R cognitive architecture
  19. 19. Aims of the project Embodied cognition constrained by perceptuo-motor processing. Goal directed behaviour. Including self generated goals. Cognitive control and resource management over different time scales (including long-term). High-level cognition. Planning, reasoning, problem solving, decision making, reflection and metacognition, analogy. Verbal and nonverbal communication. Interaction and adaptation through natural language understanding (speech, text, demonstration. Constant adaptation and learning. Human scale (i.e., relatively fast and not requiring thousands of training trials). Integration of cognitive functions
  20. 20. Take home messages Types of AI Narrow AI is going to change the world radically but is limited AGI is a much more challenging problem and is a long way off Super-intelligent AI is a very long way off AGI: Requires the integration of many aspects of intelligence (learning, reasoning, planning, self-monitoring etc.) Will require insights from cognitive science as human cognition is currently our best example of AGI Cognitive architectures are a potentially very useful approach to developing systems with AGI

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