Creating AI: A unique interplay between the development of learning algorithms and their education

1. Creating AI: A unique interplay between the development of learning algorithms and their education Authors: AnatTreister-Goren and Jason L. Hutchens Presentation by: Carlos FernándezMusoles

2. Introduction • Alan Turing “Can machines think?” = Turing Test: conversational scenario, whereby if a human interrogator cannot distinguish between a machine and a human, then it can be said to be thinking. • Artificial Intelligence NV (Ai) goal is to create a machine capable of passing the Turing Test. • Currently: HAL has a level of a 18 month child. • Equal importance to the development of learning algorithms and their training and evaluation.

3. Overview • Difficult to simulate adult level conversation • Turing suggestion: instead produce a programme to simulate the child’s, and then subject it to appropriate education to develop it to an adult level. • Traditional approach • Fixed grammatical rules are sufficient. • Failed to learn the essence of human intelligence. • Ai approach • More behaviouristic approach: stimuli from the environment (feedback) – response from the system (learning) • Black box model (architecture is not important, behaviour is)

4. HAL’s Architecture • Black box system from the trainer point of view • Internally, two models: one makes predictions about the symbol it is likely to observe next (probability distribution); the second calculates correlations between HAL’s behaviour and reinforcement from the trainer • The development team help improving learning algorithms

5. HAL’s Architecture • Conversation HAL-trainer is enough to go from babbling in characters to generating meaningful sequences of words • Feedback information is given in an intuitive way • Trainer can edit HAL’s sentences by adding / deleting characters (with gives reinforcement)

6. Training and Evaluation • Our perception of intelligence is influenced by our expectations (monkeys, children, lecturers...) • Setting different levels to be achieved by HAL helps in the learning • HAL is given specific reinforcement on each level to achieve the necessary lingual behaviours

7. ‘15 month old’ milestone HAL’s performance comparable to a 15-month-old child Example:

8. ‘18 month old’ milestone • Vocabulary grows, start combining words • Remarkable: ‘monkeys eat bananas’; HAL remembered a previous conversation about zoo.

9. Conclusion • HAL can successfully learn up to a 18 month child level with the development-training model • Good start point, but how do we jump from here to other basic aspects of our intelligence? Eg: abstract reasoning, creativity, independent thought... • This model does not seem complex enough to achieve these.