LOCATION:  TEL, Room Room 209 (2nd floor), Ernst-Reuter-Platz 7, 10587 Berlin  

Date/Time: 25.03.2019, 14:15-15:00 

SPEAKER: Kristinn R. Thórisson (School of Computer Science, Reykjavik U. & the Icelandic Institute for Intelligent Machines)

Abstract:

An important part of human intelligence is the ability to use language. Humans learn this in a society of language users, which is probably the most effective way to learn a language from the ground up, assuming a learner has the capacity for cumulative learning. I present a framework which demonstrates cumulative – continuous, incremental, life-long, non-destructive – learning. Our auto-catalytic, endogenous, reflective architecture (AERA) learns from experience. Our AERA-based S1 agent learns situated communication by observing two humans interacting in a realtime mock TV interview, using gesture and situated spoken dialog. At the outset S1 is only given a small set of knowledge – a seed – no information is provided to it about how to resolve anaphora, use co-verbal gestures, take turns, form grammatically correct sentences, or indeed, how to relate words, grammar, and question-answer pairs with the goals or content of the dialog. S1 learns through on-line observation, demonstrating unequivocal and correct interpretation and generation of all of the above: The fluent use of pragmatics, semantics, and syntax of natural unscripted dialog spoken by the human subjects using a vocabulary of 100 words, on the topic of materials recycling (aluminum cans, glass bottles, plastic, and wood). Using a novel reasoning process based around auto-generated causal-relational models of the task-environment AERA becomes able to predict, interact with, and understand complex temporal relationships, patterns, and contingencies in the environment, starting from only a small bootstrapping seed. The resulting behavior, and the knowledge thus acquired, is highly predictable and reliable.