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Building machines that better understand human goals


Caption:
An “agent” and an “observer” demonstrate how a new MIT algorithm is capable of inferring goals and plans, even when those plans might fail. Here, the agent makes a mistaken plan to reach the blue gem, which the observer infers as a possibility.
Credits:
Image courtesy of the researchers.
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In a classic experiment on human social intelligence by psychologists Felix Warneken and Michael Tomasello, an 18-month old toddler watches a man carry a stack of books towards an unopened cabinet. When the man reaches the cabinet, he clumsily bangs the books against the door of the cabinet several times, then makes a puzzled noise.  ....

Jordyn Mann , Stuart Russell , Michael Tomasello , Felix Warneken , Joshua Tenenbaum , Vikash Mansinghka , Artificial Intelligence Laboratory , Department Of Brain , University Of California At Berkeley , Cognitive Sciences , Department Of Electrical Engineering , Tan Zhi Xuan , Electrical Engineering , Computer Science , Bayesian Inverse Reinforcement Learning , Smith Zadeh Professor , Inverse Plan Search , Neural Information Processing Systems , ஜோர்டின் மான் , ஸ்டூவர்ட் ரஸ்ஸல் , மைக்கேல் டோமசெல்லோ , ஜோஷுவா டெனன்பாம் , விகாஷ் மன்சிங்க்கா , செயற்கை உளவுத்துறை ஆய்வகம் , துறை ஆஃப் மூளை , பல்கலைக்கழகம் ஆஃப் கலிஃபோர்னியா இல் பெர்க்லி ,