Artificial Tasks for Artificial Intelligence

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An era of success▪ Many breakthroughs recently▪ Object detection (Krizhevsky et al. 13)▪ Speech recognition (Hinton et al. 12)▪ Word embeddings (Collobert et al. 11) (Mikolov et al. 13)▪ Machine translation (Sutskever et al. 14)▪ Ingredients1. Models with high capacity and representation power (CNNs, RNNs, LSTMs, etc.)2. Lots of (supervised) dataExcept <strong>for</strong> word embeddings..
So AI is solved?▪ Not quite.▪ Success of Big data statistics cleverly used.▪ Labeled data is crucial.▪ So are fast and powerful computers (m/billions of examples/parameters).▪ Models and learning algorithms are not specifically new.• Reasoning is still limited.• “Concept ≠ statistics” (Bottou 15)
Page 1 and 2: Artificial Tasks for Artificial Int
Page 3: Building intelligent machines
Page 7 and 8: Caption generationFrom a talk of(Zi
Page 9 and 10: MT still does not fully understand
Page 11 and 12: Big Data => Big AI ?▪ Can we solv
Page 13 and 14: Looking backwards
Page 15 and 16: And in AI?▪ Block Worlds (Winogra
Page 17 and 18: Evaluating and learning systems for
Page 20 and 21: Stack-augmented RNNsTask a n b 2n(D
Page 23 and 24: ExampleSimple grammarCommand format
Page 25 and 26: (T1) Single supporting fact “wher
Page 27 and 28: (T3) Three supporting facts▪ Simi
Page 29 and 30: (T6) Yes/No questions▪ This task
Page 31 and 32: (T17) Positional reasoning▪ This
Page 33 and 34: (T19) Path finding▪ In this task
Page 35 and 36: Looking forward
Page 37 and 38: Simulation control panelSymbolsMemo
Page 39 and 40: Playing with the symbols knob▪ We
Page 41 and 42: Playing with the memory knob▪ We
Page 43 and 44: Playing with the linguistics knob
Page 45 and 46: Playing with the reasoning knob▪
Page 47 and 48: A virtuous circleModel design/train
Page 49 and 50: A game engine within the model?•
Page 51 and 52: On the need of artificial tasks▪

Artificial Tasks for Artificial Intelligence

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