1 / 4

Autonomous Machine Learning Panel IJCNN 2010, July 20

Autonomous Machine Learning Panel IJCNN 2010, July 20. Juyang Weng Embodied Intelligence Laboratory Department of Computer Science Cognitive Science Program Neuroscience Program Michigan State University East Lansing MI 48824 USA http://www.cse.msu.edu/~weng/.

mabyn
Download Presentation

Autonomous Machine Learning Panel IJCNN 2010, July 20

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Autonomous Machine Learning PanelIJCNN 2010, July 20 Juyang Weng Embodied Intelligence LaboratoryDepartment of Computer Science Cognitive Science Program Neuroscience Program Michigan State University East Lansing MI 48824 USA http://www.cse.msu.edu/~weng/

  2. Intent-Directed Reasoning with Pixels Luciw & Weng IJCNN 2010

  3. Autonomous Machine Learning (AML):Misconceptions • Generate own supervision: a lot of work! • Semi-supervised, reinforcement learning • Select the most informative samples: a lot of work! • Border points, bottom-up attention (e.g., saliency) • Generate own loss function: Is a loss function good? • Evaluate own performance: Without teacher? • Human supervision? • Human internal (inside the brain) intervention: Bad • Human external (outside the brain) supervision: Goodas it is essential for human intelligence, e.g., classroom teaching

  4. AML: Let Us Face Major AI Challenges • General-purpose vision: • Multiple objects, complex backgrounds • General-purpose audition: • Speaker independence, noisy environment, beyond speech (e.g, music) • General-purpose language understanding: • Acquisition of ontology, language acquisition, discourse • The bottleneck problem: Internal Representation! Emergent! • Symbolic, monolithic representation: Handcrafting a representation • Neural networks: One-shot recognition, cannot perform goal-directed reasoning, lack of autonomy of intents • The bottleneck problem seems to have a solution: A 5-chunk brain-mind model (Weng, IJCNN 2010, Tuesday AM) • The above 3 central problems of AI seem to be tractable by AML

More Related