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Deep Learning: The Good, the Bad, and the Ugly
1Department of Cognitive Linguistic and Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02818, USA;
Annual Review of Vision Science
|August 9, 2019
Summary
Deep learning advances artificial vision, but human-level machine intelligence remains a challenge. This review assesses progress and the interplay between artificial and biological vision systems.
Area of Science:
- Computer Science
- Artificial Intelligence
- Neuroscience
Background:
- Artificial vision is crucial for intelligent machines.
- Deep learning has significantly improved machine vision capabilities.
- Human-level visual intelligence is an ongoing research goal.
Purpose of the Study:
- To review recent deep learning advancements in artificial vision.
- To critically evaluate progress toward human-level visual intelligence.
- To explore the relationship between machine and biological vision.
Main Methods:
- Literature review of deep learning in machine vision.
- Analysis of current artificial vision algorithm successes and limitations.
- Discussion of neuroscience's role in future artificial vision.
Main Results:
- Deep learning has driven substantial progress in machine vision.
- Current systems still fall short of human-level visual intelligence.
- The limitations of artificial vision offer insights into biological vision.
Conclusions:
- While deep learning has accelerated machine vision, achieving human-level intelligence requires further breakthroughs.
- Understanding biological vision can guide the development of more sophisticated artificial systems.
- The synergy between AI and neuroscience is key to advancing artificial vision.
Keywords:
action recognitionartificial intelligencecomputational neurosciencedeep learningface recognitionimage segmentationneural networksobject recognitionMore Related Videos
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