Deep Learning System Surprises Researchers
June 24, 2015
Researchers were surprised when their scene-classification AI performed some independent study, we learn from Kurzweil’s article, “MIT Deep-Learning System Autonomously Learns to Identify Objects.”
At last December’s International Conference on Learning Representations, a research team from MIT demonstrated that their scene-recognition software was 25-33 percent more accurate than its leading predecessor. They also presented a paper describing the object-identification tactic their software chose to adopt; perhaps this is what gave it the edge. The paper’s lead author, and MIT computer science/ engineering associate professor, Antonio Torralba ponders the development:
“Deep learning works very well, but it’s very hard to understand why it works — what is the internal representation that the network is building. It could be that the representations for scenes are parts of scenes that don’t make any sense, like corners or pieces of objects. But it could be that it’s objects: To know that something is a bedroom, you need to see the bed; to know that something is a conference room, you need to see a table and chairs. That’s what we found, that the network is really finding these objects.”
Researchers being researchers, the team is investigating their own software’s initiative. The article tells us:
“In ongoing work, the researchers are starting from scratch and retraining their network on the same data sets, to see if it consistently converges on the same objects, or whether it can randomly evolve in different directions that still produce good predictions. They’re also exploring whether object detection and scene detection can feed back into each other, to improve the performance of both. ‘But we want to do that in a way that doesn’t force the network to do something that it doesn’t want to do,’ Torralba says.”
Very respectful. See the article for a few more details on this ambitious AI, or check out the researchers’ open-access paper here.
Cynthia Murrell, June 24, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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