CLEVRER: The first video dataset for neuro-symbolic reasoning
As far back as the 1980s, researchers anticipated the role that deep neural networks could one day play in automatic image recognition and natural language processing. It took decades to amass the data and processing power required to catch up to that vision – but we’re finally here. Similarly, scientists have long anticipated the potential for symbolic AI systems to achieve human-style comprehension. And we’re just hitting the point where our neural networks are powerful enough to make it happen. We’re working on new AI methods that combine neural networks, which extract statistical structures from raw data files – context about image and sound files, for example – with symbolic representations of problems and logic. By fusing these two approaches, we’re building a new class of AI that will be far more powerful than the sum of its parts. These neuro-symbolic hybrid systems require less training data and track the steps required to make inferences and draw conclusions. They also have an easier time transferring knowledge across domains. We believe these systems will usher in a new era of AI where machines can learn more like the way humans do, by connecting words with images and mastering abstract concepts.
Computers Already Learn From Us. But Can They Teach Themselves?
04/08/2020 - The New York Times