Neuro-Symbolic AI

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.

Top Work

CLEVRER: The first video dataset for neuro-symbolic reasoning

CLEVRER: The first video dataset for neuro-symbolic reasoning

Computers Already Learn From Us. But Can They Teach Themselves?

Computers Already Learn From Us. But Can They Teach Themselves?

04/08/2020 - The New York Times

All Work

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Natural language boosts LLM performance in coding, planning, and robotics
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In search of AI algorithms that mimic the brain
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Computational model mimics humans’ ability to predict emotions
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AI insights: Is machine intelligence capable of thinking like a human?
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Does this artificial intelligence think like a human?
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Neuro-symbolic AI brings us closer to machines with common sense
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AI Researchers Fight Noise by Turning to Biology
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Reinforcement learning challenge to push boundaries of embodied AI
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Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
 
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What’s Next in AI // Virtual Conference
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AI’s next big leap
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AI Needs To Learn Multi-Intent For Computers To Show Empathy
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How the Coronavirus Pandemic Is Breaking Artificial Intelligence and How to Fix It
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The path to real-world artificial intelligence
TechRepublic
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Neurosymbolic AI to Give Us Machines With True Common Sense
Medium
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All you need to know about symbolic artificial intelligence
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This hybrid AI system can understand causality in controlled environments
TheNextWeb
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Artificial intelligence is struggling to cope with how the world has changed
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Neuro-symbolic AI seen as evolution of artificial intelligence
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How AI and supercomputing are going green
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CLEVRER: The first video dataset for neuro-symbolic reasoning
CLEVRER: The first video dataset for neuro-symbolic reasoning
 
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Computers Already Learn From Us. But Can They Teach Themselves?
The New York Times
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Deep Symbolic Superoptimization Without Human Knowledge
 
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Neuro-symbolic A.I. is the future of artificial intelligence. Here’s how it works
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Top minds in machine learning predict where AI is going in 2020
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Visual Concept-Metaconcept Learning
 
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What happens when you combine neural networks and rule-based AI?
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The AI Breakthrough Will Require Researchers Burying Their Hatchets
PC Mag
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The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
 
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A Glimpse of A.I.’s Future? MIT-IBM Research Lab Sees Early Progress
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Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding