Oct 12, 2021 · We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence. By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we’re aiming to create a revolution in AI, rather than an evolution.

Building on the foundations of deep learning and symbolic AI, we have developed software that answers complex questions with minimal domain-specific training.

Recent neuro-symbolic systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we aim to understand the workload characteristics and potential architectures for neuro-symbolic AI.

To better simulate how the human brain makes decisions, we’ve combined the strengths of symbolic AI and neural networks.

At IBM Research, we’re inventing what’s next in AI, quantum computing, and hybrid cloud to shape the world ahead.

Research into new a neuro-vector-symbolic architecture gives AI a dramatic boost.

A new era of AI is rapidly emerging: neuro-symbolic AI combines knowledge-driven, symbolic AI with data-driven machine learning approaches. IBM is a leader in…

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A new era of AI is rapidly emerging: neuro-symbolic AI combines knowledge-driven, symbolic AI with more traditional data-driven machine learning approaches.…

To this end, we introduce a specialization of a neuro-symbolic architecture, known as SOFAI (Slow and Fast AI), inspired by the cognitive framework popularized by D. Kahneman's book "Thinking, Fast and Slow".