Connect with Supply Chain player and NeuroSymbolic AI

While delivering a session to a Supply Chain player, I was pleasantly surprised by questions about Neurosymbolic AI. It’s exciting to see businesses eager and thinking to leverage such emerging tech, and indeed it’s a promising sign for the future of our industry.

Every enterprise relies on effective supply chain management. Supply chain management isn’t merely about moving products; it’s a complex interplay of predicting demand, managing production, and ensuring timely deliveries. But imagine if we could not only react to disruptions but also proactively anticipate them.

Simplifying for the non-AI practitioners…
Neural Networks: Like having a bird’s-eye view of the supply chain, spotting patterns and trends. These networks can process vast amounts of data, learning and evolving.

Symbolic Reasoning: The rulebook supply chain managers turn to during disruptions. This isn’t just a static set of rules. It’s a dynamic system where rules can be explicitly defined by humans, allowing for understanding contexts and making decisions based on predefined logic.

Neurosymbolic AI: A blend of the above, foreseeing a delay from past trends and then consulting the rulebook for swift action. It’s like having an intuitive, experienced manager equipped with a comprehensive playbook.

Imagine a scenario where there’s an unexpected halt in production due to a raw material shortage. Neurosymbolic AI could predict such disruption by analyzing previous trends and then recommending alternative suppliers or suggesting adjustments in production schedules, all within moments.

Why does this matter? 
– Fewer hiccups in the supply chain process
– Balanced demand-supply, leading to optimal inventory levels
– Happier customers due to consistent delivery times

Neurosymbolic AI promises smoother operations and more predictable outcomes. With the global supply chain becoming increasingly complex, integrating such AI can be a game-changer, and this is true for many verticals.

Neurosymbolic AI is emerging, but you should read the below-it has some examples as well. The links provided will be helpful in understanding; if you need something more in-depth, please comment and I can suggest papers. People are trying to use this concepts in many fields including *bots, autonomous driving, healthcare areas etc.

https://scallop-lang.github.io/
https://mitibmwatsonailab.mit.edu/category/neuro-symbolic-ai/
https://www.bosch.com/stories/neuro-symbolic-ai-for-scene-understanding/
https://github.com/Xpitfire/symbolicai
https://research.ibm.com/topics/neuro-symbolic-ai#overview

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