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How Neurosymbolic AI Can Fix Generative AI’s Reliability Issues
- Generative AI is powerful but often fails to get facts accurate and relies on probabilities to make decisions, making it unreliable, particularly in fields like healthcare, law, or finance.
- Neurosymbolic AI can solve reliability issues faced by generative AI by combining the strengths of neuronal networks with symbolic logic, increasing and verifying generated information's accuracy.
- Neurosymbolic AI can bring transparency to generative AI and show exactly how the system arrived at an answer, building trust and increasing the AI's reliability.
- GraphRAG is a case study that shows the potential of combining the strengths of neurosymbolic and generative AI by organizing information into nodes using knowledge graphs and cross-referencing answers with trusted information.
- Combining neurosymbolic AI with generative AI is challenging and requires a balance between creativity and accuracy, which isn’t always easy to achieve.
- Future directions include developing hybrid systems that can switch between the two methods depending on the task's requirements and improving the trackability of the AI's reasoning to increase trust and confidence.
- Neurosymbolic AI addresses the unpredictability and lack of understanding of generative AI, making AI decision-making creative yet dependable.
- As AI continues to evolve, neurosymbolic AI offers a path forward, one where we can count on the answers AI provides, even when lives and livelihoods are at stake.
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