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Beyond Large Language Models: How Large Behavior Models Are Shaping the Future of AI
- Large Behavior Models (LBMs) are shaping the future of AI by enabling machines to learn the way humans do.
- LBMs move beyond language and replicate the way humans interact with the world. Their learning process is through continuous interactions with the surrounding which makes them adaptable and efficient.
- LLMs are primarily focused on language and struggle with real time decision making or learning from experience. However, LBMs learn from continuous experience so they can adapt and reason in dynamic, real world situations.
- LBMs have interactive learning, adaptability and multimodal understanding as their key features
- LBMs replicate human-like learning by incorporating dynamic learning, multimodal contextual understanding, and the ability to generalize across different domains.
- LBMs have real-world applications in healthcare and robotics for providing personalized healthcare recommendations, complex task handling in kitchen and more.
- The ethical concerns of LBMs are invasion of privacy and unintentional replication of biases from the data.
- The development of LBMs requires clear ethical guidelines and regulatory frameworks to ensure their responsible deployment.
- Further development of LBMs can shape the future of AI by making machines smarter and more helpful.
- LBMs shift the paradigm from what the model knows to how the model learns.
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