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See, Think, Explain: The Rise of Vision Language Models in AI
- Vision Language Models (VLMs) merge visual and language skills, enabling them to explain images with a human-like touch.
- VLMs excel in tasks like image description, video comprehension, question answering, and image generation from text.
- Their functioning involves a vision system analyzing images and a language system processing text, trained on vast image-text datasets.
- Chain-of-Thought reasoning in VLMs ensures step-by-step explanations, enhancing transparency and trustworthiness of AI decisions.
- CoT facilitates tackling complex problems, as seen in healthcare diagnostics and self-driving car decision-making.
- In industries like healthcare, self-driving cars, geospatial analysis, robotics, and education, VLMs with CoT are revolutionizing processes.
- In medicine, VLMs like Med-PaLM 2 diagnose based on symptoms, providing detailed reasoning for doctors to follow.
- Self-driving cars leverage CoT-enhanced VLMs for safer navigation and natural language explanations of actions taken.
- Google's Gemini model integrates CoT to expedite geospatial analysis for disaster response and decision-making.
- In robotics, CoT and VLM integration enhances planning and execution of multi-step tasks, boosting adaptability and response clarity.
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