<ul><li>ChatGPT-4V, a language model for vision tasks, can solve complex physics problems when text questions are converted to PNG image.</li><li>Vision-Language Models (VLMs) combine a large language model with a vision encoder to enable the model to see.</li><li>VLMs are capable of performing image analysis, visual Q&A, summarising images and video, and solving complex math and physics problems.</li><li>VLMs are useful in logistics and manufacturing where robots can sort items based on appearance and verbal guidance.</li><li>The limitations of VLMs include challenges around spatial and long-context video understanding.</li><li>Training VLMs requires large image/caption datasets and high computational power.</li><li>The ethical implications of VLMs involve job displacement and labor impacts as machines can outperform human capabilities.</li><li>The challenge is not just technological, but societal and requires embracing innovation without sacrificing the human touch that defines us.</li></ul>

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