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Beyond Language: How Multimodal AI Sees the Bigger Picture
AI chatbots have grown increasingly ubiquitous over the last year. For example, the basic version of ChatGPT is a conversational chatbot capable of understanding natural language inputs and generating highly coherent text responses. However, exciting new multimodal AI models like Google's Gemini showcase more sophisticated capabilities.
What distinguishes these two varieties of artificial intelligence? How may such multimodal systems further extend machine learning's capacities? And by what means might novel implementations leveraging multiple modalities secure patent rights?
Overview of AI Chatbots
AI Chatbots typically incorporate Large Language Models (LLMs) which are AI models designed to understand user-supplied input and generate human-like textual output. These models, such as GPT-3.5 as incorporated in ChatGPT, are trained on vast amounts of diverse textual data and can perform a wide range of natural language processing tasks. They excel at tasks like language translation, text summarization, question answering, and even creative writing. On a technical level, AI chatbots are programmed to understand and generate natural language, analyzing the text that users input in order to interpret intent and provide relevant text responses.
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