While AI is the flavor of the times, there is so much happening in this universe that one is often left gasping for breath trying to make sense of the ever-new terms being bandied about. Generative AI has been the buzzword for quite some time now, and just when one sort of came to terms with what it possibly entailed, one hears of another one - Multimodal AI. Multimodal AI comprises machine learning models that possess the ability to both process and integrate information from diverse or multiple types of data, like text, images, audio, and video. It is quite different from the old AI models that can usually only work with a particular type of data (largely text-to-text generation), because it can deal with multiple types of data inputs, analyzing them to achieve a much more broadbased understanding of the issue at hand. This enables it to come up with optimal outputs. A multimodal model, for instance, could take a picture of Switzerland and come up with a text summary of everything important that one needs to learn about that place. This wasn't something that Generative AI models like ChatGPT could come up with when it was launched. All it could do was produce outputs in text when responding to inputs provided in the same way. The subsequent incorporation of multimodal made ChatGPT that much better at understanding and interpreting data. Why are Multimodal GenAI models better? Unlike the traditional unimodal model, the multimodal GenAI model functions in a manner similar to the human brain because it can deal with sensory inputs from multiple sources, which it can then combine and analyze to obtain a comprehensive as well as nuanced understanding of actual facts. This allows multimodal Gen AI models to successfully deal with the most complex of tasks and come up with stellar results largely untainted by mistakes and glaring errors, as was often the case with unimodal AI. As more and more businesses make a move towards Multimodal AI, they will benefit from its ability to be used for a wider and more complex set of applications. The Future With Multimodal AI As we go along, Multimodal AI will greatly empower businesses to better respond to challenges that arise suddenly and out of the blue. Large-scale adoption of IoT will aid this process by providing large volumes of multimodal data to help it come up with bespoke solutions that speak to the customers’ exact needs. Given that multimodal generative AI models are able to process a range of multisensory inputs, they will be more amenable to use by even those with low or no technical skills, allowing them to enhance their productivity manifold. With Multimodal AI evolving all the time, the number of innovative uses it can be put to is changing on a daily basis. Going forward, it promises to completely reshape the paradigm of what can be achieved with its help. Multimodal AI has opened up a whole slew of opportunities for developing the next generation of applications that will fundamentally alter the way the world goes about the business of daily living and working. Feature image generated by Google's Gemini model. Feature image generated by Google's Gemini model.

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