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Source: The post is based on the article “What is multimodal artificial intelligence and why is it important?” published in “The Hindu” on 10th October 2023
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This article talks about Multimodal artificial intelligence and its importance.
What is Multimodal artificial intelligence?
Multimodal artificial intelligence refers to a type of AI system that can understand and process information from multiple modalities or sources, such as text, images, videos, audio, and other forms of data simultaneously.
This means it can analyze and extract insights from various types of data to gain a more comprehensive understanding of a situation or problem.
Some notable developments in multimodal AI include OpenAI’s GPT-3.5 and GPT-4 models, which can analyze images and engage in spoken conversations and Google’s multimodal large language model called Gemini which leverages its vast image and video database for understanding multiple modalities.
Why is Multimodal artificial intelligence important?
Enhanced Understanding: Multimodal AI can provide a richer and more nuanced understanding of data by combining information from different sources. For example, it can analyze both the text and images in a news article to gain a deeper understanding of the content.
Improved Accuracy: Combining data from multiple modalities can lead to improved accuracy in tasks like natural language processing (NLP), computer vision, and speech recognition. It helps AI systems make more informed decisions.
Real-World Applications: Multimodal AI has a wide range of practical applications, such as in healthcare (integrating medical images with patient records), autonomous vehicles (processing both visual and sensor data), and content recommendation systems (analyzing text and user behavior).
Better User Experience: In applications like virtual assistants or chatbots, multimodal AI can better understand and respond to users by considering both their spoken words and visual cues.
Problem Solving: Multimodal AI can help address complex problems that require insights from different data sources.For instance, in disaster response, it can analyze text reports, satellite images, and sensor data to assess the situation and plan a response.
