In this age defined by technological innovations and dominated by technological advancements, the field of Artificial Intelligence (AI) has successfully emerged as the driving force behind transforming the way we live and reshaping industries. AI enables computers to think and learn in a manner comparable to that of humans by imitating human brainpower. Recent advances in Artificial intelligence, Machine Learning, and Deep Learning have helped improve multiple fields, including company operations, improving medical diagnosis accuracy, and even paving the way for the development of self-driving cars and virtual assistants.
What is Multimodal AI?
Multi-modal AI incorporates data from multiple sources, including text, images, audio, and video, in contrast to standard AI models that mostly rely on textual input to produce a more thorough and detailed knowledge of the world. Multi-modal AI’s primary goal is to imitate human comprehension and interpretation of information using several senses at once. It has enabled AI systems to analyze and comprehend data in a more comprehensive way. The convergence of modalities empowers them to make more accurate predictions and judgments.
The Release of GPT-4
Large Language Models (LLMs) have recently gained a loy of attention and popularity. With the development of the latest version of LLM by OpenAI, i.e., GPT 4, this advancement has opened the way for the progress of the multi-modal nature of models. Unlike the previous version, i.e., GPT 3.5, GPT 4 can take textual inputs as well as inputs in the form of images. GPT-4, due to its multi-modal nature, can understand and process various types of data in a manner akin to that of people. With GPT-4, OpenAI has hailed this model as an important milestone in its efforts to scale up deep learning, stating that it achieves human-level performance on a variety of professional and academic standards.
What Is Multimodal AI Capable Of?
- Image recognition – Multi-modal AI can precisely identify objects, persons, and activities through the analysis and interpretation of visual data, including photos and videos. Technologies that rely on image and video analysis have developed largely thanks to the ability to analyze visual information. Improved security systems with person identification capabilities and the ability for self-driving cars to perceive and react to their environment are some of its examples.
- Text analysis – Through Natural Language Processing, Natural Language Understanding, and Natural Language Generation, multi-modal AI can comprehend printed text beyond simple recognition. This includes things like sentiment analysis, translating between languages, and drawing conclusions from textual data that are useful. Language hurdles can be overcome in a variety of applications where the ability to read and understand written language is crucial, including customer feedback analysis.
- Speech recognition – Multi-modal AI has a significant use case in the field of speech recognition. Due to its high proficiency in understanding and recording spoken words, multi-modal AI can comprehend the subtleties of human speech, such as context and intent, in addition to word recognition. Voice instructions can be used to communicate with machines seamlessly.
- Ability to integrate – Multi-modal AI combines inputs from various modalities, including text, visuals, and audio, to produce a more comprehensive understanding of a particular scenario. It can use both visual and audible signals to recognize an individual’s emotions, giving a more accurate and nuanced result. By combining data from many sources, the AI’s contextual awareness is improved, which helps it manage challenging real-world situations.
Practical Applications of Multimodal AI
- Customer service: Using a multi-modal chatbot in an online store can improve the level of support offered to customers in the field of customer service. With the addition of image comprehension and voice response capabilities, this chatbot goes above and beyond standard text-based conversations. Multi-modal AI can help provide a more dynamic and user-friendly support experience in addition to improving the effectiveness of handling customer complaints.
- Social Media Analysis: Multi-modal AI is essential for analyzing information on social media, where text, photos, and videos are frequently combined. Companies can use multi-modal AI to learn more about what consumers are saying about their goods and services on a variety of social media channels. Businesses can swiftly react to client input, see patterns, and modify their strategy to suit their needs by having a thorough understanding of both written sentiment and visual content. This proactive approach to social media research improves consumer happiness and brand perception, which makes the business model more adaptable and flexible.
- Training and development – By accommodating various learning styles and guaranteeing a more thorough comprehension of the subject matter, LLMs using multimodality can improve the efficacy of training programs. A more knowledgeable and skilled workforce is the end consequence, which can boost innovation and performance in organizations.
In conclusion, multimodal AI is a paradigm change surpassing the constraints of unimodal techniques. It expands the potential of AI applications by combining the strength of several data sources. The incorporation of multi-modal AI can definitely transform how people engage with and profit from artificial intelligence in numerous facets of everyday lives as technology advances.
Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.