Introduction
Multimodal artificial intelligence (AI) is a cutting-edge technology that focuses on revolutionising the working of AI systems. It is a type of AI that makes use of several modes of data so that determinations can be made more accurately, insightful conclusions can be drawn, and more exact predictions can be made regarding real-world problems. In other words, multimodal AI involves the use of different types of data so that information can be extracted from them and analysed to understand the situation or a problem more comprehensively. With multimodal AI, the processing and interpretation of information are carried out by incorporating several sensory modalities in a seamless manner. In other words, multimodal AI systems can interpret and use data simultaneously from audio, video, images, text, and other such distinct sources or modalities. It has a distinct characteristic of utilising the information received from varied sensory inputs, thereby replicating the manner in which humans decipher and interact with their surroundings. OpenAI’s GPT-3.5 and GPT-4 models are prominent developments in the field of multimodal AI. They are capable of processing images and narrating them in words.
Single-Modal AI vs Multimodal AI
The single-modal AI uses a single source or type of data, while the multimodal AI uses multiple sources or several types of data. That is, the single-modal AI processes the data from a single source; whereas, the multimodal AI processes the data from multiple sources, such as text, videos, images, speech, and sound.
With single-modal AI, only a limited perception of a particular situation is gained, which does not align much with human perception. On the contrary, with multimodal AI, a more comprehensive perception of the particular situation is gained, which pretty much aligns with human perception.
How Multimodal AI Systems Work
Multimodal AI systems work with the help of the following three basic components:
- Input module It refers to a sequence of neural networks, which accept and process (or encode) data. This data could be vision, speech, etc. Each neural network manages a particular type of data. Thus, in an input module, there are a number of unimodal neural networks.
- Fusion module The purpose of a fusion module is to integrate, arrange, and process the suitable data from all modalities, such as text, vision, and speech. The module transforms the entire data into a unified data set, which optimally uses the power of each data type. Different data processing and mathematical techniques, including graph convolutional networks and transformer models, are used to carry out fusion.
- Output module This module produces the output from the multimodal AI. This output can be used to make predictions, make decisions, or suggest other output to be used by a human operator or the system.
Other Components in a Multimodal AI System
Some other components/technologies embedded in the multimodal AI system are mentioned below:
- Computer vision technology This technology is meant for capturing images and videos. It facilitates the detection and recognition of the object, such as recognising humans and identifying activities like walking and hopping.
- Text analysis technology With this technology, the system can read and interpret the written text along with the intention behind it.
- Natural language processing (NLP) With this technology, speech recognition and speech output can be provided. It also includes speech-to-text translations and vice-versa along with speech-to-speech and text-to-text translations in several languages. Besides, vocal inflections can also be identified by this technology. These include sarcasm, stress, etc., which provide context at the time of processing.
- Storage and compute resources Real-time interactions and quality results are possible only when storage and compute resources are used in data mining, processing, and result generation.
- Integration system With this system, the multimodal AI can prioritise, combine, arrange, and filter data inputs available in different data types. It is through integration only that context can be developed, and in turn, decisions based on context can be made.
Compute resources are infrastructure elements that enable problem solving and solution creation by receiving, analysing and storing data. Compute resources can be physical (servers) and virtual (desktop).
Significance of Multimodal AI
Multimodal AI is significant in a number of ways. Some of them are as follows:
- Industry Multimodal AI can be used for a variety of purposes at workplaces, such as:
- In the healthcare sector, multimodal AI analyses the vital signs of a patient and complex datasets, including their CT scans and their diagnostic data and records so that better treatment can be given.
- In the industrial sector, it supervises and increases the efficiency of manufacturing processes, thereby enhancing product quality and decreasing maintenance costs.
- In the automotive sector, it keeps a watch on the driver to get the signs of fatigue, such as dozing off or leaving their line unexpectedly. It also interacts with the driver to keep him/her alert and suggest them to take a rest or change the driver.
- Robotics Multimodal AI plays a vital role in the development of robotics, as the interaction between robots and real-world environments, between robots and humans/animals/ buildings and their access points, etc., is mandatory. It understands the environment comprehensively by using data from microphones, cameras, global positioning system (GPS) and other such sensors, and then properly interacts with the environment.
- Image caption Informative captions can be created automatically by multimodal AI systems. These captions vividly describe the images. Consequently, the content becomes accessible and expressive.
- Video analysis Multimodal AI systems serve an important purpose in video analysis. They assist in recognising actions and events in videos by integrating auditory and visual data.
- Autonomous driving Autonomous vehicles take the aid of multimodal AI systems to facilitate their navigation and promote safety. For this, the systems analyse the data received from different sensors.
- Speech recognition Multimodal AI systems are also used in speech recognition. For example, OpenAI’s Whisper. These systems do the work of translating audio (or spoken language) into normal text.
- Virtual reality The virtual reality experience can be greatly improved by using multimodal AI systems, which give detailed sensory inputs, such as not only visuals and sounds but also temperature and wind.
- Cross-modal data integration The main objective of multimodal AI is to combine miscellaneous sensory data, including smell, touch, and signals sent by the brain. This leads to immersive experiences and the invention of modern applications.
- Content generation and better understanding Multimodal AI is also used in producing visual or textual content on the basis of visual or textual prompts. Thus, it facilitates the generation of content. Besides, data interpretation is advanced and more nuanced due to integrated information, collected from varied sources. For instance, both text and images can be analysed to get a better understanding of the content.
- Better user experience Multimodal AI systems are also used in virtual assistants/chatbots for interpreting the users’ queries and responding to them in a better way. They do so by taking into account their spoken lines as well as visual cues.
- Enhanced accuracy Tasks such as speech recognition, computer vision, and natural language processing (NLP) can be performed with enhanced accuracy, as multimodal AI integrates data from several modalities. Thus, more informed decisions can be made by AI systems.
- Problem solving A multitude of complex problems can be solved by multimodal AI, as it can employ useful insights from diverse data sources. For example, in case of disaster management, satellite images, text and visual reports, and sensor data are processed by multimodal AI before providing a relevant response and giving appropriate recommendations.
Conclusion
Multimodal AI is an emerging technology in the field of AI. It puts the developers on the path to innovation and enables users to gain from its practical applications in varied domains.
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