What is Multi Modal AI?

We all remember when ChatGPT by OpenAI was launched in November 2022. It took days before it shook the world with its unprecedented capabilities. This marked the beginning of the generative AI revolution and everyone had the same question to ask – what happens next?

During that time, ChatGPT and many other generative AI tools powered by Large Language Models (LLMs) were created for accepting text inputs from users and producing text outputs respectively. They could simply be referred to as unimodal AI tools.

Multimodal Models can Process a Vast Variety of Inputs (Source)

However, this was just the teaser; we barely knew what LLMs were capable of. The industry’s advancements have been so remarkable ever since that one can hardly find the limits of possibilities when it comes to the long-term implications of ChatGPT and Generative AI.

Today, if someone asked us what is next in line? Perhaps multimodal learning would be the best answer. It is one of the most encouraging trends amid the ongoing artificial intelligence revolution. Some multimodal generative AI models are developed to work on different types of inputs which may also lead to various kinds of outputs. The benefits of multimodal AI include enhanced contextual understanding and the ability to address a broader range of problems compared to unimodal systems.

In this article, we shall go through multi modal AI. We shall see the meaning of multi modal AI, multi-modal LLMS (large language models), underlying technologies and examples and how they can be implemented in real-world scenarios. Are you ready for multimodality? Then we are good to go!

What is Multi Modal AI and Different Modalities?

The first artificial intelligence (AI) algorithms that could generate new text, such as ChatGPT, were unimodal. In other words, they were limited to only one type of input and their output was always in the same format. In particular, most of these models were designed to process text prompts and generate a text response.

This makes sense because these AIs need lots of data to learn from and while there are many kinds of data available nowadays, the text is the easiest to handle and use. It should be noted that various sources on the internet provide a lot of training data for tools like ChaptGPT.

There Are Several Advantages of Multi Modal AI (Source) 

The subfield of artificial intelligence known as multimodal learning endeavors to expand the learning potential of machines by teaching them huge amounts of text, plus other types of data also called sensory information, including images, videos or audio files. This enables models to detect new patterns and associations between textual explanations and corresponding images, videos or audio.

Multi modal generative AI is unlocking fresh possibilities for intelligent systems. For example, during training, multi modal AI models combine different data types, thereby making them suitable for receiving multiple modalities of input type and generating multiple types of outputs. For instance, the foundational model of ChatGPT – GPT-4 – can take both text and image inputs and produce text outputs. Multimodal AI systems enhance capabilities by processing various types of data, including images and text, for more complex and accurate outputs. The latest offering from OpenAI – Sora – is equipped to produce text-to-video outputs.

How is MultiModal AI Different from Multimodal LLMs?

Multi-modal LLMs or large language models have lately become extremely important in transforming the way we work with computers and also how we interact with them on the internet. With billions of parameters, these models can understand and generate human-like texts, hence making them useful for various applications.

At the same time, Multi modal AI has been developing rapidly. This is the cross-section of different modalities like text, image, audio and video to create more holistic and human-like AI systems. These systems can process and generate content across multiple modalities enabling applications like speech recognition, automated image captioning, etc.

Multi-modal LLMs can Process Multiple Data Inputs to Generate More Precise Outputs

Large Language Models have revolutionized artificial intelligence by allowing for human-like generation and understanding of textual contents. They are equally transformative in multi-modal AI as they bridge the gap between text on one hand and other modes such as images or videos. This creates more flexible and comprehensive AI systems.

The field of artificial intelligence (AI) has experienced significant strides lately due to Large Language Models (LLMs). Such LLMs as GPT-3 and BERT have received massive attention and played a key part in diverse natural language processing (NLP) tasks. Nevertheless, the integration of these LLMs with other modalities like pictures or videos has exposed new avenues.

What are the Applications of Multimodal AI?

The accuracy and interpretation capacity of machines has been greatly enhanced through multimodal learning, thereby enabling them to have new ‘senses’. As a result, there are numerous possibilities for applications of multi modal AI in a wide range of industries:

• Autonomous cars – For self-driving cars to operate effectively, they depend on multi modal AI. In different formats, these vehicles use many sensors around them to process information. Therefore, these automobiles must utilize multiple sources during other periods when experiencing an intelligent decision-making phase.

This can be done by combining their effectiveness and efficiency while engaging in real-time mode with any evidence-based display technologies using multidimensional feedback loops based solely upon autonomic inputs at all times as necessary. One example is augmented reality (AR). You can expand your knowledge of how AI reshapes the automotive industry by reading ‘Top Generative AI Use Cases in Automative Industry.’

• Augmented generative AI – Most early versions of generative models were text-to-text, capable of reading user text queries and answering in text. Multi modal AI models like GPT-4 Turbo, Google Gemini or DALL-E create opportunities to improve user experience, starting from the input up to the output. Hinged on either accepting prompts that come in several modalities or developing content in multiple forms, the potentiality of multi modal generative AI agents is infinite. Students learn best when multiple modes of communication and learning styles are integrated, leading to more effective knowledge acquisition and enhanced engagement.
• Earth science and climate change – In the last decade or so, there has been an increased production of ground sensors, drones, satellite data etc. These have helped us augment our understanding of the planet. This information must be combined accurately using multi modal AI, thereby creating new tools and applications. They can assist us in multiple aspects such as monitoring greenhouse gas emissions, predicting severe weather events and enabling precision agriculture.
• Biomedicine – The rise of biobanks, data from electronic health records, clinical imaging, and medical sensors, as well as genomic data, has fueled the development of multi modal AI in medicine. These models are able to handle different kinds of data coming from various sources in different forms to help us solve questions related to human health problems and diseases as well as take logical steps in healthcare.

To get a better understanding of how generative AI is changing the face of the healthcare industry, read our blog titled ‘Chatbots to Drug Discovery: How Generative AI in Healthcare is Changing Life Sciences.’

What Technologies Does Multi Modal AI Power?

Accumulative knowledge in many subfields has given birth to multi-modal AI, an advanced category of machine learning that combines different data modalities like images, videos, and text to enhance processing and predictive capabilities. Over the recent years, researchers and practitioners in AI have demonstrated remarkable progress in the storage and manipulation of information in different formats and forms.

The domains listed below are among those that are driving the multimodal AI boom:

• Deep Learning – A subfield of AI, deep learning encompasses the use of a particular kind of algorithm known as an artificial neural network to solve complex problems. Presently, this multi modal generative AI revolution is powered by deep learning models, and especially transformers, which are a type of neural architecture.

The future multi modal AI depends on breakthroughs in this area, too. There is a need for more research to discover how to enhance the performance of transformers or other approaches to data fusion.

• – NLP forms a critical part of artificial intelligence that bridges the gap between human communication and computer understanding. As an interdisciplinary field, it enables machines to interpret, understand and generate human language, enabling immaculate interactions between humans and computers.

Given that most interactions with computers are through text, it is no wonder that NLP plays a crucial role in driving high-performance generative models including multi-modal ones.

• Computer Vision – Computer vision, which is also termed as ‘image analysis’, is a collection of methods that enable computers to visually perceive and interpret images. The advancement in this area has seen the development of multi modal AI models that can take in or give out pictures and films.

Audio Processing – These generative AI models are the most advanced ones in processing audio files for both input and output. From decoding voice messages to simultaneous translation and music creation, there are several options available in audio processing.

                                      Multi Modal Generative AI is a Revolutionary New AI Paradigm 

Some Examples of the Output of Multi Modal AI

Multimodal AI has a lot more output applications compared to unimodal AI. That makes it more valuable. Common output applications of multimodal AI include the following:

• Computer Vision – The future of computer vision goes far beyond object recognition only. Combining multiple data types enables the AI system to identify image context and make accurate decisions. Advanced AI models can also extract and convert text from images, showcasing their capability to handle image text effectively.

For instance, an image showing a dog combined with sounds produced by a dog is more likely calling for appropriate identification of the object as ‘a dog’. Alternatively, facial recognition together with NLP might be used to improve the identification accuracy of an individual.

• Industry – There are several workplace uses for multimodal AI. In terms of manufacturing processes, an industrial vertical incorporates multimodal AI in monitoring and optimizing them; improving product quality or reducing maintenance costs.

Healthcare vertical utilizes multimodal AI that processes a range of patient’s vital signs, diagnostic data and records to enhance treatment. Automotive vertical applies multimodal AI in monitoring driver fatigue such as eye closure and lane departure towards interacting with the driver advising him/her either to rest or change drivers.

• Language processing – Multimodal AI NLP tasks include sentiment analysis. For instance, a system can recognize when someone is worried depending on their voice and then combine this with the angry face the same person makes to give replies that are fitting or could act as pacifiers of some sort. Likewise, if text is combined with the sound of speech, AI can enhance articulation.
• Robotics – In developing robots that interface with the real world, people and myriad objects like pets, cars and buildings among others, Multimodal AI remains at the center of it all. With information from cameras, microphones, GPS and other sensors, multimodal AI builds a more detailed understanding of an environment to engage with it better.

What Lies Ahead for Multi Modal AI?

The revolution of generative AI is certainly going to the next phase and has been adequately dubbed as ‘multimodal generative AI’. The rapid rise of the domain of multimodal learning has resulted in many novel models and applications for various uses. We are just at the starting point of this revolution. As new techniques and technologies combine newer modalities, the range of multi modal AI will certainly become broader in the coming years. Translating content across different modalities, such as converting information from one language to another, presents significant challenges in ensuring models understand the semantic relationships between various forms of data.

However, with this newfound power comes much responsibility. Multi modal AI comes with some serious risks and challenges that must be addressed for an equitable and sustainable future. Addressing ethical concerns and user privacy in AI technology is crucial, as these systems rely on sensitive personal data, raising significant issues regarding security and potential biases.

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