What Is Multimodal AI? A Complete Introduction

How do you get more context for decision making? By looking at more, and varied, types of information and data.

Lately, we have seen artificial intelligence (AI) evolve so, so quickly. Multimodal AI is among the latest developments. Unlike traditional AI, multimodal AI can handle multiple data inputs (modalities), resulting in a more accurate output.

In this article, we'll discuss what multimodal AI is and how it works. We will also discuss the benefits and challenges that come from multimodal AI, along with potential use cases across different areas and industries. And of course, as with any meaning conversation about emerging AIs, we will discuss the privacy concerns and ethics that we need to follow while working with multimodal AI.

What is multimodal AI?

Before getting to know about multimodal AI, let's take its first word: multimodal. When it comes to artificial intelligence, modality refers to data types, or modalities. Data modalities include — but are not limited to — text, images, audio, and video.

So, multimodal AI is an AI system that can integrate and process multiple different types of data inputs. The data inputs can be text, audio, video, images, and other modalities, as we'll see below.

Combining various data modalities, the AI system interprets a more diverse and richer set of information. It soon becomes able to make accurate human-like predictions. By processing these data inputs, multimodal artificial intelligence produces a complex output that is contextually aware.

The output is different from the outputs generated by unimodal systems (as they depend on a single data type).

Multimodal AI examples

Multimodal AI is advancing across different fields, combining multiple different types of data to create powerful and versatile outputs. A few notable examples include:

• GPT-4V(ision) is upgraded GPT-4 version that can process images as well as text, meaning the AI can generate visual content.
• Inworld AI can create intelligent and interactive virtual characters in games and other digital worlds.
• Runway Gen-2 can use text prompts to generate dynamic video.
• DALL-E 3 is an OpenAI-based model that generates high-quality images based on text prompts.
• ImageBind by Meta AI uses six data modalities — text, image, video, thermal, depth, and audio — to generate outputs.
• Google's Multimodal Transformer (MTN) combines audio, text, and images to generate captions and descriptive video summaries.

Multimodal AI tools

Several advanced tools are already paving the way for enhancing multimodal artificial intelligence.

• Google Gemini can integrate images, texts, and other modalities to create, understand, and enhance content.
• Vertex AI is the machine learning platform of Google Cloud. it can also process different data and perform tasks like image recognition, analyzing video, and more.
• OpenAI's CLIP can process text and images to perform tasks like visual search and image captioning.
• Hugging Face's Transformers can support multimodal learning and build versatile AI systems by processing audio, text, and images.

All these systems prove that multimodal AI is growing in the field of content creation, gaming, and dealing with other real-world scenarios.

(Know other AIs: adaptive AI, generative AI & what generative AI means for cybersecurity.)

How multimodal AI works

Before diving into multimodal AI, let's first understand unimodal AI.

Many generative artificial intelligence systems can only process one type of input — like text — and only provide output in that data modality: text to text. This makes it unimodal, one mode only. For example, GPT-3 is a text based AI that can handle text but canont interpret or generate images. Clearly, unimodal AI has limitations in both adaptability and contextual understanding.

In contrast, multimodal AI gives users the ability to provide multiple data modalities and generate outputs with those modalities. For example, if you give a multimodal system both text and images, it can produce both text and images.

Multimodal artificial intelligence is trained to identify patterns between different types of data inputs. These systems have three primary elements:

• An input module
• A fusion module
• An output module

Bringing back the topic of modality: A multimodal AI system actually consists of many unimodal neural networks. These make up the input module, which receives multiple data types.

Then, the fusion module combines, aligns, and processes the data from each modality. Fusion employs various techniques, such as early fusion (concatenating raw data). Finally, the output module serves up the results. These vary greatly depending on the original input.

Benefits of multimodal AI

There are numerous advantages of multimodal AI since it can perform versatile tasks in comparison to unimodal AI. Some notable benefits include:

• Better context: Multimodal AI analyzes different inputs and recognizes patterns. Thereby, leading to natural and human-like accurate outputs.
• Accuracy: Since multimodal AI combines different data streams, it can result in more reliable and precise outcomes.
• Enhanced problem solving: Since multimodal artificial intelligence can process diverse inputs, it can tackle more complex challenges like analyzing multimedia content or diagnosing a medical condition.
• Cross-domain learning: It can efficiently transfer knowledge between different modalities, thereby, enhancing data adaptability to perform various tasks.
• Creativity: In domains like content creation, art, and video creation, multimodal AI is blending data and opening up new possibilities to create innovative outputs.
• Rich interactions: Augmented reality, chatbots, and virtual assistants can use multimodal AI and provide a more intuitive user experience.

Challenges of multimodal AI

Certainly multimodal AI can solve a wider variety of problems than unimodal systems. However, like any technology in its early and developmental stages, there are certain challenges and downsides, including the following. 

Higher data requirements

Multimodal AIs would require large amounts of diverse data for it to be trained effectively. Collecting and labeling these data is expensive and time-consuming.

Data fusion

Multiple modalities display various kinds and intensities of noise at various times, and they aren't necessarily temporally (time) aligned. The diverse nature of multimodal data makes the effective fusion of many modalities difficult, too.

Alignment

Related to data fusion, it's also challenging to align relevant data representing the same time and space when diverse data types (modalities) are involved.

Translation

Translation of content across many modalities, either between distinct modalities or from one language to another, is a complex undertaking known as multimodal translation. Asking an AI system to create an image based on a text description is an example of this translation.

One of the biggest challenges of multimodal translation is making sure the model can comprehend the semantic information and connections between text, audio, and images. It's also difficult to create representations that effectively capture such multimodal data.

Representation

Managing various noise levels, missing data, and merging data from many modalities are some of the difficulties that come with multimodal representation.

Ethical and privacy concerns

As with all artificial intelligence technology, there are several legitimate concerns surrounding ethics and user privacy.

Because AI is created by people — people with biases — AI bias is a given. This may lead to discriminatory outputs related to gender, sexuality, religion, race, and more.

What’s more, AI relies on data to train its algorithms. This data can include sensitive, personal information. This raises legitimate concerns about the security of social security numbers, names, addresses, financial information, and more.

(Related reading: AI ethics, data privacy & AI governance.)

Multimodal AI use cases

Multimodal AI is an exciting development, but it has a long way to go. Even still, the possibilities are nearly endless. A few ways we can use multimodal artificial intelligence include:

• Improving the performance of self-driving cars by combining data from multiple sensors (e.g. cameras, radar, and lidar).
• Developing new medical diagnostic tools that use data such as images from scans, health records, and genetic testing results.
• Improving chatbot and virtual assistant experiences by processing a variety of inputs and creating more sophisticated outputs. (Meta has a fun prompt, if you’d like to try it out.)
• Employing improved fraud detection and risk assessment in banking, finance, and other industries.   
• Analyzing social media data — including text, images, and videos — for improved content moderation and trend detection.
• Allowing robots to better understand and interact with their environment, leading to more human-like behavior and abilities.

Between the challenges of executing these complex tasks and the legitimate privacy and ethical concerns raised by experts, it may be quite some time before multimodal AI systems are incorporated into our daily lives.

The many paths for multimodal AI

Throughout this post, we have understood how multimodal AI has proven to be a significant development in AI systems. With more research, this innovative technology can enhance AI's capability and revolutionize domains like self-driving technology, healthcare, and more.

Despite the promising future, multimodal AI still comes with certain challenges like biases, ethical concerns in terms of privacy, and a high volume of data requirements.

As technology is evolving, we need to deal with these challenges appropriately in order to unlock the full potential of multimodal artificial intelligence. Although it may take time to become widespread, with continued development, multimodal AI is expected to become more advanced in solving complex problems in a human-like manner in different sectors.