Multimodal AI Explained

Here we go again! Multimodal AI is the “next big thing” in artificial intelligence technology. But what exactly does multimodal mean, and how does it differ from the AI models we’ve all grown familiar with?

Let’s take a look.

What is multimodal AI?

Multimodal AI refers to artificial intelligence systems that can process multiple data inputs to produce more accurate, sophisticated outputs than unimodal systems.  

An example of a multimodal AI system is OpenAi’s GPT-4V(ision). The major difference in this V version over GPT-4? Vision can process image inputs in addition to text.  Other examples include Runway Gen-2 for video generation and Inworld AI for character creation in games and digital worlds.

Today, multimodal AI is mostly wrapped up in its potential to do a lot of things, as we’ll see below. Beware, though, that multimodal AI is far from being fully figured out.

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

What is modality?

With regard to artificial intelligence, modality refers to data types. Data modalities include — but are not limited to — text, images, audio, and video. 

Multimodal vs. unimodal AI

Many generative artificial intelligence systems can only process one type of input — like text — and only provide output in that data modality. This makes it unimodal. 

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. 

How multimodal AI works

Multimodal AI systems are 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

Remember how we discussed modality? A multimodal AI system actually consists of many unimodal neural networks. These make up the input module, which receives multiple data types. 

Then there’s the fusion module, which 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 results. These vary greatly depending on the original input. 

Benefits of multimodal AI

One of the major upsides of multimodal AI models is context. Because these systems can recognize patterns and connections between different types of data inputs, the output is more accurate, natural, intuitive, and informative. And, of course, it’s more human. 

Multimodal AI can also solve a wider variety of problems than unimodal systems — more on the possibilities below. 

Challenges & drawbacks of multimodal AI

As with any new technology, multimodal AI comes with several downsides, including…

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 aligned. The diverse nature of multimodal data makes the effective fusion of many modalities difficult, too.

Alignment

It’s challenging to properly 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. An example of this translation is asking an AI system to create an image based on a text description. 

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.)

What’s next for multimodal AI?

Multimodal AI is an exciting development, but it has a long way to go. Even still, the possibilities are nearly endless. A few ways multimodal AI may be used 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 — be on the lookout for further developments.