Multimodal AI: What It Is and How It Works

Generative AI has advanced significantly in recent years, from generating simple text answers to producing complex literature. The development of multimodal AI, an advanced kind of artificial intelligence that processes and generates various sorts of data at the same time, such as text, images, video, and audio, is pushing the boundaries of this technology even farther. This cutting-edge technology is creating waves in a variety of fields, including healthcare and robotics, with digital heavyweights such as Google, OpenAI, Anthropic, and Meta building their own multimodal models.

Understanding Multimodal AI

Multimodal AI refers to systems that use a variety of data kinds to gain insights, make predictions, and create content. Unlike traditional AI models, which typically deal with a single form of data (e.g., text for big language models or images for convolutional neural networks), multimodal AI incorporates many data types to gain a more complete awareness of its surroundings. This method simulates human experience by blending sensory inputs such as sight, sound, and touch to generate a complex knowledge of the world.

Aaron Myers, CTO of AI-powered recruiting platform Suited, explains, “We have five different senses, each of which provides us with different data that we can utilize to make judgments or take actions. Multimodal models are striving to accomplish the same objective.

Applications of Multimodal AI

Chatbots:

Multimodal AI chatbots outperform text-only equivalents in terms of responsiveness. Users can, for example, upload a photo of a dying houseplant and receive care recommendations, or they can connect to a video for a more extensive explanation.

AI Assistants:

Devices such as Amazon’s Alexa and Google Assistant use multimodal AI to execute voice requests, retrieve images and videos, and manage smart home devices, delivering information in both audio and text formats.

Healthcare:

Multimodal AI is used in the medical profession to analyze a variety of data sources, including medical imaging, clinical notes, and lab tests, to aid in medical diagnosis and provide holistic care.

Self-driving cars:

Use multimodal AI to evaluate visual input from cameras, detect objects with radar, calculate distances with LiDAR, and navigate with GPS data. This connection enables cars to understand their environment in real time and respond accordingly.

Robotics: Robots equipped with multimodal AI integrate data from cameras, microphones, and depth sensors, enabling them to perceive and respond to their environment accurately. This technology is essential for both humanoid robots and collaborative robots (cobots) on assembly lines.

Benefits of Multimodal AI

Better Context Understanding: Multimodal models integrate various data types to provide a well-rounded contextual understanding, making them more adept at recognizing patterns and connections between different types of data.

More Accurate Results: By analyzing multiple data types, multimodal AI can offer more accurate predictions and interpretations, answering questions more comprehensively than unimodal systems.

Wider Range of Tasks: These systems can handle a broader range of tasks, from generating images based on text prompts to explaining videos in plain language, providing a versatile tool for various applications.

Better Understanding of User Intent: Multimodal AI allows users to interact in multiple ways, capturing their true intent more accurately, whether through speech, text, gestures, or other forms of expression.

More Intuitive User Experience: Users can interact with AI systems in a way that feels natural to them, enhancing the overall user experience.

Challenges of Multimodal AI

Requires More Data: Multimodal models need vast amounts of data from various sources, increasing the complexity and scale of the data required.

Limited Data Availability: Certain data types, like temperature or hand movements, are less readily available and often need to be sourced from private repositories or generated independently.

Data Alignment: Aligning different data types can be challenging, requiring careful processing to ensure seamless integration.

Computationally Intensive: Multimodal AI demands substantial computational power, leading to significant costs and environmental impact.

Potential to Exacerbate AI Issues: Multimodal models can amplify existing AI issues such as bias, privacy concerns, and the generation of misleading information.

How Does Multimodal AI Work?

Multimodal models are based on transformer topologies that process numerous input kinds using embedding, a method for encoding raw data into numerical forms. These models frequently employ strategies such as early fusion, which combines raw data from each modality at the same time, and late fusion, which merges information obtained through independent analysis. Reinforcement learning and human feedback are used to fine-tune the models, improving accuracy and reducing detrimental responses.

The Future Of Multimodal AI

Many experts believe that multimodal AI could be critical to obtaining artificial general intelligence (AGI), a theoretical version of AI capable of comprehending and completing any intellectual job as well as a human. Multimodal AI could gain a comprehensive picture of the environment by combining various data kinds and applying knowledge to a wide range of tasks. Brendan Englot, an associate professor at Stevens Institute of Technology, says, “In the quest for an artificial intelligence that looks a little bit more like human intelligence, it has to be multimodal.”

AI’s future depends on its ability to process a variety of data sources, generating richer, more accurate insights and transforming how humans engage with technology across domains.

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