What is "Multimodality", Anyway?

What is Multimodal Data?

“Multimodal” refers to data that comes from various sources, forms, or “modalities,” providing a more complete and nuanced understanding of a situation or query. Just as humans rely on multiple senses to process information, multimodal AI uses different types of data to achieve a deeper and more context-aware understanding of its environment.

For example, when you’re engaged in a conversation at a cocktail party, you interpret your conversation partner’s words (hearing) and facial expressions (vision). In the meantime, a host of other senses such as smell, taste, and touch add context to your understanding of what’s going on in the background. In a similar way, AI uses multiple modalities to gain greater contextual awareness of the problem or situation facing it.

These modalities can take the form of a wide variety of data types. Here are a few examples:

• Text: Written or spoken language in textual form. Examples include documents, webpages, transcripts, or chat logs.

• Visual: Static images and video streams. Examples include photographs and videos captured on phone cameras or film, drones, or security cameras.

• Audio: Sound data such as human speech, nonverbal vocalizations like laughter, or environmental noise like traffic or birdsong. 

• Sensor data: Readings from sensors detecting distance, depth, or motion in the physical world. Examples include LiDAR, radar, or depth cameras.

• Geolocation data: Position or movement data, often also captured from sensors  to allow a system to locate an object or person in the physical world.

• IoT & wearable data: Measurements from wearable devices or smart gadgets. Examples include heart rate data from a smart watch or temperature or humidity data from a smart refrigerator.

• Behavioral: Data capturing user interactions with digital environments, like websites or mobile apps. Examples include clickstreams, keystrokes, or interaction patterns.

This data is used to train multimodal AI: models that can process and integrate multiple data types. Unlike traditional models, which can only process one form of data, multimodal models can combine disparate streams of data to create a complete and contextually rich understanding of a real-world event, situation, or scene. 

What are some real-world applications for multimodal models? 

Right now, most people interact with multimodal large language models (LLMs) that read, write, listen, speak, and interpret and generate visual content (images and videos). As these models train on increasingly diverse datasets, they grow more capable of handling a broader range of tasks and interacting in more natural, human-like ways. The greater the range of modalities models can interpret, the more sophisticated and able to engage in humanlike reasoning and interaction they will become. 

Multimodal AI is already transforming cutting-edge industries by allowing AI to operate in fast-changing environments. Here are a few groundbreaking use cases:

• Vision-language models: Models like GPT-4 and Google’s Gemini can analyze, “understand”, and respond to visual inputs. One of these models could generate an image from text, explain a chart, or analyze the content of a video. 

• Autonomous systems: Multimodal data is essential in the training and deployment of autonomous systems like self-driving cars and buses. Extensive visual data is required to recognize traffic signs, pedestrians, or other obstacles while driving, while sensor data enables the vehicles to “see” while driving around corners or in adverse weather conditions. 

• Voice assistants and chatbots: Audio data is the lifeblood of virtual assistants like Siri, Alexa, or Google Home; speech data from diverse datasets allows them to recognize and respond to voices in a variety of languages and accents, while environmental data allows them to distinguish speech from background noise. Sentiment data allows chatbots to better mimic human conversation and show empathy in customer service interactions.

• Robotics: Robots rely on vision and sensor data to navigate the world around them; in home robotics, an autonomous vacuum uses vision to map a room and sensors to detect collisions. In a factory, robots on an assembly line combine camera vision with sensors to place parts accurately without damaging them. In human-robot interaction, robotic secretaries will be trained to interpret body language, voice commands, and gestures to enable intuitive and natural interactions.

Where does Aris come in?

Aris provides the multimodal, human-generated data needed to train next-generation AI models by addressing a fundamental challenge: scaling the collection of high-quality, human-sourced data at speed. 

We leverage a global network of real users to capture multimodal data in diverse environments. Our contributors provide visual, audio, behavioral, text, sentiment data, and more: anything real people can collect anywhere they work, live, or spend time. They can capture rare, edge-case scenarios to improve a model’s robustness, provide geographically or demographically representative data collected from across different regions, or perform tasks to fill key gaps in AI companies’ datasets. These contextually rich inputs train more contextually aware, higher-performing models and autonomous systems.

In a world where AI systems must interact with and adapt to real-world environments, Aris delivers the on-demand fuel source needed to power truly intelligent next-generation multimodal models. As AI continues to evolve, the quality of the multimodal data it is trained on will determine which models succeed in navigating a myriad of real-world applications.