According to Dennis Hassabis, CEO and co-founder of Google DeepMind, Gemini is the result of a large-scale collaborative effort by teams across Google, including our colleagues at Google Research. This AI was built from the ground up with the goal of being multimodal, which means it can generalize and seamlessly understand, work with, and interact with different types of information, including text, code, audio, images, and video.

According to a recently published study, Gemini will have a surprising impact on the information industry soon “Welcome to the Gemini era: Google DeepMind and the information industry“

Multimodal refers to the integration or combination of different modes of communication or expression. In the context of technology and communication, it often refers to the use of multiple forms of media, such as text, images, audio, and video, to convey information or interact with users. Multimodal interfaces and applications are designed to provide a more engaging and inclusive user experience by leveraging various modes of communication simultaneously or interchangeably.

When we say that Gemini is built to be multimodal, it means that it is designed to process and understand information from multiple modalities simultaneously. Modalities refer to different forms of input, such as text, speech, images, videos, and more. Gemini’s multimodal capabilities enable it to integrate and analyze data from different sources, allowing for a more comprehensive and holistic understanding of the information it encounters. This multimodal approach enhances Gemini’s ability to perform tasks and interact with users across various modalities, creating a more versatile and effective AI system.

Multimodal technology has various applications across different fields.

1. Human-Computer Interaction: Multimodal interfaces enable more natural and intuitive interactions between humans and computers. By combining modalities such as speech recognition, gesture recognition, and touch interfaces, users can interact with computers in more versatile and personalized ways.
2. Assistive Technology: Multimodal systems can greatly benefit individuals with disabilities. For instance, a multimodal interface can enable someone with limited mobility to control devices using voice commands or eye-tracking, providing them with greater independence and accessibility.
3. Healthcare: Multimodal systems can aid in medical diagnostics and treatment. For example, integrating data from various modalities like medical images, patient records, and real-time physiological signals can help doctors make more accurate diagnoses and develop personalized treatment plans.
4. Autonomous Vehicles: Multimodal perception is crucial for autonomous vehicles to navigate and interact with their environment. Combining information from sensors like cameras, lidar, and radar allows the vehicle to detect and interpret objects, pedestrians, and road conditions, enhancing safety and decision-making capabilities.
5. Education: Multimodal learning platforms can improve educational experiences. By incorporating text, images, videos, and interactive elements, students can engage with content in different ways, catering to different learning styles and enhancing comprehension and retention.
6. Virtual and Augmented Reality: Multimodal systems are fundamental in creating immersive virtual and augmented reality experiences. By integrating visual, auditory, and haptic feedback, users can have more realistic and engaging interactions with virtual environments.

What does multimodality cause in artificial intelligence?

Multimodality in artificial intelligence refers to the ability of AI systems to process and understand information from multiple modalities, such as text, image, speech, and video. This capability allows AI to have a more comprehensive understanding of data, leading to enhanced performance and improved user experiences. By integrating multiple modalities, AI systems can leverage the strengths of each modality to complement and cross-validate information, leading to more accurate and robust results. For example, a multimodal AI system can combine text and image inputs to provide more accurate object recognition or sentiment analysis. Overall, multimodality in AI enables more sophisticated and human-like interactions, as well as more effective data analysis and decision-making.