Shaping the Future, One Algorithm at a Time
Imagine perceiving the world through only one of your senses, or worse, a fraction of one sense — static images without action or spoken words without music. While Artificial Intelligence (AI) excels at recognizing language or imagery in isolation, there's still a significant gap when it comes to combining these interpretations or integrating action sequences.
The key to true intelligence lies in reasoning across different sensory inputs. To improve AI's capabilities and usher in new applications like visual question answering, image captioning, visual dialogue, and virtual assistants, we need to bridge the gap between different modes of data interpretation.
… Enter Multimodal AI … The Next Generative AI Frontier.
Unimodal AI vs. Multimodal AI — The Future of Generative AI
Multimodal AI, also known as Mixed Modality AI, represents a leap forward in artificial intelligence. Unlike traditional AI models that focus on a single data type, Multimodal AI thrives on the synergy of multiple data sources, including text, images, audio, and video. This holistic approach enables a deeper and more comprehensive understanding of the input data, transcending the limitations of unimodal AI.
Unimodal vs. Multimodal AI
From Solo Senses to Super Senses
Evolving Beyond the Basics
Multimodal AI revolutionizes how we interact with AI systems. Multimodal AI transcends simple demonstrations to facilitate interpersonal collaboration, advanced robotics, and even the elusive goal of continuous learning. It can process multiple inputs simultaneously, decipher complex scenes incorporating imagery, actions, and sound, and mimic human-like perception and cognition. This opens up exciting possibilities across industries, making processes more efficient and expanding horizons.
Building a Multimodal AI LLM
Imagine building a model for each input type and then fusing them together: an image model, a text model, and a model to learn the relationships between them. This is analogous to constructing a Multimodal AI Large Language Model (LLM).
Multimodal LLM = A model for image embeddings + A text model for text embeddings + A model to establish relationships between them
Components of Multimodal AI Systems
Multimodal AI systems typically consist of three core components:
Input Module: This module comprises neural networks designed to process different data types, such as speech and vision, with each data type handled by its dedicated neural network.
Fusion Module: Responsible for aligning, combining, and processing data from each modality (speech, text, vision) to create a cohesive dataset using techniques like transformer models and graph convolutional networks.
Output Module: Generates output from the Multimodal AI, including predictions, decisions, or actionable recommendations.
The illustration below depicts a Multimodal model that fuses different data entities:
Technologies Empowering Multimodal AI
Multimodal AI systems integrate various technologies across their stack. These systems draw upon technologies such as Natural Language Processing (NLP), Computer Vision, Text Analysis, Integration Systems, and robust storage and compute resources to facilitate real-time interactions and results.
Natural Language Processing (NLP): Enables speech recognition, speech-to-text, and text-to-speech capabilities, adding context through vocal inflections.
Computer Vision: Enhances image and video processing by enabling object detection, recognition, and contextual understanding.
Text Analysis: Allows the system to understand written language and intent.
Integration Systems: Crucial for aligning, combining, prioritizing, and filtering data inputs to develop context and context-based decision-making.
Storage and Compute Power: Ensuring quality real-time interactions and results through data processing.
Multimodal AI Use Cases — Unlocking possibilities with the power of NexGen AI
Multimodal AI offers a plethora of applications:
Search (Visual Language Model - VLM): Combining computer vision and natural language processing for comprehensive information retrieval, surpassing traditional keyword searches. This technology powers the next-gen user experience.
Computer Vision: Beyond object identification, it considers context and sound, improving object recognition.
Industry: Optimizing manufacturing processes, healthcare diagnostics, automotive safety (monitor driver fatigue) and improving product quality, by processing and understanding data from multiple modalities.
Language Processing: Enhancing user interactions by analyzing vocal and facial cues to tailor responses and improve pronunciation in various languages.
Robotics: Enabling robots to interact successfully with real-world environments through data from various sensors (cameras, microphones, GPS, and other sensors) to navigate and perform tasks autonomously.
The Future of Multimodal AI
Recent advancements in foundation models like large pre-trained language models (LLMs) have empowered researchers to tackle complex problems by combining modalities. The coming years will witness Multimodal AI revolutionizing tasks from visual question-answering to robotics navigation.
While large models like Google's PaLM LLM and OpenAI's GPT-4 play a role, many mixed modality solutions will orchestrate different components to achieve desired outcomes, augmenting AI capabilities significantly. The potential of these programs far surpasses today's Generative AI.
In the illustration below, the Robot leverages PaLM-E (Google's Multimodal Language Model) to follow a series of instructions such as "bring me the rice chips from the drawer," which the language model breaks down into atomic instructions, such as "go to the drawer," "open the drawer," "pick the green rice chip bag," etc.
Robot leverages PaLM-E to follow a series of instructions to complete task —> Bring me the rice chips from the drawer.
Why Multimodal AI is a Game-Changer for Businesses
Multimodal AI models bring several advantages:
Enhanced Decision-Making: Multimodal AI facilitates better-informed decisions by analyzing data from various sources, leading to more accurate predictions and insights.
Streamlined Workflows: Simultaneously processing multiple data types simplifies and automates complex workflows, saving time and resources.
Improved Customer Experience: Offers personalized experiences by analyzing customer behavior through various channels, elevating customer engagement & enhancing customer satisfaction.
New Business Opportunities: Enables innovative applications and services previously unimaginable with traditional AI models..
Multimodal AI is not just the future; it's the present, transforming the way we interact with technology and empowering individuals and businesses alike to reach new heights of innovation and understanding.
Be My AI — Empowering the Visually Impaired
On the front lines of AI innovation, Be My Eyes has introduced Be My AI, a digital AI-powered visual assistant powered by OpenAI's GPT-4. This virtual volunteer provides real-time, instantaneous visual assistance to individuals who are blind or have low vision, revolutionizing accessibility and support and is available 24/7 through the Be My Eyes app, providing real-time visual assistance and answering questions about images.
Multimodal AI in Action — Be My Eyes: Bridging the Visual Gap.