Imagine you're trying to understand a joke. You might hear the words (text), see the person's facial expressions (visual), and pick up on their tone of voice (audio). All these inputs together help you get the full meaning. That's the core idea behind multimodal AI.
Multimodal means multiple ways. In AI, it refers to using different types of data, like text, images, speech, and even sensor data to understand the context more accurately or improve a query's relevance.
This ability to leverage multiple data sources is what sets multimodal AI apart from traditional AI approaches. Traditional AI, often referred to as unimodal AI, focuses on just one type of data. For instance, an image recognition system might only look at pictures. While this can be effective for specific tasks, it can miss the bigger picture.
Difference between unimodal and multimodal AI
Regular AI (unimodal) focuses on one type of data. An image recognition system might only look at pictures whereas a multimodal AI combines multiple data types for a richer understanding. It's like having multiple senses to analyze a situation.
Technically Multimodal AI uses algorithms to find patterns across these combined data streams. This is done by fusing the data early on (think merging all the info) or later (focusing on insights from each data type separately).
Example: Imagine a customer service chatbot that can understand your text, analyze the sentiment in your voice, and even gauge your expression from a video chat. This lets it provide a more helpful and nuanced response. This is possible using multimodal.
How does the multimodal AI work? Concept of multimodal AI
Traditional AI models often only do object recognition. An image recognition system might identify a doctor in a picture. Whereas Multimodal AI goes a step further. It strives to understand the meaning behind the object.
How does multimodal AI compile data?
Multimodal AI compiles diverse data types like text, images, audio, or sensor readings, into one format through a process called embedding. These are essentially compressed representations that capture the underlying meaning of the data, not just the raw information itself.
Think of it as summarizing a book into its core themes - that's the essence of embedding.
Each data type goes through its own embedding process. Techniques like Convolutional Neural Networks (CNNs) work well for visual data, while Recurrent Neural Networks (RNNs) might be used for textual data.
Once embedded, these representations are mapped into a shared space. Imagine translating different languages into a universal one. This allows the AI model to compare and understand data across different formats.
Context mapping and information retrieval using multimodal AI
Query preprocessing: When user provides query, which can be text, an image, or even a combination of both.Query is then fed through encoders, which are like special AI translators that convert your input into a uniform data format called embedding.
Embedding similarity: Now that query has been transformed into an embedding, the system uses a technique called cosine similarity to find similar embeddings within its knowledge base. Think of cosine similarity as a way to measure how closely these embedding vectors resemble each other, with higher scores indicating a greater match.
Information retrieval: Based on the closest match identified in the previous step, the AI model retrieves the most relevant information from its knowledge base. It's like using the best match it found to pinpoint the most relevant data for your query.
Response generation: Depending on the specific application of the multimodal AI system, it might use the retrieved information to generate the most appropriate response to the query. In other words, the system takes the most relevant data it found and uses it to craft an answer tailored to the specific needs.
Here's an analogy for multimodal AI:
Imagine a library with books in various languages (text), pictures (images), and even audiobooks (audio). A multimodal AI system acts like a super translator. It converts all these formats into a single code (embeddings) and then searches the library based on your query (also converted to code).
This allows it to find relevant information across different formats in a efficient manner, just like finding books, pictures, and audiobooks related to your search topic.
Use cases of multimodal AI
Use of multimodal AI in retail
Personalized recommendations: Analyze customer data (browsing behavior, past purchases, demographics) to suggest relevant products with audio descriptions or personalized messages on digital displays.
Smart dressing rooms: Use cameras and AI to suggest complementary outfits and offer real-time feedback on fit and style.
Seamless search: Allow customers to search for products using images or voice descriptions, with the AI understanding the underlying features.
Sentiment analysis for reviews: Analyze text reviews alongside product images to identify fake reviews or spot buying trends.
Content recommendations: Analyze a viewer's facial expressions to recommend movies or shows that match their mood.
Real-time summarization: Generate captions or summaries of live streams for accessibility or multitasking viewers.
Content moderation: Analyze audio and video content to identify inappropriate behavior or hate speech for real-time moderation.
Personalized news feeds: Consider a viewer's location and browsing history to curate personalized news feeds with relevant video clips or articles.
Use of multimodal AI in e-commerce
Visual and In video search : Allow customers to search for products using a picture, with the AI understanding the underlying features and suggesting matches.
Virtual try-On: Use augmented reality to virtually try on clothes or makeup based on a customer's image or video.
Multimodal customer support: Allow customers to describe technical issues through text, voice, and even video demonstrations for faster and more accurate solutions.
Use of multimodal AI in customer support
Virtual assistants with emotional intelligence: Train chatbots to understand the sentiment behind customer questions and tailor their responses accordingly.
Automated troubleshooting and assistance: Enable customers to describe issues through text, voice, and video demonstrations for faster troubleshooting.
Use of multimodal AI for media and entertainment
Automated content tagging: Use AI to analyze video and audio data to automatically tag content with relevant keywords, actors, genres, or emotions.
Personalized content discovery: Go beyond text analysis to consider user location and browsing history to curate personalized content recommendations.
Automated accessibility features: Generate audio descriptions of images and videos or create captions in multiple languages for broader accessibility.
Use of multimodal AI in sports
Enhanced player performance analysis: Analyze player movements (video data) and physiological data (sensor data) to identify areas for improvement, predict injury risks, and optimize training programs.
Real-time officiating assistance: Use AI to analyze video feeds and sensor data to support referees in making close calls or identifying potential fouls.d
Use of multimodal AI in edtech:
Personalized learning: Track facial expressions and voice inflections to adjust the pace and difficulty of learning materials in real-time, catering to individual needs.
Automated feedback on student presentations: Analyze body language, vocal delivery, and presentation slides to provide constructive feedback on communication skills.
Post product and video creation: Multimodal ai can capture micro events
Use of multimodal AI for live streaming:
Real-time translation and captioning: Translate live streams into multiple languages or generate captions for viewers who are deaf or hard of hearing.
Content Creation Tools: Generate captions, add background music based on video content, or suggest transitions based on the emotional tone of the video.
Use of multimodal AI for information retrieval:
Multimodal search engines: Allow users to search for information using text, images, or voice descriptions, providing more relevant and comprehensive results.
Enhanced historical research: Analyze historical documents (text and images) alongside audio recordings of speeches or interviews for a deeper understanding of past events.
Use of multimodal AI in video editing and production:
Automated video tagging and organization: Use AI to automatically categorize and tag video clips based on their content for faster editing and organization.
Smart content creation tools: Generate captions, add background music, or suggest transitions based on the video's emotional tone.
Use of multimodal AI in surveillance
Enhanced anomaly detection: Analyze video feeds alongside sensor data to identify unusual activity or potential security threats with greater accuracy.
Person Identification with multi-factor verification: Combine facial recognition with gait analysis or voice recognition for more secure access control systems.
Use of multimodal AI in insurance:
Automated damage assessment: Analyze images and video footage of accident scenes to assess vehicle damage and streamline the claims process.
Fraud detection: Analyze video calls with claimants to detect inconsistencies or signs of potential fraud.
Use of multimodal AI in healthcare:
Multimodal diagnosis: Combine medical images with patient records and voice analysis during consultations to improve diagnostic accuracy and personalize treatment plans.
Real-time patient monitoring: Analyze facial expressions and physiological data in intensive care units to detect early signs of complications or emotional distress.
Use of multimodal AI in agriculture:
Precision agriculture: Analyze drone footage and sensor data to identify crop health issues, optimize resource allocation, and predict yield.
Livestock monitoring: Use cameras and AI to track animal behavior and identify signs of illness or stress in livestock, promoting animal.
FastPix video search: Multimodal AI for videos
Building a multimodal AI engine from scratch can be an arduous and intricate task, requiring significant time and expertise. But what if you could leverage advanced AI without the hassle of development? FastPix Video Search streamlines this process for you. Our state-of-the-art technology simplifies the retrieval of images and videos, making visual content management a breeze.
We tailor our solutions to your unique needs, ensuring our AI-driven search and retrieval capabilities are a perfect fit. With FastPix, you can save time and resources while enhancing your ability to access and utilize visual media.
Don't wait – unlock the power of multimodal AI for your visual content today!
What is the difference between generative and multimodal AI?
Generative AI focuses on creation from single data types, while multimodal AI uses its many senses to understand and create.
Is ChatGPT multimodal?
Yes, ChatGPT is multimodal. It recently gained the ability to understand and respond to images and voice prompts, along with its original text-based strengths. This makes it a multimodal AI system.
Is Gemini Multimodal?
Yes, Gemini is specifically designed to be multimodal. It’s infra built from the ground up for multimodality
What is are AI models that can use images as an input prompt?
Image modal AI in a larger context, multimodal AI can recognize image, speech and sound prompts as input
What is modality in AI?
Modality refers to types of data that the AI model can handle, unimodal AI can handle only one kind of data whereas multimodal can different data types like text, image, sound etc.
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