Our environment is one in which technology can read text, hear our voices, and recognize facial expressions, as well as collect other data. Multimodal AI is capable of processing various types of data simultaneously, such as words, sounds, and images. Artificial intelligence that integrates several kinds, or modes, to produce more accurate judgments, more accurate predictions, or more insightful conclusions regarding real-world issues is known as multimodal AI. From robotics to healthcare, multimodal AI is making inroads into a number of areas. Several IT behemoths, like OpenAI, Anthropic, and Meta, have produced their multimodal models since Google’s Gemini ushered in the era of multimodal AI.
Multimodal AI: What is it?
A form of artificial intelligence known as multimodal AI is capable of simultaneously processing and integrating several data sources, including text, sounds, and visuals. A modality is a specific type of data used in machine learning. The core of multimodal AI systems is their architecture, which uses deep learning models, neural networks, and specialized AI frameworks made to handle and integrate multimodal input.
Multimodal models may handle text, photos, video, speech, and more to do a variety of tasks, like as translating an audio sample into various languages or creating a recipe from a food photo.
One cannot stress the importance of multimodal models in AI. They provide novel approaches to simultaneously process and provide insights from different data sources, which makes them invaluable in a variety of industries.
For example, in the field of education technology, multimodal models can improve e-learning platforms by integrating visual, audio, and textual content to produce individualized learning experiences. A student who is having trouble understanding a math idea, for example, could get a verbal explanation, a visual step-by-step animation, and interactive feedback based on their answers—all in real-time—to accommodate various learning preferences and enhance comprehension.
Multimodal AI: What Is It Used For?
Multimodal AI is now being used in these domains.
1. Robotic Systems
By combining information from depth sensors, cameras, and microphones, multimodal AI-enabled robots may better comprehend their surroundings and react accordingly. They can utilize microphones to comprehend spoken orders, for instance, or cameras to observe and identify objects. Brendan Englot, an associate professor in the Stevens Institute of Technology’s mechanical engineering department, stated that they can even be equipped with sensors that allow them to experience the five senses that humans do: touch, smell, and taste.
2. Medical Care
Interpreting a variety of data types, such as lab tests, clinical notes, electronic health records, and medical imaging, is essential in the medical industry. Within particular modalities, unimodal AI models carry out particular healthcare tasks, including X-ray analysis or genetic variation identification. Additionally, LLMs are frequently utilized to provide straightforward answers to health-related queries.
3. Conversational AI
Compared to their text-only predecessors, AI chatbots with multimodality are able to respond to consumers more efficiently and provide more insightful responses. Users can, for instance, upload a photo of a dying houseplant to receive tips on how to revive it or a thorough description of a movie they’ve linked to.
What Applications does Multimodal AI have?
Compared to unimodal AI, multimodal AI is more valuable since it can handle a greater variety of use cases. The following are typical uses for multimodal AI:
1. Customer service:
By examining a customer’s written words, voice tone, and facial expressions, multimodal AI assists customer service personnel in better comprehending their intents and feelings. Additionally, it can allow sophisticated chatbots to offer immediate customer service. For instance, a consumer can upload a photo and describe a fault with a product by speech or text, allowing the AI to automatically fix the issue without human assistance.
2. Language processing:
NLP activities like sentiment analysis are carried out by multimodal AI. For instance, a system can adjust or moderate responses to a user’s demands by recognizing indicators of stress in a user’s voice and combining them with signs of anger in the user’s facial expression. Similar to this, AI can enhance speech and pronunciation in various languages by fusing text with speech sounds.
3. Retail:
Multimodal AI is utilized in retail to provide more individualized shopping experiences. It makes product recommendations based on a customer’s past purchases, browsing history, and social media activity. An excellent illustration of this is Zara’s AI-powered recommendation system, which employs computer vision to examine in-store consumer behavior (such as the things people pick up or try on) and merge it with textual information from past purchases and internet searches. Zara can customize the shopping experience to each customer’s interests in real time by using this multimodal technique to offer appropriate clothing items both online and in-store.
The Top 5 Greatest Multimodal AI Models You Must Be Aware Of
1. GPT-4V by OpenAI
An improved OpenAI GPT-4 model, OpenAIGPT-4V, has multimodal features that enable it to process and produce data from both text and images. comprehension of both visual information and textual words. Furthermore, GPT-4V can receive voice input and translate it into text for additional processing thanks to its speech capabilities. Likewise, it can produce spoken responses in a variety of human-sounding voices in response to input prompts.
2. GPT-4o OpenAI
OpenAI’s most recent multimodal model, GPT-4o, is made to process and produce text, audio, pictures, and video in real time. It’s excellent at combining various inputs during discussions, which makes interactions more organic and contextually aware. External red-teaming, or hiring outside contractors to perform risk assessments and rigorously evaluate their models’ propensity to provide biased or harmful information, was a strategy used by OpenAI to make their models safer.
3. LLaVA.
Big language and vision helper): Vision and language comprehension are combined in this approach. It is open-source, so anybody can make changes or add to it. Based on visual inputs, it generates complex, interactive replies by combining verbal comprehension with visual data. When combined with textual data, LLaVA is especially helpful for tasks like image captioning, visual question answering, and image reasoning.
The University of Wisconsin-Madison, Columbia University, and Microsoft collaborated on the research initiative that produced LLaVA. It was created by employing a technique called visual instruction tuning, which fine-tunes an LLM to comprehend and process visual cue suggestions.
4. DALL-E 3 by OpenAI
OpenAI’s most recent picture creation model, DALL-E 3, has been merged with ChatGPT to enable users to produce intricate visuals from text prompts while improving comprehension of user intent. This technology, which focuses on word-to-image creation, can comprehend challenging text and generate images that faithfully represent particular artistic styles.
Similar to how words are represented by tokens in LLMs rather than continuous vectors, one of the main breakthroughs of the DALL-E family is the use of a discrete latent space, or discrete tokens, to represent data. This improves output by allowing DALL-E 3 to learn a more stable and structured representation of created images.
5. Google Gemini
Google’s most recent multimodal AI model, Gemini, can combine a number of modalities, such as text, graphics, audio, code, and video. Gemini is a text, image, audio, and video processing tool created by Google DeepMind. For good reason, its image creation feature was just suspended. Gemini was pre-trained on many data kinds from the beginning and was intended to be naturally multimodal. Another noteworthy feature of Gemini is its expanded context window; the Gemini 1.5 Pro model allows for multimodal data processing and supports up to 10 million tokens.
Final Thoughts
In conclusion, multimodal AI integrates various data types, including text, voice, images, and video, to facilitate more intelligent and human-like interactions. This represents a huge advancement in artificial intelligence. With improved accuracy, personalization, and context awareness, its applications are found in robotics, healthcare, retail, and customer service, among other fields.
Advanced models like as Google’s Gemini and OpenAI’s GPT-4o are setting the standard for multimodal AI, which is changing how machines see and react to their environment. The capacity of the technology to comprehend intricate, real-world situations will only increase with time, establishing it as a fundamental component of upcoming technological advancements in several sectors and revolutionizing our relationship with electronics.
