What comes to your mind when I say AI, aka artificial intelligence?
For some, it might be images of robots from sci-fi movies, with superhuman intelligence and maybe even villainous intentions. Others might think of helpful tools like chatbots or the algorithms that recommend products online. The truth is, AI is a broad field with a lot of potential, and what you think of first depends on your experiences and interests.
However, traditional or generative AI faces difficulties to understand the variations of information presented in a single format, like audio, images, or videos. This is where multimodal AI comes in.
The Multimodal AI gives superpower (extra senses) to AI, I mean it allows AI to process and learn from a wider range of information such as text, images, videos, audio, and even sensor inputs, just like we do as humans.
This approach allows AI to make more informed decisions, solve complex problems, and interact more intelligently with you and your surroundings. So let’s start with the very first question.
- What is Multimodal AI?
- Difference between multimodal AI and generative AI
- Technologies associated with Multimodal AI
- How top brands are using Multimodal AI (examples)
- Some famous projects based on Multimodal AI
- Challenges & drawbacks of multimodal AI
- Future of Multimodal AI
- Multimodal AI – power, potential, promise
What is Multimodal AI?
Multimodal AI is a type of artificial intelligence that combines, processes and understands information from multiple sources and formats. This includes images, voice, text, video, and more using which it makes accurate determinations, precise predictions or generates insightful data.
This helps you make better decisions, save time, and come up with new ideas in many areas like business, healthcare, and everyday tasks.
For example, in healthcare, it can help doctors analyze medical images, patient records, and research data to make accurate diagnoses and suggest effective treatments.
Even in everyday tasks, like planning schedules or organizing information, multimodal AI can save time by quickly processing and presenting relevant information. Plus, its ability to understand and combine different types of data can spark new ideas and innovations, making it a valuable tool across various fields and activities.
Examples of Multimodal AI
- In autonomous vehicles, multimodal AI processes data from various sensors such as cameras, LIDAR (Light Detection and Ranging – a technology that measures distance using laser light), radar, and GPS. It combines these information to navigate safely, detect obstacles, interpret traffic signals, and make real-time driving decisions.
- Multimodal AI enhances healthcare diagnostics by analyzing medical images (like X-rays and MRIs), patient records, genetic data, and even voice inputs. This comprehensive analysis aids in accurate disease detection, personalized treatment plans, and medical research.
- Devices like smart speakers and smart TVs utilize multimodal AI to understand voice commands, display visual content, control connected devices, and learn user preferences, creating a hasslefree and interconnected home environment.
- Multimodal AI is used in social media platforms to analyze and understand user behavior. It can process text, images, and videos to detect sentiments, identify trends, and personalize content recommendations, improving user engagement and satisfaction.
Difference between multimodal AI and generative AI
Multimodal AI focuses on integrating and processing information from multiple sources and formats, such as text, images, videos, and audio. It excels at understanding and analyzing diverse data types simultaneously, leading to comprehensive insights and smarter decision-making.
For example, multimodal AI powers virtual assistants, autonomous vehicles, healthcare diagnostics, and smart home devices by combining various modalities to enhance functionality and user experience.
On the other hand, generative AI and its tools are centered around creating new content or data based on learned patterns and algorithms. This type of AI can generate realistic images, videos, text, music, and even entire conversations.
Generative AI models, like GANs (Generative Adversarial Networks) and language models such as GPT (Generative Pre-trained Transformer), are used for creative purposes, content generation, and improving user interaction.
To understand more clearly. Imagine you’re a chef and want to prepare a delicious meal.
The generative AI approach to this will be having a recipe book filled with amazing dishes. You can choose a recipe (text prompt) and follow the instructions (algorithms) to create a new dish (generated content) like a poem, image, or music piece.
Generative AI excels at creating new things based on existing information, but it might not understand the nuances of different ingredients or the creativity involved in combining them in new ways.
Now, the multimodal AI approach will be having a skilled chef who can understand various ingredients (data from different sources like text, images, audio). They can not only follow recipes (instructions) but also improvise based on the available ingredients and your preferences (user context).
Multimodal AI can analyze your past orders (past interactions), dietary restrictions (user preferences), and even the weather (external data) to suggest a completely new and delicious dish (generate something relevant) that you might enjoy more.
Feature | Generative AI | Multimodal AI |
Input | Single modality (text, image, etc.) | Multiple modalities (text, image, audio, etc.) |
Output | New content based on prompt | New content relevant to context |
Strengths | Creates new things from existing information | Understands and combines information |
Limitations | May lack creativity or context-awareness | Requires large amounts of diverse data |
Technologies associated with Multimodal AI
Multimodal AI refers to systems that can process and interpret multiple types of data (modalities). Some key technologies associated with multimodal AI.
Natural Language Processing (NLP)
NLP allows the AI to understand the meaning behind written words, just like you translate languages to grasp their intent. Techniques like part-of-speech tagging help identify nouns, verbs, and adjectives, the building blocks of a sentence, similar to how you break down a sentence into parts of speech in grammar class.
Computer Vision (CV)
CV gives AI the ability to interpret visual data. Object recognition, image segmentation, and image to text AI using optical character recognition (OCR) are some of the crucial CV technologies used in multimodal AI.
For example, imagine you show a photo of someone to your friend. Your friend can instantly recognize that person in a crowded place. Similarly, computer vision (CV) enables machines to recognize and identify objects, faces, and scenes in images or videos.
Data mining
Data mining means extracting patterns and insights from large datasets. In multimodal AI, data mining is used to effectively combine information from various sources for better comprehension.
Have you ever searched through a stack of papers to find a specific receipt? That’s similar to what data mining does, sifting through large amounts of data to find important information.
Machine learning (ML)
ML is the foundation of AI that allows it to learn from data and improve its ability to process information from various modalities. It’s like training a dog with treats.
Machine learning uses algorithms to learn from data, similar to how the dog learns which behaviors get rewarded. The more data the AI processes, the better it becomes at handling different types of information.
Speech processing
Just like you talk with your friends, you can converse with AI. AI can understand and respond to spoken language through speech processing. It breaks down speech into sounds, much like how you recognize individual words in a conversation.
Techniques like keyword recognition help AI identify specific commands or questions within the speech.
Deep learning
Deep learning is a specialized form of machine learning inspired by the human brain. It uses artificial neural networks to excel at recognizing patterns across various data types, like text, images, and sound.
You can understand the concept of deep learning with an example. Imagine a child learning to identify different animals. They start by recognizing basic types like cats and dogs, and with more practice, they learn to distinguish different breeds and sizes.
Deep learning works in the same way, constantly improving its ability to understand and process information.
Knowledge representation and reasoning (KRR)
KRR allows AI to store and utilize knowledge from various sources (text, images, past experiences) to make inferences and solve problems using multimodal data.
Imagine a lawyer with years of experience – they can use their knowledge of past cases to connect seemingly unrelated facts in a new case. Similarly, multimodal AI can leverage its accumulated knowledge to make informed decisions based on various data types.
How top brands are using Multimodal AI (examples)
By combining information from different sources, multimodal AI can achieve better results than traditional AI systems that rely on a single type of data. This opens up a wide range of possibilities for applications in various fields. Now, let’s look at some famous brands using Multimodal AI.
Amazon’s multimodal AI innovations
Amazon’s Multimodal AI is seen in Amazon Alexa, which responds to voice commands, understands natural language, and controls smart home devices. Amazon Rekognition is another example, analyzing images and videos to recognize faces, objects, and moderate content.
Tesla Motors – Multimodal AI in autonomous driving
Tesla is a prominent player in autonomous driving (self-driving cars with AI) with its Autopilot system, which integrates cameras, sensors, and AI algorithms to enable features like navigation, lane-keeping, and collision avoidance. This helps cars drive themselves safely.
Google’s multimodal AI enhancing user experience
Google uses Multimodal AI in Google Photos to organize and search photos by recognizing what’s in them and understanding written descriptions. Google Assistant also uses this technology to understand voice commands, written messages, and images to give helpful responses and information.
Apple’s multimodal AI
Apple uses Multimodal AI in Face ID, where your face is scanned securely to unlock your iPhone or iPad. Additionally, Siri, Apple’s virtual assistant, also uses Multimodal AI to understand voice commands, text, and context to help you with tasks and questions.
Microsoft’s multimodal AI solutions
Microsoft offers Multimodal AI through services like Microsoft Azure Cognitive Services, which analyze text, recognize speech, understand images, and languages for developers and businesses.
Microsoft Kinect is another example. It uses special cameras and tracking to let you play games and control things by moving your body, showing how Multimodal AI can make entertainment and using things more interactive.
Some famous projects based on Multimodal AI
Multimodal AI is making big progress by combining different senses like text, vision, and sound. This helps create amazing projects that are changing the future. Let’s take a look at some of the most fascinating creations that are shaping the future with this powerful technology.
Amazon Style Snap
Amazon Style Snap is an exciting feature that lets you explore fashion styles using AI technology. You can upload a photo or take a picture of an outfit you like, and Style Snap will find similar items available for purchase on Amazon.
It uses computer vision to analyze patterns, colors, and styles, making it easier for you to discover fashion inspiration and shop for clothing that matches your taste. It’s a convenient way to stay updated with the latest trends and find clothing options that suit your preferences.
Sephora Virtual Artist
Sephora’s Virtual Artist is a cool app that helps you try on makeup without actually putting it on. It uses smart technology to look at your face and suggest makeup styles that might suit you.
This way, you can see how different products would look on you before buying them. It’s a handy tool that makes shopping for makeup more fun and personalized.
Meta
Meta, previously known as Facebook, is a prime example of using Multimodal AI across its platforms. They use this technology extensively for image recognition, allowing automatic tagging and content analysis on platforms like Facebook and Instagram.
Multimodal AI does a lot of cool stuff on Meta’s apps. For example, it changes voice messages into text on Messenger and WhatsApp, making chatting easier. It also makes fun filters on Instagram and recommend personalized content, friend suggestions, and targeted advertising based on what you like. This makes using Meta’s apps better and more fun for everyone!
But that’s not all! Let me share that Meta has teamed up with Ray-Ban to launch its own Multimodal AI glasses. All you need to say is just “Hey Meta” and you can access a range of features like object recognition, navigation assistance, information about what you’re looking at, and even suggest matching items of clothing.
Google Gemini
Google Gemini offers many possibilities with Multimodal AI. For example, you can describe a scene and see it turn into a picture, or take a photo of a dish and get the recipe. Gemini can understand and work with different types of information, like text and images.
It can turn your ideas into pictures or help you cook by showing you recipes. It also helps you communicate easily by breaking language barriers. This is just a small part of what Gemini can do, making it perfect for creativity and information.
Ohhh, I’ve been chatting so much about food, recipes, and the kitchen because I absolutely love exploring different cuisines. But enough about that—let’s switch gears and start our journey!
This time, I gave Gemini a sketch and asked it to name the item, show me a real image of it, and write a poem about it—all at once. Let’s see how it goes.
Netflix
Netflix uses Multimodal AI to make watching movies and shows better for you. It suggests things to watch based on what you like. It also helps make cool pictures and videos to show you. Plus, it makes sure the videos look and sound good.
Multimodal AI also helps with subtitles so you can watch in different languages. Overall, Netflix uses this technology to make your streaming experience awesome!
Challenges & drawbacks of multimodal AI
Although multimodal AI is the new upcoming trend in the AI industry, it still has some drawbacks and challenges as well.
Data volume
Multimodal AI requires vast amounts of data from various sources (text, images, audio, sensor data) for effective training. Collecting, storing, and managing this diverse data can be complex and expensive.
Also, the quality of the data used to train multimodal AI models is crucial. Biases present in the data can be amplified and lead to discriminatory or unfair outcomes.
Model complexity
Developing multimodal AI models requires sophisticated algorithms to handle the complexity of integrating different kinds of data, like pictures, text, and sounds.
This makes the computational process tricky because the AI needs to understand each type of data and put them together to make good decisions. Additionally, this process can be expensive and needs a lot of computer power and data to work well.
Data alignment and fusion
Another big challenge of making multimodal AI is to ensure different types of data work well together. For example, text descriptions might not exactly match images, and sensor data can come in different formats and quality levels. The AI needs to combine this information accurately to get good results.
Decision-making and transparency
Decision-making with multimodal AI is understanding how these models arrive at their decisions. Because these AI systems process and combine different types of data, it can be difficult to see the steps they take to make a final decision.
This lack of transparency can raise concerns about accountability and fairness, especially in critical applications like healthcare or finance, where knowing how decisions are made is crucial.
Making sure these AI models are clear and understandable is important for building trust and ensuring their decisions are fair and reasonable.
Missing data
Another drawback of multimodal AI is dealing with missing data. Sometimes, not all types of data are available at the same time. For example, you might have text but no images, or sensor data might be incomplete. This can make it hard for the AI to make accurate decisions since it doesn’t have all the information it needs.
In these situations, unlike humans who can use common sense and reasoning to fill in the gaps, AI can struggle because it lacks the ability to understand the context and improvise. In simple terms we can say that these are things AI cannot do.
Future of Multimodal AI
The future of multimodal AI is very exciting. As technology improves, these AI systems will get even better at understanding and using different types of data together. This will help in many areas.
For example, in healthcare, AI could look at medical images, patient records, and genetic information all at once to give better diagnoses and treatments.
In education, multimodal AI will create more personalized learning by using visual, audio, and text data to meet each student’s needs. In entertainment, it will make experiences more fun and immersive by combining video, sound, and interactive elements.
As these AI systems become easier to understand and more transparent, people will trust them more, especially in important fields like finance and law.
Overall, in the future, multimodal AI will bring incredible advancements to various fields like healthcare, retail, finance, manufacturing, and more. These developments will not only make technology more helpful but also streamline business workflows, enhance customer experiences, and improve decision-making processes.
Multimodal AI – power, potential, promise
I believe now you’ve got a clear idea of what Multimodal AI entails and its immense potential. It’s not just about processing data, it’s about putting and combining different types of information together to understand things better, much like how we humans perceive and interact with the world.
This transformative technology is already reshaping industries, from virtual assistants to autonomous vehicles and healthcare. By enabling machines to make informed decisions, solve tough problems, and interact more intelligently, multimodal AI holds immense promise for the future.
But there are challenges, like dealing with lots of data and making sure decisions are fair. With responsible development, Multimodal AI has a bright future, making things better in many areas and making our world smarter.
Hope you enjoyed reading about Multimodal AI. For more exciting AI stories and tools, don’t forget to visit Unrola! 🌎