Multimodal LLM – Disrupting the AI Game

Updated on January 4 2024
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As a technologist with deep interest in artificial intelligence, witnessing the advent of Multimodal LLMs (MLLMs) signifies a groundbreaking moment for me; they promise not just to understand words but also interpret images, sounds, and even videos, transforming how machines comprehend our world.

The power of Multimodal LLMs lies in their ability to seamlessly blend various forms of data into one experience that mirrors human perception. Imagine an AI that doesn’t just read your emails but identifies emotions through your tone or facial expressions during video calls.

I have used some real world analogies to make the concept easy to understand. Let’s explore how these intelligent networks could redefine interaction…

Key Takeaways

  • Multimodal LLMs use text, images, sounds, and videos to understand and respond like humans do.
  • Multimodal LLMs can help in many areas including making art, teaching kids, looking at medical pictures for doctors, helping robots get smarter, and improving how companies talk to customers.
  • Big models like GPT-4 are already showing us how powerful this technology can be.
  • There are worries about how fair Multimodal LLMs are since they sometimes make mistakes based on what a person looks or sounds like. Keeping data safe is also very important.
  • These systems need lots of power and information which can be hard to get and expensive. People are trying new ways to make them better without using as much energy.

Understanding Multimodal LLMs

Multimodal LLMs are disrupting AI space by enabling interaction and content generation across text, images, audio, and video formats.

As these advanced neural networks juggle multiple forms of information, they shatter previous boundaries—opening up new dimensions in machine understanding and communication.

Concept of multimodality and its role in Multimodal LLMs

Multimodality means mixing different ways of sharing ideas, like using words, pictures, sounds, and videos. In Multimodal LLMs, this mix helps the AI understand and respond to information just like humans do.

Think of it as a smart friend who can chat, see your drawings, listen to music with you, or watch videos – all at once! This makes MLLMs very special because they can handle many types of data.

Their role in AI is big. They bring together text from books, voices from recordings, images from cameras and scenes from movies to learn how people communicate. By knowing all these things, MLLMs get better at helping us out.

They turn into helpers that seem more like real people because they understand the world in many ways.

Now let’s explore what makes these powerful tools stand apart from usual language models that only focus on text..

Also read: Google Gemini – The most advanced Multimodal LLM yet

Multimodal LLMs and contrast them with traditional LLMs

Multimodal LLMs are like super-smart tools that handle more than just words. As they work with pictures, sounds, and videos too, this make it a big step up from traditional language models that only understand text.

MLLMs can look at an image and tell a story, listen to music and write lyrics, or read handwritten notes—things old-school models can’t do.

Traditional LLMs, they’re good with writing essays or making chatbots, but they miss out on what you see or hear. Imagine if someone only listened to you but couldn’t see any gestures—you’d lose some meaning, right? That’s where MLLMs shine. They get the full picture by mixing all kinds of information together.

This makes them smarter in figuring out what people really mean. It’s like having a friend who doesn’t just hear your words but gets your jokes and sees when you’re excited too!

Different types of modalities MLLMs can handle

Moving from the general idea of multimodal LLMs, let’s dive into the details. These advanced AI systems are not limited to just understanding and generating text. Here’s how they can work with different types of information:

  • Text: At their core, MLLMs are experts in dealing with words. They read, understand, and write text in many languages. This includes creating stories, answering questions, or even writing code.
  • Images: Vision Transformers within MLLMs allow them to recognize and understand pictures and graphics. They can describe what’s in an image, make new images like those made by Dall E 3, or find pictures that match certain words.
  • Audio: They listen to sounds or music and know what they hear. These models might turn spoken words into written ones or even create new pieces of music.
  • Video: Videos combine moving images and sound – MLLMs handle both at once. They can figure out what’s happening in a video clip or even create short videos based on a description.
  • Other Sensory Data: Beyond these common types, MLLMs can also work with data from other senses – like touch or smell – if it’s converted into a digital form they can understand. However, for now, it is only theoretical. But for how long?

Existing Multimodal LLMs and their capabilities

Multimodal LLMs are reshaping AI by integrating various data types. They synergize text with visuals, sounds, and more, enhancing the AI’s contextual understanding. Below is a table showcasing some cutting-edge MLLMs and their remarkable capabilities:

Model NameModalities HandledCapabilitiesDeveloper/Institution
GPT-4Text, ImagesContextual understanding, content creation, reasoningOpenAI
KOSMOS-1Text, Images, Videos, AudioHuman-level performance on benchmarks, multimodal interactionsMicrosoft
CLIPText, ImagesVisual recognition, zero-shot learningOpenAI
DALL-EText, ImagesImage generation from textual descriptionsOpenAI
VILBERTText, ImagesVisual question answering, image-caption matchingFacebook AI Research (FAIR)

MLLMs such as GPT-4 have demonstrated impressive abilities, including human-level performance on various benchmarks. These models are increasingly accurate, creating content that feels contextually rich and nuanced. With an understanding of their current state, we turn our attention to the underlying mechanisms that power these MLLMs.

How Multimodal LLMs Work?

The architecture of Multimodal LLMs unveils a fascinating world where AI interprets and generates content across various senses, helping us to explore how these digital brains are trained to understand our complex human experiences.

The Multimodal LLM architecture

The Multimodal LLM architecture is smart and powerful. It lets computers understand and make things using words, pictures, sounds, and videos. Computers use a special part called multimodal encoding to look at different kinds of data all together.

Then comes the LLM understanding stage where the computer thinks about what it sees or hears. Finally, in the generation stage, it makes new stuff like writing a story or creating an image.

Big projects like NExT-GPT show us how this works. They can take in any mix of text, images, video, and audio then share something back in a similar way. Imagine telling your computer to draw a dragon with a funny hat; that’s possible because of this impressive design! With these steps—looking at everything mixed up together, thinking hard about what it means, and making something new—computers can do really cool things we never thought they could before!

How Next GPT works

Training Multimodal LLM on multimodal data

Training Multimodal LLM on multimodal data helps computers understand and use different types of information like text, pictures, sounds, and videos all at once. This kind of training makes them smarter in dealing with real-world tasks.

  • MLLMs need lots of examples to learn from. We feed them big sets of data that have things like articles, photos, audio clips, and movie snippets.
  • Combining these data types is a must. The models learn to connect words with pictures and sounds so they can get better at handling tasks that need more than one type of information.
  • We teach the models using special techniques. For example, we might show them a photo and ask the model to describe it or play a sound and have it write down what’s happening.
  • It’s not just about matching things together. The models also need to understand the deeper meaning behind the information they receive.
  • Experts check the work too. They help make sure that what the models are learning is correct and useful.
  • Sometimes things go wrong. If an MLLM doesn’t do well with certain tasks or data, researchers work hard to fix it by making changes in how they train the model.
  • Keeping everything fair is important too. Trainers watch out for biases in the data so that the models will be fair and treat everyone equally.

Approaches to multimodal fusion and representation learning

Multimodal fusion is like putting puzzle pieces together. Each piece is different – one might be a word, another an image, or a sound. In multimodal AI, machines learn to connect these pieces.

This means they get smarter at understanding the world by looking at photos while listening to sounds and reading text all at once.

For this to work well, we use smart ideas like “deep learning.” Think of deep learning as teaching machines to find patterns the way our brains do. We show them lots of examples — pictures with words, for instance — until they start getting really good at knowing what those combinations mean.

That’s how these AI models help robots understand us better and can even look at medical images to tell doctors if something might be wrong. It’s not just about seeing or hearing; it’s about making sense of everything together.

Applications of Multimodal LLMs

Diving into the practicality, Multimodal LLMs are revolutionizing how we interact with and shape our world—think of a canvas that listens, interprets, and paints your thoughts. From virtual classrooms to intelligent robots, these systems are not just tools but partners in expanding human creativity and capability.

Creative industries

Multimodal LLM is changing how we create art, music, and stories. It mixes words, sounds, images, and videos to make amazing new things.

  • Artists now use AI to turn ideas into real paintings or digital images. Midjourney and Dall-E are making this such an easy task that you dont need to be an artist to create images. Just a simple prompt would do.
  • Musicians work with AI that can listen to beats and tunes. The AI then comes up with its own songs or helps to change existing music. Suno and MusicFx are doing this in real time.
  • Writers help their stories come alive with AI tools. They start by telling the AI what their story is about. The AI can offer plot ideas or even write parts of the story. ChatGPT 3.5, 4 and Bard are doing this to perfection.
  • People making movies use this smart tech too. They ask the AI for help in writing scripts or creating scenes that are hard to film in real life. Runway ML is making huge strides every single day to make the outcome life like.
  • In video games, creators are using multimodal LLMs. This makes characters that talk and act more real because they understand both voice commands and player actions.
  • Social media folks use AI to decide what kind of art or music might go viral. The tech looks at lots of data to know what people like best.

Also read: How to use Midjourney V6?

Education

Multimodal Large Language Models are changing how we learn. They offer new ways for students to experience lessons and get help with their studies.

  • Immersive learning experiences: These models create environments where students feel like they are inside the topic they’re studying. Say a class is learning about the rainforest; a multimodal LLM can show them real images, sounds, and texts about the plants and animals there. This helps kids remember better because they see, hear, and read all at once.
  • Personalized tutoring: Just like a good teacher knows what each student needs, these smart models learn about each kid’s way of learning. They can give extra help in math or reading by figuring out what problems a child struggles with. Then, they offer special exercises just for them.
  • Accessibility tools: Not every student learns in the same way, especially those with disabilities. Multimodal LLMs are great at making learning easier for everyone. A child who can’t see well might get information through audio. Another who finds reading tough could get help from videos that explain things simply.

Healthcare

How Multimodal LLM is used in Healthcare
Example of how Multimodal LLM is used in Healthcare

Multimodal Large Language Models are transforming healthcare. They have started to help doctors analyze images, support diagnosis, and talk to patients. Although the accuracy levels are yet to be established and regulated, healthcare can be one of the areas that get hugely disrupted in 2024 as these models get better.

  • Doctors use AI models to look at medical pictures like X-rays and MRIs. This helps them find problems faster.
  • These AI models can understand both words and pictures. This means they can read reports and look at images together for better diagnosis.
  • The AIs are trained with many examples to get good at their jobs. This includes lots of medical data.
  • When a doctor needs help, the AI can suggest what might be wrong with a patient by looking at their information.
  • Patients sometimes have questions or concerns. Multimodal LLMs can talk to them using natural language that is easy to understand.
  • Privacy is very important when dealing with health information. These AIs must keep all patient data safe.
  • Large companies are creating their own versions of these multimodal LLMs. They want to make healthcare better and easier for everyone.
  • The money spent on natural language processing is going up fast. More investment in AI could really change how we do healthcare.

Robotics and Human-Computer Interaction

Just as multimodal LLMs are changing healthcare by helping doctors with diagnoses, they’re also making big waves in the world of robots and computers. Here’s how these smart systems are creating better ways for us to chat and work with machines:

  • Robots get smarter with multimodal LLMs. They can see, hear, and even feel their surroundings. This helps them understand what humans need.
  • Talking to robots feels more natural. Thanks to AI, we can speak to machines like we do to people. Robots understand our words and even our tone of voice.
  • Machines learn from pictures and sounds, not just text. Multimodal LLMs help them recognize images and noises to know what’s happening around them.
  • Wearable devices use AI to help us more. They can figure out if you’re standing or sitting just by “feeling” your movements.
  • Computers turn spoken words into written ones. With this tech, machines write down what we say without missing a beat.
  • Signs that robots notice help keep us safe. They spot things like warning lights and alert us right away.
  • Personal assistants on phones seem smarter than before. They answer questions and help with tasks by understanding both speech and text.

Business and Marketing

Multimodal LLMs are changing how we do business and market products. Some areas that are starting to see the usage of AI for business getting deepened and intuitive:

  • Customer service chatbots are getting smarter. They can understand what we say or type and help answer our questions quickly. These chatbots make shopping online easier. Customers can ask for help just like talking to a real person in the store.
  • Companies can use multimodal LLMs to learn more about what people say on social media platforms. This helps them know better what people think about their stuff.
  • In business talks, these models can pull out the important parts from lots of information, saving time and effort.
  • These models also make sure ads reach the right eyes and ears by knowing who’s looking at them on electronic data exchange like websites or apps.
  • Personalized advertising uses what these models know about a person to show ads that fit just right. For example, if someone likes sports, an ad for running shoes might pop up.
  • Product recommendations get better with these smart tools. They look at what you’ve bought before and suggest new things you’re likely to buy.

Also read: How to use AI for Digital Marketing in 2024

Challenges and Limitations of Multimodal LLMs

While the potential of Multimodal LLMs is undeniably vast, they’re not without their hurdles—navigating ethical quandaries and technical constraints remains a complex dance.

Teasing out solutions for these multifaceted challenges is pivotal as we propel MLLMs toward their full promise in shaping our digital landscape.

Ethical concerns surrounding Multimodal LLMs

Multimodal LLMs can sometimes be unfair. They might decide things based on a person’s details like where they live or how they look. This isn’t good because it means that not everyone is treated the same way.

People are working hard to fix this, but it’s tough because these AI systems learn from lots of data and sometimes that data has these unfair bits in it.

When you talk to an MLLM, like when you ask your phone for help, it knows what you say and may remember what you like. But if someone else gets this info, they could use it in ways we don’t want them to.

Keeping our chats with AI safe is really important so nobody can sneak a peek at our private stuff or mess with us using the information the AI knows about us.

Technical limitations of Multimodal LLMs

To make multimodal large language models smart, you need lots of data. This can be tough and costly. These models learn from different types like text, images, and sounds. But getting enough training data for all these kinds is a big job.

People also find it hard to figure out how these models think. They mix up words, pictures, and sounds in complex ways. Because they work this way, it’s tricky to see why they make certain choices.

On top of that, these models use a lot of power which can be bad for the environment and cost a lot of money too.

Ongoing research efforts to address these challenges

Researchers are working hard to improve Multimodal LLMs. They tackle big problems so these smart systems can understand and use different kinds of information better.

  • Scientists are finding ways to mix data better. They want text, pictures, sounds, and videos to work together smoothly.
  • Making MLLMs smarter is key. The goal is for them to get the meaning from all data types without confusion.
  • New learning methods help MLLMs understand things like humans do. This means they need good examples to learn from.
  • Keeping data private and fair is important. Researchers create rules so that MLLMs treat everyone’s information with care and respect.
  • Energy – saving techniques are in the works. It’s like teaching MLLMs to use less power but still be smart.
  • Teams across the world work on making MLLMs able to handle more data. Big or small, they want all kinds of info included.

The Future of Multimodal LLMs

As we stand on the brink of an evolutionary leap in AI, the horizon for Multimodal LLM is expansive. These advanced systems promise to redefine our interaction with technology, merging human-like understanding with machine efficiency to unlock unprecedented possibilities.

Future of Multimodal LLM development and its potential impact on society

Multimodal LLMs, are growing fast. They’re changing how we live and work. Soon, they might help doctors look at medical images better or make learning more fun for kids.

Imagine a world where you can talk to machines just like you talk to friends.

MLLMs will also be big in business. Shops could use them to figure out what people want to buy. Robots could become smarter helpers at home and in factories. But there’s a lot to think about too—making sure these AI tools are safe and fair is really important!

Opportunities and challenges that lie ahead for Multimodal LLMs

Multimodal LLMs stand at the edge of changing how machines understand our world. They can juggle text, images, and sounds to do amazing things like drive cars without a person’s help or find out what’s wrong when we’re sick.

This kind of AI could also make it easier for us to talk with computers as if they were people.

Still, making these smart systems work well is tough. They need lots of different kinds of information and have to figure out how to mix it all together in the best way. As they get more complex, they need more power and clever designs so that we can trust what they say and know they’re fair.

Techies are working on solving these puzzles every day.

Also read: Public and AI Predict the future of AI in 2024

Conclusion

Multimodal LLMs are changing our world, and fast. They help us talk to machines like we do with friends. From art to helping doctors, they’re everywhere! Big challenges wait, but so does success.

Let’s get ready for a future full of smart helpers that understand us better than ever. These smart helpers will impact and be of tremendous help in every area of our personal and professional lives. I am excited and confident that they will get better by each passing day and techies will address every concern being raised. 2024 will be the year of disruptions and Multimodal LLMs will be at the forefront of it.

FAQs on Multimodal LLMs

What are Multimodal LLMs?

Multimodal LLMs, like GPT-4 and Imagen 2, mix artificial intelligence to understand different things – words, pictures, and even spoken commands.

Can these AI models actually “see” images or “hear” voices?

Sort of! These models don’t see or hear, but they use fancy math like convolutional neural networks to recognize patterns in images and voice recognition for sounds.

Do these AI systems learn on their own?

Yes – they learn from lots of data using machine learning techniques that make them smarter over time… just like how we learn from experience!

Is Midjourney a multimodal model?

Midjourney is not fully multimodal. It primarily takes text input (prompts) and generates image output. While it can incorporate some other types of information, such as existing images for style reference or additional text keywords, its core functionality revolves around text-to-image generation.

What is the difference between generative AI and multimodal AI?

Generative AI focuses on creating new content, such as text or images, while Multimodal AI processes information from various sources simultaneously, enhancing understanding across different modalities like text and images.

What are the 5 types of multimodal?

Multimodal is defined in terms of the five modes of communication – linguistic, visual, gestural, spatial, audio.

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