Today, companies face numerous pain points that hinder their growth and efficiency. A big headache arises when it’s necessary to effectively integrate and use different types of data like text, images, audio, and video.
Traditional single-modal AI models, which focus on just one data type, often fall short. This can lead to a fragmented understanding and less effective decisions. Businesses also deal with the dilemma of how to offer personalized experiences. These problems can challenge customer satisfaction and slow down innovation and competitiveness.
That’s where multimodal AI comes in. This game-changing technology tackles these issues. It integrates and processes different types of data seamlessly. With multimodal AI, companies better understand their data, leading them to more informed decisions. Multimodal models help businesses analyze content more thoroughly and create personalized user experiences. In short, multimodal AI solves current pain points and opens up new opportunities for business efficiency in the digital age.
In this article, our Technical Lead will share valuable insights on multimodal AI that you should consider for your company to enhance and pave the way for sustained growth. Let’s dive in.
Exploring the Basics of Multimodal AI
Multimodal generative AI is a type of artificial intelligence that uses a mix of images, voice, text, video, and more to make predictions or create new content. It goes beyond the traditional text-based AI. Multimodal AI can answer more sophisticated and specialized questions in real time. Moreover, it relies on meaningful media, audience, and context, similar to humans.
Today, we’re observing that multimodal AI models go mainstream. Google has announced its Gemini AI models, trained from the beginning to handle text, images, audio, video, and more. GPT has also released GPT-4o, a multimodal model with high performance capable of creating and analyzing images and interpreting data.
According to the latest report, the multimodal AI market is projected to reach $4,5 billion by 2028, with an annual growth of 35% between 2023-2028. As you can see, multimodal AI is picking the steam and can’t leave the business community indifferent.
How multimodal AI differs from single-model AI
In AI, modality refers to different data types, such as images, voice, text, and video. Single-modal AI applies artificial intelligence to one data type at a time. For example, a chatbot that communicates through text messages is a common example of a single-modal AI, often used in website apps.
Multimodal AI is a more advanced and sophisticated form of artificial intelligence compared to single-model AI. Multimodal models can handle and generate multiple types of data simultaneously. For example, OpenAI’s CLIP (Contrastive Language-Image Pre-Training) is a multimodal model that can understand and generate content based on both text and images.
Contact the Forbytes team to kickstart your journey with multimodal AI today! Our experts are here to help you tap into integrated data, boost insights, and spark innovation.
How Multimodal AI Works
The work of multimodal AI consists of the following stages: input, model processing, and output.
- Stage 1: Input
Separate neural networks process different data types. For example, a customer might ask a virtual agent if their bookshelf is assembled correctly and send pictures of the assembled parts. - Stage 2: Model Processing
The data is combined, analyzed, and compared to trained data models. The multimodal AI then assesses the bookshelf and creates a relevant response. - Stage 3: Output
The model delivers predictions, decisions, and recommendations using all the data, and then presents them to the customer. In this case, the bookshelf is assembled correctly, and the customer can start using it.
By using separate neural networks and combining their outputs, multimodal AI delivers precise and relevant responses.
Technologies That Fuel Multimodal AI
Multimodal AI is an advanced and sophisticated system powered by various technologies, such as the following:
- Natural language processing (NLP) technologies can recognize speech, turn it into text, and read it aloud. They can even detect tones like stress or sarcasm, enhancing speech understanding. NLP significantly contributes to the high performance of multimodal AI.
- Deep learning is a part of AI that uses artificial neural networks to tackle complex tasks.
- Computer vision tech helps cameras identify and understand objects and people. It can recognize humans and differentiate actions like running or jumping.
- Text analysis allows multimodal tools to read and comprehend written language and intent.
- Integration systems enable multimodal AI to align, sort, and manage various data types from different sources. Integration is crucial for building context and making context-based decisions.
- Audio processing: Some of the most advanced AI models can handle audio files as both inputs and outputs. They can understand voice messages, translate languages in real time, and even create music.
Best Multimodal AI Tools to Consider for Your Business
Multimodal AI is everywhere, with companies like Microsoft, Google, and OpenAI using this term to promote their latest AI models and services. Thus, we dare to suggest the best multimodal AI tools to consider for your business.
Google Gemini
Google Gemini is a multimodal AI that can identify and create text, images, video, audio, and code. It has three main versions: Gemini Ultra, Gemini Pro for multiple tasks, and Gemini Nano for mobile users. In May 2024, there were 143,1 million desktop visits and 170,8 million mobile visits to Gemini. This means that this multimodal AI tool is popular among users.
Strengths. First, Google Gemini has a vast training dataset. It can deeply understand language nuances and provide more accurate responses. Another winning point for this AI tool is that it can address many tasks, including text and code generation and translation.
Weaknesses. One of the disadvantages of Google Gemini is its small emphasis on open-ended conversation and its focus on text creation. Plus, many users complain that the tool is inaccurate in image generation.
How you can benefit. With Google Gemini, you can boost your marketing by using advanced data analytics to target the right audience better. You can also create more personalized customer experiences by understanding and predicting customer preferences across text, images, and voice interactions.
ChatGPT (GPT-4V)
GPT-4V is a multimodal version of GPT-4 that enables users to apply text, voice, and images in their prompts. Plus, ChatGPT has five different AI-generated voices to respond to users.
Strengths. First, ChatGPT is publicly available. Second, it excels at open-ended conversation, engaging in natural dialogues. Finally, it can produce original texts like poems, musical pieces, scripts, and letters.
Weakness. A smaller training dataset is one of the weaknesses of ChatGPT. That’s why it fails to solve complex tasks. Its primary focus on text generation limits its applicability to other tasks.
How you can benefit. With this multimodal AI tool, you can improve client interactions by offering more personalized support through text, images, and voice inputs.
Inworld AI
Inworld AI is a multimodal AI in gaming that helps create non-playable characters and virtual people, populating digital worlds and metaverse environments.
Strengths. One of the main advantages of Inworld AI is that it creates characters that can communicate diverse mediums, including natural language, voice, animations, and emotion. Thanks to this, non-playable characters are smart and engaging.
Weaknesses. Users find the customization options of Inworld AI restricted, limiting the tool’s adaptability to specific needs.
How you can benefit. With Inworld AI, not only can gaming businesses grow, but other companies can also fuel their brand strategy with immersive digital experiences.
Meta ImageBind
Meta ImageBind is a multimodal AI model that can process text, audio, visual, movement, thermal, and depth data. This tool was the first AI model capable of combining information across all these modalities.
Strengths. Meta ImageBind can perform diverse tasks, such as understanding multiple modalities, searching for multimodal content via image, text, and audio, and creating images from audio clips.
Weaknesses. The new features ImageBind offers can be tricky to learn if you’re not familiar with AI and machine learning. Even though it’s open source, you’ll need a lot of computing power and know-how to get the most out of it.
How you can benefit. You can use this multimodal AI to enhance image quality and create unique designs quickly. E-commerce businesses can optimize product images to attract customers.
Runway Gen-2
Runway Gen-2 is a multimodal AI that generates videos with text, image, or video input. This visual AI tool is great for creating original video content.
Strengths. Runway Gen 2 offers significant benefits for content creators. It ensures consistent and stable video elements, even during motion, for a seamless viewing experience. Plus, it renders videos quickly using platform servers, saving time and effort.
Weaknesses. While Gen 2 offers many benefits, it has some limitations. The videos it creates are still experimental and not quite ready for finished products. Content creators might need to use other platforms for higher-quality results.
How you can benefit. You can use Runway Gen-2 to speed up video content generation, but just improve it using other tools.
How to Integrate Multimodal AI into Your Business
Before integrating multimodal AI into your system, it is vital to create a roadmap with clear goals to move step-by-step. So, here are our recommendations on how you can achieve this.
Step 1: Identifying your needs and setting goals
The first step to integrating multimodal AI into your system is knowing what your needs are and what goals you pursue. This often is the most challenging part, as you should understand how your business will benefit from AI.
Step 2: Choosing a multimodal AI platform
Once you’ve identified your needs and goals, your next step is choosing a relevant multimodal AI tool. Evaluate on-shelf options based on their scalability, support, ease of use, and integration capabilities, along with the pros and cons of each model.
Step 3: Collecting all relevant data
Collecting diverse data types (text, images, audio, video) can help you use multimodal AI with more benefits for your business. Ensure your data are clean, well-organized, and labeled correctly for effective AI training.
Step 4: Training models
Team up with AI experts to develop and train your multimodal AI models. Use your prepared data to train the AI to recognize patterns and generate accurate predictions.
Step 5: Integrating with existing systems
With your training complete, it’s time to integrate the AI models with your current business system. It’s vital to ensure that AI can access and process data from various sources within your company.
Step 6: Educating your team
You’re almost ready to use multimodal AI. However, before you jump into it, you should educate your team on how to use the new AI tool effectively. Provide training sessions and resources to help them understand the capabilities of multimodal AI.
How Forbytes Can Help You
No doubt, you can integrate multimodal AI into your system on your own, but if you have questions or need help, our team is here for you. Why choose Forbytes for AI implementation?
First, we offer top-notch AI solutions and services. We work closely with our clients to meet their expectations and deadlines. We know how to fuel your system with multimodal AI.
Second, our skilled team can tackle your challenges and adopt the latest AI trends. Our solutions are scalable, advanced, and adaptable to your changing needs.
At Forbytes, we provide AI development services to help you unlock new business opportunities with sophisticated AI solutions. We can add AI tools to your system to boost its efficiency and value.
Want to integrate multimodal AI into your software? Contact our team! We’re excited to discuss how AI can meet your business goals.
