The power of multimodal AI lies in its ability to bridge the gap between traditional, single-source data analysis and a more holistic understanding of the world
It’s through a multi-step process involving the input module, the fusion module and the output module
The race to harness multimodal AI is fierce as tech giants and smaller companies advance their capabilities
Artificial intelligence has transcended science fiction and firmly rooted itself in our reality. We’ve seen incredible progress, moving from deep learning and natural language processing (NLP) to advanced computer vision and now to generative AI.
But the most recent exciting development is multimodal LLMs, which sit at the fascinating intersection of language, voice and vision. Research predicts that by 2028, the multimodal AI industry will soar to $4.5 Bn, a monumental increase that can significantly drive AI adoption.
But can it truly lead us toward more natural, human-like conversations with AI?
Enhancing User Experience with Multimodal AI
The power of multimodal AI lies in its ability to bridge the gap between traditional, single-source data analysis and a more holistic understanding of the world. Unlike unimodal LLMs, multimodal LLMs integrate various modalities, enabling models to effectively understand inputs across different formats.
This capability enhances their ability to make informed decisions and deliver outputs that seamlessly integrate multiple modalities, resulting in more natural and fluid conversations.
How does this happen? It’s through a multi-step process involving the input module, the fusion module and the output module. The input module uses different neural networks to handle various types of data like text, images and audio.
The fusion module then combines and processes this data using methods like merging raw data or combining features. Finally, the output module produces results based on the integrated data, which can vary depending on the input types. This enhances the user experience by providing more accurate and comprehensive insights, leading to smarter and more intuitive interactions.
Experts in the field have noted, “conversation is the future interface.” This is quite accurate, as some of it is already happening. Instead of just clicking buttons and typing, we can now talk to AI at scale and show it pictures, with gestures possibly becoming a reality in the future.
The outputs from multimodal AI are more precise, adaptable and user-friendly. The closer AI can come to mimicking human interaction, the better it will meet the diverse needs of users. Essentially, multimodal AI aims to make our interactions with technology as seamless and intuitive as possible.
What Does Multimodal AI Look Like In Action?
The race to harness multimodal AI is fierce as tech giants and smaller companies advance their capabilities. OpenAI has integrated multimodal functionality into its tools and launched Sora, a text-to-video platform that creates high-quality videos from textual descriptions and GPT-4o, an enhanced version of GPT-4 with advanced, context-aware interactions.
Google’s Gemini AI model offers state-of-the-art natural language understanding and generation, pushing AI’s boundaries in processing and generating text and visual information. Other examples include Runway’s Gen-2, which generates novel videos from text, images, or video clips.
This opens doors to a whole new level of understanding. From creating images based on sounds to transforming a basketball game audio recording into a vibrant scene, the applications extend far beyond the tech industry.
Multimodal AI is poised to transform how we interact with machines, making it more immersive and engaging than ever before. For instance, in healthcare, doctors can leverage multimodal AI to supercharge diagnostics by weaving together medical images, patient information and clinical stories.
In finance, it can revolutionise risk assessment and trading strategies. In education, it can personalise learning materials based on how students interact with text, images and videos, catering to individual needs. Businesses that embrace multimodal learning will gain a significant edge.
What’s The Upside For Customer Service?
We’ve seen how unimodal LLM-powered chatbots automate many interactions, but multimodal LLM-powered AI agents take it to the next level by handling complex issues that usually need human help. According to research by McKinsey, multimodal AI, which includes generative AI as a key component, can significantly increase customer service efficiency.
For instance, if a customer is assembling furniture and encounters a problem, explaining the issue via text can be frustrating. With multimodal AI, they can take photos or videos of their progress and send them to the support system.
The multimodal LLM-powered AI agent can respond with customised help, such as diagrams, 3D videos, or step-by-step audio guides, all while maintaining the patience and friendliness of a human agent.
The flexibility, precision and scale afforded by multimodal AI optimise customer service operations, improving the internal functioning of contact centres and freeing human agents to focus on specialised tasks.
This efficient allocation of resources enhances employee satisfaction and reduces churn, a common issue in support centres. Additionally, models like GPT-4o bring AI agents closer to human-like interactions.
Multimodal LLMs-powered voice AI agents could adjust their tonality in real-time conversations, responding to users’ emotions such as frustration or happiness.
Multimodal AI has the potential to enhance consumer interactions across the board, from personalised recommendations to supply chain and manufacturing. Leading brands are expected to invest heavily in this technology, signalling others to join the bandwagon. In 2024, the key challenge for businesses is learning how to effectively leverage this technology.
Challenges And Future Outlook
Multimodal AI isn’t without hurdles. Training demands vast amounts of diverse data, making it relatively cost-intensive. Ensuring data compatibility and coherence across different modalities is also complex.
Ethical concerns persist regarding bias and privacy. Despite these hurdles, the potential is immense; multimodal AI could serve as a bridge to human-level understanding. Building models capable of handling diverse modalities at scale will take time.
However, businesses that embrace and invest in this future will not only stay ahead but also drive innovation and efficiency across domains. The goal is clear: harness the power of multimodal AI to create a more intuitive, versatile and user-friendly world.
