Traditional AI models are trained on a singular, ‘structural’ data type.
But we live in a world where we constantly observe, absorb, and process the sights, sounds, and words around us. We should be able to harness the power of all that ‘unstructured’ data. This is the goal of multimodal AI.
With multimodal AI, you take a more holistic approach to data. There’s scope for better context because you’re using multiple sources. This leads to increased accuracy in output and reduces the likelihood of AI hallucinations. Most importantly, multimodal AI unlocks insights you’re just not going to get when only using structured data.
Deeper insights with multimodal AI
What does this mean when it comes to business?
When you use your ‘entire data estate’ in training models and your RAG (retrieval augmented generation) pipelines, you get analytics and predictions that take advantage of all the data you have, regardless of modality.
This can allow you to, for example, perform enterprise searches or build a chatbot across all your documents and materials, taking in not just text but also images, tables, and graphs. The result: more contextually relevant responses and more efficient and accurate problem-solving via automated support. Which saves everyone time and frustration.
The potential of multimodal AI extends across various industries, bringing each of them specific value-driving use cases.
- Healthcare: Analyzing patient records and images, accelerating research, and helping doctors more rapidly diagnose diseases like cancer to improve patient outcomes
- Retail: Combining images with a user’s shopping history to enhance personalized recommendations, elevate the user experience and generate more sales
- Finance: Analyzing records, charts, and tables, to detect fraudulent activity
The road to multimodal AI
How do you make multimodal AI a reality for your business, integrating it into your systems, and enabling more effective analysis and searches that encompass all data, rather than just words?
The key lies in storing all these data types at once, in a single place, and in a format that allows everything to be searched simultaneously. This is tackled by storing all data types as vector embeddings – numerical representations of the original raw data that can be searched with a single query. By doing this, you eliminate the need to search within images and other visuals by using an image.
Instead, you simply ask your question, and the system retrieves relevant information drawn from across your text, images, audio, and video. This is then fed to your LLM/GenAI model as a comprehensive, insightful answer. And unsurprisingly, we’re big fans of this line of thinking at KX – it’s precisely what we do with KDB.AI.
| Learn | Connect | Build |
|---|---|---|
| Learn the stages of multimodal RAG and how KDB.AI powers the retrieval process Read now |
Get faster responses to your questions from KX and community experts Join now |
Get hands-on with our code repositories and try out sample projects Explore now |
Where next for multimodal AI?
The world of AI is evolving rapidly, and we’re increasingly going to see more models that not only understand a variety of data but also output images and audio alongside text. So now is the ideal time to get on board and fully understand how multimodal AI can benefit your projects.
Because, again, we live in a multimodal world. By harnessing the power of all available data, we’ll discover the true capabilities of AI, fueling exciting applications and use cases we’ve not even dreamed of before.
Curious about how you can take advantage of multimodal AI in your organization? Learn more on our KDB.AI page. And if you’re keen to get hands-on with our tech and see it in action, book a demo.
