In this episode of Valorem Visions, Ryan McCamy talks about the potential of Multimodal Artificial Intelligence and what Valorem Reply is doing to help customers leverage this technology.

Transcript

What is the trend?

So within the trend of Multimodal Artificial Intelligence, multimodal AI for the integration of different modalities into an AI-powered system. So, what that could mean is for example the ingestion of text video, audio or other data sources or information streams that are then brought together to provide a deeper understanding of a situation. In 2023, so much of the AI world was focused on large language models, so chatbots, and the like. In 2024, we're looking to take the next step and bring together these different types of AI to help provide deeper insights, understandings, and analytics for different situations.

Why is this trend important?

So, generally speaking, a multimodal system will have greater utility and a more holistic understanding of a situation than a system that only consists of a single data source. For example, a system that analyzes only text can only do so much with that whereas if you pair that with other streams you can gain deeper insights. For example, in an auto-racing context, a system could analyze photos and videos of a car audio that's coming directly from the driver and text entered into various systems about performance and setup of the car and you can pull those three things together to optimize a car's setup and give instructions back to the team to put into play at the next pit stop. By incorporating all of these sources, the system would have greater insight and make better recommendations than it have only had a single source.

What is Valorem Reply doing to prepare for this trend?

So Valorem Reply is preparing to support this trend by building out accelerators using Azure's Advanced AI tool sets to help clients quickly stand up different types of individual models and then integrating them into a cohesive system. So we begin with an initial design workshop where we help our clients identify the specific business cases that align well to an AI solution. Then we move into solution design implementation planning finally working alongside your data and product teams to bring the solution to life and we can bring that solution to life slowly, and incrementally through proofs of concept and then scaling it up to solve your different business challenges.