The article also explores potential applications of multimodal AI, such as smarter AI chatbots that can handle more than just text and UX/UI feedback apps that can evaluate both the visual and written content of a webpage. The article concludes by offering Vellum's services to help interested parties prototype, choose the best model for their needs, push to production, and monitor the results.
Key takeaways:
- Multimodal AI refers to models that can understand and interpret different types of data simultaneously, including text, images, video, and audio. This broadens the understanding of AI, allowing it to tackle new tasks and offer unique experiences for end users.
- There are several multimodal models available today, including GPT4-V by OpenAI, LLava 1.5, and Fuyu-8B by Adept. Each of these models has its own strengths and limitations.
- Multimodal AI can be used to build smarter AI chatbots and UX/UI feedback apps, among other applications. These models can handle more than just text, allowing for a more comprehensive understanding of user input.
- Vellum offers a platform that can help prototype, choose the best model for the job, push to production and monitor the results for those interested in using multimodal AI for their apps.