Generative & Multimodal AI:

Generative & Multimodal AI are the leading technologies in 2025. These AI models can generate new content but also understand and process several types of data — text, images, audio, video and more. Between the two of them, they’re revolutionizing how humans create, communicate and connect with machines.

What is Generative AI?

Generative AI are systems that generate new content based on the data they have been trained on. These may be included:

·         Text (for example, ChatGPT, Claude)

·         Images (for example, DALL·E, Midjourney)

·         Audio (for example, A ElevenLabs, Suno)

·         Video (e.g., Pika, RunwayML)

·         Code (e.g., GitHub Copilot, Replit Ghostwriter)

Rather than analyzing or classifying the data to which it is traditionally applied, generative AI creates new content, which can be used to power limitless new creative, productivity and automation opportunities.

What is Multimodal AI?

Multimodal AI is capable of processing and interpreting inputs from different modalities simultaneously, like:

·         Reading text while viewing related images

·         Listening to audio and producing captions

·         Watching a video and then answer questions about it

A multimodal model in high demand is GPT-4V(ision) which allows you to put forth an image and ask questions about it. Google Gemini, Anthropic’s Claude and Meta’s LLaVA are also making strides in this space.

Current Leaders in the Space:

·         OpenAI: GPT-4, DALL·E, Sora (video generation)

·         Google DeepMind: Gemini (multimodal LLM)

·         Anthropic: Claude 3.5 w/visual – Anthropic

·         Meta: LLaMA + LLaVA (vision-language)

·         Runway, Pika, Synthesia: Video production platforms

·         Adobe Firefly, Canva Magic Studio: Generative design

Multimodal generative AI examples:

1.     GPT-4o (OpenAI):

Modality Support: Text, Image, Audio (Input and Output)

GPT‑4o (short for “omni”) is OpenAI’s premier multimodal model, released in 2024. It can read and write text, understand and generate images and audio — and now it can do all of these things in one interaction. For instance, they can show it an image and ask questions about it, or provide it with audio input and get text answers back. GPT‑4o supports real-time voice interaction (with emotional tone), can generate visual content, and respond to questions on screenshots, documents, or live camera feeds. Its seamless multimodal capabilities are creating a new wave of natural, human-like AI assistant.

2.     Emu (Meta):

Modality Support: Text and Image (Bidirectional Generation)

Emu is the latest foundational model in Meta’s family of vision and language models and is capable of understanding and generating images and texts. It enables you to do multimodal pretraining (e.g. you can convert text to images like DALL·E, or generate text from visual inputs, which is handy for captioning photos, scenes, or product images). Instead of performing such tasks separately like previous models, Emu is directly capable of many multimodal tasks with a single model. This has applications in areas such as e-commerce (creating product descriptions), accessibility (explaining visuals to users who are visually impaired), and graphic design.

3.     LLaVA-Interactive:

Modality Support: Image+Text (Interactive Visual Dialogue)

LLaVA-Interactive is the interactive visual chat extension of the LLaVA (Large Language and Vision Assistant) project. You can upload an image and then go back and forth with the model about the image – identify objects, suggest edits, or answer questions about visual scenes. It supports text-to-image editing (e.g., modifying objects in an image using conversational prompts). This model is a significant leap towards AI systems that can interactively interpret and manipulate visual content, which is particularly beneficial for applications in design, marketing and educational tools.

4.     CoDi (Composable Diffusion, Microsoft Research):

Modality Support: Text, Image, Audio, Video (From-to-To Generation)

CoDi is a research project on the full power of “any-to-any” multimodal generation, that allows one or many types of inputs (e.g., image + audio) and outputs one or many types (e.g., video or text). Inspired by composable diffusion architecture, CoDi enables a dynamic recomposition of different modalities. This might enable revolutionary applications such as converting a text script and voice track into an animated video, or fusing visual and auditory signals into on line experiences. Its generalist, mix-and-match form factor signals a world of incredibly creative AI apps.

5.     Wu Dao (Beijing Academy of AI, China):

Modality support: Text, Image (Multimodal Understanding and Generation)

Wu Dao is a large-scale AI model developed by an organization in China, which stands out with its multilingual and multimodal features. It was trained with 1.75 trillion parameters and uses image-text data, meaning it can create images based on descriptions and the other way round. Wu Dao is also said to be promising in art creation, academic writing and medical applications. It is a national-scale model and shows how generative AI research is global. Its architecture enables a multilayer decoder to generate content in a multimodal setting that is more regionally and culturally sensitive.