What is Multimodal Artificial Intelligence and What to Eat It With?

2023 can be called the year of Large Language Models (LLM). They won their place in almost every field of business and became a new trend. However, this year – 2024 will surprise you much more because it can become the year of Multimodal Artificial Intelligence. It increasingly relies on human senses than its predecessors and can process multiple inputs such as text, voice, video, and thermal data. So this year, Multimodal Artificial Intelligence models such as GPT-4V, Google Gemini, and Meta ImageBind will be revealed even more.

What is Multimodal Artificial Intelligence ?

Developments and breakthroughs in generative Artificial Intelligence are making ever greater strides toward its ability to perform a wide range of cognitive tasks (AGI). Despite this, it still cannot think like a human. The human brain relies on 5 senses, which serve as collectors of information from the surrounding environment. After that, the information is processed and stored in our brains.?

Generative models such as ChatGPT can only accept and generate one type of data. That is, they are unimodal. They were mostly used to provide text prompts and generate a text response.

Multimodal learning aims to increase the ability of machines to learn by presenting them with sensory types of data, i.e. images, videos or audio recordings. With this model, the correlation between textual descriptions and associated images, audio or video is studied. Currently, multimodal learning opens many perspectives for the modern technological world. Their ability to generate multiple types of outputs allows us to see new opportunities and developments.

How Does Multimodal Artificial Intelligence Work?

The development of transforms opened new possibilities for multimodal AI. The structure of transformers made it easier to experiment with the models’ architecture. Transformer consists of two parts – an encoder (transforms the input into a feature vector – a meaningful representation of the input information) and a decoder (generates information based on the encoder’s output). At first, transformers were used in language processing and text generation (LLMs) and later on were trained for image captioning, visual question answering, visual instruction, and other multimodal tasks. This leads to the possibility of creating a Large Vision-Language Models that have visual and textual encoders, can combine the representation of these two modalities, and can generate language responses (such as GPT-4V or LLaVA).?

Another common approach is a combination of the Large Language Model with other capable models where LLM handles reasoning processes and other models (such as Latent Diffusion Models or TextToSpeech Models) generate new modalities. Such ansamble of models is more suitable to handle differences in architectures (transformers are great in handling language processing but diffusers are better for image generation) and allow higher modularity.

How Do Various Input Types Affect the Operation of Multimodal Models?

The multimodal AI architecture functions as follows for various input kinds. To facilitate comprehension, we have incorporated actual instances as well.

Text-to-image Generation and Image Description Generation

Some of the most revolutionary models of text-to-image generation and image description generation are GLIDE, CLIP, and DALL-E. Their specialty is the ability to create images from text and help describe images.

OpenAI CLIP has specific text and image encoders. They predict specific images in a dataset by training on massive datasets. In addition, if the model is subject to both an image and a corresponding textual description, it can use multimodal neurons. All this together represents a merged multimodal system.

DALL-E has about 13 billion parameters. It generates an image according to the input request. CLIP is used to rank images. This allows for accurate and detailed images.

CLIP also ranks images for GLIDE. However, it uses a diffusion model, which allows it to obtain accurate and realistic results.

Visual Question Answering

This query assumes correct answers to the questions based on the presented image. Microsoft Research is at the forefront of this, offering creative and innovative approaches to visual responses.

METER for example uses sub-architectures for rendering encoders, decoding modules, text encoders, and multimodal fusion modules.

The Unified Vision-Language Pretrained Model (VLMo) suggests the use of different encoders. Among them are dual encoders, fusion encoders, and a network of modular transformers. The flexibility of the model is primarily due to its levels of self-control and blocks with experts in specific modalities.

Image-to-text Search And Text-to-Image

Web search is also not aloof from the multimodal revolution. Datasets such as WebQA, which was created by researchers and developers at Carnegie Mellon University and Microsoft, allow models to identify sources of text and images with exceptional accuracy. This helps to answer the request correctly. However, the model will still need multiple sources to provide accurate predictions.

Google ALIGN (Large-scale Image and Noisy-Text Embedding model) uses alt text data from images on the Internet to train text (BERT-Large) and clear visual coders (EfficientNet-L2).

After that, the results of the encoders are combined using a multimodal architecture. This creates powerful models with multimodal representation. They can provide web searches in several modalities. At the same time, no additional configuration is required.

Video-Language Modeling

To bring Artificial Intelligence closer to natural, multimodal models designed for video were created.

Microsoft’s Florence-VL project uses a combination of transformer models and convolutional neural networks (CNN) in its ClipBERT project. They work with a thin selection on cards.

SwinBERT and VIOLET, iterations of ClipBERT, use Sparse Attention and Visual-token Modeling to perform better in answering video questions/subtitles/searches.

ClipBERT, SwinBERT, and VIOLET work similarly to them. Their ability to acquire video data from multiple modalities relies on a transformer architecture along with parallel learning modules. That is why they can integrate responses into a single multimodal representation.

Advantages of Using Multimodal Models of Artificial Intelligence

Contextual Understanding

By analyzing words, surrounding concepts, or sentences, multimodal systems can understand them. This is especially difficult when processing natural language. After all, it makes it possible to understand the concept and essence of the sentence to give an appropriate answer. A combination of NLP and multimodal Artificial Intelligence can understand the context by combining linguistic and visual information.

Thanks to the ability of Multimodal AI to consider both textual and visual cues, they are a convenient method of interpreting and combining information. In addition, they can understand the temporal relationship between sounds, events, and dialogues in a video.

Much Higher Accuracy

Combining multiple modalities, such as text, image, and video, can provide greater accuracy. With a comprehensive and detailed understanding of the input data, multimodal systems can achieve better performance and provide more accurate predictions.

Modalities provide more descriptive and accurate signatures, and improve natural language processing operations or face recognition to obtain more accurate information about the speaker’s emotional state. They can fill in missing gaps or correct errors using information from multiple modalities.

Natural Interaction

Facilitate interactions between users and machines. This is due to the ability of multimodal models to combine multiple input modes, including text, speech, and visual cues. In this way, they can more fully understand the needs and intentions of the user.

Humans can easily interact with machines in conversation. A combination of multimodal systems and NLP can interpret a user’s message and then combine it with information from visual cues or images. This will allow you to fully understand the meaning of the user’s sentences, his tone, and emotions. Thanks to this, your chatbot will be able to provide answers that will satisfy the user.

Improved Capabilities

Multimodal models make it possible to achieve a greater understanding of the context, because they use information from several modalities, and thereby significantly improve the overall capabilities of the artificial intelligence system. In this way, AI can be more productive, more accurate and more efficient.

Multimodal systems also bridge the gap between people and technology. They help machines to be more natural and understandable. AI can perceive and respond to combined queries. This increases customer satisfaction and allows you to use technology more effectively.

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