We have already witnessed AI solving real world problems. Multi-modal AI is a new paradigm in the world of artificial intelligence. The term ‘modality’ refers to the channels of communication and sensation. Multi-modality refers to the use of multiple data modalities such as images, video, text, and speech. Multi-modal AI is a new branch of artificial intelligence that allows AI learning systems to process and relate multi-modal data.
Why is multi-modal AI much more powerful than unimodal AI?
- As human beings, the world that we see around us is multimodal.
- In the surroundings, we can see objects, listen to sounds, feel the texture, taste flavors, and even smell odors.
- Traditionally, the standard AI systems that are built are unimodal. This means that the designed AI systems are specifically trained to do a specific task.
- In unimodal AI, the AI systems are trained using a single training data. For example,
- The drawback of a unimodal AI system is that it completely overlooks the important contextual and supporting information for making the best possible deductions.
- AI advancements like multi-modal AI possess the ability to process multimodal signals simultaneously just like human beings do.
- For example, when a content text and image are changed and replaced with some other image, humans are able to experience this and how the juxtaposition of images and text can go seriously wrong.
- When it comes to AI systems, a unimodal AI system would not be able to grasp the right meaning when texts and images change.
- However, with multi-modal AI, that uses various data modalities, better understanding and analysis of the data is possible.
Core challenges in multi-modal AI
The main challenges in the field of multi-modal AI are:
- Representation
- Transparency
- Alignment
- Fusion
- Co-learning
Multi-modal AI developments
AI Researchers around the world have made some exciting and interesting breakthroughs in multimodal development. Some of the developments are as below.
DALL.E
- It is an AI system developed by OpenAI.
- It converts a text-based description into a digital image.
- Essentially, this multimodal AI system is a neural network that consists of 12 billion parameters.
ALIGN
- This AI model is trained by Google over a noisy dataset using a large number of image-text pairs.
- In various image-text retrieval benchmarks, this model was able to achieve the highest accuracy.
CLIP
- OpenAI has developed this AI system.
- It is capable of performing a range of visual recognition tasks.
- If natural language descriptions of categories are provided, CLIP can quickly classify images into their respective categories.
MURAL
- It is an AI system developed by Google AI.
- MURAL is a digital workspace that enables teams around the world to virtually collaborate and communicate with each other to unleash their potential and solve real-world challenges as well.
FLAVA
- This multimodal AI system has been trained by Meta over images and 35 different languages.
- It has performed well in various multimodal tasks.
Florence
- It is released by Microsoft Research.
- Florence makes it possible to model time, space, and modality.
- Several common video language tasks can be solved using this model.
NUWA
- It is a multimodal AI pre-trained model developed by a collaboration between Microsoft Research and Peking University.
- This multimodal AI system is capable of generating new or modifying existing visual data (images and videos).
- The NUWA model is trained with images, videos, and text.
- When given a text prompt or sketch, it can predict the next video frame and fill in the gaps in any incomplete video.
Cross modal applications
Robust multi-modal AI systems have multiple applications across various industrial verticals. These include robotic assistants, driver monitoring systems, advanced driver assistance, and obtaining valuable business insights through context-based data mining.
The recent development of multi-modal AI has resulted in many cross-modal applications. These include:
Text-to-Image generation
AI can generate an image depending on the input text.
Image caption generation
It involves recognizing the image context and generating a caption for it using deep leading and computer vision.
Visual Question Answering (VQA)
In VQA, an image and text-based question is taken as the input, and a text-based answer is given as the output.
Text to Image and Image to Text search
Based on multiple modalities, the search engine identifies the sources.
Text-to-speech synthesis
This technology involves reading digital text and transferring it into spoken speech automatically. It is used in several digital devices such as smartphones, laptops, personal computers, and tablets.
Speech to Text Transcription
This technology recognizes the spoken language and translates it into text format. Various virtual assistants like Google Assistants, Amazon Alexa, and Apple’s Siri make use of speech-to-text transcription.
Human beings have an inborn potential to process multi modalities. The real-world that we live in is inherently multi-modal in nature. The multimodal AI, which is one of the AI advancements, has the potential to progress towards multiple modalities from a single modality. With the advancements in multimodal AI systems, some existing challenges in the world of artificial intelligence can be overcome.
