Recently Microsoft-backed OpenAI made ChatGPT multimodal giving the bot the ability to analyse images and speak to users via its mobile app.
What is Multimodal AI?
Multimodal AI- It is artificial intelligence that combines multiple types or modes of data to create more accurate determinations, draw insightful conclusions or make more precise predictions about real-world problems.
Multiple modalities include video, audio, speech, images, text and a range of traditional numerical data sets.
Example - OpenAI’s text-to-image model, DALL.E, upon which ChatGPT’s vision capabilities are based, is a multimodal AI model that was released in 2021.
Coventional AI - Most AI systems today are unimodal that are designed and built to work with one type of data exclusively.
Example- ChatGPTuses natural language processing (NLP) algorithms to extract meaning from text content, and the only type of output the chatbot can produce is text.
How does multimodality work?
Multimodal AI architecture typically consists of the following components.
Input module – It consists of unimodal neural networks which receive and pre-process different types of data separately.
This module may use different techniques, such as natural language processing or computer vision, depending on the specific modality.
Fusion module – It is meant for integrating information from multiple modalities, such as text, images, audio, and video.
Its goal is to capture the relevant information from each modality and combine it in a way that leverages the strengths of each modality.
Output module – It is responsible for generating the final output or prediction based on the information processed and fused by the earlier stages of the architecture.
The Artificial Intelligence Race
ChatGPT-maker OpenAI has announced that it had enabled its GPT-3.5 and GPT-4 models to study images and analyse them in words, while its mobile apps will have speech synthesis so that people can have full-fledged conversations with the chatbot.
The Microsoft-backed company had promised multimodality during the release of GPT-4.
Google’s new yet-to-be-released multimodal large language model called Gemini, is already being tested in a bunch of companies.
OpenAI is also reportedly working on a new project called Gobi which is expected to be a multimodal AI system from scratch, unlike the GPT models.
What are the advantages of multimodal AI over the current AI?
Versatility- It can handle multiple types of data, making it more adaptable to different situations and use cases.
Natural interaction- By integrating multiple modalities, multimodal AI can interact with users in a more natural and intuitive way, similar to how humans communicate.
Improved accuracy- Multimodal AI can also improve the accuracy of its predictions and classifications.
Enhanced user experience- It can enhance the user experience by providing multiple ways for users to interact with the system.
Robustness against noise- Multimodal AI can be more robust against noise and variability in the input data.
For example, in a speech recognition system, the system can continue to recognize speech even if the audio signal is degraded or the speaker's mouth is partially obscured.
Efficient usage of resources- It can help to make more efficient use of computational and data resources by enabling the system to focus on the most relevant information from each modality.
Better interpretability- It can help to improve interpretability by providing multiple sources of information that can be used to explain the system's output.
What are the applications of multimodal AI?
Healthcare- It can help doctors and patients communicate more effectively, especially for those who have limited mobility or are non-native speakers of a language.
According to a report, the healthcare industry is expected to be the largest user of multimodal AI technology, with a CAGR of 40.5% from 2020 to 2027.
Education- It can improve learning outcomes by providing more personalized and interactive instruction that adapts to a student’s individual needs and learning style.
Entertainment- It can create more immersive and engaging experiences in video games, movies, and other forms of media.
Agriculture- It can help monitor crop health, predict yields, and optimize farming practices.
By integrating satellite imagery, weather data, and soil sensor data, farmers can gain a richer understanding of crop health and optimize irrigation and fertilizer application, resulting in improved crop yields and reduced costs.
Manufacturing-It can be leveraged to improve quality control, predictive maintenance, and supply chain optimization.
By incorporating audio visual data, manufacturers can identify defects in products and optimize manufacturing processes, leading to improved efficiency and reduced waste.
Voice assistants- It canenable more sophisticated and personalized voice assistants that can interact with users through speech, text, and visual displays.
Smart homes- It can create more intelligent and responsive homes that can understand and adapt to a user’s preferences and behaviours.
Virtual shopping assistants- It can help customers navigate and personalize their shopping experience through voice and visual interactions.
Law and order- Microblogging platform X has updated policies to fight a stream of misleading videos and hate speech on the platform since the renewed conflict between Israel and Palestine.
In 2020, Meta was working on a multimodal system to automatically detect hateful memes on Facebook.
What lies ahead?
Businesses can unlock insights that were previously hidden, enabling them to make better decisions and improve outcomes, by combining the strength of different modalities.
As technology continues to evolve and become more advanced, we can expect even greater innovation and impact from multimodal AI in the years ahead.
