Large multimodal models involve interpreting a wide range of data for better and intelligent systems. But even though they are here to make a revolutionary impact, they do suffer from certain drawbacks. Machine learning models have always operated on...
Large multimodal models involve interpreting a wide range of data for better and intelligent systems. But even though they are here to make a revolutionary impact, they do suffer from certain drawbacks.
Machine learning models have always operated on data from a single modality. For instance, text input was generally used for tasks like translation and language modeling. Image for object detection and image classification, and audio for applications like speech recognition. But the integration of multiple modalities or Large Language Models (LLMs) are reshaping things drastically.
LMMs are models that can generate a range of output including text, image, audio, and video based on input. The models receive training on specific data to learn patterns for producing similar data and adding richness to the AI applications. LMMs are undoubtedly going to be in focus and demand in the coming years along with other players in this race.
LMMs are opening up new application avenues by making the models more interactive, creating brand new user experiences, and identifying solutions to new types of tasks. When compared to LLMs, LMMs are very similar to human intelligence. For instance, LMMs enable its users to use an image as a prompt to query the model in lieu of creating elaborate text prompts.
As of now, the LMM landscape mirrors the LLM landscape with winners being those who have the resources to train their models on a wide range of diverse datasets. Even though its a competitive scenario, the rewards are huge. Tech giants may dominate the foundation models through modalities, however, there is a possibility that specialized models might overpower the mightiest of players.
Multimodal applications will have a marked effect in various fields with pilot tests and talks already being carried out.
Applications of LMMs
Let’s discuss their impact in the fields highlighted below to gain a better understanding.
Healthcare: LMMs facilitate medical analysis, communication among healthcare providers and patients speaking different languages, and act as a central repository for a wide range of unimodal AI applications within hospitals.Robotics: Leaders in robotics have incorporated LMMs into human-machine interface along with automation. This facilitates better coordination between robots and humans, as well as, in performing sensitive and precision-related tasks allocated by humans in an easy manner.
Self-driven vehicles: LMMs are already playing a key role in Advanced Driver Assistance Systems (ADAS) and In-Vehicle Human Machine Interfaces (HMI) assistants. In the coming days, they will come equipped with similar sensory perception and decision-making abilities as human drivers.
Education: This involves developing Adaptive Learning Systems that can comprehend and attune to each student’s needs.
Entertainment: LMMs can be used to translate movies real-time into various languages as per cultural context.
Market Trends in LMMs
Leading technology firms and startups are trying their level best to go beyond the AI terrain with the hope of creating new AI models that can work with text as well as images interchangeably.
According to a research by Microsoft, “As a natural progression, LMMs should be able to generate interleaved image-text content, such as producing vivid tutorials containing both text and images to enable comprehensive multimodal content understanding and generation. Additionally, it would be beneficial to incorporate other modalities, such as video, audio, and other sensor data to expand the capabilities of LMMs.”
Limitations of LMMs
Developing LMMs that can do everything is a very costly exercise as it involves huge computing costs and data constraints. These two factors restrict even the most well-funded companies from building these incredible foundation models. Apart from these, there are other factors too which are highlighted below.
It is a complicated task to meaningfully link text with visual data. It is a challenge to teach the models to comprehend abstract ideas including humor or irony. Biases in training data may result in ethical issues. Creating and using these models is an expensive task as it requires immense computational power.Conclusion
The fine-tuning of foundation models with data that’s aimed at fulfiling a specific purpose represents a brand new way for democratizing AI and solutions for a bigger and targeted impact. All in all, developing LMMs requires a great deal of resources and expertise and startups are faced with a chance to devise innovative solutions that can address real-world challenges across industries. LMMs that are fine-tuned focus on specific industries with a proper audience can deliver outcomes that are at par with leading tech firms.
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