An Introduction to Large Multimodal Models
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We're all aware of the rapid advancements in generative artificial intelligence (AI) and its applications in language translations, image recognition, and voice-to-text conversion. In the past couple of years, we've witnessed advancements in large language models (LLMs) and their successful business applications. However, a fundamental limitation of currently famous LLMs is that they work only with a single data modality. This restricts artificial intelligence(AI) from grasping the complexities of the real world, which comprises the simultaneous presence of images, sound, and text. This is the gap that large multimodal models (LMMs) are beginning to close by working with various data modalities simultaneously. Therefore, this blog will delve deeper into this transformative advancement and its potential for enhancing business operations.
What are Large Multimodal Models (LMMs)?
LMMs are AI models that can understand and process different forms of input. These inputs are of different "modalities," including images, videos, and audio. Modalities are data for the AI models. LMMs' ability to process and interpret information from diverse sources simultaneously mimics how humans interact with the world. However, it is essential to note that not all multimodal systems qualify as LMMs. For instance, DALL-E is multimodal since it converts text to images. However, it does not include language model components.
For ease of comprehension, think of it this way: a multimodal system can generate inputs and process outputs in multiple modalities. For instance, Gemini, a LMM, achieves input generation and output processing across multiple modalities by integrating diverse data types, such as text, videos, and audio, into its training process, allowing it to understand and generate content in a multimodal fashion.
Difference Between Large Multimodal Models(LMMs) and Large Language Models(LLMs)
Despite their differences, which we will delve deeper into in this section, LMMs and LLMs are similar in training, design, and operation. Both models rely on similar training and reinforcement strategies and have a similar underlying transformer architecture. LMMs are the advanced versions of LLMs as they work on multiple modalities, whereas LLMs are restricted to text. LLMs can be transformed into LMMs by incorporating multiple modalities in the model.
Understanding the differences between LMMs and LLMs is crucial for leveraging them for business use cases. Hence, here is a tabular description of the differences between LMMs and LLMs:
| Aspect | LMM | LLM |
| Data Modalities | LMMs can understand and process different data modalities, including text, audio, videos, and sensory data | LLMs specialise in processing and generating only textual data |
| Applications and tasks | LMMs can understand and integrate information across data modalities, making them suitable for various business applications. For instance, an LMM could analyze textual, pictorial, and video-based information from an informative article | LLMs are suitable for processing textual data and are restricted to text-based applications |
| Data collection and preparation | LMM training involves complex data collection as it involves a variety of content in different formats and modalities. Therefore, techniques such as data annotation are crucial for aligning different data types for usage | LLM training involves collecting textual data from books, websites, and other sources to increase linguistic diversity and breadth |
| Model architecture and design | LMMs require a complex architecture as they integrate different types of data modalities. Therefore, LMM utilises a combination of neural network types and mechanisms to fuse these modalities effectively. For instance, a LMM architecture could use convolutional neural networks (CNNs) for images and transformers for text | LLMs utilise transformer architecture to process sequential data such as text |
| Pre-training | LMM pre-training involves using multiple data modalities. The task consists of the model learning to correlate text with images or understanding sequences in videos | LLM pre-training involves vast amounts of text. Pre-training an LLM also involves techniques like masked language modelling, in which the model predicts missing words in a sentence. |
| Fine-tuning | LMM fine-tuning involves datasets that help models learn cross-modal relationships. | LLM is fine-tuned using specialised text datasets, which are tailored to specific tasks such as question answering or summarising |
| Evaluation and iteration | LMMs are evaluated using multiple metrics as they support several data modalities. Some common evaluation metrics for LMMs include image recognition accuracy, audio processing quality, and integrating information across modalities | An LLM's evaluation metrics focus on language comprehension and text generation, such as relevance, fluency, and coherence |
LMM Architecture
LMMs are trained on vast amounts of multiple modalities such as text, images, audio, video, code, and any other modality the AI model can understand. The training occurs simultaneously. To put that into perspective, here's an example: the LMM's underlying neural network learns the word cat, its concept, and what it looks and sounds like. It becomes as capable of recognizing a cat's photo as identifying a "meow" sound from an audio clip. After this pre-training, the results are further fine-tuned.
For a detailed description, here's a general overview of how LMMs function:
Data encoding: LMMs use specialised encoders for each modality to transform raw input data into vector representations called embeddings. These embeddings capture the crucial features of the data, making them suitable for further processing.
Multimodal fusion: The embeddings from different modalities are combined using fusion mechanisms. These mechanisms align and integrate the embeddings into a unified multimodal representation.
Task-specific processing: Depending on the task, LMMs may employ additional processing layers or components. For example, in generative tasks, a decoder might be used to generate output (e.g., text or images) based on the multimodal representation.
Output generation: In generative tasks, LMMs generate output step-by-step. For example, considering the multimodal context and previously generated words, the model might predict each word sequentially during text generation.
Training and optimization: LMMs are trained on large datasets using optimization algorithms. The training process involves adjusting the model's parameters to minimise the loss function, which measures the difference between the model's predictions and the ground truth data.
Attention mechanisms: Attention mechanisms are often used in LMMs to enable the model to focus on relevant parts of the input data. This is particularly important in multimodal settings, where the model must selectively attend to information from different modalities.
It's important to note that LMMs are a rapidly evolving field, and researchers are continuously exploring new architectures, alignment mechanisms, and training objectives to improve multimodal representation and generation capabilities. LMMs are applicable for various tasks beyond text generation, including classification, detection, and more complex generative tasks involving multiple output modalities. The architecture and components of an LMM can vary depending on the specific task and modalities involved.
Despite their potential, LMMs also face particular challenges and limitations. Training LMMs requires significant computational resources and expertise, making them inaccessible to smaller research groups or organisations with limited resources. Additionally, integrating multiple modalities into a single model can introduce complexities and potential performance issues, requiring careful optimization and tuning.
By using LMMs' capabilities to process and interpret multiple data types, AI systems can become more sophisticated and effective in addressing real-world problems across different domains.
Examples of Large Multimodal Models
Over the past year, AI-based organisations have launched their LMMs. In this section, we will discuss five of them, along with their origins, functions, and business applications:
GPT- 4V: GPT-4V was developed by Open AI, and it is mainly used for the smooth integration of text-only, vision-only, and audio-only models. It performs well on text summarization tasks. Its primary use cases include text generation from written/graphical inputs and versatile processing of various input data formats.
Gemini: Gemini was developed by Google's DeepMind. It is inherently multimodal and can effortlessly manage text and diverse audiovisual inputs. Its primary use case lies in effortlessly handling tasks across text and audiovisual domains. It is capable of generating outputs in text and image formats.
ImageBind: ImageBind was developed by Meta. It integrates six modalities: text, image/videos, audio, 3D measurements, temperature, and motion data. Its everyday use cases involve connecting objects in photos with attributes like sound, 3D shapes, temperature data, motion, and scene generation from text/sound.
Unified-IO 2: Unified-IO 2 was developed by the Allen Institute for AI. It is an autoregressive multimodal model that can understand/generate images, text, audio, and action. It tokenizes inputs into shared space. It has promising use cases such as captioning, free-form instructions, image editing, object detection, audio generation, and more.
LLaVa: LLaVa was jointly developed by the University of Wisconsin-Madison, Microsoft Research, and Columbia University. It is a multimodal GPT4 variant which utilises Meta's Llama LLM. Furthermore, it incorporates CLIP visual encoder for robust visual comprehension. It has applications in healthcare for answering enquiries related to biomedical images.
Applications of LMMs
LMMs hold promising and diverse applications for businesses across various industries. Here are five compelling business applications of LMMs that show their transformative potential:
1. Research and Development (R&D): LMMs can contribute to scientifically backed research by analysing vast amounts of data. They can assist R&D teams with identifying patterns and trends and enhancing their discovery. LMMs accelerate innovation by creating realistic scenarios for launching new products and process efficient decision-making.
Potential: LMMs hold promises for accelerated product development and innovation.
Challenges: Integrating LMMs for R&D requires robust computational infrastructure and challenges related to data quality, model interpretability, and scalability are required to ensure substantial research outcomes.
2. Skill Development: LMMs help create adaptive learning systems tailored to every employee's pace and skill level. Businesses can leverage interactive simulations and practical skill development for their employees. A hands-on learning experience can facilitate critical thinking and problem-solving skills.
Potential: Leveraging LMMs for organisation-wide skill development helps businesses prepare the workforce for a rapidly evolving marketplace.
Challenges: Integrating LMMs for employee skill development requires investing in learning management systems capable of supporting multimodal learning material. It also comes with challenges related to measuring the effectiveness of personalised learning interventions.
3. Safety Inspection: Businesses can use LMMs for safety inspections due to their effective monitoring of compliance with personal protective equipment (PPE). LMMs have been used to count the number of employees wearing helmets, demonstrating their suitability for identifying safety violations. LMMs foster a safe work environment by promptly helping address safety concerns.
Potential: LMMs can help identify safety hazards and facilitate timely intervention, reducing workplace injuries.
Challenges: It is difficult to ensure LMMs compatibility with existing safety protocols and reliability in safety-critical applications.
4. Defect Detection: LMMs offer efficient defect detection, which can help during the manufacturing process. LMMs can analyse product images using computer vision techniques with natural language capabilities to help identify faults or defects in products.
Potential: Integrating LMMs for default detection will help businesses enhance product quality and build customer trust.
Challenges: Ensuring robustness and generalisation of defect detection across diverse product categories is challenging.
5. Content generation and recommendations: LMMs enable real-time translations and products based on individual tastes after analysing vast amounts of data.
Potential: LMMs can empower businesses to deliver customised marketing messages tailored to individual tastes.
Challenges: Delivering real-time personalised experiences at scale while maintaining user trust and satisfaction is challenging.
Conclusion
Large multimodal models (LMMs) represent a revolutionary leap in AI, processing information across modalities like text, images, and audio. Unlike traditional LLMs, LMMs mimic human perception, offering a comprehensive understanding of the world. This transformative technology unlocks vast potential for businesses, from accelerating R&D to personalising learning experiences. While challenges like computational cost and data integration exist, LMMs are poised to reshape various industries, paving the way for a future powered by intelligent and versatile AI.
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