We are all aware of the rapid advances in the field of generative artificial intelligence (AI) and its applications in the areas of language translation, image recognition and speech-to-text conversion. In recent years, we have witnessed advances in large language models (LLMs) and their successful applications in business. However, a fundamental limitation of currently known LLMs is that they only work with a single data modality. This prevents artificial intelligence (AI) from capturing the complexity of the real world, which consists of the simultaneous presence of images, sound and text. Large Multimodal Models (LMMs) are beginning to close this gap by working with different data modalities simultaneously. In this blog, we will therefore take a closer look at this transformative advance and its potential for improving business processes.
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What are Large Multimodal Models?
Large Multimodal Models (LMMs) are AI models that can understand and process various forms of input. These inputs consist of various "modalities", including images, videos and audio. Modalities are data for the AI models. The ability of LMMs to process and interpret information from different sources simultaneously mimics how humans interact with the world. However, it is important to note that not all multimodal systems are considered LMMs. DALL-E for example, is multimodal, as it converts text into images. However, it does not contain any language model components.
To make it easier to understand, you should visualise it like this: A multimodal system can generate input and process output in multiple modalities. For example, Gemini, an LMM, can generate input and process output in multiple modalities by integrating different types of data such as text, videos and audio into its training process so that it can understand and generate content in a multimodal way.
Large Multimodal Models vs. Large Language Models
Despite their differences, which we will discuss in more detail in this section, Large Multimodal Models (LMMs) and Large Language Models (LLMs) are similar in training, design and operation. Both models are based on similar training and reinforcement strategies and have a similar underlying transformer architecture. LMMs are the advanced versions of LLMs as they work with multiple modalities, while LLMs are limited to text. LLMs can be transformed into LMMs by integrating multiple modalities into the model.
Understanding the differences between LMMs and LLMs is critical to utilising them for business use cases. Therefore, a tabular description of the differences between LMMs and LLMs follows:
| Feature | Large Multimodal Model (LMM) | Large Language Model (LLM) |
|---|---|---|
| Data modalities | LMMs can understand and process different data modalities, including text, audio, video and sensory data. | LLMs specialise exclusively in the processing and generation of text data. |
| Applications and tasks | LMMs can understand and integrate information across different data modalities, making them suitable for various business applications. For example, an LMM could analyse textual, pictorial and video-based information from an informative article. | LLMs are suitable for processing textual data and are limited to text-based applications. |
| Data acquisition and processing | Training LMMs requires complex data collection as it involves a variety of content in different formats and modalities. Therefore, techniques such as data annotation are crucial to match the different types of data for use. | LLM training involves the collection of text 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, LMMs use a combination of neural network types and mechanisms to effectively fuse these modalities. For example, an LMM architecture could use convolutional neural networks (CNNs) for images and transformers for text. | LLMs use a transformer architecture to process sequential data such as text. |
| Pre-training | The pre-training of the LMM involves the use of several data modalities. The task is for the model to learn to correlate text with images or to understand sequences in videos. | LLM pre-training involves large amounts of text. The pre-training of an LLM also includes techniques such as masked language modelling, where the model predicts missing words in a sentence. |
| Fine-Tuning | The fine-tuning of LMM includes data sets that help the models to learn cross-mode relationships. | LLM is fine-tuned using specialised text datasets that are tailored to specific tasks such as answering questions or summarising texts. |
| Evaluation and iteration | LMMs are evaluated on multiple metrics because they support multiple data modalities. Common evaluation metrics for LMMs include the accuracy of image recognition, the quality of audio processing and the integration of information across different modalities. | The assessment metrics of an LLM focus on language comprehension and text production, e.g. relevance, fluency and coherence. |
Architecture and functionality of large multimodal models
Large Multimodal Models (LMMs) are trained using large amounts of different modalities such as text, images, audio, video, code and any other modality that the AI model can understand. The training takes place simultaneously. To illustrate this, here is an example: The LMM's underlying neural network learns the word cat, its concept and what it looks and sounds like. It is then able to recognise a photo of a cat as well as identify a "meow" from an audio clip. After this preliminary training, the results are further refined.
For a detailed description, here is a general overview of how Large Multimodal Models (LMMs) work:
- Data EncodingLMMs use specialised encoders for each modality to convert the raw input data into vector representations, known as embeddings. These embeddings capture the key features of the data and make it suitable for further processing.
- Multimodal FusionThe embeddings from different modalities are combined with the help of fusion mechanisms. These mechanisms harmonise the embeddings and integrate them into a uniform multimodal representation.
- Task-specific processingDepending on the task, LMMs can use additional processing levels or components. With generative tasks For example, a decoder can be used to generate the output (e.g. text or images) based on the multimodal representation.
- Output creation In generative tasks, LMMs generate the output step by step. For example, the model could predict each word in turn during text generation, taking into account the multimodal context and the previously generated words.
- Training and optimisationLMMs are trained on large data sets using optimisation algorithms. The training process involves adjusting the model parameters to minimise the loss function, which measures the difference between the model's predictions and the actual data.
- Attention mechanismsAttention mechanisms are often used in LMMs to allow the model to focus on relevant parts of the input data. This is particularly important in multimodal environments where the model must selectively attend to information from different modalities.
It is important to note that Large Multimodal Models (LMMs) are a rapidly evolving field and researchers are continuously exploring new architectures, alignment mechanisms and training targets to improve multimodal representation and generation capabilities. LMMs are suitable for various tasksthat go beyond text generation, including classification, recognition 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, large multimodal models also face particular challenges and limitations. The Training of LMMs requires considerable computing resources and expertisewhich makes them inaccessible to smaller research groups or organisations with limited resources. Furthermore, integrating multiple modalities into a single model can lead to complexity and potential performance issues that require careful optimisation and tuning.
By utilising the ability of Large Multimodal Models to process and interpret multiple data types, AI systems can become more sophisticated and effective at tackling real-world problems in different domains.
Find out all about foundation models and how they can be used effectively in companies to give you a competitive edge and accelerate business processes in our basic article.
Examples of large multimodal models
Over the past year, AI-based organisations have launched theirLarge Multimodal Models (LMMs). In this section, five of them are discussed along with their origins, features and business applications:
- GPT-4VGPT-4V was developed by Open AI and is mainly used for the smooth integration of text-only, image-only and audio-only models. It performs well on text summarisation tasks. Its main use cases include text generation from written/graphic input and versatile processing of various input data formats.
- GeminiGemini was developed by Google's DeepMind. It is multimodal by nature and can effortlessly process text and various audiovisual inputs. Its main use case is to effortlessly handle tasks in textual and audiovisual domains. It is capable of generating output in text and image formats.
- ImageBindImageBind was developed by Meta. It integrates six modalities: text, images/videos, audio, 3D measurements, temperature and motion data. Everyday use cases include the connection of objects in photos with attributes such as sound, 3D shapes, temperature data, motion and the creation of scenes from text/sound.
- Unified-IO 2Unified-IO 2 was developed by the Allen Institute for AI. It is an autoregressive multimodal model that can understand/generate images, text, audio and actions. It tokenises inputs into a shared space. It has promising use cases such as subtitles, free-form instructions, image processing, object recognition, audio generation and more.
- LLaVaLLaVa was jointly developed by the University of Wisconsin-Madison, Microsoft Research and Columbia University. It is a multimodal GPT4 variant that utilises Meta's Llama LLM. It also contains the visual CLIP encoder for robust visual understanding. It is used in the healthcare sector to answer enquiries about biomedical images.
Large language models are transforming interaction with technology and expanding its application from content creation to customer service. Our overview presents 14 relevant representatives in detail:
Application examples of LMMs in companies
Large Multimodal Models (LMMs) offer promising and diverse applications for companies in various industries. Here are five compelling business applications of LMMs that demonstrate their transformative potential:
Research and development (R&D)
Large Multimodal Models (LMMs) can contribute to scientifically sound research by analysing large amounts of data. They can help R&D teams recognise patterns and trends and improve their discovery. LMMs accelerate innovation by creating realistic scenarios for new product launches and efficient decision-making.
PotentialLMMs promise accelerated product development and innovation.
ChallengesThe integration of LMMs for research and development requires a robust computing infrastructure, and the challenges in terms of Data qualityThe challenges of model interpretability and scalability must be overcome in order to ensure substantial research results.
Skills development
Large Multimodal Models (LMMs) can be used to create adaptive learning systems that are customised to the pace and skill level of each employee. Organisations can use interactive simulations and hands-on skills development for their employees. A hands-on learning experience can promote critical thinking and problem-solving skills.
PotentialThe use of LMMs for organisation-wide skills development helps companies to prepare their employees for a rapidly evolving market.
ChallengesThe integration of LMMs for employee skills development requires investment in learning management systems that can support multimodal learning material. Measuring the effectiveness of personalised learning interventions is also a challenge.
Safety inspection
Organisations can use Large Multimodal Models (LMMs) for safety inspections as they effectively monitor compliance with personal protective equipment (PPE). LMMs have been used to count the number of employees wearing helmets, proving their suitability for identifying safety violations. LMMs promote a safe working environment by helping to address safety issues promptly.
PotentialLMMs can help to identify safety risks and enable timely intervention, thereby reducing injuries in the workplace.
ChallengesIt is difficult to ensure the compatibility of LMM with existing safety protocols and reliability in safety-critical applications.
Defect detection
Large Multimodal Models (LMMs) provide efficient defect detection that can be helpful during the manufacturing process. LMMs can analyse product images using computer vision techniques and natural language capabilities to detect faults or defects in products.
PotentialThe integration of LMMs for fault detection will help companies to improve product quality and increase customer confidence.
ChallengesEnsuring the robustness and generalisation of fault detection across different product categories is a challenge.
Generation of content and recommendations
Large Mulitmodal Models (LMMs) enable real-time translations and products based on individual preferences after analysing large amounts of data.
PotentialLMMs can enable companies to deliver customised marketing messages that are tailored to individual tastes.
ChallengesProviding personalised experiences in real time while maintaining user trust and satisfaction is a challenge.
Whether text or code generation: ChatGPT is currently on everyone's lips. Find out what use cases could look like in your company and what integration challenges await you.
Benefit from the versatility of the applications
Large Multimodal Models (LMMs) represent a real leap forward in artificial intelligence as they process information across different modalities such as text, images and audio. Unlike traditional large language models, LMMs mimic human perception and provide a comprehensive understanding of the world. This transformative technology opens up enormous potential for companies, from accelerating research and development to personalising learning experiences. Even though there are challenges such as computational costs and data integration, LMMs are capable of transforming various industries and paving the way for a future fuelled by intelligent and versatile AI.
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