What Is Multimodal in Deep Learning?
Everything around us consists of multimodality. We constantly use multimodal learning in our daily lives. Simply put, when we combine different senses- auditory, visual, and kinaesthetic- to learn or perceive our surroundings, we use a multimodal type of learning.
In multimodality, the term ‘Modality’ refers to the way we experience something. The most common way we experience something is through touch or vision. We utilize different modalities (senses) to understand the environment around us.
In the same way, machine learning utilizes multimodality. Just as humans use multimodality-speech, vision, etc., to communicate, machine learning aims to understand the patterns in human communication through multiple modalities.
It might sound ridiculous, but the multimodal deep learning era has already begun and is gaining momentum as artificial intelligence progresses. Machine learning models intend to understand and interpret multimodality.
Multimodal machine learning is not a new concept in the field of computer science. It first took was initiated in the 1970s. The turning point for multi-modal systems came in the deep learning stage, where neural networks helped push this concept to new heights.
Multimodal data in deep learning
Data from various channels are related based on their semantics. We can refer to modes as sources of information. Different modes contain patterns that can't be distinguished through a single modality. Therefore, we can analyze multiple modes that offer information to one another.
Multi-modal systems obtain data from different sensors and combine this diverse, non-related information to make strong predictions. Various AI-based chatbots integrate this multimodal system to enhance their Chatbot responses.
Use of MultiModal Sytem in OpenAI GPT-4
OpenAI finally released ChatGPT-4, which is a multimodal model. The model stands up to its promise of a multimodal machine learning system by creating a model that can accept image or text inputs and display text-based information. It has the ability to comprehend complex inputs and provides outputs based on images and text simultaneously.
How does multimodal deep learning work?
We can apply neural networks for singular modularities through unsupervised feature learning. For multiple modes, we can divide the task into three major components, i.e., fusing the data, understanding features, and testing. Let's examine how the process runs.
Step 1:- Representation
The first step is describing input data. We need to summarise the data presented to us in a way that conveys the different modalities. This is a difficult task due to the diverse data type we work with.
Step 2:- Translation
In this step, we must learn how to map (translate) one data modal to another. Two or more distinct modalities can show some relation. The relation might be unclear, but we must find similarities between different modalities to map one modal to another.
Step 3:- Feature extraction
After translating the features, it's time to extract the essential ones needed for prediction from all data sources. We build models that best match the data type of individual information sources to extract features.
For example, feature extraction in image sources that will show higher quality, like environmental surroundings. Alternatively, feature extraction in the text is represented as tokens.
Step 4:- Fusion & Co-learning
In this step, we merge the information extracted from multiple modalities and form a prediction. All the features combine to configure a single shared illustration. You can perform deep learning among varying modalities. For example, if one modality has more resources, we may transfer knowledge between the two modalities.
What are the challenges in multimodal machine learning?
There are some challenges that multimodal deep learning faces. Some of the main issues are explained below.
Challenge 1:- Representation of modalities
As stated earlier, representation poses a major problem as it is difficult for a model to summarize the data from varying modalities. This redundancy can slow down the process when dealing with two similar pieces of data present in different areas within a dataset.
For example, 'nice' can mean a person likes something. But if we read their face or understand the intonation behind the word, this can also be expressed in a sarcastic tone. In this case, the whole meaning of the word changes. So, being able to represent multiple modalities is important.
Challenge 2:- Alignment
Aligning the various modalities created from multiple devices or sensors can be difficult. They may possess varying degrees of space/time resolutions which can provide complexity in constructing a meaningful interpretation of the data.
Challenge 3:- Missing modalities
Multimodal learning models can handle multiple modalities, but the data might be unavailable or missing in some cases. Recovery of missing values/modalities is still a big issue, although we may use cross-profiling for such cases.
Challenge 4:- Explainability and Generalization
Multimodal models are complex, making it hard to explain their decision-making process. We must learn the factors affecting the prediction to prevent biases and solve the bugs.
We usually train the models on specific data sets and modalities. Therefore, the models might become unreliable when presented with a new or unseen data set. This type of generalization problem can also lead to an explainability problem.
Challenge 5:- Scalability
These models are expensive, even for big companies. Training and deploying them is costly as they usually work with large data sets and fast processing. Those utilizing the models must carefully implement them to avoid encountering monetization problems.
Top 6 Applications of multimodal learning
The multimodal deep learning field is progressing rapidly. We can already witness some of its applications in various fields where multimodal systems provide enhanced applications.
1. Computer vision
Computer vision is one of the leading multimodal deep learning applications. We can merge image data with audio or text input to better understand the contextual information from an image. This also leads to improve predictions.
2. Robotics
Robotics can be an obvious use case, where multimodal deep learning algorithms can enhance communication with the surrounding environment. Models can use sensor data such as microphones or cameras to interact by understanding their environment and predicting an accurate response.
3. Natural Language Processing
NLP is a revolutionary technology that can enhance a model's understanding of its environment. For complex tasks like language translation or sentiment analysis, multimodal machine learning applications work with NLP to make accurate predictions.
4. Neural search
Neural search can construct each element of a search engine/system utilizing state-of-the-art neural network models. It is powered by deep neural networks to obtain data.
Neural search can use multimodal learning to map different modalities in a single enclosed space.
For example, by transferring text and image data, neural search engines can provide images; as a result, using textual inputs. They can also use images as input to provide text documents, resulting in efficient searching capabilities.
5. Healthcare
Multimodal data sources like images, text, audio, and video are abundant in a medical environment. Doctors obtain enriched knowledge of patients' health to make accurate predictions using multimodal deep-learning applications.
6. Generative AI
It is very evident that Generative AI is one of the most significant multimodal machine learning applications. They use neural network models to create image, text, or video content. Multimodal AI is modifying how we interact with machines.
One exciting recent example would be OpenAI GPT-4, which can generate human-like conversations on command with more precision than previous GPT-3 models. Another invention by OpenAI is DALL-E 2, which can create images from textual commands.
What is the future of multi-modal artificial intelligence?
With multimodal deep learning, we are advancing toward an era of AI innovation that has never been seen before.
The future scopes of multimodal artificial intelligence have reached an extent where it can enhance and improve the accuracy of chatbot systems, healthcare diagnosis, autonomous vehicles, and much more. The integration of multimodal AI may readily incorporate many intelligent processing algorithms, which can increase an AI technology's overall performance.
To learn more about multimodal AI, it is crucial first to study Artificial Intelligence and Machine Learning in depth. The Artificial Intelligence and Machine Learning Advance Program is the finest choice for interested candidates to study and practice live applications of topics such as multi-modal systems.
Follow us on Twitter, YouTube, and LinkedIn to stay up to date on such technology.
