Highlights:
Multimodal AI refers to systems capable of analyzing data from multiple sources. These types of AI models offer wide application areas in industries such as retail, healthcare, finance, and entertainment.
Multimodal AI has the capability to analyze multifaceted inputs, similar to humans. This can increase communication accuracy and provide the ability to see and hear simultaneously.
Multimodal AI systems go through stages including data collection, data integration, data preprocessing and analysis, learning, modeling, and application.
Humans perceive the world in a multifaceted way, through touch, hearing, sight, smell, and taste. Standard artificial intelligence (AI) systems are typically unimodal, trained to perform only one task, such as image or language processing. These systems can perceive words or images by using a single data source. Although working with a single data source is simple, these systems may fall short in reducing unstructured data due to a lack of context and supporting information. Like humans, the ability to analyze different inputs is a prerequisite for advanced AI systems.
Multimodal AI refers to systems that can analyze data from multiple sources. These types of AI models offer extensive application areas in industries such as retail, healthcare, finance, and entertainment. Multimodal AI can enhance communication power by more effectively conveying these sensory experiences. Additionally, similar to the way humans communicate with each other, it can provide the ability to see, hear, and speak simultaneously.
Tech Stack in Multimodal AI:
NLP (Natural Language Processing): Enables speech recognition, allowing the system to understand and transform spoken language.
Image processing technologies: Analyze and decipher complex visual inputs, facilitating the contextualization of actions, objects, and people. This, in turn, simplifies video and image recognition processes.
Textual analysis: Allows for the understanding of written materials, sentiment analysis, and language translation.
Fast processing and data mining technologies: Enable quicker real-time calculations.
How does a multimodal system work?
The human brain can perceive and understand its surroundings better by using different sensory inputs, such as hearing and sight. This allows the brain to perform and evaluate various actions.
Multimodal AI systems act like the human brain, capable of processing different sensory data and deriving meaning from this information. These systems typically use machine learning (ML) algorithms. ML algorithms are trained with different sensory data to detect patterns and relationships in this data.
Why is Multimodal AI necessary?
Multimodal AI can help us better understand the world. The world is a complex system comprised of different sensory data. Multimodal AI systems, by integrating these varied sensory data, enable us to comprehend the world in a more holistic manner.
How does Multimodal AI differ from other AI systems?
Unlike other AI systems, it can process a variety of sensory data. This allows multimodal AI systems to achieve more accurate and comprehensive results.
Multimodal AI systems typically use multiple representations. Multiple representations are data structures that represent the same data in different ways. This enables multimodal AI systems to link different sensory data and extract more meaningful information.
What is the current state of Multimodal AI?
Multimodal AI is currently still in the development phase, but significant progress has been made in recent years.
In terms of technological maturity level, Multimodal AI systems can now be considered to be in the "early adoption phase." This means that Multimodal AI is still under development and has its limitations. However, these systems have already achieved significant successes in various applications.
For example, multimodal AI systems offer key application areas in artificial vision, artificial hearing, and natural language processing. These systems can use image and sound data together to achieve more accurate and comprehensive results."
Multimodal AI systems typically go through the following stages:
Data collection: In the data collection phase, the Multimodal AI system gathers data from different sources. This data can be in the form of camera images, audio files, text documents, or other formats.
Data integration: This is the stage where the Multimodal AI system combines the collected data. This means transforming the data into a single dataset.
Data preprocessing and analysis: The Multimodal AI system applies preprocessing operations to prepare the data for analysis. These operations can include cleaning, standardizing, and denoising the data.
Learning and modeling: The Multimodal AI system creates a model using the data. This model is used to derive meaning from the data and to predict future data.
Application: The Multimodal AI system uses the created model in real-world applications. These applications can be autonomous systems, artificial vision, artificial hearing, natural language processing, or other tasks.
How can we help?
PwC possesses a robust framework to help you understand the impact of various innovations in technology on your business. Our experienced and dedicated staff can assist clients in creating any application according to desired specifications. We can help you identify and prioritize Multimodal AI use cases based on their potential to create business value.
