Multimodal AI is an advanced form of Artificial Intelligence that can analyse and interpret multiple modes of data simultaneously, allowing it to generate more accurate and human-like reasoning and decision-making.
Traditional Unimodal AI vs Multimodal AI:
The fundamental difference between multimodal AI and traditional singlemodal/unimodel AI is the use of data.
- Unimodal AI is generally designed to work with a single source or type of data. E.g., Unimodal AI system like ChatGPT uses natural language processing (NLP) algorithms to understand and extract meaning from text content, and the only type of output the chatbot can produce is text. That is, the unimodal AI is tailored to a specific task.
- Multimodal AI processes data from multiple sources, including video, images, speech, sound and text, allowing more detailed and nuanced perceptions of a particular environment or situation. In doing this, multimodal AI more closely simulates human perception and enhances the accuracy of AI systems.
E.g., SeamlessM4T, launched by Meta, is a multimodal AI translation and transcription model that is capable of performing various tasks including speech-to-text, speech-to-speech, text-to-speech, and text-to-text translations.
- Improved accuracy: By leveraging information from multiple sources, multimodal AI can achieve higher accuracy compared to singlemodal AI. E.g., Any system that analyses customer feedback for a product, combining text, image, and audio data can provide more comprehensive understanding of customer sentiment.
- Enhanced user experience: Multimodal AI can enhance user experience by providing multiple ways for users to interact with the system. E.g., Users can interact with a multimodal virtual assistant system using voice, text, or gesture, providing greater convenience and accessibility.
- Efficient usage of resources: Multimodal AI can help to make more efficient use of computational and data resources by enabling the system to focus on the most relevant information from each modality. This would help reduce the amount of irrelevant data that needs to be processed.
- Better interpretability: Multimodal AI can help to improve interpretability by providing multiple sources of information that can be used to explain the system’s output. E.g., Any system that analyses medical images for the diagnosis, combining images with textual descriptions and other data can help to explain the reasoning behind the system’s diagnosis and provide more transparency and accountability.
Applications of Multimodal AI:
- Healthcare: Multimodal AI can help improve medical imaging analysis, disease diagnosis, personalised treatment planning and better patient outcomes. E.g., By combining medical images with patient records and genetic data, healthcare providers can gain more accurate understanding of patient’s health, enabling them to tailor treatment plans.
- Retail: In retail, it can be used to enhance customer experience and increase sales. By utilising user behaviour data, product images, and customer reviews, retailers can provide personalised recommendations and optimise product searches.
- Agriculture: Multimodal AI can help monitor crop health, predict yields, and optimise farming practices. By integrating satellite imagery, weather data, and soil sensor data, farmers can gain deep insights into crop health and optimise irrigation and fertilizer application.
- Manufacturing: Multimodal AI can be leveraged to improve quality control, predictive maintenance, and supply chain optimisation.
- Robotics: Multimodal AI is central to robotics development using which robots could successfully interact with real-world environments.
- Entertainment: Multimodal AI algorithms can be used to extract features about emotions, speech patterns, facial expressions, and actions which can create content targeted for specific demographics.
Multimodal AI challenges:
- Data Storage: The data sets needed to operate a multimodal AI, involve a huge variety of data (text, images, audio, video). Such data volumes are expensive to store, and costly to process.
- Data integration: Combining and synchronizing different types of data can be challenging because the data from multiple sources will not have the same formats. Ensuring the seamless integration of multiple modalities and maintaining consistent data quality can be difficult and time-consuming.
- Data bias: Data bias and maintaining data integrity can be a problem in training the AI model.
Read also: ChatGPT and Open AI