Syllabus
GS Paper 3 – Awareness in the fields of IT, Space, Computers, robotics, nano-technology, bio-technology and issues relating to intellectual property rights.
Context
OpenAI, with Microsoft’s backing, has recently upgraded ChatGPT to be multimodal, enabling it to analyze images and engage with users through its mobile app using speech.
What is Multimodal AI?
- A type of artificial intelligence that can process and understand multiple types of data, such as text, images, audio, and video.
- A multimodal AI system could be used to generate text descriptions of images, or to translate speech into text in real time.
- Multimodal AI systems can also be used to develop more natural and intuitive human-computer interaction interfaces.
- Example: OpenAI’s text-to-image model, DALL.E, upon which ChatGPT’s vision capabilities are based, is a multimodal AI model that was released in 2021.
What is Conventional AI?
- It predominantly functions in a unimodal manner, primarily designed to handle one specific data type.
- For instance, ChatGPT utilizes natural language processing (NLP) algorithms to extract meaning from textual content, generating text as its sole output.
How does it work?
- Multimodal AI systems are trained to recognize patterns and relationships between different types of data.
- It works by first extracting features from each type of data. Like; color, texture, and shape from an image. Letters from an audio etc.
- Then it uses a machine learning algorithm to learn the relationships between the features.
- Then perform tasks such as generating text descriptions of images, or translating speech into text.
Competing in the Field of Artificial Intelligence:
- OpenAI, the creator of ChatGPT, has announced that they’ve empowered their GPT-3.5 and GPT-4 models to understand and describe images, while their mobile apps will soon incorporate speech synthesis, enabling comprehensive conversations with the chatbot.
- OpenAI, supported by Microsoft, had initially pledged to introduce multimodality when they launched GPT-4.
- Google is currently testing a new, unreleased multimodal large language model named Gemini in various companies.
- OpenAI is also reportedly in the process of developing a brand-new project known as Gobi, which is expected to be a multimodal AI system built from the ground up, distinct from the GPT models.
What are the advantages of multimodal AI over the current AI?
- Versatility: Its ability to handle various data types makes it more adaptable to diverse situations and applications.
- Natural Interaction: By integrating multiple modalities, multimodal AI can engage with users in a way that feels natural and intuitive, resembling human communication.
- Enhanced Precision: Multimodal AI can also enhance the precision of its predictions and classifications.
- Improved User Experience: It elevates the user experience by offering multiple avenues for users to interact with the system.
- Resilience to Disturbance: Multimodal AI exhibits greater resilience against disruptions and variations in input data.
- Efficient Resource Utilization: It optimizes the use of computational and data resources by enabling the system to focus on the most pertinent information from each modality.
- Enhanced Explainability: It contributes to enhanced explainability by providing multiple information sources that can be used to clarify the system’s output.
Applications of multimodal AI:
- Natural language processing such as machine translation, text summarization, and question answering.
- Computer vision tasks such as image classification, object detection, and image segmentation.
- Speech recognition tasks such as automatic transcription and voice translation with improved performance.
- Intuitive human-computer interaction interfaces like virtual assistants that can understand and respond to spoken commands while simultaneously processing visual cues from the environment.
Some Specific Applications:
- Medical diagnosis by combining information from multiple sources, such as medical images, patient records, and lab results.
- Self-driving cars which need to be able to process and understand information from a variety of sources, such as cameras, radar, and LIDAR sensors.
- Customer service chatbots that can understand and respond to customer queries in a more natural and efficient way.
- Education: Multimodal AI can be used to develop educational tools that can engage students in a more immersive and interactive way. For example, developing virtual learning environments that allow students to interact with objects and people in a simulated world.
Conclusion
By harnessing the synergies of diverse modalities, businesses can reveal concealed insights, empowering them to enhance decision-making and achieve superior results. As technology continually advances, we anticipate even more significant innovation and influence from multimodal AI in the future.
Practice Question
What is multimodal artificial intelligence? How it differs from Conventional AI? Discuss its applications. (Answer in 250 words)