Multimodal AI

11 Oct Multimodal AI

This article covers “Daily Current Affairs” and the topic details “Multimodal AI”. This topic has relevance in the “Science and Technology” section of the UPSC CSE exam.

For Prelims:

What is Multimodal AI? 

For Mains:

GS2:  Science and Technology

Why in the news?

OpenAI recently revealed that they have extended the capabilities of their GPT-3.5 and GPT-4 models to understand and describe images in textual form. Additionally, they have integrated speech synthesis into their mobile apps, enabling users to engage in complete conversations with the chatbot.

Multimodal AI

• Multimodal AI, or multimodal artificial intelligence, is an AI system that can understand and interact with information from multiple sensory modalities, such as text, images, speech, and more. 
• It combines various types of data and information to make sense of the world in a way that resembles human perception and cognition. 
• Multimodal AI can be used for various applications, including natural language processing, computer vision, speech recognition, and more.

Working of Multimodal AI

• Data Integration: Combine text and images for a unified dataset.

• Training: Use diverse data for model training.

• Cross-Modal Learning: Teach the model to link text and images.

• Inference & Generation: The model performs tasks like image-to-text and speech recognition.

• Feedback & Iteration: Improve accuracy through iterative training.

• Deployment: Apply in applications like virtual assistants and content recommendation.

Examples of the use of multimodal artificial intelligence:

• Self-Driving Cars: They rely on multimodal AI for environment perception and safe navigation, using cameras, radar, and lidar sensors to collect data about the road and surroundings.

• Medical Diagnosis: Multimodal AI enhances medical diagnoses by analysing X-rays, MRI scans, and patient data to identify diseases and risk factors, resulting in more accurate and personalised assessments.

• Education: Multimodal AI enriches educational experiences, enabling personalised learning plans and interactive content like simulations and games to engage and inform students.

Challenges in Multimodal artificial intelligence:

• Data Volume: Storing and processing large, diverse datasets is costly and challenging.

• Learning Nuance: Teaching AI to understand context and nuanced meanings from identical inputs can be problematic.

• Data Alignment: Aligning data from various sources to represent the same context is difficult.

• Limited Data Sets: Incomplete or hard-to-find data can hinder AI training, leading to data integrity and bias issues.

• Missing Data: AI’s reliance on multiple data sources can lead to malfunctions when one source is missing or provides incomplete information.

• Complex Decision-Making: Understanding how AI evaluates data and makes decisions can be challenging, making the AI unreliable and unpredictable for users.

Some examples of multimodal artificial intelligence models:

• Meta’s project CAIRaoke: Meta, Facebook’s parent company, is developing a digital assistant project based on multimodal AI, capable of human-like interactions.

• Google’s video-to-text research: Google has recently researched a multimodal system that predicts dialogues in video clips.

• OpenAI’s GPT-3.5 & GPT-4 models: These models can analyse images in text and feature speech synthesis in their mobile apps.

• Google’s Gemini: Currently undergoing testing in various companies.

• OpenAI’s Gobi: OpenAI is creating Gobi, a multimodal AI system from the ground up.

Sources: What is multimodal artificial intelligence and why is it important?

Download plutus ias current affairs eng med 11th Oct 2023

Q1. With reference Multimodal artificial intelligence, consider the following statements: 

1. Multimodal AI combines data from different sources, such as text, images, and audio, to better understand information and the world more comprehensively.
2. It is a system that can decode the thoughts and dreams of individuals by analysing brainwaves and predicting their future actions.
3. It can understand and process data from multiple sensory modalities, enabling more human-like interactions and decision-making.

Which of the statements given above is/are correct?

(a) 1 and 2 only

(b) 2 and 3 only

(c) 1 and 3 only 

(d) None 

Check Answer

Q2. Consider the following:

• CAIRaoke

• DALL-E

• SpiNNaker

• Gemini

How many of the are examples of Multimodal Artificial Intelligence?

(a) Only one 

(b) Only two 

(c) Only three 

(d) All Four 

Check Answer

Q3. Examine the applications and importance of Multimodal AI across domains, its impact on human-computer interactions, and its challenges for researchers and developers.

Gaurav N