GS Paper 3
Syllabus: Science and Technology- Developments and their Applications and Effects in Everyday Life
Source: TH
Context: AI can traditionally be divided into Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI).
AGI and ANI: The difference lies in their scope of intelligence and their ability to generalise knowledge across different contexts.
| AGI | ANI |
| ● Flexible and adaptable
● Designed to perform a wide variety of intellectual tasks without human intervention ● Unsupervised learning means that the AI system can learn from data without being explicitly programmed to do so ● Lack of control continues to learn and make decisions (based on incomplete or uncertain information) that even its creators cannot possibly predict. ● In the realm of theoretical research and development. |
● Designed to perform a single or a narrow set of related tasks.
● Not necessarily capable of reasoning or learning in the way that humans do. ● Typically trained using machine learning algorithms such as supervised learning, unsupervised learning, or reinforcement learning. ● Widespread use in a variety of industries and applications ● For example, ChatGPT – is a chatbot which allows users to engage in a conversation about a variety of topics. |
Advantages of AI:
- Disruptive technology creates new jobs and skill sets by creating demand for expertise in machine learning, data science and natural language processing.
- It will transform industries by creating new opportunities for growth and innovation.
- In industries like healthcare, AI can optimise transportation networks, develop new materials, and even simplify manufacturing processes.
Threats: ChatGPT/similar solutions are proficient at automating routine and repetitive tasks (data entry, customer service) which could perhaps replace low-skill level workers.
Conclusion: The impact of AI on jobs and industries is likely to be uneven. Hence, significant investment in education and training programs and proactive policies is the need of the hour.
The science behind AI: Neural Network
A neural network is a computer system designed to learn and recognize patterns, like a simplified version of the human brain. It consists of layers of interconnected nodes or “neurons,” each of which performs a simple computation.
Neural networks have become increasingly popular in recent years due to their ability to learn complex patterns and make accurate predictions. They have been used in a variety of applications, including self-driving cars, speech recognition, and medical diagnosis.
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