Artificial Intelligence

1. Based on Capabilities:

• Artificial Narrow Intelligence (ANI): Also known as Weak AI, ANI is designed to perform specific tasks. It operates under a limited set of constraints and cannot function beyond its predefined capabilities. Examples include voice assistants like Siri and Alexa, which can perform tasks such as setting reminders or playing music but cannot perform functions outside their programming.

• Artificial General Intelligence (AGI): Referred to as Strong AI, AGI would possess the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to a human being. AGI remains theoretical and has not yet been realized.

• Artificial Superintelligence (ASI): ASI surpasses human intelligence in all aspects, including creativity, problem-solving, and decision-making. This level of AI is purely hypothetical and is a common subject in Science fiction discussions.

2. Based on Functionalities:

• Reactive Machines: These AI systems can only react to current scenarios and cannot use past experiences to inform decisions. They lack memory-based functionality. An example is IBM's Deep Blue, which was designed to play chess and could evaluate possible moves but had no memory of past games.

• Limited Memory: This type of AI can use past experiences to inform future decisions. Many current AI applications fall into this category, such as self-driving cars that observe other vehicles' speed and direction to make decisions.

• Theory of Mind: Still in the research phase, this type of AI aims to understand human emotions, beliefs, and thought processes, enabling more natural interactions.

• Self-Aware AI: This represents the most advanced form of AI, where machines possess self-awareness and consciousness. Currently, self-aware AI is theoretical and not yet developed.

Hello!

It seems like you're interested in pursuing an M.Tech degree in the AIDS (Artificial Intelligence and Data Science) stream, or something similar. To help you get started, M.Tech in AI and Data Science is offered by many universities, and each institution has its own eligibility requirements, entrance exams, and fee structures.

Typically, to be eligible for M.Tech, you need to have a Bachelor's degree in a related field such as Computer Science, Electronics, or Information Technology. Most institutes conduct entrance exams like GATE (Graduate Aptitude Test in Engineering) for admission into M.Tech programs.

Hope this helps, and all th very best with your studies!