Narrow AI, General AI, and Super AI

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Context:

Artificial Intelligence (AI) is machine intelligence that mimics a human mind’s problem-solving and decision-making capabilities to perform various tasks.
AI uses algorithms and techniques such as machine learning and deep learning to learn, evolve, and get progressively better at assigned tasks.
AI is categorized into three types based on the human characteristics it can replicate, its real-world applications, and the theory of mind prerequisites:

Artificial narrow intelligence (ANI):

Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is application- or task-specific AI.
It is programmed to perform singular tasks such as facial recognition, speech recognition in voice assistants, or driving a car.
Narrow AI simulates human behavior based on a limited set of parameters, constraints, and contexts.
Some of the common examples of ANI include speech and language recognition demonstrated by Siri on iPhones, the vision recognition feature showcased by self-driving cars and recommendation systems such as Netflix’s recommendations that suggest shows based on users’ online activity.
Google’s RankBrain is another example of narrow AI that Google uses to sort results.
Such systems only learn or are trained to complete specific tasks.

Artificial general intelligence (AGI):

Artificial general intelligence (AGI), also referred to as strong AI or deep AI, is the ability of machines to think, comprehend, learn, and apply their intelligence to solve complex problems, much like humans.
Strong AI uses a theory of mind AI framework to recognize other intelligent systems’ emotions, beliefs, and thought processes.
A theory of mind-level AI refers to teaching machines to truly understand all human aspects, rather than only replicating or simulating the human mind.

Artificial superintelligence (ASI):

It is a type of AI that surpasses human intelligence and can perform any task better than a human.
ASI systems not only understand human sentiments and experiences but can also evoke emotions, beliefs, and desires of their own, similar to humans.
Although the existence of ASI is still hypothetical, the decision-making and problem-solving capabilities of such systems are expected to be far more superior to those of human beings.
Typically, an ASI system can think, solve puzzles, make judgments, and take decisions independently.

What is Generative AI?

Generative Artificial Intelligence is any type of AI that can be used to create new and original content based on patterns and examples it has learned.
This content can be text, images, video, code, or synthetic data. Examples include DALL-E, Midjourney, and ChatGPT.
An example is an AI trained on trees might be able to create images of trees that don’t exist in the real world, based on patterns it has learned.
These AIs often take written prompts from humans as input, and turn it into the desired output.
Generative AI models are often used in unsupervised machine learning problems.

Generative AI vs Discriminative AI:

Discriminative AI is a different type of AI that is trained to recognise and classify patterns in existing data.
Like its namesake, it “discriminates” between classes and categories based on examples it’s learned.
An example would be choosing if a picture has an image of a cat or a dog.

Generative AI has been used for things like fraud detection (“Is this person engaging in sketchy behavior?”) to image recognition.
Discriminative AI is normally used for supervised machine learning.

Syllabus: Prelims; Science and Technology

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