What are the different algorithm techniques you can use in AI and ML?

Some algorithm techniques that can be leveraged are:

Learning to learn
Reinforcement learning (deep adversarial networks, q-learning, and temporal difference)
Semi-supervised learning
Supervised learning (decision trees, linear regression, naive bayes, nearest neighbor, neural networks, and support vector machines)
Transduction
Unsupervised learning (association rules and k-means clustering)

What is FOPL?

First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.

What methods are used for reducing dimensionality?

Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.

What are the two different kinds of steps that we can take in constructing a plan?

a) Add an operator (action)

b) Add an ordering constraint between operators

What are the different types of AI?

Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory.
Example: Deep Blue
Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory.
Theory of Mind AI: Advanced AI that has the ability to understand emotions, people and other things in the real world.
Self Aware AI: AIs that posses human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
Artificial Narrow Intelligence (ANI): General purpose AI, used in building virtual assistants like Siri.
Artificial General Intelligence (AGI): Also known as strong AI. An example is the Pillo robot that answers questions related to health.
Artificial Superhuman Intelligence (ASI): AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.

Why is game theory important to AI?

Game theory, developed by American mathematician Josh Nash, is essential to AI because it plays an underlying role in how these smart algorithms improve over time.

At its most basic, AI is about algorithms that are deployed to find solutions to problems. Game theory is about players in opposition trying to achieve specific goals. As most aspects of life are about competition, game theory has many meaningful real-world applications.

These problems tend to be dynamic. Some game theory problems are natural candidates for AI algorithms. So, whenever game theory is applied, multiple AI agents that interact with each other will only care about utility to itself.

Data scientists within this space should be aware of the following games:

Symmetric vs. asymmetric
Perfect vs. imperfect information
Cooperative vs. non-cooperative
Simultaneous vs. sequential
Zero-sum vs. non-zero-sum

List the advantages of an expert system.

Consistency
Memory
Diligence
Logic
Multiple expertise
Ability to reason
Fast response
Unbiased in nature

What is an autoencoder? Name a few applications.

An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:

Data denoising
Dimensionality reduction
Image reconstruction
Image colorization

What is Prolog in AI?

In AI, Prolog is a programming language based on logic.

In ‘Artificial Intelligence’ where you can use the Bayes rule?

In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.