What’s the difference between inductive, deductive, and abductive learning?

Inductive learning describes smart algorithms that learn from a set of instances to draw conclusions. In statistical ML, k-nearest neighbor and support vector machine are good examples of inductive learning.

There are three literals in (top-down) inductive learning:

Arithmetic literals
Equality and inequality
Predicates
In deductive learning, the smart algorithms draw conclusions by following a truth-generating structure (major premise, minor premise, and conclusion) and then improve them based on previous decisions. In this scenario, the ML algorithm engages in deductive reasoning using a decision tree.

Abductive learning is a DL technique where conclusions are made based on various instances. With this approach, inductive reasoning is applied to causal relationships in deep neural networks.

What is a bidirectional search algorithm?

In a bidirectional search algorithm, the search begins in forward from the beginning state and in reverse from the objective state. The searches meet to identify a common state. The initial state is linked with the objective state in a reverse way. Each search is done just up to half of the aggregate way.

What are the TensorFlow objects?

Constants
Variables
Placeholder
Graph
Session

What does a production rule consist of?

The production rule comprises of a set of rule and a sequence of steps.

What’s the most popular programming language used in AI?

The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior.

Its popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK.

There’s a chance that the interviewer might keep the conversation going and ask you for more examples. If that happens, you can mention the following:

Java
Julia
Haskell
Lisp

Is it possible to solve logical inference in propositional logic?

Yes, logical inference can easily be solved in propositional logic by making use of three concepts:

Logical equivalence
Process satisfaction
Validation checking

What is “Generality” in AI ?

Generality is the measure of ease with which the method can be adapted to different domains of application.

What is the purpose of Deep Learning frameworks such as Keras, TensorFlow, and PyTorch?

Keras is an open source neural network library written in Python. It is designed to enable fast experimentation with deep neural networks.
TensorFlow is an open-source software library for dataflow programming. It is used for machine learning applications like neural networks.
PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing.

What is collaborative filtering?

Collaborative filtering can be described as a process of finding patterns from available information to build personalized recommendations. You can find collaborative filtering in action when you visit websites like Amazon and IMDB.

Also known as social filtering, this approach essentially makes suggestions based on the recommendations and preferences of other people who share similar interests.

What combines inductive methods with the power of first order representations?

Inductive logic programming combines inductive methods with the power of first order representations.