Artificial Intelligence Interview Questions – Set 08

by

How would you go about choosing an algorithm to solve a business problem?

First, you have to develop a “problem statement” that’s based on the problem provided by the business. This step is essential because it’ll help ensure that you fully understand the type of problem and the input and the output of the problem you want to solve.

The problem statement should be simple and no more than a single sentence. For example, let’s consider enterprise spam that requires an algorithm to identify it.

The problem statement would be: “Is the email fake/spam or not?” In this scenario, the identification of whether it’s fake/spam will be the output.

Once you have defined the problem statement, you have to identify the appropriate algorithm from the following:

  • Any classification algorithm
  • Any clustering algorithm
  • Any regression algorithm
  • Any recommendation algorithm
    Which algorithm you use will depend on the specific problem you’re trying to solve. In this scenario, you can move forward with a clustering algorithm and choose a k-means algorithm to achieve your goal of filtering spam from the email system.

List different methods for sequential supervised learning.

  • Sliding window methods
  • Recurrent sliding windows methods
  • Hidden Markov models
  • Maximum entropy Markov models
  • Conditional random fields
  • Graph transformer networks

Which property is considered as not a desirable property of a logical rule-based system?

“Attachment” is considered as not a desirable property of a logical rule based system.

Explain the commonly used Artificial Neural Networks.

Feedforward Neural Network

  • The simplest form of ANN, where the data or the input travels in one direction.
  • The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.
    Convolutional Neural Network
  • Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations.
  • Mainly used for signal and image processing

Recurrent Neural Network(RNN) – Long Short Term Memory

  • Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
  • Here, you let the neural network to work on the front propagation and remember what information it needs for later use
    This way each neuron will remember some information it had in the previous time-step.

Autoencoders

  • These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them.
  • The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs.
  • Typically for the purpose of dimensionality reduction and for learning generative models of data.

What would you say are common misconceptions about AI?

Many AI-related misconceptions are making the rounds in the age of “fake news.” The most common ones are:

  • AI will replace humans
  • AI systems aren’t safe
  • AI will lead to significant unemployment
  • While these types of stories are common, they’re far from the truth. Even though some AI-based technology is able to complete some tasks—for example, analyzing zettabytes of data in less than a second—it still needs humans to gather the data and define the patterns for identification.

List the different algorithm techniques in Machine Learning.

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Transduction
  • Learning to Learn

What is an A* algorithm search method?

A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.

What are the components of the generative adversarial network (GAN)? How do you deploy it?

Components of GAN:

Generator
Discriminator
Deployment Steps:

Train the model
Validate and finalize the model
Save the model
Load the saved model for the next prediction

What is a breadth-first search algorithm?

A breadth-first search (BFS) algorithm, used for searching tree or graph data structures, starts from the root node, then proceeds through neighboring nodes, and further moves toward the next level of nodes.

What are the steps involved in the gradient descent algorithm?

Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.

Step 1: Allocate weights (x,y) with random values and calculate the error (SSE)

Step 2: Calculate the gradient, i.e., the variation in SSE when the weights (x,y) are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized

Step 3: Adjust the weights with the gradients to move toward the optimal values where SSE is minimized

Step 4: Use new weights for prediction and calculating the new SSE

Step 5: Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error