Artificial Intelligence Interview Questions – Set 14

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Can you list some disadvantages related to linear models?

There are many disadvantages to using linear models, but the main ones are:

  • Errors in linearity assumptions
  • Lacks autocorrelation
  • It can’t solve overfitting problems
  • You can’t use it to calculate outcomes or binary outcomes

In Inductive Logic Programming what needed to be satisfied?

The objective of an Inductive Logic Programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied.

What is regularization in Machine Learning?

Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.

What would you do if data in a data set were missing or corrupted?

Whenever data is missing or corrupted, you either replace it with another value or drop those rows and columns altogether. In Pandas, both isNull() and dropNA() are handy tools to find missing or corrupted data and drop those values. You can also use the fillna() method to fill the invalid values in a placeholder—for example, “0.”

What are the components of relational evaluation techniques?

  • Data acquisition
  • Ground truth acquisition
  • Cross validation technique
  • Query type
  • Scoring metric
  • Significance test

Do you have research experience in AI?

At present, a lot of work within the AI space is research-based. As a result, many organizations will be digging into your background to ascertain what kind of experience you have in this area. If you authored or co-authored research papers or have been supervised by industry leaders, make sure to share that information.

In fact, take it a step further and have a summary of your research experience along with your research papers ready to share with the interviewing panel.

However, if you don’t have any formal research experience, have an explanation ready. For example, you can talk about how your AI journey started as a weekend hobby and grew into so much more within a space of two or three years.

List different activation neurons or functions.

  1. Linear neuron
  2. Binary threshold neuron
  3. Stochastic binary neuron
  4. Sigmoid neuron
  5. Tanh function
  6. Rectified linear unit (ReLU)

Which is the best way to go for Game playing problem?

Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.

In top-down inductive learning methods how many literals are available? What are they?

There are three literals available in top-down inductive learning methods they are

a) Predicates

b) Equality and Inequality

c) Arithmetic Literals

What’s the difference between strong AI and weak AI?

The difference between the two is just like the terms sound. Strong AI can successfully imitate human intelligence and is at the core of advanced robotics.

Weak AI can only predict specific characteristics that resemble human intelligence. Alexa and Siri are excellent examples of weak AI.

Strong AI

  • Can be applied widely
  • Extensive scope
  • Human-level intelligence
  • Processes data by using clustering and association
    Weak AI
  • Can be great at performing some simple tasks
  • Uses both supervised and unsupervised learning
  • The scope can be minimal