Top 10 Artificial Intelligence (AI) Interview Questions and Answers

Artificial Intelligence (AI) Interview Questions

Here you will learn essential Artificial Intelligence (AI) Interview Questions and Answers. However, these questions are mostly asked in AI interviews and exams, so keep learning this AI knowledge with a box of learn.

1) what is artificial intelligence?

Answer: Artificial Intelligence is a powerful and advanced level technology made by humans and works like the human brain.

It’s especially designed to think and make decisions, learn and perform tasks, understand language, solve problems, and perform image recognition.

2) types of AI(Artificial Intelligence)?

Answer:

• Narrow AI (Weak AI): Designed for specific tasks.
  • For example: ChatGPT, Siri and Google Assistant.
• General AI (Strong AI): This AI can perform any intelligence task like a Human.
  • It is not yet developed
• super AI: It surpasses human intelligence. advanced level intelligence.
  • Currently, this super AI is only a theoretical subject.

3) what is the difference between AI, Machine Learning and Deep Learning?

Answer:

Artificial Intelligence (AI)

AI is a wide range of concepts. It refers to machines or systems that can perform intelligent tasks like humans. such as decision-making, problem-solving, understanding language, and much more.

• Example: Chatbots, self-driving cars, voice assistants like Alexa.

Machine Learning (ML)

ML(Machine Learning) is a subpart of AI. It is giving training to a machine to learn from data patterns and improve its performance for every task.

• Example: A spam filter machine that learns which emails to block based on patterns.

Deep Learning (DL)

DL(Deep Learning) is a subpart of Machine Learning. It uses neural networks (similar to the human brain) with multiple layers to process data and make decisions. It is used for special tasks that need high accuracy and complex pattern recognition.

• Example: Facial recognition, voice recognition, and automatic language translation.

4) how machine learning work in AI?

Answer: Machine Learning (ML) is a technique of Artificial Intelligence. It allows machines to learn from a large amount of data and improve their performance.

Below we describe how it works:

1. Collect Data
2. Train the Model
3. Test the Model
4. Make Predictions
5. Improve Over Time

Real-Life Example:

• In Netflix, Machine Learning checks which kind of content you show to recommend similar content to you.
• Google Maps learns from traffic data to suggest faster routes to users.

5) What is a Neural Network?

Answer: A Neural Network is a type of machine learning model that is inspired by the human brain. It is created by layers of interconnected nodes (neurons) that work together to process information, find data patterns, and make problem-solving decisions.

For example: Image recognition, Language translation, Medical diagnosis, and more.

6) what are the components of AI?

Answer: Artificial Intelligence is like a toolbox that contains multiple components to make intelligent machines. These components help AI to think, learn, decide, and interact like humans.

There are several AI components such as:

1. Machine Learning (ML)
2. Deep Learning
3. Natural Language Processing (NLP)
4. Computer Vision
5. Robotics
6. Expert Systems
7. Reinforcement Learning
8. Speech Recognition

7) what is NLP(Natural Language Processing)?

Answer: NLP stands for “Natural Language Processing“. It is a branch of Artificial Intelligence (AI) that helps machines to understand, interpret, and respond to human language, both spoken and written.

this natural language allows computers to read, listen, speak, and generate language like humans.

What Can NLP Do?

• Understand what you say (voice input)
• Understand what you write (text input)
• Translate languages
• Give Answers to the questions
• Make content summaries
• Check grammar and spelling

8) what is the difference between supervised and unsupervised learning?

Answer: Supervised Learning and Unsupervised Learning are two main types of Machine Learning, but they work in different ways.

a) Supervised Learning

This model learns from the labelled data, where humans give the data to the machine along with the correct answers. like a teacher teaches a student.

Example:

• You give 1000 emails to the machine, and each email is already labelled as “spam” or “not spam.” (This means we already give data with answers.)
• First, the machine learns the data pattern.
• Later, whenever you give a new email to the machine, it can easily predict whether it’s spam or not.

Main Goal:

• Its main goal is to train the machine to predict the correct output using past labelled data.

b) Unsupervised Learning

This model learns from unlabeled data (data without correct answers). In this scenario, the humans give data to the machine, but without the correct answers.

Example:

• You give 1000 customers’ shopping data to the machine, but you don’t tell what each customer buys.
• First, the machine looks at the data itself and finds that some people buy electronics, and some buy groceries, and then it automatically creates groups without any help.

Main Goal:

• This model’s main goal is to find hidden patterns or groups in the data without any labels.

9) what are AI algorithms?

Answer: An AI has powerful algorithms that allow computers to learn huge data patterns, analyze data, and make smart decisions without being manually programmed for every task.

10) what is the role of Big Data in AI?

Answer: Big Data is the most essential part of AI because AI systems learn and solve problems by a large amount of data, and that data is provided by the big Data sources.

What Is Big data?

Big Data refers to extremely large and complex sets of data that are generated from various sources, like:

• Social media posts
• Online shopping activity
• Search engines
• Mobile apps
• Sensors and IoT devices

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