Artificial Intelligence Viva Questions

Introduction

Artificial Intelligence (AI) has made a huge impact across several industries, such as healthcare, finance, education, business, telecommunications, and social media, and is expected to disrupt even more industries in the future. Artificial Intelligence has the potential to give rise to such new industries, which we can’t imagine in the present. There is a huge demand for AI Engineers and AI professionals to implement AI to use its potential to the maximum.

In this blog, we have compiled a list of Artificial Intelligence viva questions and their answers:

Give differences between Artificial Intelligence, Machine learning, and Deep learning.

Artificial Intelligence  Machine Learning  Deep Learning
In 1950-Alan Turing proposes the ‘Turing test’ to check the intelligence of machines.  In 1952-Arthur Samuel wrote the first computer learning program.  In the mid-1960s-Mathematician, Alexey Ivakhnenko created small but functional neural networks.
AI represents imitated intelligence in machines.  ML is the practice of getting machines to make decisions without being programmed. DL uses artificial neural networks to solve complex problems without using any inputs.
Artificial Intelligence is a tool. Machine Learning is a way to build the tool. Deep Learning is a type of ML to achieve Artificial Intelligence.
Aims to build machines capable of thinking like humans. Aim to enable the machines to recognize past experiences.  Aim to solve complex problems, like the human brain, through various algorithms.
Requires a huge amount of data to work. Can work with fewer data as compared to AI and Deep Learning. Requires a huge amount of data as compared to ML.

                                                     

What is Artificial Intelligence?

Artificial Intelligence is a computer science field wherein the human brain’s cognitive functions are studied and replicated on a machine or a system. AI is used in widely used for various applications like computer vision, speech recognition, decision-making, perception making, reasoning, and so on.

Give examples of Artificial Intelligence. 

1. Face Detection and Face Recognition technology:

Snapchat uses Face Detection Technology to provide virtual filters, while Apple uses Face Recognition for FaceID unlock.

2. Text Editor

Grammarly uses Natural Language Processing algorithms to identify incorrect grammar usage and suggest corrections. It can also provide readability and plagiarism grades.

3. Social Media

Facebook, Twitter, and Instagram rely heavily on AI to personalize what you see on your feeds.

4. Chatbots

Computer scientists train chatbots to impersonate the conversational style of customer representatives using Natural Language Processing. 

5. Recommendation Algorithm

Netflix, Spotify, and YouTube rely on smart recommendations provided by AI.

6. Search Algorithm

Google, Bing, and DuckDuckGo use a quality control algorithm to recognize high-quality content.

7. Digital Assistants

Siri, Bixby, and Alexa use AI algorithms to understand and process the commands.

8. Smart Home Devices

Google Nest uses AI to set room temperatures according to the preference of users.

What are the different AI?

1. Reactive Machines AI

The simplest machine that can provide output after giving a certain input.

Example- IBM’s chess-playing AI- DeepBlue

2. Limited Memory AI

The system can store some memory and make decisions accordingly. 

Example- Self-Driving cars.

3. Theory of Mind AI 

Artificial Intelligence is capable of understanding human emotions, beliefs, intents, and knowledge. Artificial Intelligence expert teams are still working on reaching this stage of AI. 

4. Self-Awareness AI: 

Self-aware machine capable of human-level consciousness with the ability to think, desire, and understand feelings. 

What are the domains of AI? 

  1. Machine learning 
  2. Deep Learning
  3. Natural Language Processing
  4. Expert system
  5. Machine vision
  6. Speech Recognition
  7. Face Recognition

What is Machine learning? 

Machine learning is a part of AI which provides intelligence to machines with the ability to learn with experiences without being explicitly programmed automatically. It is based on the idea that machines can learn from past data, identify patterns, and make decisions. 

What are the types of Machine Learning? 

  1. Supervised Learning: It is a type of machine learning in which machines learn from known datasets and then predict the output. Popular algorithms include- Linear Regression, Logistic Regression, support vector machine, etc.
  2. Reinforcement Learning: It is a type of learning in which an AI agent is trained by giving commands, and on each action, the agent gets a reward as feedback. Popular algorithms- Q-learning, SARSA, etc. 
  3. Unsupervised Learning: the algorithms are trained with neither labeled nor classified data. The AI agent has to learn from patterns without any corresponding output values. Popular Algorithms- K-means, C-means, etc.

Examples of Machine learning 

1. Akinator

It is a software trained to learn famous people’s properties through people answering the questions in the game.

2. Image recognition:

The machine had to train itself by looking at pictures of dogs, cats, stairs, and other objects and comparing them with similar-looking pictures of other objects.

What is Deep Learning? 

Deep Learning is an AI function that imitates the working of the human brain in processing data and creating patterns for decision making. The neurons and neuron connections inspire the structure of Deep learning in the human brain. 

Artificial Neural Networks (ANNs) are a subset of machine learning. They are comprised of node layers containing one or multiple hidden layers and an output layer. If the number of layers, including the input and output layers, is more than three, it is called a Deep Neural Network.

Any Deep Neural network will comprise three types of layers:

  1. Input layer: It receives the inputs and transfers them to hidden layers for analysis.
  2. Hidden layer: Various computations are carried out, and the result is transferred to the output layer. There can be n number of hidden layers. 
  3. Output layer: The layer responsible for transferring the information from the neural network to the outside world.

Applications of Deep learning

  1. Natural Language Processing is the method through which computers can understand the text and spoken words the same way human beings can. An advanced NLP model is capable of understanding sarcasm, slang, inner meaning and contextual definitions of the language in which the text was written.

Uses of NLPs: 

  1. Text and speech processing
  2. Syntactic analysis
  3. Lexical semantics (meaning of individual words in a context)
  4. Relational semantics (meaning of individual sentences)
  5. Discourse (Meaning beyond Individual sentences)
  1. Computer Vision: It enables computers to derive meaningful information from digital images, videos, and other visual inputs and take actions, make decisions, and make recommendations on that information. 

Uses of Computer Vision:

  1. Defect detection
  2. Security
  3. Intruder Detection
  4. Assembly verification
  5. Editing

Conclusion

We compiled the most relevant questions on topics of Artificial Intelligence, Machine Learning, and Deep learning most concisely and understandably. You can refer to the links below if you need more help with your artificial intelligence training. Several good AI certification courses are being offered worldwide, and Jigsaw Academy is one of the proponents of such studies. 

The above-mentioned artificial intelligence interview questions and answers will help you gain extensive knowledge about AI and its related fields. UNext Jigsaw offers PG and diploma degrees in Management and Artificial Intelligence. Learn from industry experts and broaden the horizons of your knowledge.

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