Q1. What is AI?
AI stands for Artificial Intelligence.
Artificial Intelligence is a technology that mimics human behavior(acts like a human). For example— Humanoid Robots,
Alexa, Siri, etc.
Q2 What is DATA?
Data is the smallest unit of any information. In other words, Data is raw facts and figures.
For example: Roll-18, name-Aakash, Std -7
Q3. What is Information?
Information is an organized collection of data.
Let’s see the Student_Info Table:
Name Std Sec Roll
Aakash 7 A 18
Rajesh 7 B 15
In this Table data is well organised and we can form information such as, Aakash studies in standard 7 section A and his roll number is 18. Now this organised data is more meaningful and informative.
Q4. What is Digital Footprint?
While browsing, we leave a trail of data behind us that describes our browsing habits or trends. This browsing habit the
trend is called digital footprint.
Q5. What do you mean by analytics?
Website collects huge amount of data on daily basis due to digital footprints. AI algorithms compile such huge data
chunks from millions of visitors daily and find out the intelligent results out of it. The process of analyzing enormous
amount of data to find useful trends in visitors browsing habit is called analytics.
Q6. What is data science? Why is it so important?
A computer understands data in the form of numbers only. All the data collected from various sources is not in numeric
form. Data is converted in a suitable form for using it to teach the AI or Machine Learning (ML) algorithms. The practices
and techniques that collect and prepare the data for this purpose are called data science.
Q7. What do you mean by Computer Vision?
Computer vision is the AI domain that deals with analyzing visual data such as images, spatial data, video frames and live
feed of data like face, video recording etc.
Q8. How does computer recognize any visual data such as images or videos?
For a computer everything should be in number, when we click a picture or record a video, it gets converted into
numbers, AI algorithms analyses the pattern of numbers and then identify a picture out of several stored pictures or
videos.
Q9. How does computer understand Colors’?
In computer, Colors are combination of three primary colors RGB(Red, Green, Blue). Every color is combination of these
three colors.
For each color, Color intensity varies from 0-255. It means each color have 256 varieties. If we set all three colors 0 such
as 000 000 000, It will return black colors. If we set all three colors 255, such as 255 255 255, It will return white color.
Based on different color intensity combination we can form 256*256*256 = 16777216 color combinations.
Q10. How does computer understand Images?
Computer memory stores images in the form of pixels. An image is grid of several thousands of tiny pixels.
Q11. What do you mean by Natural Language Processing? Explain with examples.
Ans:- The ability of machines to understand and speak human languages is called Natural Language Processing (NLP).
ChatGPT is the most popular example of that can generate any kind of text in request.
Alexa also interacts in Natural human language.
NLP is useful in :
documents classification
Sentiment analysis
Handwritten text recognition, etc.
12. How is speech to text conversion done?
Ans: When we speak it forms analog waves in the air and computer works on digital wave.
Phase 1- The spoken word, analog signal is converted into digital signal by the modulating device. Sound card on the
computer is one such common device. Sound card also normalizes the audio signals to a constant volume level, filters
the noise and adjusts the speed of the spoken words to a constant speed.
Phase2- The digital form of sound is broken down into tiny segments.
Phase-3 The machine compares the tiny segments with the English language dictionary of words and phrases.
13. List the several applications of AI.
Ans: – The domain of AI consists various applications. Some of them are listed below:
Predictive smart search
Product recommendation
Fraud Detection and Prevention
Location and Directions
Sentiment Analysis
Object Detection
Language Processing
14. What is Machine Learning?
Ans: – Machine learning is a sub-set of AI.
It involves feeding a machine with a lot of data (training data) and make it learn from that data to perform useful tasks
such as identifying objects, processing text, classifying items, identifying patterns in data and do predictions.
15. What is deep learning?
Ans: – Deep Learning is an advanced form of machine learning. It works on the concepts of human brain.
Deep learning is used when we have to train more complex data and need high accuracy in predictions.
16. Explain Artificial Neural Network (ANN) .
Ans: – ANN is an algorithm that works on the pattern of human brain neuron. It is trained on the data for higher accuracy
and predictions. We can create multiple layers of artificial neurons to get high level of accuracy in predictions. It is often
used for high level of applications such as fraud detection, healthcare services, medical sciences, etc.
17. How many types of Machine learning are?
Ans; There are 3 types of machine learning identified by AI scientist:
Supervised learning
Unsupervised learning and
Reinforcement Learning
18. What are the differences between different types of machine learning?
There are three types of Machine learning:
- Supervised Machine Learning: In this type of ML models, input data and output data are labelled. When we collect data for Ml model building, Along with Input data output data(for future predictions ) are also available. We Use regression and classification ML models to develop supervised ML models. Weather Forecasting, Stock market trends, Fraud Detection are some popular ML models of this category.
2. Unsupervised ML:
In this type, Input data is available but output levelled data is not available for training purpose. SVM, KNN models, RNN algorithms are used for development of such models. Sentiment Analysis, Image classifications are some popular examples of Unsupervised Machine Learning.
3. Reinforcement Learning: In such type of models, Models are developed in a restricted environment. Models are trained and gets rewards for each correct movement and penalties for incorrect learning. Robotics, Self Driving Cars is popular application of reinforcemet learning. Artificial General Intelligence (AGI) is the concept working for developing such type of models.
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