Basics of AI Class 10
People all around the world have long been fascinated by the concept of artificial intelligence. Many organizations have come up with their own definitions of artificial intelligence. Following are a few of them:
NITI Aayog: National Strategy for Artificial Intelligence
AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Initially conceived as a technology that could mimic human intelligence, AI has evolved in ways that far exceed its original conception.
World Economic Forum
Artificial intelligence (AI) is the software engine that drives the Fourth Industrial Revolution. Its impact can already be seen in homes, businesses and political processes. In its embodied form of robots, it will soon be driving cars, stocking warehouses and caring for the young and elderly.
European Artificial Intelligence (AI) leadership, the path for an integrated vision
AI is not a well-defined technology and no universally agreed definition exists. It is rather a cover term for techniques associated with data analysis and pattern recognition. AI is not a new technology, having existed since the 1950s.
Artificial intelligence (AI), is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
AI, ML & DL
You must have encountered a very common conflict between artificial intelligence (AI) and machine learning while you worked to develop your AI readiness (ML). These phrases are frequently used synonymously, but are they the same? Is there no distinction between artificial intelligence and machine learning? Is Deep Learning (DL) an instance of artificial intelligence? What is Deep Learning, exactly?
Artificial Intelligence (AI)
Refers to any method that makes it possible for computers to simulate intelligence. Machines can now detect human faces, move and manipulate items, comprehend human voice commands, and perform a variety of other jobs thanks to this technology. The AI-enabled machines operate in an intelligent manner and think algorithmically.
Machine Learning (ML)
It is a branch of artificial intelligence that allows robots to get better at tasks over time (data). The goal of machine learning is to give computers the ability to learn on their own utilizing the supplied data and arrive at reliable predictions and decisions.
Deep Learning (DL)
Software can use it to teach itself how to carry out tasks using enormous volumes of data. Massive amounts of data are used to train the machine in deep learning, allowing it to learn from the data. These devices possess the intelligence to create algorithms on their own.
Introduction to AI Domains
AI models can be broadly categorized into three domains:
- Data Science
- Computer Vision
- Natural Language Processing
Data sciences is an area of AI that deals with data systems and processes. In this area, a system gathers a lot of data, maintains data sets, and extrapolates meaning from the data. A decision can be made based on the data science-extracted information.
Example of Data Science
Price Comparison Websites – Price comparison websites include PriceGrabber, PriceRunner, Junglee, Shopzilla, and DealTime, to name a few. There is a huge amount of data powering these websites. If you have ever used one of these websites, you are aware of how convenient it is to compare a product’s price among various merchants in one location.
The field of artificial intelligence known as computer vision, or CV for short, demonstrates the ability of a machine to gather and analyze visual data before making predictions about it. Image acquisition, screening, analysis, identification, and information extraction are all part of the process.
Example of Computer Vision
- Self-Driving cars/ Automatic Cars
- Face Lock in Smartphones
Natural Language Processing
NLP, or natural language processing, is a subfield of artificial intelligence that deals with the use of natural language in communication between machines and people. Natural language processing (NLP) aims to use algorithms to extract information from spoken and written words in natural language, which is language spoken by people.
Example of Natural Language Processing
- Email filters
- Smart assistants like – Apple Siri and Amazon Alexa
AI ethics is a set of moral guidelines and methods meant to guide the creation and ethical application of artificial intelligence technologies. Organizations are beginning to create AI codes of ethics as AI has become ingrained in goods and services.
These days, the Information Age is giving way to the Age of Artificial Intelligence. We now construct solutions using intelligence gathered from the data rather than data or information. Even Netflix TV and movie recommendations can be made using these technologies.
Data is the center of the artificial intelligence. Every business, no matter how big or small, is collecting data from as many sources as they can. The fact that more than 70% of the data acquired to date was just gathered in the previous three years demonstrates how crucial data has grown in recent years. The adage that data is the new gold is not untrue.
When outcomes in AI cannot be broadly generalised, bias occurs. We frequently imagine bias as the product of preferences or exclusions in training data, but bias can also be introduced through the methods used to collect data, the algorithms used to process it, and the methods used to interpret the results of AI.
Not everyone can use artificial intelligence because it is still a developing technology. People who can afford AI-enabled technology benefit from it to the fullest, while those who cannot are left behind. Due to this, a gap between these two social strata has developed, and it is becoming wider due to the rapid growth of technology.
AI creates unemployment
The use of AI is improving people’s lives. Nowadays, most tasks can be completed with a few clicks. AI will soon be able to complete all the difficult activities that humans have been performing for a long time. Maybe all of the laborer’s will be replaced by AI-enabled machines in the near future.
AI for kids
Kids today are intelligent enough to grasp technology from a young age. As their capacity for thought grows, they begin to become tech-savvy and eventually pick everything up more quickly than an adult. But should technology be introduced to young children?
Employability skills Class 10 Notes
- Unit 1- Communication Skills Class 10 Notes
- Unit 2- Self-Management Skills Class 10 Notes
- Unit 3- Basic ICT Skills Class 10 Notes
- Unit 4- Entrepreneurial Skills Class 10 Notes
- Unit 5- Green Skills Class 10 Notes
Employability skills Class 10 MCQ
- Unit 1- Communication Skills Class 10 MCQ
- Unit 2- Self-Management Skills Class 10 MCQ
- Unit 3- Basic ICT Skills Class 10 MCQ
- Unit 4- Entrepreneurial Skills Class 10 MCQ
- Unit 5- Green Skills Class 10 MCQ
Employability skills Class 10 Questions and Answers
- Unit 1- Communication Skills Class 10 Questions and Answers
- Unit 2- Self-Management Skills Class 10 Questions and Answers
- Unit 3- Basic ICT Skills Class 10 Questions and Answers
- Unit 4- Entrepreneurial Skills Class 10 Questions and Answers
- Unit 5- Green Skills Class 10 Questions and Answers
Artificial Intelligence Class 10 Notes
- Unit 1 – Introduction to Artificial Intelligence Class 10 Notes
- Unit 2 – AI Project Cycle Class 10 Notes
- Unit 3 – Natural Language Processing Class 10 Notes
- Unit 4 – Evaluation Class 10 Notes
- Advanced Python Class 10 Notes
- Computer Vision Class 10 Notes
Artificial Intelligence Class 10 MCQ
- Unit 1 – Introduction to Artificial Intelligence Class 10 MCQ
- Unit 2 – AI Project Cycle Class 10 MCQ
- Unit 3 – Natural Language Processing Class 10 MCQ
- Unit 4 – Evaluation Class 10 MCQ