What is Artificial Intelligence (History of AI , Application, Tools)

Full form of AI : Artificial Intelligence (AI) applications refer to the practical use of AI technologies to solve real-world problems or tasks.

What is Artificial Intelligence?

Artificial Intelligence is a part of computer science that is focused on developing such machines or systems which could solve the problems that may otherwise require human intelligence. Artificial intelligence combines the features of computer science, physiology and philosophy. The idea is to make a machine artificially intelligent by incorporating such programs and equipment’s that are capable of taking decisions on their own in case of problems in a particular domain for which the system is made. Researcher are creating such systems that can imitate the thoughts of human, recognize human speech and interact with them, question and answering systems, moving the chess moves according to the move of the human opponent. Expert Systems employs the use of artificial intelligence. MYCIN and DENDRAL were one of the earliest expert systems designed for chemical analysis and medical diagnosis respectively.


Artificial Intelligence is made up of two words:


It means something that is not natural but is made by human skills or produced by the humans. It implies creating a copy or imitation of human. Though we can make a machine artificially similar to human but it lacks spontaneity and naturalness.

It implies injecting intelligence into a machine so that it can perform the work which would otherwise require human brain. The device should be able to take its own decision according to a particular situation. For example, the game chess on a computer is artificially intelligent. The computer plays its moves according to the moves of the opponent rather than having any fixed moves.

History of Artificial Intelligence (AI)

The history of artificial intelligence (AI) is marked by key milestones and breakthroughs that have shaped the field over several decades. Here are some key dates and names associated with the history of AI:

1943 – McCulloch and Pitts Model: Warren McCulloch and Walter Pitts developed a mathematical model of a simplified neuron, laying the foundation for artificial neural networks.

1950 – Alan Turing’s “Computing Machinery and Intelligence”: British mathematician and computer scientist Alan Turing published a paper discussing the idea of a machine capable of imitating any human intelligence. He proposed the Turing Test as a measure of a machine’s ability to exhibit intelligent behavior.

1956 – Dartmouth Workshop: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon organized the Dartmouth Workshop, often considered the birth of AI as a field. The workshop proposed that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

1957 – John McCarthy’s LISP: John McCarthy developed the LISP programming language, one of the earliest high-level languages designed for AI research. LISP became a fundamental tool in AI development.

1965 – ELIZA: Joseph Weizenbaum created ELIZA, one of the first chatbots, which could engage in text-based conversations with users, demonstrating natural language processing capabilities.

1969 – Shakey the Robot: The Stanford Research Institute developed Shakey the Robot, one of the earliest autonomous robots, capable of basic navigation and problem-solving.

1980s – Expert Systems: The 1980s saw the rise of expert systems, AI programs that emulated the decision-making abilities of human experts in specific domains. Names like MYCIN (for medical diagnosis) and Dendral (for chemistry) were notable in this era.

1997 – Deep Blue vs. Garry Kasparov: IBM’s Deep Blue defeated world chess champion Garry Kasparov, showcasing the power of AI in strategic game-playing.

2000s – Machine Learning Renaissance: Advances in machine learning algorithms and the availability of large datasets led to significant progress in AI, including the development of systems like IBM’s Watson for natural language processing and knowledge retrieval.

2010s – Deep Learning and Neural Networks: The 2010s witnessed a resurgence of interest in neural networks, particularly deep learning, which led to breakthroughs in image recognition, speech recognition, and natural language processing. Key figures in this period include Geoff Hinton, Yann LeCun, and Yoshua Bengio.

2011 – IBM Watson on Jeopardy!: IBM’s Watson competed on the game show Jeopardy!, demonstrating its ability to understand and answer questions in natural language.

2016 – AlphaGo: Google’s AlphaGo AI defeated the world champion Go player, Lee Sedol, marking a significant achievement in complex board game playing.

2020s – Ongoing Advancements: AI continues to advance rapidly in areas like autonomous vehicles, robotics, healthcare, and language generation, with notable developments from companies like Tesla, OpenAI, and DeepMind.

What is full form of AI

The full form of AI is “Artificial Intelligence.”

Artificial Intelligence (AI) Applications Name, Definition,  Type of (AI) Applications Name

Artificial Intelligence (AI) applications refer to the practical use of AI technologies to solve real-world problems or tasks. AI has a wide range of applications across various industries and domains. Here are some common types of AI applications and their definitions:

Natural Language Processing (NLP):

Definition: NLP is a branch of AI that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language.

Applications: Chatbots, language translation, sentiment analysis, voice assistants (e.g., Siri, Alexa), and text summarization.

Computer Vision:

Definition: Computer vision involves teaching machines to interpret and understand visual information from the world, such as images and videos.

Applications: Image recognition, facial recognition, object detection, autonomous vehicles, medical image analysis, and quality control in manufacturing.

Machine Learning and Predictive Analytics:

Definition: Machine learning algorithms are used to analyze large datasets and make predictions or decisions based on patterns and data.

Applications: Credit scoring, fraud detection, recommendation systems (e.g., Netflix, Amazon), demand forecasting, and personalized marketing.


Definition: Robotics combines AI with physical machines (robots) to perform tasks in the physical world, often involving sensors and actuators.

Applications: Industrial automation, healthcare robots, autonomous drones, self-driving cars, and home automation.

Expert Systems:

Definition: Expert systems are AI programs that emulate the decision-making abilities of human experts in a specific domain. They use knowledge-based rules to solve complex problems.

Applications: Medical diagnosis, financial analysis, troubleshooting complex machinery, and legal advice.

Autonomous Systems:

Definition: Autonomous AI systems are capable of operating and making decisions without human intervention.

Applications: Self-driving cars, drones, and smart home devices (e.g., thermostats, security systems).

Reinforcement Learning:

Definition: Reinforcement learning is a machine learning paradigm where agents learn to make decisions by interacting with an environment and receiving feedback.

Applications: Game playing (e.g., AlphaGo), robotics, and optimization of processes.

AI in Healthcare:

Definition: AI is applied to healthcare to improve patient care, diagnostics, drug discovery, and medical imaging.

Applications: Disease diagnosis, drug discovery, radiology image analysis, virtual health assistants, and personalized treatment plans.

AI in Finance:

Definition: AI is used in the financial industry to analyze market data, manage investments, and detect fraudulent activities.

Applications: Algorithmic trading, risk assessment, fraud detection, and customer service chatbots.

AI in Education:

Definition: AI applications in education aim to enhance learning experiences, provide personalized education, and automate administrative tasks.

Applications: Intelligent tutoring systems, adaptive learning platforms, plagiarism detection, and administrative automation.

AI in Gaming:

Definition: AI is employed in the gaming industry to create intelligent non-player characters (NPCs), simulate human-like behaviors, and improve game realism.

Applications: Game AI, virtual worlds, and character behavior modeling.


Best  AI Software Tool Name

AI software tools given in the table.

No. AI Software Tool Name
1 TensorFlow
2 PyTorch
3 Keras
4 scikit-learn
5 OpenCV
6 Microsoft Azure AI
7 IBM Watson
8 Google Cloud AI
9 Amazon SageMaker
10 H2O.ai
11 Caffe
12 Theano
13 RapidMiner
15 IBM Watson Studio
16 DataRobot
17 Nvidia Deep Learning AI
18 BigML
19 Cognitive Toolkit (CNTK)
20 Orange



What is TensorFlow:

Developed by Google, TensorFlow is an open-source deep learning framework widely used for building and training machine learning models, especially neural networks.


PyTorch is another popular open-source deep learning framework, known for its dynamic computation graph and flexibility, making it favored by researchers and developers.


Keras is an open-source neural network library that can run on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It offers a high-level interface for building and training neural networks.


scikit-learn is a versatile and user-friendly machine learning library in Python, offering tools for classification, regression, clustering, and more.


OpenCV is an open-source computer vision library that provides tools and functions for image and video analysis, making it essential for computer vision and image processing tasks.

Microsoft Azure AI:

Microsoft’s Azure AI platform offers a suite of AI and machine learning services, including Azure Machine Learning, for building, training, and deploying AI models.

IBM Watson:

IBM Watson is a suite of AI-powered services and solutions that includes tools for natural language processing, chatbots, and machine learning.

Google Cloud AI:

Google Cloud AI provides various AI and machine learning services, such as AutoML, Vision AI, and Natural Language AI, hosted on Google Cloud Platform.

Amazon SageMaker:

Amazon SageMaker is an integrated development environment for building, training, and deploying machine learning models using Amazon Web Services (AWS).


H2O.ai offers a platform for machine learning and artificial intelligence, including tools for data preparation, model building, and deployment.


Caffe is an open-source deep learning framework developed by the Berkeley Vision and Learning Center, known for its speed and efficiency in training convolutional neural networks (CNNs).


Theano is a Python library that specializes in optimizing and evaluating mathematical expressions, particularly used for deep learning.


RapidMiner is a data science platform that offers tools for data preprocessing, machine learning, and predictive analytics.


KNIME is an open-source data analytics platform that allows users to create data workflows, perform data analysis, and build machine learning models.

IBM Watson Studio:

Watson Studio is part of IBM’s AI offerings, providing tools for data scientists and developers to collaborate on data analysis and model building.


DataRobot is an automated machine learning platform that helps organizations build, deploy, and manage machine learning models.

Nvidia Deep Learning AI:

Nvidia provides AI and deep learning software libraries, frameworks, and hardware for accelerating AI workloads, particularly in the field of deep learning.


BigML is a machine learning platform that offers a range of tools for building and deploying machine learning models.

Cognitive Toolkit (CNTK):

CNTK is an open-source deep learning framework developed by Microsoft, designed for efficient training of deep neural networks.


Orange is an open-source data visualization and analysis tool that also includes machine learning components, making it suitable for data exploration and model building.

Difference Between Weak AI Vs Strong AI

Weak AI (Narrow AI):

Definition: Weak AI refers to AI systems that are designed and programmed for a specific task or a narrow range of tasks. These systems are not conscious and do not possess general intelligence or self-awareness.


Limited to a specific task or domain.

Lacks consciousness and self-awareness.

Does not have the ability to understand or reason beyond its programmed capabilities.

Examples include virtual assistants (e.g., Siri, Alexa), recommendation systems, and chatbots.

Strong AI (General AI or AGI – Artificial General Intelligence):

Definition: Strong AI, or General AI, refers to AI systems that possess human-level intelligence and consciousness. These systems would have the ability to understand, learn, reason, and adapt to a wide range of tasks and domains, similar to human intelligence.


Exhibits human-like intelligence and consciousness.

Can perform a wide variety of tasks, adapt to new situations, and learn from experience.

Possesses general reasoning and problem-solving abilities.

Can understand, learn, and apply knowledge across different domains.

Strong AI has not yet been achieved and remains a goal of AI research.


FAQ Question based on Artificial Intelligence (AI)

What is Artificial Intelligence (AI)?

AI refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and understanding natural language.

What are the main branches of AI?

AI can be categorized into two main branches: Narrow AI (or Weak AI) and General AI (or Strong AI). Narrow AI specializes in performing specific tasks, while General AI would have human-like intelligence and versatility.

What is machine learning, and how does it relate to AI?

Machine learning is a subset of AI that focuses on developing algorithms that allow computers to learn from and make predictions based on data. It’s a crucial component of many AI applications.

What is deep learning?

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to process and learn from large datasets. It has been instrumental in tasks like image and speech recognition.

What are some real-world applications of AI?

AI is used in various industries, including healthcare (diagnostics, drug discovery), finance (algorithmic trading, fraud detection), robotics, autonomous vehicles, and natural language processing (chatbots, voice assistants).

What is the Turing Test?

The Turing Test is a test of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. If a machine can convince a human evaluator that it’s human during a text-based conversation, it’s said to have passed the Turing Test.

What is the difference between AI and automation?

AI involves machines that can perform tasks intelligently, adapting to changing situations. Automation typically refers to the use of machines to perform repetitive tasks without intelligence.

Is AI going to replace human jobs?

AI may automate certain tasks, but it also has the potential to create new jobs and enhance productivity. The impact on jobs depends on how AI is integrated into different industries.

What are the ethical concerns surrounding AI?

Ethical concerns include bias in AI algorithms, privacy issues, the potential for job displacement, and the responsible use of AI in areas like autonomous weapons.

What is the current state of AI research and development?

AI research is active and rapidly evolving. Advances in deep learning, natural language processing, and computer vision have led to significant breakthroughs in recent years.

Can anyone learn AI and machine learning?

Yes, AI and machine learning are accessible to anyone interested in learning. There are online courses, tutorials, and resources available to help individuals get started.

What are some popular AI programming languages and frameworks?

Python is a popular programming language for AI, and frameworks like TensorFlow, PyTorch, and scikit-learn are widely used for AI development.

Is AI safe?

AI safety is a concern, particularly in applications like autonomous vehicles and healthcare. Researchers are actively working on developing safe and responsible AI systems.

What is the future of AI?

The future of AI holds the promise of more advanced AI systems, increased integration into various industries, and continued research into achieving human-level AI.


Multiple-choice questions (MCQs) based on Artificial Intelligence (AI)

  1. What does AI stand for? a) Advanced Intelligence b) Artificial Intelligence c) Automated Information d) All-Inclusive
  2. Which of the following is not a subset of AI? a) Machine Learning b) Deep Learning c) Neural Networks d) None of the above
  3. Which programming language is commonly used for AI development? a) Java b) C++ c) Python d) Ruby
  4. What type of learning allows AI systems to learn from data without explicit programming? a) Supervised Learning b) Unsupervised Learning c) Reinforcement Learning d) Machine Learning
  5. What is the primary goal of natural language processing (NLP) in AI? a) To create art b) To understand and generate human language c) To simulate natural environments d) To optimize machine performance
  6. What is the Turing Test used for? a) Testing computer performance in video games b) Measuring a machine’s ability to exhibit intelligent behavior indistinguishable from humans c) Evaluating internet speed d) Assessing the accuracy of AI-generated weather predictions
  7. Which of the following is an example of a narrow AI application? a) A self-driving car b) A virtual assistant like Siri c) A robot with human-like intelligence d) An AI that can perform any task
  8. What does the acronym “NLP” stand for in AI? a) Natural Learning Process b) Neural Language Processing c) Natural Language Processing d) Numeric Language Protocol
  9. What is the term for a computer vision task that involves identifying and labeling objects within an image or video? a) Object Detection b) Facial Recognition c) Image Classification d) Optical Character Recognition (OCR)
  10. Which AI system defeated the world chess champion Garry Kasparov in 1997? a) Watson b) AlphaGo c) Deep Blue d) HAL 9000


  1. b) Artificial Intelligence
  2. d) None of the above
  3. c) Python
  4. d) Machine Learning
  5. b) To understand and generate human language
  6. b) Measuring a machine’s ability to exhibit intelligent behavior indistinguishable from humans
  7. b) A virtual assistant like Siri
  8. c) Natural Language Processing
  9. a) Object Detection
  10. c) Deep Blue