What is the history of Artificial Intelligence?
The famous Dartmouth conference in summer 1956 started AI as a field. The modern day AI has covered a long distance.
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1952: Arthur Samuel, a computer scientist developed a checker-playing game system which could learn itself playing game.
1958: McCarthy developed LISP(LISt processing) popular programming language which is used in AI applications.
1959: Samuel used the term Machine learning when speaking about computer program which can play chess better than human.
1960s
1961: Unimate an industrial robot made by George Devol became the first to work in assembly line in New Jersy.
1965: Joseph Weizenbaum a computer scientist developed ELIZA, an interactive computer program which can interact in English with a person.
1966: Shakey, the Robot, developed by Charles Rosen with his team which was the first mobile robot, known as first electronic person.
AI (Artificial Intelligence) Winters:
AI applications needed to process large amount of data. Computers weren’t able to process large volume of data, so government and corporations were losing their interest in AI. Therefore they stopped funding for AI research. Now computer scientists weren’t able to continue their research work, so from the mid 1970s to the mid 1990s the computer scientists were in shortage of funds. These years were known as the AI Winters.
1070s
1970: WABOT, an intelligent robot, aiming to develop like a person in Waseda University in Japan which had features like moveable limbs, ability to see, and ability to talk.
1980s
1980: WABOT – 2 was developed in Waseda University in Japan. This humanoid could communicate with people.
1986: A driverless car was built by Mercedes in the direction of Ernst Dickmanns, used cameras, sensors. It could drive up to 55 mph.
1990s
1995: Computer scientist Richard Wallace developed a chatbot named ALICE (Artificial Linguistic Internet Computer Entity)
1997: Deep Blue a chess-playing system developed by IBM became the first system to defeat a world chess champion.
2000s
2000: Professor Cynthia Breazeal developed Kismet a humanoid that could recognize and simulate its emotion.
2002: i-Robot developed Roomba a Robot vacuum cleaner that could clean avoiding obstacles.
2004: NASA’s robotic developed rover which could navigate Mars’ surface without human intervention.
2009: Google developed a driverless car which passed driving test in 2014.
2011: Apple developed Siri, a virtual assistant on Apple iOS. It could interact in English language.
2014: Microsoft developed Cortana, a virtual assistant on Window OS. It was similar to Siri on Apple iOS.
2014: Amazon developed Alexa, a home assistant. It could work as a personal assistant.
2015: Google Deep Mind, computer system that defeated many champions in board games.
2016: Sophia, a humanoid developed by Hanson Robotics which is known as the first robot citizen.
2016: Google developed Google Home, a smart speaker AI based which could work as a personal assistant.
2018: Samsun developed Bixby, a virtual assistant which could interact in English like a personal assistant.
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Class 11 AI
Chapter - AI for Everyone
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1950s1952: Arthur Samuel, a computer scientist developed a checker-playing game system which could learn itself playing game.
1958: McCarthy developed LISP(LISt processing) popular programming language which is used in AI applications.
1959: Samuel used the term Machine learning when speaking about computer program which can play chess better than human.
1960s
1961: Unimate an industrial robot made by George Devol became the first to work in assembly line in New Jersy.
1965: Joseph Weizenbaum a computer scientist developed ELIZA, an interactive computer program which can interact in English with a person.
1966: Shakey, the Robot, developed by Charles Rosen with his team which was the first mobile robot, known as first electronic person.
AI (Artificial Intelligence) Winters:
AI applications needed to process large amount of data. Computers weren’t able to process large volume of data, so government and corporations were losing their interest in AI. Therefore they stopped funding for AI research. Now computer scientists weren’t able to continue their research work, so from the mid 1970s to the mid 1990s the computer scientists were in shortage of funds. These years were known as the AI Winters.
1070s
1970: WABOT, an intelligent robot, aiming to develop like a person in Waseda University in Japan which had features like moveable limbs, ability to see, and ability to talk.
1980s
1980: WABOT – 2 was developed in Waseda University in Japan. This humanoid could communicate with people.
1986: A driverless car was built by Mercedes in the direction of Ernst Dickmanns, used cameras, sensors. It could drive up to 55 mph.
1990s
1995: Computer scientist Richard Wallace developed a chatbot named ALICE (Artificial Linguistic Internet Computer Entity)
1997: Deep Blue a chess-playing system developed by IBM became the first system to defeat a world chess champion.
2000s
2000: Professor Cynthia Breazeal developed Kismet a humanoid that could recognize and simulate its emotion.
2002: i-Robot developed Roomba a Robot vacuum cleaner that could clean avoiding obstacles.
2004: NASA’s robotic developed rover which could navigate Mars’ surface without human intervention.
2009: Google developed a driverless car which passed driving test in 2014.
2011: Apple developed Siri, a virtual assistant on Apple iOS. It could interact in English language.
2014: Microsoft developed Cortana, a virtual assistant on Window OS. It was similar to Siri on Apple iOS.
2014: Amazon developed Alexa, a home assistant. It could work as a personal assistant.
2015: Google Deep Mind, computer system that defeated many champions in board games.
2016: Sophia, a humanoid developed by Hanson Robotics which is known as the first robot citizen.
2016: Google developed Google Home, a smart speaker AI based which could work as a personal assistant.
2018: Samsun developed Bixby, a virtual assistant which could interact in English like a personal assistant.
What do you mean by Machine Learning in AI?
Machine Learning is a discipline that deals with programming a system so as to make them automatically learn and improve with experience. Here learning implies understanding the input data and taking informed decisions based on the supplied data. In other words, Machine learning is a subset of AI which predicts results based on incoming data.
Machine Learning is the application of AI which makes the system to automatically learn from experience without any coding. Machine learning develops an algorithm based on the given data.
Advantages of Machine Learning:
1) Continuous Improvement: As Machine learning gets large volume of data with experience the algorithm starts improving and it gives accurate results. Machine learning continuous improves with experience.
2) Wide Application: Machine learning can be used in wide range of applications. It can be used in customer care for giving personal experience and in healthcare for patients.
3) Understands trends and patterns: Machine learning can deal with large amount of data and can recognize the trends and patterns with is difficult to human brain. In e-commerce website it can find browsing pattern and buying patterns of online customers.
4) Non human intervention needed: Machine learning improves itself with experience and write algorithm without human intervention. For example Machine learning is used in antivirus to filter virus and recognize spam.
5) Handling multidimensional data: Machine learning is capable to handle multidimensional data and process it dynamically.
Disadvantages of Machine Learning:
1) Time and Resources: Machine Learning needs a lot of time to develop an algorithms. It also needs large volume of data to give accurate and reliable results.
2) Data Acquisition: Machine learning requires a massive amount of data to train on. Without data machine learning can’t predicts future results.
3) Accurate Results: It isn’t possible to predict future with machine learning system. It doesn’t give rational reason for a prediction.
Machine Learning is the application of AI which makes the system to automatically learn from experience without any coding. Machine learning develops an algorithm based on the given data.
Advantages of Machine Learning:
1) Continuous Improvement: As Machine learning gets large volume of data with experience the algorithm starts improving and it gives accurate results. Machine learning continuous improves with experience.
2) Wide Application: Machine learning can be used in wide range of applications. It can be used in customer care for giving personal experience and in healthcare for patients.
3) Understands trends and patterns: Machine learning can deal with large amount of data and can recognize the trends and patterns with is difficult to human brain. In e-commerce website it can find browsing pattern and buying patterns of online customers.
4) Non human intervention needed: Machine learning improves itself with experience and write algorithm without human intervention. For example Machine learning is used in antivirus to filter virus and recognize spam.
5) Handling multidimensional data: Machine learning is capable to handle multidimensional data and process it dynamically.
Disadvantages of Machine Learning:
1) Time and Resources: Machine Learning needs a lot of time to develop an algorithms. It also needs large volume of data to give accurate and reliable results.
2) Data Acquisition: Machine learning requires a massive amount of data to train on. Without data machine learning can’t predicts future results.
3) Accurate Results: It isn’t possible to predict future with machine learning system. It doesn’t give rational reason for a prediction.
What are the Applications of Machine Learning?
1) Spam Detection: Getting unwanted advertising mails, messages from sites, and banks reports are called spamming which fills mail inbox and takes a lot of time to filter it. Machine learning is used to filter spam mails.
2) Speech Recognition: Machine learning is used to convert speech into text by using speech recognition system. This technology recognizes spoken language and converts into words.
3) Optical Character Reader (OCR): OCR is the technology which converts text image, handwritten note, or printed text into a soft document which can be edited easily.
4) Handwriting Recognition (HWR): HWR is a technology which receives handwritten text on touch-screen and converts into a real text.
5) Youtube’s Recommended Video: Youtube Recommendation is another application of machine learning. It recognizes the past watching patterns and recommends videos.
6) Personal Assistant: Google’s Alexa and Apple Siri is the personal assistant which recognizes speech pattern and gives answer to the asked questions.
7) Face Recognition (FC): Face Recognition can unlock your phone. It also used for daily attendance in offices.
8) Self Driving Cars: Machine learning is also used in self driving car. It can drive anywhere for example Tesla, Google and Uber have already invested in driver less cars.
2) Speech Recognition: Machine learning is used to convert speech into text by using speech recognition system. This technology recognizes spoken language and converts into words.
3) Optical Character Reader (OCR): OCR is the technology which converts text image, handwritten note, or printed text into a soft document which can be edited easily.
4) Handwriting Recognition (HWR): HWR is a technology which receives handwritten text on touch-screen and converts into a real text.
5) Youtube’s Recommended Video: Youtube Recommendation is another application of machine learning. It recognizes the past watching patterns and recommends videos.
6) Personal Assistant: Google’s Alexa and Apple Siri is the personal assistant which recognizes speech pattern and gives answer to the asked questions.
7) Face Recognition (FC): Face Recognition can unlock your phone. It also used for daily attendance in offices.
8) Self Driving Cars: Machine learning is also used in self driving car. It can drive anywhere for example Tesla, Google and Uber have already invested in driver less cars.
What is the difference between Conventional programming and Machine learning Approach?
Conventional Programming Approach
Conventional programming refers to a program developed by a programmer which accepts input data and after processing the data according to the algorithm, produces the output.
Conventional programming refers to a program developed by a programmer which accepts input data and after processing the data according to the algorithm, produces the output.
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| Conventional Programming Approach |
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| Machine Learning Approach |
Machine Learning Approach
On the contrary, in Machine Learning the input data and output data is given to an algorithm (Machine Learning algorithm) to create a program.
On the contrary, in Machine Learning the input data and output data is given to an algorithm (Machine Learning algorithm) to create a program.
What are the Domain of Artificial Intelligence (AI)?
AI is working on different fields where traditionally human intelligence is used. AI is trying to simulate human intelligence and take decisions without any help. AI Domains are basically divided into three categories:
1) Data Science: Data Science deals with large volume of data which is in numeric, alphabetic, alphanumeric form. It analysis and interpret the large amount of data and get the insight and pattern using machine learning.
2) Natural Language Processing (NLP): NLP processes text and speech input which helps computer to understand, interpret, and generate human language. It translates language, summarize text, and helps to communicate between human and machine using natural language interface.
3) Computer Vision (CV): CV focuses on visual data in the form of image or video which helps computer to interpret and understand visual data. It performs many tasks such as facial recognition, object detection, and helping to self driving cars.
1) Data Science: Data Science deals with large volume of data which is in numeric, alphabetic, alphanumeric form. It analysis and interpret the large amount of data and get the insight and pattern using machine learning.
2) Natural Language Processing (NLP): NLP processes text and speech input which helps computer to understand, interpret, and generate human language. It translates language, summarize text, and helps to communicate between human and machine using natural language interface.
3) Computer Vision (CV): CV focuses on visual data in the form of image or video which helps computer to interpret and understand visual data. It performs many tasks such as facial recognition, object detection, and helping to self driving cars.
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