Artificial Intelligence

In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day.

Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines. The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc.

AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human.

What Is Artificial Intelligence?

Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems. The term "Artificial Intelligence" refers to the simulation of human intelligence processes by machines, especially computer systems. It also includes Expert systems, voice recognition, machine vision, and natural language processing (NLP).

 Visions of AI

      Systems that think like humans.

      Systems that act like humans.

      Systems that think rationally.

      Systems that act rationally.

Definition

Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power."

So, we can define AI as:

 "It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions." 

What Comprises to Artificial Intelligence?

Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.

To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:

• Mathematics
• Biology
• Psychology
• Sociology
• Computer Science
• Neurons Study
• Statistics

 Foundation of AI.

A brief history of the disciplines that contributed ideas, viewpoints, and techniques to AI are as follows:

1. Philosophy(the study of the fundamental nature of knowledge):

 

 Can formal rules be used to draw valid conclusions?

 How does the mind arise from a physical brain?

 Where does knowledge come from?

 How does knowledge lead to action?

 

 Aristotle (384–322 B.C.), was the first to formulate a precise set of laws governing the rational part of the mind. He developed an informal system of syllogisms for proper reasoning, which in principle allowed one to generate conclusions mechanically, given initial premises.

 

Eg.

all dogs are animals;

all animals have four legs;

therefore all dogs have four legs

 Thomas Hobbes (1588–1679) proposed that reasoning was like numerical computation that ―we add and subtract in our silent thoughts.‖

 Rene Descartes (1596–1650) gave the first clear discussion of the distinction between mind and matter and of the problems that arise.

 The empiricism movement, starting with Francis Bacon's (1561— 1626).

 The confirmation theory of Carnap and Carl Hempel (1905-1997) attempted to analyze the acquisition of knowledge from experience.

 Carnap's book The Logical Structure of the World (1928) defined an explicit computational procedure for extracting knowledge from elementary experiences. It was probably the first theory of mind as a computational process.

 The final element in the philosophical picture of the mind is the connection between knowledge and action. This question is vital to Al because intelligence requires action as well as reasoning.

 

2. Mathematics

 What are the formal rules to draw valid conclusions?

 What can be computed?

 

Formal science required a level of mathematical formalization in three fundamental areas: logic, computation, and probability.

Logic:

George Boole (1815–1864), who worked out the details of propositional, or Boolean, logic.

In 1879, Gottlob Frege (1848–1925) extended Boole’s logic to include objects and relations, creating the firstorder logic that is used today.

First order logic – Contains predicates, quantifiers and variables

E.g. Philosopher(a) ⇒ Scholar(a)

∀x, effect_carona(x) ⇒ quarantine(x)

∀x, King(x) ^ Greedy (x) ⇒ Evil (x)

Alfred Tarski (1902–1983) introduced a theory of reference that shows how to relate the objects in a logic to objects in the real world.

Logic and Computation: The first nontrivial algorithm is thought to be Euclid’s algorithm for computing greatest common divisors(GCD).

 Beside logic and computation, the third great contribution of mathematics to AI is the probability. The Italian Gerolamo Cardanao (1501-1576) first framed the idea of probability, describing it in terms of the possible outcomes of gambling events.

 Thomas Bayes (1702-1761) proposed a rule for updating probabilities in the light of new evidence. Baye’s rule underlies most modern approaches to uncertain reasoning in AI systems.

 

3. Economics

 How should we make decisions so as to maximize payoff?

 How should we do this when the payoff may be far in the future?

 

 The science of economics got its start in 1776, when Scottish philosopher Adam Smith treat it as a science, using the idea that economies can be thought of as consisting of individual agents maximizing their own economic well being.

 Decision theory, which combines probability theory with utility theory, provides a formal and complete framework for decisions (economic or otherwise) made under uncertainty— that is, in cases where probabilistic descriptions appropriately capture the decision maker’s environment.

 Von Neumann and Morgenstern’s development of game theory included the surprising result that, for some games, a rational agent should adopt policies that are randomized. Unlike decision theory, game theory does not offer an unambiguous prescription for selecting actions.

 

4. Neuroscience: How do brain process information?

 Neuroscience is the study of the nervous system, particularly the brain.

 335 B.C. Aristotle wrote, "Of all the animals, man has the largest brain in proportion to his size."

 Nicolas Rashevsky (1936, 1938) was the first to apply mathematical models to the study of the nervous system.

 

Fig. A neuron cell of human brain.

 The measurement of intact brain activity began in 1929 with the invention by Hans Berger of the electroencephalograph (EEG).

 The recent development of functional magnetic resonance imaging (fMRI) (Ogawa et al., 1990; Cabeza and Nyberg, 2001) is giving neuroscientists unprecedentedly detailed images of brain activity, enabling measurements that correspond in interesting ways to ongoing cognitive processes.

 

5. Psychology: How do humans and animals think and act?

 

 Behaviorism movement, led by John Watson(1878-1958). Behaviorists insisted on studying only objective measures of the percepts(stimulus) given to an animal and its resulting actions(or response). Behaviorism discovered a lot about rats and pigeons but had less success at understanding human.

 Cognitive psychology, views the brain as an information processing device. Common view among psychologist that a cognitive theory should be like a computer program.(Anderson 1980) i.e. It should describe a detailed information processing mechanism whereby some cognitive function might be implemented.

 

6. Computer engineering: How can we build an efficient computer?

 

 For artificial intelligence to succeed, we need two things: intelligence and an artifact. The computer has been the artifact(object) of choice.

 The first operational computer was the electromechanical Heath Robinson, built in 1940 by Alan Turing's team for a single purpose: deciphering German messages.

 The first operational programmable computer was the Z-3, the invention of KonradZuse in Germany in 1941.

 The first electronic computer, the ABC, was assembled by John Atanasoff and his student Clifford Berry between 1940 and 1942 at Iowa State University.

 The first programmable machine was a loom, devised in 1805 by Joseph Marie Jacquard (1752-1834) that used punched cards to store instructions for the pattern to be woven.

 

7. Control theory and cybernetics: How can artifacts operate under their own control?

 

 Ktesibios of Alexandria (c. 250 B.C.) built the first self-controlling machine: a water clock with a regulator that maintained a constant flow rate. This invention changed the definition of what an artifact could do.

 Modern control theory, especially the branch known as stochastic optimal control, has as its goal the design of systems that maximize an

objective function over time. This roughly OBJECTIVE FUNCTION matches our view of Al: designing systems that behave optimally.

 Calculus and matrix algebra- the tools of control theory

 

The tools of logical inference and computation allowed AI researchers to consider problems such as language, vision, and planning that fell completely outside the control theorist’s purview.

8. Linguistics: How does language relate to thought?

 

 In 1957, B. F. Skinner published Verbal Behavior. This was a comprehensive, detailed account of the behaviorist approach to language learning, written by the foremost expert in the field.

 Noam Chomsky, who had just published a book on his own theory, Syntactic Structures.Chomsky pointed out that the behaviorist theory did not address the notion of creativity in language.

 Modern linguistics and AI were ―born‖ at about the same time, and grew up together, intersecting in a hybrid field called computational linguistics or natural language processing.

 The problem of understanding language soon turned out to be considerably morecomplex than it seemed in 1957. Understanding language requires an understanding of the subject matter and context, not just an understanding of the structure of sentences.

 knowledge representation (the study of how to put knowledge into a form that a computer can reason with)- tied to language and informed by research in linguistics.

 

History of Artificial Intelligence

 

Maturation of Artificial Intelligence (1943-1952)

• Year 1943: The first work which is now recognized as AI was done by Warren McCulloch and Walter pits in 1943. They proposed a model of artificial neurons.
• Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning.
• Year 1950: The Alan Turing who was an English mathematician and pioneered Machine learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test.

The birth of Artificial Intelligence (1952-1956)

• Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program"Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems.
• Year 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic field.

At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time.

The golden years-Early enthusiasm (1956-1974)

• Year 1966: The researchers emphasized developing algorithms which can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA.
• Year 1972: The first intelligent humanoid robot was built in Japan which was named as WABOT-1.

The first AI winter (1974-1980)

• The duration between years 1974 to 1980 was the first AI winter duration. AI winter refers to the time period where computer scientist dealt with a severe shortage of funding from government for AI researches.
• During AI winters, an interest of publicity on artificial intelligence was decreased.

A boom of AI (1980-1987)

• Year 1980: After AI winter duration, AI came back with "Expert System". Expert systems were programmed that emulate the decision-making ability of a human expert.
• In the Year 1980, the first national conference of the American Association of Artificial Intelligence was held at Stanford University.

The second AI winter (1987-1993)

• The duration between the years 1987 to 1993 was the second AI Winter duration.
• Again Investors and government stopped in funding for AI research as due to high cost but not efficient result. The expert system such as XCON was very cost effective.

The emergence of intelligent agents (1993-2011)

• Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion.
• Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner.
• Year 2006: AI came in the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI.

Deep learning, big data and artificial general intelligence (2011-present)

• Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve the complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly.
• Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction.
• Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test."
• Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and also performed extremely well.
• Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had taken hairdresser appointment on call, and lady on other side didn't notice that she was talking with the machine.

Now AI has developed to a remarkable level. The concept of Deep learning, big data, and data science are now trending like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon are working with AI and creating amazing devices. The future of Artificial Intelligence is inspiring and will come with high intelligence.

AI Programming Cognitive Skills

AI programming focuses on three cognitive aspects, such as learning, reasoning, and problem solving.

• Learning Processes
• Reasoning Processes

·         Self-correction processes / problem solving

Learning Processes

This part of AI programming is concerned with gathering data and creating rules for transforming it into useful information. The rules, which are also called algorithms, offer computing devices with step-by-step instructions for accomplishing a particular job.

Reasoning Processes

Picking up the specific algorithms to resolve a specific task

Self-correction processes

 Refining the algorithms to ensure the most accurate results.                                        

Types of AI with examples.

1. Weak AI: Weak AI is also known as narrow AI. It is an AI system that is designed and trained for a specific type of task.

Eg.IBM’s Watson,Siri and Alexa are weak AI. This categorization happens with the help of unsupervised programming.

2. Strong AI: Strong AI is more like the human brain and is also known as artificial general intelligence. It has cognitive abilities that help to perform unfamiliar tasks and commands. It can find the solution to a problem and works beyond a preprogrammed algorithm.

Eg.Visual perception, speech recognition, decision making, and translations between languages.

3. Super AI: Super AI is AI that to go beyond in excellence than human intelligence and ability. It’s also known as artificial superintelligence (ASI) or super intelligence.

Eg. It’s the best at everything — maths, science, medicine, hobbies, you name it.

Examples of AI-Artificial Intelligence

The following are the examples of AI-Artificial Intelligence:

1. Google Maps and Ride-Hailing Applications
2. Face Detection and recognition
3. Text Editors and Autocorrect
4. Chatbots
5. E-Payments
6. Search and Recommendation algorithms
7. Digital Assistant
8. Social media
9. Healthcare
10. Gaming
11. Online Ads-Network
12. Banking and Finance
13. Smart Home devices
14. Security and Surveillance
15. Smart Keyboard App
16. Smart Speaker
17. E-Commerce
18. Smart Email Apps
19. Music and Media Streaming Service
20. Space Exploration

Artificial Intelligence in Education

Education is an important part of life for everyone, and a good education plays a vital role to have a successful life. In order to improve the education system for the students, there are always a lot of changes happening around the world, ranging from the way of teaching to the type of curriculum. Artificial Intelligence is a thriving technology that is being used in almost every field and is changing the world. One place where artificial intelligence is poised to make big changes is (and in some cases already is) in education

Artificial Intelligence in Education is developing new solutions for teaching and learning for different situations. Nowadays, AI is being used by different schools and colleges across different countries. AI in education has given a completely new perspective of looking at education to teachers, students, parents, and of course, the educational institutions as well. AI in education is not about humanoid robots as a teacher to replace human teachers, but it is about using computer intelligence to help teachers and students and making the education system much better and effective. In future, the education system will have lots of AI tools that will shape the educational experience of the future. In this topic, we will discuss the impact and application of Artificial Intelligence on Education. To better understand this topic, let's first understand what AIED is?

Overview Of AIED(Artificial Intelligence in Education)

Artificial Intelligence (AI) is a simulation of human intelligence into a computer machine so that it can think and act like a human. It is a technology that helps a computer machine to think like a human. Artificial Intelligence aims to mimic human behaviour. AI has various uses and applications in different sectors, including education.

In the 1970s, AIED has occurred as a specialist area to cover new technology to teaching & learning, specifically for higher education. The main aim of AIED is to facilitate the learners with flexible, personalized, and engaging learning along with the basic automated task. Some popular trends in AIED include Intelligent tutor systems, smart classroom technologies, adaptive learning, and pedagogical agents. Below diagram shows the relationship between all these trends:

Applications/roles of Artificial Intelligence in Education

1. Automate basic activities in education with AI
   In the education system, there are various activities which take lots of time of teachers such as grading tests and home-works. These tasks require lots of time and effort, while this time could be used in interacting with students, letting them know their errors, teaching new things, and many more.
   To save this time, Artificial Intelligence can be used.

AI tools can perform various functions like 

1. it is possible to automate the grading system
2. Report cards and other correspondence to the parents automatically
3. Plan and schedule meetings
4. Automate routine student forms, enrollments, and other paperwork to the correct department
5. Shorten the time spent on progress reports
6. Streamline any other record-keeping tasks. 

2. Additional Support for students with AI tutor
   As it is obvious that teachers can't be present with students all the time while they study, as teachers in colleges have fixed timings. But each student is not smart enough to grasp all the things at once, and they need additional supports from someone to help them in the understanding study material. This additional support can be provided by the AI tutors.
   Currently, there are various AI-driven tutoring programs that can help students in learning the basics of mathematics, writing, and other subjects.
   With these AI programs, students can learn fundamentals, but still, they are not suitable to learn high-level concepts of any subject. In order to learn such complex concepts, students still require a professor. However, in future, it is possible that AI might be able to help students with complex problems also that require analytical thinking and reasoning.
3. Helpful feedback to students and teachers with AI-driven programs
   AI is not only helping the students to learn the customized course as per their requirements, but it can also give feedback to both the teachers and students about the success level of the course. Some online course providers are currently using such feedback-based AI systems to analyse the progress of the student and also alert the professors for the critical performance issue of the student.
   These type of AI-driven systems enables the student to get the proper support, and professors can determine the areas of teaching where it requires improvement. Instant feedback to students helps them understand where they are going wrong and how they can do it better.
4. Finding improvement required in course with AI
   In the education system, it is very hard to find out the gaps in learning. Teachers have limited time to teach in the classroom, and they may not always know where the students are lacking and what concepts have confused the student. To solve this problem, AI-driven programs can help the education system.
   Coursera and some other learning platforms are already using AI-driven programs in practice. For example, when a large number of students are found to submit the wrong answer to a homework assignment, the system alerts the teacher and gives future students a customized message that offers hints to the correct answer. Such type of programs helps in filling the gaps while learning that can occur in courses, and also ensures that each student understands the concepts successfully. With AI, instead of waiting for feedback from the professor, students get an immediate system generated response, which helps them to understand a concept and remember their mistakes, and also how to do it correctly the next time around.
5. AI could change the role of the teacher.
   Teachers always have a critical role in the education system, but this role and its requirement may change with the new technologies. As in the above points, we have already discussed that Artificial Intelligence can automate different tasks such as grading, reports, help students while learning, and may also be an option of real-world tutor in some cases. AI can be included in different aspects of teaching. AI systems can be programmed for providing expertise to students, a place where students can ask their doubts and could take the place of teacher for teaching basis course materials. In such cases, AI could change the role of the teacher as a facilitator.
6. Personalize education with AI
   The main aim of Artificial Intelligence in education is not to completely replace teachers. Instead, it aims to act as helping hands for teachers as well as students.
   AI systems can be programmed to provide personalized learning to students. With personalized learning, each student can have their own way of learning as per their level of understanding and need. By understanding the needs of every student, teachers can come up with a tailor-made study plan for every student. As AI is developing day-by-day, it is possible that machines can identify the facial expressions of students while learning the concepts can understand if they are finding any difficulty in learning, and according to that make changes in the way of teaching. However, currently, such things are not possible, but they might be possible in the near future with AI-Powered machines and software.
7. Generating Smart content with AI
   With AI, it is possible to generate smart content in three ways:

1. Digital Lessons: Nowadays, everything is becoming digital, and so the education. Digital learning is being preferred in colleges with customization options, e-books, study guides, bite-sized lessons, and many other things with the help of AI.
2. Information Visualization: Visualizing things rather than listening is much more efficient to understand in a better way and keep in mind for a long time. With Artificial Intelligence, the study information can be perceived in new ways of visualization, simulation, web-based study environment.
3. Learning content Updates: Moreover, AI also helps in preparing the content of lessons, keeping information up to date, and make it adaptable as per different learning curves.
7. Ensure Access to Education for Students with Special Needs
   Life is full of challenges for those students who have some learning disabilities such as deaf or hard of hearing, visually impaired, etc. Such students may face various difficulties while learning and studying. Moreover, they also need extra care & time. With the adoption of innovative AI technology, there will be new ways of interacting with such students. AI-enabled tools can be successfully trained to help a group of students with special needs.
8. Universal Access
   One of the great uses of Artificial Intelligence of digital learning in education is universal access to study material. Each student has his own grasping capability, and with the use of universal access, they can learn anywhere and anytime. Students can explore things whenever they want to learn without waiting for the tutor. Moreover, students get the facility of high-quality courses and material from all over the world at their place only without travelling away from their home.

10. Voice assistants

Voice assistants are an engaging and convenient way to bring learning at home while also helping users schedule study calendars, listen to coaching instructions while on the go, and give instant answers to students’ basic questions in class. The benefits of voice assistants in education include:

• Efficient saving of time for students and teachers
• Providing community learning opportunities
• Providing personalized education within seconds

These AI-powered voice assistants can be used in apps on the smartphone even if they don’t have smart speakers. 

Benefits of AI For Students

• 24*7 access to Learning
  With AI-driven digital Learning, students can learn anywhere, anytime. Every learner is free to plan their schedule, rather than being linked to a specific place only. Everyone can make their learning easier and effective as per their most productive hours.
• Better Engagement
  With personalized learning, custom tasks, and digital visualisation, the study becomes more interactive and engaging. Personalized learning and great experience with AI-driven programs make students feel much confident and smarter as they can explore many things apart from their syllabus without any hesitation or fear of asking. All these things and new AI technologies are increasing the interest of students in studies.
• Less Pressure
  With AI-driven programs and personalized learning, students feel less pressure of studies. AI-enabled virtual assistants help the students whenever they ask a question, with a complete explanation. In the traditional learning methods, a student needs to ask queries in class in front of everyone, which might hesitate some students, and these issues can be resolved with the help of virtual assistants. However, all the questions can't be correctly answered by these virtual assistants. But for basic queries, they can be much helpful that can boost the confidence of each learner and reduce the pressure.

Artificial Intelligence