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Artificial Intelligence

What is A.I

Artificial intelligence (AI) is the ability of a machine or a computer program to think and learn. The concept of AI is based on the idea of building machines capable of thinking, acting, and learning like humans.

AI is accomplished by studying how the human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

Goals of AI

  • To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
  • To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans.

The difference between AI and “machine learning”

Chances are, if you’ve heard the term AI ballooning over the last few years, you’ve also heard “machine learning” as a buzzword. Many have questions like “Is AI The Same As Machine Learning?”

AI and machine learning have a similar relationship to rectangles and squares. Just as all squares are rectangles, but not all rectangles are squares; machine learning is one application of AI, but AI is a broader concept that has other uses, too.

Hype around A.I

AI is already transforming web search, advertising, e-commerce, finance, logistics, media, and more. Surprisingly, despite AI’s breadth of impact, the types of it being deployed are still extremely limited. Almost all of AI’s recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B).

For example: The technical term for building this  A→B software is supervised learning. A→B is far from the sentient robots that science fiction has promised us. Human intelligence also does much more than A→B. These A→B systems have been improving rapidly, and the best ones today are built with a technology called deep learning or deep neural networks, which were loosely inspired by the brain. But these systems still fall far short of science fiction. Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal.

Today’s supervised learning software has an Achilles’ heel: It requires a huge amount of data. You need to show the system a lot of examples of both A and B. For instance, building a photo tagger requires anywhere from tens to hundreds of thousands of pictures (A) as well as labels or tags telling you if there are people in them (B).

Artificial intelligence is such a thing that has been talked about for a very long time. Literally a hundred years, maybe even more. But everything that has been done so far shows that we are still very far from the intellect per se.

Reading emotions, showing empathy, be spontaneous and creative on its own, be ethically and politically correct, make a clear and consistent decision — all these aspects are so-called AI weak points. However, we can’t say all these aspects are impossible for the future. In fact, more than half of this has already been implemented. Although there are not so well, we at least have first tries.

AI For Students

AI’s digital, dynamic nature also offers opportunities for student engagement that cannot be found in often out-dated textbooks or in the fixed environment of the typical four-walled classroom. In synergistic fashion, they each have the potential to propel the other forward and accelerate the discovery of new learning frontiers and the creation of innovative technologies. Below are some examples of ways in which AI is being pioneered and applied in education.

Examples of Artificial Intelligence in Education

  • Smart Content –  Technology that attempts to condense text books into useful tool for exam preparation such as true or false questions
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  • Intelligent Tutoring Systems – Personalized electronic tutoring customized to the learning styles and preferences of the pupil
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  • Virtual Facilitators and Learning Environments – Virtual human guides and facilitators for use in a variety of educational and therapeutic environments

Smart Content

“Smart content” creation, from digitized guides of textbooks to customizable learning digital interfaces, are being introduced at all levels, from elementary to post-secondary to corporate environments.

Content Technologies, Inc., an artificial intelligence development company specializing in automation of business processes and intelligent instruction design, has created a suite of smart content services for secondary education and beyond. Cram101, for example, uses AI to help disseminate and breakdown textbook content into digestible “smart” study guide that includes chapter summaries, true-false and multiple choice practice tests, and flashcards. JustTheFacts101 has a similar, though more streamlined purpose — highlighting and creating text and chapter-specific summaries, which are then archived into a digital collection and made available on Amazon.

Other companies are creating smart digital content platforms, complete with content delivery, practice exercises, and real-time feedback and assessment.

Intelligent Tutoring Systems

Mastery learning, a set of principles largely tied to the work of Educational Psychologist Benjamin Bloom in the 1970’s, supports the effectiveness of individualized tutoring and instruction in the classroom. Curriculum organized around a student’s progress, combined with timely targeted feedback, immediate opportunities for corrected practice, and enrichment activities, are fundamental mastery learning practices. Developing a one-on-one tutoring system that can provide these elements has been a coveted goal of AI researchers since the 1970’s and 1980’s.

Carnegie Learning’s “Mika” software, for example, uses cognitive science and AI technologies to provide personalized tutoring and real-time feedback for post-secondary education students, particularly incoming college freshman who would otherwise need remedial courses. Carnegie states the cost of such remedial learning as costing colleges $6.7 billion annually, with only a 33% success rate for math courses. ITS provides the potential for students to more conveniently access flexible and more personalized modes of learning on an ongoing basis.

Virtual Facilitators and Learning Environments

The goal in this field is to create virtual human-like characters who can think, act, react, and interact in a natural way, responding to and using both verbal and nonverbal communication.

Captivating Virtual Instruction for Training (CVIT), for example, is a distributed learning strategy that aims to integrate live classroom methods with best-fit virtual technologies—including virtual facilitators, augmented reality, intelligent tutor, and others—in remote learning and training programs.

Opportunities And applications

AI has been dominant in various fields such as

Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.

Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For exampleA spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

Doctors use clinical expert system to diagnose the patient.

Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.

Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment

Need For AI

We don’t really need artificial intelligence, but it is proving ever more useful. This is a function of what is known as utility–the capability of an algorithm to perform a task adequately. The new utility of AI has upsides and downsides.

Weak AI: Less capable than a human

At the lower end of the scale, there might be a situation where a human would be better, but the work is so dangerous, or expensive for humans, that we use automatons instead. (Space exploration is a good example. The AI on a deep space probe or Mars rover.)

Semi-Strong AI: About as capable as a human

Here we have AI or automation that can do tasks as well as humans, such as on an assembly line, but where automation is more efficient.

As Machine Learning continues to get more effective, the range of tasks that AI can perform as well as humans will surely grow. This in turn creates new opportunities for humans and mostly eliminate repetitive, less fulfilling work. 

Strong AI: Exceeds human capability

Machine Learning has demonstrated greater than human capability in a number of tasks, and the range of such tasks will surely grow. (AlphaGo was a milestone because the game of Go is unsolvable and notoriously difficult for AI, prior to AlphaGo. Now it’s unclear if unmodified humans will ever again be able to beat strong AI at these types of games.)

Although game AIs are not directly useful, except for recreational purposes, the methods used to create them can be extended to real-world problems.

Strong AI is useful because it has greater utility than humans. It is desirable because it increases efficiency and expected return on investment.

What is needed for AI

Some of the tools required for developing AI are

Python

Python is very popular in machine learning programming.Python is one of the first programming languages that got the support of machine learning via a variety of libraries and tools. Scikit and TensorFlow are two popular machine learning libraries available to Python developers.

C++

 C++ is one of the oldest and most popular programming languages. Most of the machine learning platforms support C++ including TensorFlow.

Some of the other widely used languages for AI are C#, R, Java and JavaScript

What is TensorFlow?

TensorFlow is now a semi-open-source library that allows developers to perform numerical computations. AI developers can use the TensorFlow library to build and train neural networks in pattern recognition. It is written in Python and C++, two powerful and popular programming languages, and allows for distributed training.

Scikit-Learn

Scikit-learn is an open-source machine learning framework for Python that is useful for data mining, data analysis, and data visualization. It is good for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing, to name a few. It is built on NumPy, SciPy, and matplotlib.

Google ML Kit

Google ML Kit, Google’s machine learning beta SDK for mobile developers, is designed to enable developers to build personalised features on Android and IOS phones. The kit allows developers to embed machine learning technologies with app-based APIs running on the device or in the cloud. These include features such as face and text recognition, barcode scanning, image labelling and more.

Cloud-based services(AWS, GCP) provide tools for deploying predictive models as analytic solutions. It can also be used to test machine learning models, run algorithms, and create recommender systems, to name a few.

PLAYING WITH AI 

https://experiments.withgoogle.com/collection/ai

https://quickdraw.withgoogle.com/

https://experiments.withgoogle.com/collection/ai/move-mirror/view/mirror

Scribbling Speech demo 

http://xinyue.de/scribbling-speech.html

https://research.google.com/semantris/

https://www.autodraw.com/

https://thing-translator.appspot.com/

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