Artificial Intelligence Tutorial

Artificial Intelligence Tutorial – Learn AI from Experts

Last updated on 04th May 2020, Blog, Tutorials

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What is AI?

A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solving problems are deemed to hold an artificial intelligence.Artificial intelligence exists when a machine has cognitive ability. The benchmark for AI is the human level concerning reasoning, speech, and vision.

Introduction to AI Levels

  • Narrow AI: Artificial intelligence is said to be narrow when the machine can perform a specific task better than a human. The current research of AI is here now
  • General AI: An artificial intelligence reaches the general state when it can perform any intellectual task with the same accuracy level as a human would
  • Strong AI: An AI is strong when it can beat humans in many tasks

Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale. According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bringing 50 percent more incremental value in banking compared with other analytics techniques. In transport and logistics, the potential revenue jump is 89 percent more.

Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on tasks like relationship building, lead nurturing, etc. A company name Gong provides a conversation intelligence service. Each time a Sales Representative makes a phone call, the machine records, transcribes and analyzes the chat. The VP can use AI analytics and recommendation to formulate a winning strategy.

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In a nutshell, AI provides a cutting-edge technology to deal with complex data which is impossible to handle by a human being. AI automates redundant jobs allowing a worker to focus on the high level, value-added tasks. When AI is implemented at scale, it leads to cost reduction and revenue increase.

A brief History of Artificial Intelligence

Artificial intelligence is a buzzword today, although this term is not new. In 1956, a group of avant-garde experts from different backgrounds decided to organize a summer research project on AI. Four bright minds led the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose of the research project was to tackle “every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it.”The proposal of the summits included

  1. Automatic Computers
  2. How Can a Computer Be Programmed to Use a Language?
  3. Neuron Nets
  4. Self-improvement

It led to the idea that intelligent computers can be created. A new era began, full of hope – Artificial intelligence.

Type of Artificial Intelligence

Artificial intelligence can be divided into three subfields:

  • Artificial intelligence
  • Machine learning
  • Deep learning
Artificial-Intelligence-Types

Machine Learning

Machine learning is the art of study of algorithms that learn from examples and experiences.Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.The difference from hard coding rules is that the machine learns on its own to find such rules.

Deep learning

Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it means the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, Google LeNet model for image recognition counts 22 layers.In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

AI vs. Machine Learning

Most of our smartphone, daily device or even the internet uses Artificial intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.

AI- artificial intelligence- is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own.

Artificial-Intelligence-vs-Machine-Learning

Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can do the same. It can be said that AI is the large science that mimics human aptitudes. 

Machine learning is a distinct subset of AI that trains a machine how to learn. Machine learning models look for patterns in data and try to conclude. In a nutshell, the machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples.

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Where is AI used? Examples

AI has broad applications-

  • Artificial intelligence is used to reduce or avoid the repetitive task. For instance, AI can repeat a task continuously, without fatigue. In fact, AI never rests, and it is indifferent to the task to carry out
  • Artificial intelligence improves an existing product. Before the age of machine learning, core products were building upon hard-code rule. Firms introduced artificial intelligence to enhance the functionality of the product rather than starting from scratch to design new products. You can think of a Facebook image. A few years ago, you had to tag your friends manually. Nowadays, with the help of AI, Facebook gives you a friend’s recommendation.

AI is used in all the industries, from marketing to supply chain, finance, food-processing sector. According to a McKinsey survey, financial services and high tech communication are leading the AI fields.

Why is AI booming now?

A neural network has been out since the nineties with the seminal paper of Yann LeCun. However, it started to become famous around the year 2012. Explained by three critical factors for its popularity are: 

  • Hardware
  • Data
  • Algorithm

Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches. With the boom of the internet, data became more easily accessible. Besides, giant companies like NVIDIA and AMD have developed high-performance graphics chips for the gaming market.

Hardware

In the last twenty years, the power of the CPU has exploded, allowing the user to train a small deep-learning model on any laptop. However, to process a deep-learning model for computer vision or deep learning, you need a more powerful 

machine. Thanks to the investment of NVIDIA and AMD, a new generation of GPU (graphical processing unit) is available. These chips allow parallel computations. It means the machine can separate the computations over several GPU to speed up the calculations.For instance, with an NVIDIA TITAN X, it takes two days to train a model called ImageNet against weeks for a traditional CPU. Besides, big companies use clusters of GPU to train deep learning models with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.

Data

Deep learning is the structure of the model, and the data is the fluid to make it alive. Data powers artificial intelligence. Without data, nothing can be done. Latest Technologies have pushed the boundaries of data storage. It is easier than ever to store a high amount of data in a data center.

The Internet revolution makes data collection and distribution available to feed machine learning algorithms. If you are familiar with Flickr, Instagram or any other app with images, you can guess their AI potential. There are millions of pictures with tags available on these websites. Those pictures can be used to train a neural network model to recognize an object on the picture without the need to manually collect and label the data.

Artificial Intelligence combined with data is the new gold. Data is a unique competitive advantage that no firm should neglect. AI provides the best answers from your data. When all the firms can have the same technologies, the one with data will have a competitive advantage over the other. To give an idea, the world creates about 2.2 exabytes, or 2.2 billion gigabytes, every day.

A company needs exceptionally diverse data sources to be able to find the patterns and learn and in a substantial volume.

AI-with-Data

Algorithm

Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms. Primary neural networks are a simple multiplication matrix without in-depth statistical properties. Since 2010, remarkable discoveries have been made to improve the neural network.

Artificial intelligence uses a progressive learning algorithm to let the data do the programming. It means, the computer can teach itself how to perform different tasks, like finding anomalies, and become a chatbot.

What is Intelligence Composed of?

The intelligence is intangible. It is composed of −

  • Reasoning
  • Learning
  • Problem Solving
  • Perception
  • Linguistic Intelligence
Components-of-intelligence

Let us go through all the components briefly

  • Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types
  • Learning − It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. Learning enhances the awareness of the subjects of the study.The ability of learning is possessed by humans, some animals, and AI-enabled systems. Learning is categorized as 
  • Auditory Learning − It is learning by listening and hearing. For example, students listening to recorded audio lectures
  • Episodic Learning − To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly.
  • Motor Learning − It is learning by precise movement of muscles. For example, picking objects, Writing, etc.
  • Observational Learning − To learn by watching and imitating others. For example, child tries to learn by mimicking her parent.
  • Perceptual Learning − It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations.
  • Relational Learning − It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding ‘little less’ salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt.
  • Spatial Learning − It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create a roadmap in mind before actually following the road.
  • Stimulus-Response Learning − It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on the hearing doorbell.
  • Problem Solving − It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles.
    Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal.
  • Perception − It is the process of acquiring, interpreting, selecting, and organizing sensory information.
    Perception presumes sensing. In humans, perception is aided by sensory organs. In the domain of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner.
  • Linguistic Intelligence − It is one’s ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication.

Artificial Intelligence – Research Areas

The domain of artificial intelligence is huge in breadth and width. While proceeding, we consider the broadly common and prospering research areas in the domain of AI 

Research-areas-of-Intelligence

Speech and Voice Recognition

These both terms are common in robotics, expert systems and natural language processing. Though these terms are used interchangeably, their objectives are different.

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Speech RecognitionVoice Recognition
The speech recognition aims at understanding and comprehending WHAT was spoken.The objective of voice recognition is to recognize WHO is speaking.
It is used in hand-free computing, map, or menu navigation.It is used to identify a person by analysing its tone, voice pitch, and accent, etc.
Machine does not need training for Speech Recognition as it is not speaker dependent.This recognition system needs training as it is person oriented.
Speaker independent Speech Recognition systems are difficult to develop.Speaker dependent Speech Recognition systems are comparatively easy to develop.

Working of Speech and Voice Recognition Systems

The user input spoken at a microphone goes to the sound card of the system. The converter turns the analog signal into an equivalent digital signal for the speech processing. The database is used to compare the sound patterns to recognize the words. Finally, a reverse feedback is given to the database.

This source-language text becomes input to the Translation Engine, which converts it to the target language text. They are supported with interactive GUI, large database of vocabulary, etc.

Real Life Applications of Research Areas

There is a large array of applications where AI is serving common people in their day-to-day lives 

The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks.

expert-systems-artificial-intelligence
  • Expert Systems:Examples − Flight-tracking systems, Clinical systems. 
  • Natural Language Processing : Examples: Google Now feature, speech recognition, Automatic voice output.
NLP-applications

3.Neural Networks :Examples − Pattern recognition systems such as face recognition, character recognition, handwriting recognition.

Applications-of-Neural-Networks

4.Robotics: Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving, etc.

Robotic-applications

Task Classification of AI

The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks.

Domain-of-AI
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Mundane(Ordinary)TasksFormal TasksExpert Tasks
Perception,Computer Vision,Speech,Voice Mathematics,Geometry,Logic,Integration and Differentiation Engineering
Fault Finding,Manufacturing,Monitoring
Natural Language Processing,Understanding Language Generation,Language TranslationGames,Go,Chess(Deep Blue),Checkers Scientific Analysis
Common SenseVerificationFinancial Analysis
Reasoning Theorem ProvingMedical Diagnosis
Planning Creativity

Humans have learned mundane (ordinary) tasks since their birth. They learn by perception, speaking, using language, and locomotives. They learn Formal Tasks and Expert Tasks later, in that order.For humans, the mundane tasks are easiest to learn. The same was considered true before trying to implement mundane tasks in machines. 

Earlier, all work of AI was concentrated in the mundane task domain.Later, it turned out that the machine requires more knowledge, complex knowledge representation, and complicated algorithms for handling mundane tasks. This is the reason why AI work is more prospering in the Expert Tasks domain now, as the expert task domain needs expert knowledge without common sense, which can be easier to represent and handle.

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