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Frequently Asked Questions on A.I. Solutions

- Natural Language Processing
- Speech Recognization
- Computer Vision
- Expert Systems
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Artificial Intelligence Solution with Codigion, Shillong
Artificial Intelligence Solution with Codigion, Shillong
Knowledge Center

Artificial Intelligence & Data Science

Artificial Intelligence is machine intelligence or ability to think and process information like natural human intelligence in order to create expert systems with human intelligence (reasoning, learning, and problem solving) with help from science and technology disciplines such as Mathematics, Engineering, Biology, Computer Science, Linguistics and Psychology.

The term intelligence, literally, means the ability to acquire and apply knowledge and skills. The term Artificial Intelligence ( Artificial Intelligence ) is pretty self-explanatory. It is the ability to acquire and apply knowledge and skills artificially.

In 1956, a group of researchers from different disciplines of technology gathered for the summit called Dartmouth Summer Research Project. The term Artificial Intelligence was first introduced and used by John McCarthy in the summit with the underlying agenda of the summit - “Thinking Machines”. The researchers from Dartmouth Summer Research Project defined Artificial Intelligence the study of a conjecture of every aspect of learning and intelligence so that a machine can simulate and solve a given task, by itself, without the requirement of human intervention. However, Artificial Intelligence was not much welcomed initially with respect to its threats towards the human race. Even modern scientists like Stephen Hawking expressed their opinion of the threats caused by Artificial Intelligence.

In recent times, Artificial Intelligence has become a mandatory aspect of modern technology. The concept of Artificial Intelligence was laid on the foundations of algorithms. An algorithm is a set of rules to be followed, in order to solve a given problem. At the initial stages of Artificial Intelligence , scientists used to device algorithms with the step-by-step approach in solving a given problem. However, these algorithms have been developed to solve or at least deal with uncertain problems based on the previous outcomes, which is technically called Learning, just like humans do.

For a given problem, humans follow the step-by-step approach to find a solution. This step-by-step approach includes analysis, calculation, and estimation of outcomes. For example, when intense light falls into the eyes, we close our eyes and use our hands to protect our eyes. The steps we actually follow are analyzing the threat (light being incident in the eyes), calculating the possible solutions (closing the eyes and/or using hands to protect eyes from the light) and estimating the outcome (avoiding being hurt by the light). These steps are usually performed in a cognitive sense i.e., decision making is done just within a fraction of seconds. Artificial Intelligence is intended to perform complex decision making just like human beings, through repetitive learning based on previous outcomes.

Usually, the inputs to the Artificial Intelligence computers are events from the real world. These inputs cannot be easily understood by the Artificial Intelligence computers. Hence, it needs to be represented, which is called Knowledge Representation and Reasoning (KRR) in the context of Artificial Intelligence . KRR helps an Artificial Intelligence machine to use findings from psychology and logic in order to implement different reasonings in the process of solving a given problem. The process of using the knowledge acquired through KRR is called Knowledge Engineering (KE).

Application of Artificial Intelligence

Natural Language Processing (NLP) is a subfield of Artificial Intelligence concerning to machine's ability to understand (process and analyze) natural language is spoken by a human to use it for speech recognition, chatterbot, language generation, translation, etc.

Vision System is machine's ability to understand, interpret and process visual input from digital images and videos to achieve the human visual system to use it for visual surveillance, identification, image processing, autonomous vehicle, handwriting recognization, etc.

Speech Recognization is a sub-field of linguistic computing in Artificial Intelligence concerning to machine's capability of hearing and comprehending human languages to use it for hands-free computing, interactive voice response, voice detection, etc.

Expert System is a decision making the ability of a machine designed to solve the complex problem based on the knowledge base (facts and rules) and inference engine (applying rules to deduce new facts) to use it for prediction, planning, controlling, monitoring, diagnosing, etc.

The artificial intelligence has a lot of application in today's society for all sort of intelligent work from numerous fields and industries such as - intelligent tutoring system, automated online assistance, self-driving cars, virtual reality, strategic gaming, medical diagnosis, algorithmic trading, simulated air traffic controllers, resume screening, media analysis, music composition, and so much more.

Here are some of the updates and the abilities of Artificial Intelligence that can really help humans to perform tasks with high accuracy and extracting the best utility.

  • In 2016, John Seymour and Philip Tully, two researchers with ZeroFOX developed a bot called SNAP_R. The SNAP_R uses the LSTM (Long Short-Term Memory) algorithm to analyze the series of tweets made by a user and determine the common topics posted by the user. The catching part of SNAP_R is that it is a phishing bot. After analyzing more than two million tweets, SNAP_R was able to make a tweet that exactly matched the characteristics of the manual tweet by a user.

  • In 2017, Facebook (F Artificial Intelligence R - Facebook Artificial Intelligence Research) has shut down its Artificial Intelligence messenger bots because they were able to develop their own machine language and imitate human speech. Initially, two bots were developed to communicate with each other in order to teach one another and learn from each other, using machine learning algorithms. After a certain trial, F Artificial Intelligence R has noted that the bots were trying to imitate human speech and later it was found that they have developed their own machine language for communication. However, the new machine language was not suitable to perform any tasks but was useful just for communication.

  • A group of computer scientists from Columbia University has created FontCode. FontCode is the method of sending secret messages just by encoding messages in the shapes of letters. Scientists assumed that FontCode could be the best method to hide messages for the modern world. But, these assumptions were shattered when the recent Artificial Intelligence machines were able to decode most of the message encoded in FontCode.

  • In August 2018 researchers from the Allen Institute of Artificial Intelligence have developed an Artificial Intelligence machine, that is capable of completing a sentence in English. When competed with humans, Artificial Intelligence machines were capable of completing the sentences with 60% accuracy whereas humans at 88% accuracy. Later in October, Google researchers unveiled another Artificial Intelligence system for the same purpose, called Bert, almost matched the autocomplete sentences to the humans.

  • Another Artificial Intelligence company Arm has started in research on Artificial Intelligence that can figure out the body odor and select an appropriate scent that can nullify the bad odor. However, this is still under development and no further details were released.

  • These are just a few of the instances where Artificial Intelligence was successfully implemented and tested, which were released to the public. There are many other kinds of research and developments going on in the field of Artificial Intelligence for a better tomorrow.

Machine Learning (ML)

Machine Learning is the science behind artificial intelligence using statistical techniques to give machines an ability to learn from data without human intervention while exploring algorithms to follow static program instruction to learn to make predictions and decision based generalizing from examples. The idea behind Machine Learning is to build a system that improves itself from experiences without being explicitly programmed.

The branches of Machine Learning are as follows:

Supervised Learning is a method in which examples data-set is fed to a machine with input and desired output and the end goal is to figure the rules that map the input to output such that the inferred function can be used to map new unseen instances.

Unsupervised Learning is a method to classify none labeled data by discovering hidden structures and patterns.

Semi-Supervised Learning is a machine learning method in which a training data-set misses many of the output with a small amount of labeled data-set to achieve improved learning accuracy as compared to unsupervised learning and not even costing time as compared to supervised learning.

Active Learning is a machine learning method of labeling unlabeled data using the learning algorithm that actively queries the user for the label, for a case where when we have abundant unlabeled data that are expensive to label manually.

Reinforcement Learning is a machine learning model of learning from previous experiences and outcomes making a sequence of decisions that are either rewards or penalties from the previously performed actions, and the idea is to maximize the reward.

What is the importance of Artificial Intelligence in the modern world?

  • The process of logical analysis of information in the least amount of time possible is called recursive learning. Artificial Intelligence automates recursive learning process. This is to extract maximum utility out of a given piece of data/information. But, it is to be made clear that Artificial Intelligence is different from the other technologies robotics and Machine Learning (ML), where robotics is making a machine to work according to given set of instructions and Machine Learning is the process in which machines learn based upon the previous outcomes of a task. But, Artificial Intelligence is differentiated with robotics and ML as it is capable of applying its intelligence in decision making from what it has learned through ML.

  • When compared to the contemporary technologies, the purpose of Artificial Intelligence is to perform highly complex and high volume data analysis tasks reliably. However, human intervention is at the time of setting up a given system. Hence Artificial Intelligence automates recursive learning process.

  • Artificial Intelligence adds little more intelligence to any system. For example, conversation with a voice assistant like Siri and OK Google. They respond to the user in real-time, by analyzing the input from the user. Alongside voice assistants, chatbots is another perfect example of applications of Artificial Intelligence.

  • Artificial Intelligence helps extract more out of data. In the modern day, where technology is literally everywhere, data is being generated enormously. In order to draw utility from this data, it is too much for humans to analyze.

  • Artificial Intelligence adapts progressive learning. Artificial Intelligence works on patterns of data. Artificial Intelligence recognizes structures and regularities of data so that the learning becomes progressive. This learning process helps the machines teach themselves and this learning is adaptive and progressive.

  • Artificial Intelligence can achieve higher accuracy. A saying, ‘To err is human’. Artificial Intelligence eliminates this through deep neural networks. Google Photos is the best example to contemplate accuracy - face recognition. The accuracy of face recognition, image classification and object recognition in Google Photos is an adaptive learning.

What is Data Science & Engineering?

Data Science is the study of different tools, machine learning principles and algorithms in order to figure out the patterns in given data. Data Engineering is the process of using the pattern discovered with the help of data science in order to achieve a give task. To differentiate, Data Science can be compared to building a race car whereas Data Engineering to riding the car.

Artificial Intelligence is the area where Data Science and Data Engineering should co-exist in order to achieve an good autonomous system and derive the best out of it.

Data Visualization:

A picture is worth thousand words. Data Visualization is the process of representing given set of data, that can be easily understood. Different mechanisms like graphs, pie charts and maps can be used to visualize different forms of data. Representing number of visitors for a website in a given year can be represented as a graph for better visualization than mere numbers. Representing a firm’s expenses for different department can fit into a pie chart better than that of a numerical table.

Data Visualization helps Artificial Intelligence to understand and learn faster when compared to presenting data in its raw form.

Data Strategy:

Data Strategy is a strategy to utilize the available data in a smart way, in order to achieve the objectives of a business or an organization. It is to be understood that data strategy does not mean collecting huge amount of data but it simply is collecting right data based upon the goals set.

With Artificial Intelligence , having huge amount of data is not any of its objectives, rather having the right data to achieve the goal of reduction in human effort and ease the lifestyle.

For example, let us consider the context of an Artificial Intelligence machine identifying the shape of an object. It is very useful when we have data of different geometries instead of same geometries of different sizes.

Data Architecture:

Data Architecture deals with principles and models of data storage, data management, data integration and other aspect of data. An Artificial Intelligence system can either be homogeneous or heterogeneous in nature. Everything falls in place for homogeneous systems easily but with heterogeneous systems, the same data should be able to serve the purpose of the system. In order to make data independent of the system, the data should be “architectured” based upon the needs of the system. There are three traditional architectural processes.

  • 1. Conceptual - Gives an overview of the business entity, in terms of data.
  • 2. Logical - Represent how the data entities are related to each other.
  • 3. Physical - Realization of different data mechanisms in order to achieve the goal of the system.

Data Pipelining:

Data Pipelining can be defined as the set of actions that extracts data, for further processing, from different sources. Extracting, processing and storing the data into the database are the basic operations done during Data Pipelining, which is usually called a Job, where pipelines are made of several jobs.

There are two approaches of pipelining implemented in Artificial Intelligence - Manual pipelining and Automated pipelining. The steps of Manual pipelining depends upon how the Artificial Intelligence system is designed. There are four generic stages of Automated pipelining - Ingest, Classify/Transform, Analyze/Train and Insights.

Data Management:

Data Management is defined as an organization’s way of managing the proprietary data. This helps in privacy of the organization. Data Management of an organization is built upon seven principles - Data access, Data quality, Data integration, Data federation, Data governance, Master Data Management (MDM) and Data streaming.

In case of Artificial Intelligence , as the data is processed by a machine, Data Management is needed for both controlled and fast access of data. A simple flaw in Data Management may lead to catastrophic damage to an organization.

More Applications of Artificial Intelligence and Data Science:

The applications of Artificial Intelligence can be found in wide range of disciplines. Above all, it is splendid to say that Artificial Intelligence is creating new disciplines for extracting its utmost utility.

Humanizing Artificial Intelligence:

Emotions are the very characteristics of human beings. In the context of Artificial Intelligence , the concept of humanizing involves in the metrics identity and demographic data along with emotions. There are many such humanized Artificial Intelligence platforms that analyze the emotion of the individual and proceed accordingly.

For example, people who live individually need a lot of help in managing the situations. If an Artificial Intelligence machine is capable of recognising human emotions, then a better environment can be created for them.

Customer Intelligence:

Business is one of the best applications of Artificial Intelligence . For example, if an Artificial Intelligence machine is capable of understanding and analyzing how a particular customer experienced his visit to the store, the feedback can be used to improve the customer experience, which ultimately result in better business. On the other hand, the data collected can be used to narrow down the needs of the customers based upon their history.

Conversational Artificial Intelligence:

Suppose that you’re bathing and suddenly remembered to book a flight ticket for your trip. If there is a machine that can take instructions, book ticket for you based upon your previous trips and best prices available, then the effort to sort out the prices and book tickets accordingly can be eliminated. Google Now, Alexa and Siri are the best examples we come across the Conversational Artificial Intelligence . However, as of now, these voice assistants are not as efficient as a human assistant.

Risk and Fraud Intelligence:

You always want your payments to be secure. We never know where a payment link is redirected to. Suppose that the outcome of clicking a payment link is “sandboxed” and verified. This, most probably, eliminates the chances of risk and fraud. Artificial Intelligence helps to serve the purpose, thus making web a better place for financial affairs.

Adaptive Data Foundation:

Adaptive Data Foundation (ADF) is a data transformation technique that follows the “data-first” approach, which allows data to be made ready for utilization, in order to achieve a given task.

Responsive architecture, an operating model that delivers at scale and an Artificial Intelligence driven intelligent data management system are the objectives of ADF.

Predictive Modelling:

Predictive Modelling is the method of using statistics in order to predict the outcomes. There are different model in the Predictive Modelling like Group method of data handling, k-nearest neighbor algorithm, Support vector machines, Boosted networks etc. The Predictive models can be used in two ways for a given set of inputs - directly predicting the most possible response and indirectly to initiate the choice of decisions. In Artificial Intelligence , Predictive Modelling serves as an alternate way of machine learning, being very much similar to it.

Forecasting and Optimization:

For a given set of inputs, Forecasting deals with using statistical and modelling techniques in order to enhance the predictions whereas Optimization deals with the mathematical, statistical, simulation and other techniques in order to figure out the best outcome out of the predicted outcomes. The method of Forecasting and Optimization is really helpful, in order to give a definitive decision for a given characteristics of a task.

For example, let us consider a given Artificial Intelligence machine identified that a person can be cured by using, say, drug A. This is done by the Forecasting process. But, determining the amount of drug to be consumed by the patient is figured out by the Optimization process.’

Natural Language Processing:

(as described above)The accent and the common literature of UK English is different from that of the US English. For a system to process the voice instruction irrespective of the accent, Artificial Intelligence can be used. Alexa is the Amazon’s voice assistant that power all the Amazon Echo devices. As these devices are used across the world, the accent of the-same English varies with region. But, Alexa responds by analyzing the dynamics of the speech.

Computer Vision:

(as describe above)Computer Vision falls more into a kind of image and video recognition. For example, Apple has introduced face recognition with iPhone X. In real-time, the physical appearance of the face of a human being varies every now and then - sometimes wearing spectacles, sometimes goggles, sometimes contact lenses, sometimes bearded, sometimes pierced and so on. But, how did Apple manage to identify a person irrespective of these changes? The answer is with the help of Artificial Intelligence . Just like Apple’s face recognition, there are other applications of Artificial Intelligence for identifying a person or an object in a video or in an image, which helps in various aspects of our daily life.

More on Technologies of Artificial Intelligence and Data Science:

Artificial Intelligence is creeping into every part of human life in order to ease the lifestyle and living. Because of its potential, various technologies were developed in order to enhance the current state of Artificial Intelligence . Though these technologies are developed by different associations and firms, the primary objective of all these technologies is to provide a developer platform to address the challenges faced by developers against Artificial Intelligence . Each of these languages have their own advantages and disadvantages as well. It’s completely up to the user to adopt which technology is suitable utmost.


Python is one of the most efficient languages for Artificial Intelligence development. A wide range of libraries are available in Python for Artificial Intelligence development. For example, NumPy is a Python library that can be used for scientific computation involved in Artificial Intelligence . SciPy is a Python library that was developed for advanced computing involved in Artificial Intelligence . Scikit-Learn is another Python library exclusively developed for data mining and data analytics for Artificial Intelligence . Along with these technical advantages, Python comes with the managerial and development advantages like less lines of code, extensive library support and flexibility of usage.


TensorFlow is the open source machine learning framework developed by Google Inc. and was released in 2015. The service providers like Intel, DropBox and Uber, and the online platforms like Twitter and eBay are using the TensorFlow technology. Actually, TensorFlow is the library that allows to perform various numerical computations using data flow graphs. For developers, a development kit called TensorBoard is available for easy understanding and debugging. TensorBoard allows the developers to visualize the data through the TensorFlow graph, to plot different parameters that affect the performance and efficiency of the Artificial Intelligence machine, and extract other useful data from the inputs.


Keras is an open source library developed by Francois Chollet as project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) and was released in 2015. The objective of Keras was to simplify the process of creation of deep learning models. The Keras library was developed to be an interface for a developer working on Artificial Intelligence rather than a generic machine learning framework. Keras is capable of running on both CPU and GPU, supporting convolutional and/or recurrent networks.


PyTorch is the open source machine learning and research prototyping library developed especially for natural language processing. Though the library was developed in Python, it has the underlying implementation using C language. There are three major modules available in PyTorch - Autograd module for building efficient neural networks, Optim module for the optimization of algorithms and nn module for defining complex raw Autograd neural networks.


spaCy is the open source machine learning library designed especially for natural language processing. Cy in spaCy represents another new language called Cython, the superset of Python to provide performance characteristics similar to that of C language. spaCy supports more than 34 languages across the globe and provides 13 statistical models for 8 different languages, which make spaCy the mostly used library for natural language processing.


The process of representing a given text document as arrays of identifiers is called vector space modelling. This modelling enables quick data access and hierarchy of text within the document. The Gensim toolkit was developed exactly to serve the purpose. The Gensim library estimates the importance of a word in a given text document (called term frequency - inverse document frequency). The tool was developed to handle huge amounts of text, in the form of documents, using data streaming methods and incremental algorithms.


scikit-learn is a Python-based open source library for machine learning. It is the implementation of various algorithms for Classification, Regression, Clustering, Dimensionality reduction, Model selection and Preprocessing.

  • Classification is the process of identifying the categorization of a given object. Spam detection, Image recognition are the most common examples of Classification.
  • Regression is the process of estimating a constantly changing parameter of a given object. Drug response and stock prices are the most common examples of Regression.
  • The process of automatic grouping of different objects is called Clustering. Customer segmentation to narrow down the target customers is an example of Clustering.
  • The process of reducing number of random variable in a given set of data is called Dimensionality reduction. Data visualization is the most common application of Dimensionality reduction.
  • The process of comparing, validating and choosing appropriate parameters and models to achieve a given objective is called Model selection. Parameter tuning is one of the best examples of Model selection.
  • Preprocessing is the method of feature extraction and normalization of data based upon the requirements.

Business Applications of Artificial Intelligence and Data Science

With the help of Artificial Intelligence , businesses are able to extract the following applications and advantages.

  • Better personalised shopping experience
  • Automation of customer interaction
  • Real-time assistance to the customer
  • Data mining for analytics
  • Avoid mistakes due to human-error-virtue
  • Best business optimizations and solutions


Healthcare is one of the basic and major branches that can extract the best utility from Artificial Intelligence . Here is the list of extremely useful application of Artificial Intelligence in healthcare.

  • Artificial Intelligence -assisted robotic surgeries
  • Virtual nursing assistants
  • Dosage error reduction
  • Healthcare bots
  • Medical report, image and diagnosis analysis assistance
  • Digital consultation
  • Remote/Virtual health monitoring
  • Enhanced study of anatomy

Energy & Utilities:

Production of energy is limited whereas the demand is comparatively high. Though different methods of power generation are being explored, supply and the demand are not meeting. Artificial Intelligence can come into rescue with the following applications in the Energy and Utility sector

  • Balancing supply-demand load by forecasting
  • Yield optimization of power through winds, water, tides or solar
  • Demand management
  • Power supply based on exact demand


Insurance is one of the most unexpected applications of Artificial Intelligence . Following are the applications of Artificial Intelligence in the context of insurance.

  • Behavioral policy pricing
  • Customer experience and coverage personalization
  • Faster and appropriate claims settlement
  • Verification of procedures during claims

Banking and Financial Services:

The world runs on money and it is not all the time possible to manually, at least using computers, to manage every single transaction. Artificial Intelligence can be used for accurate and flawless banking operations. Here are some of the applications of Artificial Intelligence in the banking sector.

  • Anti-Money Laundering (AML) detection
  • Automatic trading at the appropriate point
  • Fraud detection and prevention
  • Automation of customer experience and security
  • Personalized and cyclic transactions
  • Ease of processing and issuance of loans


Communication is what driving the world today. Artificial Intelligence can be implemented in the following applications for communication.

  • Automate connections based upon the schedules
  • Using individual data for improving connectedness
  • Voice assistants
  • Enhanced information security
  • Insights for providing better connectivity
  • Delivery of personalized content
  • Elimination of opportunity loss

The list of applications becomes endless. In simple words to say, Artificial Intelligence is predicted to become an integral part of human life for a better life. However, scientists like Stephen Hawking have a retrogative perception of Artificial Intelligence . Therefore, all the developments should be “controlled” in order to avoid disasters out of such powerful technologies.

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