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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).
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.
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 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.
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.
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 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 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.
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 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.
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.
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.
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.
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.
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 (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 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.
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.’
(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.
(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.
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.
With the help of Artificial Intelligence , businesses are able to extract the following applications and advantages.
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.
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
Insurance is one of the most unexpected applications of Artificial Intelligence . Following are the applications of Artificial Intelligence in the context of insurance.
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.
Communication is what driving the world today. Artificial Intelligence can be implemented in the following applications for communication.
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.