Become A Web Developer With NodeJS: The Blueprint To A Successful Career
Rated 4.5/5 based on 19 customer reviews
Top 5 Benefits Of Using AngularJS
Rated 4.5/5 based on 14 customer reviews
Top 9 Benefits Of Learning Apache Spark and Scala
By Susan MayWhat Is Apache Spark and Scala All About?Big Data and Analytics are transforming the way businesses take informed market-oriented decisions, craft strategies for targeting customer segments that are optimally promising, and remain shielded from market quirks and economic volatilities. These abilities are impacted by mining information that is locked in large data volumes generated online or from other connected sources.Big Data can be reliably processed with the Apache Spark interface. Apart from facilitating seamless programming for data clusters, Spark also offers proper tolerance for faults and data parallelism. This implies that large datasets can be processed speedily by this open source platform. Apache Spark has an edge over Hadoop in terms of better and sophisticated capabilities on data handling, storing, evaluation and retrieving fronts. Spark framework comes integrated with modules for ML (Machine Learning), real-time data streaming, textual and batch data, graphics, etc., which makes it ideal for different industry verticals.Scala or Scalable Language is a general-purpose object-oriented language with which Spark is written for supporting cluster computing. Scala offers support with immutability, type interference, lazy evaluation, pattern matching, and other features. Features absent in Java such as operator overloading, named parameters, no checked exceptions, etc. are also offered by Scala.Why Should I Learn Apache Spark and Scala?Data science offers unparalleled scope if you want to scale new heights in your career. Also, as part of an organization, if you are strategizing on cornering your niche market, you need to get focused insights into how the market is changing. With Apache Spark and Scala training, you can become proficient in analyzing patterns and making conclusive fact-driven assumptions.There are many incentives for learning this framework-language combination as an aspirant or by exposing your organization’s chosen employees to this.1) Ideal for Implementing IoTIf your company is focusing on the Internet of Things, Spark can drive it through its capability of handling many analytics tasks concurrently. This is accomplished through well-developed libraries for ML, advanced algorithms for analyzing graphs, and in-memory processing of data at low latency.2) Helps in Optimizing Business Decision MakingLow latency data transmitted by IoT sensors can be analysed as continuous streams by Spark. Dashboards that capture and display data in real time can be created for exploring improvement avenues.3) Complex Workflows Can Be Created with EaseSpark has dedicated high-level libraries for analyzing graphs, creating queries in SQL, ML, and data streaming. As such, you can create complex big data analytical workflows with ease through minimal coding.4) Prototyping Solutions Becomes EasierAs a Data Scientist, you can utilize Scala’s ease of programming and Spark’s framework for creating prototype solutions that offer enlightening insights into the analytical model.5) Helps in De-Centralized Processing of DataIn the coming decade, Fog computing would gain steam and will complement IoT to facilitate de-centralized processing of data. By learning Spark, you can remain prepared for upcoming technologies where large volumes of distributed data will need to be analyzed. You can also devise elegant IoT driven applications to streamline business functions.6) Compatibility with HadoopSpark can function atop HDFS (Hadoop Distributed File System) and can complement Hadoop. Your organization need not spend additionally on setting up Spark infrastructure if Hadoop cluster is present. In a cost-effective manner, Spark can be deployed on Hadoop’s data and cluster.7) Versatile FrameworkSpark is compatible with multiple programming languages such as R, Java, Python, etc. This implies that Spark can be used for building Agile applications easily with minimal coding. The Spark and Scala online community is very vibrant with numerous programmers contributing to it. You can get all the required resources from the community for driving your plans.8) Faster Than HadoopIf your organization is looking to enhance data processing speeds for making faster decisions, Spark can definitely offer a leading edge. Data is processed in Spark in a cyclic manner and the execution engine shares data in-memory. Support for Directed Acyclic Graph (DAG) mechanism allows Spark engine to process simultaneous jobs with the same datasets. Data is processed by Spark engine 100x quicker compared to Hadoop MapReduce.9) Proficiency EnhancerIf you learn Spark and Scala, you can become proficient in leveraging the power of different data structures as Spark is capable of accessing Tachyon, Hive, HBase, Hadoop, Cassandra, and others. Spark can be deployed over YARN or another distributed framework as well as on a standalone server.Learn Apache Spark and Scala To Widen Your Performance HorizonCompleting an Apache Spark and Scala course from a renowned learning center would make you competent in leveraging Spark through practice sessions and real-life exercises. Once you become capable of using this cutting-edge analytics framework, securing lucrative career opportunities won’t be a challenge. Also, if you belong to an organization, gaining actual and actionable insights for decision making would be a breeze.
Rated 4.5/5 based on 12 customer reviews
A Comprehensive Guide to Machine Learning With Python Training
By Susan MayIf you're someone looking to build a career as a data scientist, you must have heard about Machine Learning. It is an incredibly beneficial tool that allows you to get hidden insights from large sets of data and predict future trends accurately.Technically speaking, ML is a prominent aspect of artificial intelligence (AI) domain and has been in the news for quite some time now. It allows computers to learn without being explicitly programmed. This area offers attractive opportunities for aspirants willing to make a career in this domain.Machine Learning can be broadly separated into three categories:Supervised learningHere, the machine learning program is given the input data, as well as corresponding labeling. This means that the learning data needs to be labeled by a human being beforehand.Unsupervised learningIn unsupervised learning, there are no labels provided to the learning algorithm. This means that the algorithm has to figure out the clustering of the input data.Reinforcement learningIn this type of machine learning, the computer program interacts with its environment dynamically. This means that the computer program receives positive and/or negative feedback to be able to improve its performance.Why Start Machine Learning With Python?To master Data Science and Machine Learning, it is imperative to master at least one coding language and continue using it confidently. For a satisfying and successful Machine Learning journey, Python is an ideal choice as the coding language, especially if you want to jump into the field of machine learning and data science.It is an extremely approachable, intuitive, and minimalistic language that comes with a full-featured library line that significantly reduces the time to get desired results.How Can You Learn Machine Learning With Python?Machine Learning with Python course is specifically designed to let you learn the fundamentals of machine learning using a well-known programming language, Python.The course contents are usually divided into two componentsTo learn about the purpose of Machine Learning and its applications in the real world.A general understanding of various Machine Learning topics including Machine Learning algorithms supervised vs unsupervised learning, and model evaluation.The course allows you to explore various algorithms and models as listed below:Algorithms: Classification, Clustering, Regression, and Dimensional Reduction.Models: Root Mean Squared Error, Train/Test Split, and Random Forests.Topics Covered In the Machine Learning with Python CourseBelow are the topics covered in Machine Learning with Python course:Neighbour ClassifierNeural networks:Neural Networks from Scratch (in Python).Dropout Neural Networks.Neural Network in using Numpy (in Python).Neural Networks with Scikit (in Python).Machine Learning with Scikit and Python.Naive Bayes Classifier.Introduction to Text Classification using Python and Naive Bayes.Skills You Will Acquire In Machine Learning With Python TrainingBelow are some of the essential skills you will acquire after completing this training:Setting up a Python development environment accurately.Various algorithm concepts such as regression, clustering, classification, sci-kit learn and SciPy.Applications of Machine Learning.Creation of accurate data science models.About Python libraries most suitable for Machine Learning.Importance of data analysis and its relevance in the present scenario.Learning how to predict future outcomes to make informed business decisions by using Python.How to apply predictive algorithms to data.Conceptual understanding of how Python works in the Hadoop distributed file ecosystem, PIG, and Hive.How to use Python packages for data analysis applications.Who Is Eligible for Doing This Course?You can do this course even if you have little to no experience in math or programming. The only important element you require is interest in the field and motivation to learn. That being said, a course in Machine Learning with Python is ideal for anyone who is:Passionate about learning the fundamentals of machine learning algorithm with Python.People who wish to kick-start or make a transition to a career as a data scientist.EXCEL users (intermediate and advanced both) who are unable to work with large sets of data.Professionals keen on learning practical application aspects of machine learning to real-world problems.Professionals looking to learn ways to apply machine learning to their respective domain.
Rated 4.5/5 based on 12 customer reviews
If you're someone looking to build a career as a data scientist, you must have heard about Machine Learning. It is an incredibly beneficial tool that allows you to get hidden insights from large s...Continue reading
8 Top Reasons to Enrol for a PostgreSQL Course
By Susan MayAs we foray deeper into the digital world, there is an increasing demand for open source database management systems. Although PostgreSQL has been around for over 30 years, in the last decade, there has been a steep rise in its popularity. Now it plays a key role in many integrated data centers across the globe.PostgreSQL is a highly reliable enterprise-class RDBMS that supports both SQL and JSON. It offers exclusive features which were earlier available only with expensive commercial databases such as Oracle.If you are a professional working in the tech domain and wish to expand your knowledge, then learning PostgreSQL can get you promoted as a database administrator. While it is difficult to master PostgreSQL on your own, you can enroll online in a PostgreSQL Course.Why Learn PostgreSQL?Relational Databases have been the backbone of applications for many decades and they still rule the roost despite the advent of NoSQL. PostgreSQL is a free open-source RDBMS that is used by many multinational companies worldwide as it not only helps developers in building apps and fault-tolerant environments but it also manages and protects data irrespective of the dataset. The biggest advantages of PostgreSQL are:Compatibility: PostgreSQL is compatible with various platforms and all major languages. Apart from this, it also supports JSON and can be linked with other databases such as SQL & NoSQL, etc.SQL: PostgreSQL also features ordered sets within a group, recursive SQL, table sampling, partial aggregates with filter clause, and hypothetical aggregates within a group to name a few.Compliance: It has been developed keeping in mind international compliance standards such as ANSI. It can also be used to build HIPAA and ACID compliant applications.Unparalleled Performance: It provides advanced locking mechanisms, tablespaces, partitioned tables, and many different types of indices. It can also run parallel queries and offers advanced cost-based query optimization. Such features ensure that it delivers unparalleled performance as an RDBMS.Security Features: Its promising security features also set it apart from other DBMS. It extends full support for SSL, database encryption, single-sign-on, and lets you manage users, roles, etc. as per the needs of the project. It also offers a sophisticated locking mechanism.Replication: It can be used for synchronous/asynchronous, logical/physical, log-based/trigger-based, and partial replication. One of its best features is point-in-time-recovery.Geo-Tagging: It can be used to store geospatial data as it supports geographic objects so it can efficiently manage location-based services and geographical information systems.Other Prominent Features: It supports stored procedures in various languages, custom aggregates, multi-version concurrency control, and professional triggers. It has a mature server-side programming function with complete support for client-server network architecture.What are the Benefits of Online PostgreSQL Training?Clearly, PostgreSQL has a number of advantages in terms of compatibility, scalability, security, and other features as compared to other database management systems. Unlike other DBMS, it is backed by a big network of companies that form a strong united community.If you are a professional or a student wondering how to learn PostgreSQL in your spare time, then the best way to go forward is to enroll in an online PostgreSQL Training program.Such programs offer online classes tutored by certified industry experts and exclusive mentor support. They cover the basics of relational databases and fundamentals of PostgreSQL teaching you installation, configuration, and best practices. Apart from theory, you will also get hands-on training experience via demos, practice sessions and mock exercises to make sure you can leverage everything that has been taught.Studying online lets you study at a comfortable pace and as per your convenience. You also get to participate in group discussions and Q&A sessions for doubt solving.In a NutshellPostgreSQL can be used to develop and run dynamic applications across multi-platforms as it supports various programming languages.It is being widely used in top companies across various industry verticals such as IT, HR, Health Care, Media, Hotels, Education, Telecommunications, Financial Services, Computer Software/Hardware, Advertising, and Marketing. It is equally popular with small and medium-sized enterprises as it is a free open-source tool.Despite being around for so long, PostgreSQL is in demand now more than ever. Adding PostgreSQL as a skill in your profile will certainly help you climb up the ladder of success.So, what are you waiting for? Enroll today!
Rated 4.5/5 based on 9 customer reviews
Top Benefits Of Using MEAN Stack For Application Development
Rated 4.5/5 based on 14 customer reviews
What is Machine Learning?
By Gaurav BharadwajMachine Learning is an umbrella term used to describe a variety of different tools and techniques which allow a machine or a computer program to learn and improve over time. ML tools and techniques include but are not limited to Statistical Reasoning, Data Mining, Mathematics and Programming.This definition can be primarily divided into 2 subsets; formal and informal, while formal deals with the specifics of what constitutes a Machine Learning technique the latter deals with simplifying this definition making it easier to grasp by a broader audience.1) Formal Definition : Before I quote a definition which effectively captures the essence of Machine Learning, let's understand the prerequisites. To learn, a machine needs Data, Processing Power/Performance and Time. It could be said that if a machine gets better at something over time and improves its performance as more data is acquired, then this machine is said to be learning and we could call this process Machine Learning.Tom Mitchell very aptly describes Machine Learning as follows :A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.2) Informal Definition : In relatively simple terms we can summarize Machine Learning as giving machines/computers an ability to learn the way humans do, ie without explicitly telling them what to do. Instead we let them learn on their own and even fail in some instances so they learn from that failure. OFC this is an oversimplification but I think it gets the point across.Arthur Samuel explains Machine Learning as :The field of study that gives computers the ability to learn without being explicitly programmed.History of Machine Learning - How did it Evolve?The History of Machine Learning is quite Convoluted(Pun intended), since Machine Learning, the term I mean could be deceptive. Machine Learning is not a monolithic concept but a collection or tools and techniques which have their own separate origins throughout the past 70 years or more. But still there were points in time which could be labeled significant enough to have manifested Machine Learning in the form we see it today.Before we take a tour down memory lane, let's pay homage to the father of Automata, Alan Turing.Year 1950 : Alan Turing developed the Turing Test during this year.The Turing Test also called the "Imitation Game" had 1 objective; to predict if a machine is able to think like a human. While the technique is quite primitive by today's standards, the philosophical implications have had a big impact on the development of AI.Turing Test is defined as a game of question and answers played by a human and a machine, and the person asking those questions is also a machine. This machine's job is to process the response provided by the machine and human player and judge whether the machine is a human or otherwise. Turing predicted that by the 21st century we would have machines capable of passing as humans, unfortunately that is not the case. I mean just take ChatBots for example, even when we do not explicitly know that it's a ChatBot we are talking to, we are easily able to see through it's disguise and identify it as a program and not a real person.Year 1957 : PerceptronDeemed the first ever Neural Network was designed this year by Frank Rosenblatt. Neural Networks comprise a very popular and promising subset of Machine Learning called Deep Learning. It is one of the most promising Machine Learning tools we have at our disposal today.Year 1960 : MIT developed a Natural Language Processing program to act as a therapist. The program was called ELIZA, It was quite a success experimentally. But it was still using scripting to do its magic. Nonetheless it was a key milestone for the development of NLP - Natural Language Processing which is again a subset of Machine Learning and is widely used today.Year 1967 : The advent of Nearest Neighbor algorithm, very prominently used in Search and Approximation. K-Nearest Neighbor or KNN is one of the most popular Machine Learning algorithms.Year 1970 : Backpropagation takes shape. Backpropagation is a set of algorithms used extensively in Deep Learning, they dynamically alter the Deep Learning Neural Network to effectively do self correction. Backpropagation scientific paper was published by Seppo Linnainmaa but at that time it was called Automatic Differentiation(AD).Year 1980 : Kunihiko Fukushima successfully built a multilayered Neural Network called ANN - Artificial Neural Network which acted as a platform for the development of Convoluted Neural Networks down the line.Year 1981 : Gerald Dejong built a new way to teach machines and he called it Explanation Based Learning, this was a very early Machine Learning implementation and it processed Data to create a set of rules which is another way of saying that it created an algorithm.Year 1989 : Reinforcement Learning is finally realized. Q-Learning algorithm is developed by Christopher Watkins which made it possible to use Reinforcement Learning in practical applications, for example, teaching a machine to play a risk vs reward game.Year 1995 : Rise of 2 very important algorithms in the Machine Learning space; Random Forest Algorithm and Support Vector Machines.Year 1997/98 : LSTM was introduced by Sepp Hochreiter and Jürgen Schmidhuber, LSTN revolutionized NLP research and application. Along with this MNIST database was also developed courtesy of a team led by Yann LeCun. MNIST database is regarded as a benchmark in training Machine Learning algorithms for Handwriting RecognitionYear 2006 : Geoffrey Hinton, regarded as the father of Deep Learning, coined this very term this year along with Netflix starting a competition to beat its Recommender System's accuracy in predicting user scores by 10%. This competition was won in 2009.Year 2009 : ImageNet is created, which facilitated Computer Vision research by giving researchers access to a vast database categorized by objects and features. It was a project initiated by Fei-Fei Li from Stanford University.Year 2010 till now : Google Brain and Facebook's DeepFace are now revolutionizing Machine Learning and pushing boundaries. Google Brain has successfully reached a Cat's level of intelligence and can now even browse and use youtube and correctly predict or identify which videos contain a cat, on the other hand Facebook's DeepFace can now identify people with an accuracy figure exceeding 97%.Benefits of Machine Learning - Why is ML important to usTo understand the need for Machine Learning and it's subsequent benefits, we need to go back to the roots.Let's ask ourselves what is a computer program ?Isn't it a set of rules applied on a certain input to get a desired output !In other words explicitly programming a machine to do a task based on some parameters is what loosely defines traditional programming. While it has served us well till now, at the current pace of technological progress it is getting very complex and hard to write code for higher order problems.To substantiate what I just said, let's compare 2 programs, one which has to deal with only 2 parameters and another which has to deal with n number of parameters, n being a very very large number. Coding explicitly for the former seems plausible and while it isn't impossible to code explicitly for the latter, it will be a mammoth task and the complexity will rise to a level where it will be very difficult to maintain such a convoluted code.With the data explosion in recent decades accompanied by the advent of big data and huge strides made in the sector of performance computing; Industry leaders across multiple disciplines are now asking bigger and better questions, to improve customer experience(Entertainment), to improve yield(Semiconductor), to reduce wait times(E-Commerce) and also to improve diagnosis(Healthcare).How does Machine Learning work ?The graph above does a very good job of explaining what Machine Learning Model encompasses.To delve deeper - Machine Learning covers such a vast variety of techniques and algorithms, there is no simple answer to this question, but we definitely can deduce the essence of it considering an algorithm.Linear Regression, Logistic Regression and Neural Networks pretty much work on the same core principles :It takes data as input.It maps the data using a mathematical function to develop a hypothesis which tries to predict a desired output.It calculates the cost which simply defines the accuracy of the hypothesis.It defines another mathematical function to reduce this cost.It produces an estimated output to the best of its ability, given the amount of data and time.The performance of these algorithms is then fine tuned by providing them with varied sets of data.Types of Machine Learning ApproachesMachine Learning comes in 3 different flavors : Supervised Learning:This is a technique in which Machine Learning algorithm takes labeled data as input and then predicts output for an unlabeled set of data. What that means is that along with a question we are also providing our algorithm with the right answer and then we let the algorithm figure out a relationship between the answer and the given question. Once the algorithm is able to figure this out, it can effectively use this knowledge to predict the answers for new questions which we feed it.Deep Learning is a type of Supervised Learning and has been very successful at things like Object Detection and Analyzing Medical Scans for Tumor to name a few. Unsupervised Learning:Unlike Supervised Learning, Machine Learning algorithm is not given a labeled data, meaning it does not have an answer to a given question, or more aptly put; we do not provide any context to the algorithm about the data, the algorithm is expected to mine that data and derive patterns and relationships to formulate the context.Anomaly Detection is a type of Unsupervised Learning technique used to detect fraudulent transactions in the Finance Industry.Autoencoders is a technique used for compression, but it does more than that, it's capable of capturing the essence of the object it’s compressing. It uses this essence to create the original object back from the compressed version of it with a very high degree of accuracy. Examples include removing noise from an image. Reinforcement Learning: I am sure most of us have played a video game or two while growing up(or still do). In those games whenever we did well, we were rewarded with some coins or with a new ability or a high score for that matter. This constant feedback gave us incentive to try and try again to get better at the game, Reinforcement Learning algorithms work on the same principal. The goal here is to improve the efficiency of a machine by providing it cues about how it's doing, if it does well, we need to reward it, and if it does bad, we shouldn't reward it. Repeating this technique a numerous times has positive implications when it comes to the performance of the algorithm.Video Games are a great example of where these techniques are being used and researched the most. I mean it is now possible to teach a computer program to successfully beat the very famous game Doom.Challenges or Limitations of Machine LearningDespite Machine Learning's newfound success, it is not the be all end all solution to all of our problems.Machine Learning is plagued by the following limitations : 1. Data Dependency Their effectiveness depends on the amount of data you can provide the algorithms, and sometimes this need is too high and finding that much amount of labeled data to train the algorithm will not be an easy task. And even if we could find a lot of data for our use case, it's possible to run into a dead end when the algorithm faces an unforeseen situation where the previous data cannot empower the algorithm to produce the desired output.It should also be noted that if the data set being fed to the algorithm itself has discrepancies or inadequacies then the algorithm's output will also be less than ideal.2. Lack of verifiability By verifiability we define whether a system's inner workings are clear and understandable. As often is the case with complex Machine Learning algorithms, even the best of researchers struggle to diagnose and understand the key points affecting the decisions made by the algorithms. The best example of this would be a Convoluted Neural Network or CNN for short, it's so utterly complex in it's working that if you aren't careful, you could start rolling down a rabbit hole.In short, unlike traditional programs which can be reverse engineered relatively easily to understand key metrics, Machine Learning algorithms are a much tougher nut to crack. 3. Time and PerformanceMachine Learning algorithms need a lot of time and computing power to reach an acceptable level of performance and even with state of the art technology, complex problem can take months to properly train. It is not an optimal situation as quite a few times developers realize quite late in the training process that they could improve the algorithm. By then, thanks to the long time taken to iterate over just 1 version of the Machine Learning algorithm, they have already wasted a lot of time. 4. Top-Down nature of AIAI or even Machine Learning could be categorized as Top-Down or Bottom-Up in nature, and the jury is still out on which is the best type of approach here.To flesh it out a little,A bottom-Up AI approach means we have a good grasp of the underlying logic which dictates what our algorithm does, think of it like having a brain and understanding that the brain works by using a billion cells called neurons, which collectively makes up a very complex neural network. Deep Learning is a very prominent example of this.On the other hand Top Down approach says that we have a good understanding of what we want the algorithm to do, but we do not care about it's inner workings. Taking the example of a brain again, let's say we write a few rules and give those rules to our brain, based on that and the question being asked brain will provide us an answer which adheres to the rules we had previously given it. A good example of this would be Reinforcement Learning.How is Machine Learning being used in Today's world? - Applications of ML ?Machine Learning has invaded most of our daily lives and we barely notice it. Let's go through a couple of examples which exemplify the extent to which Machine Learning has invaded our daily routine.1. Spotify -The music streaming platform is liked unanimously by most users due to its Machine Learning algorithm which does a pretty good job at understanding what the user likes, and it's definitely not as simple as understanding what genre you like and then picking up popular music from that genre for you, it's much deeper than that, you know you have got a gem of an algorithm if it can segregate music by sub genres, tone, tempo and general mood of the song. This is why despite there being competing services out there which arguably provide higher bit rate music, people tend to stick with spotify.2. Amazon - Ever wondered how amazon is able to suggest you products which are pretty close to something you might be interested in. Yep you guessed it right, thats Machine Learning at work here. It basically tracks your past purchases and browsing patterns to create a profile based on which it is able to suggest you useful products.3. Facebook - Facebook is investing heavily into AI and the results are quite evident for someone who has a keen eye. How do you think facebook is able to tag people in photographs and able to suggest you people you might know ? Former is a technique called Face Recognition and the latter is more complicated than that, but for simplicity's sake, let's call it a Recommender System.4. Gmail - Spam has become quite an issue in the last decade or so with the emergence of cheap and accessible internet with people trying to come up with new ways to scam you or new ways to gather data, bombarding you with information you do not need nor desire. In lieu of that google and other mailing platforms employ Spam Detection mechanisms which segregate arriving mails into 2 classes, Spam and Not Spam, and send them to appropriate mail folders.5. Online Assistants - We have all used either Siri or Google Assistant in our daily lives and know how incredibly utilitarian those assistants are. Isn't it great that they can understand you irrespective of your dialect or accent ? This application of Machine Learning is called Natural Language Processing or NLP for short and it's getting better day by day. Soon it will be integrated with all our devices and with the help of IOT has the potential to revolutionize our lives.6. Fraud Detection - Banking Services is one area which requires extra measures to build a brand and assure customers of great security in this day and age of technology. Banks have a really large user base these days and they simply do not have the bandwidth to monitor each and every transaction by employing an actual person, so these Fraud detection systems come into play and alert bank employees who in turn get in touch with the customers to verify if the transaction was legit. Fraud is a big concern for any Financial Institution and Machine Learning is being employed in this sector to identify fraudulent transactions.7. Self Driving Cars - This is the last but one of the most important Machine Learning systems in development right now. Just imagine not having to spend all that time behind the wheel and using it instead to work or relax or for recreational activities without the risk of violating any traffic rules or being in an accident. This tech has the potential to get rid of unnecessary traffic jams and untimely deaths caused each year due to accidents. Most major players in the automobile industry are investing heavily into this right now.Difference between ML and AIIt's unfortunate to see how even today people tend to confuse AI with ML and tend to use the terms interchangeably. It's like saying there is no difference between calling a potato a potato vs calling a potato a vegetable. To clarify Vegetable loosely represents AI and Potato loosely represents ML, So with this it should be clear that ML is a subset of AI which has a much broader scope and an inadequately defined boundary.1. AI - Let's discuss about the superset ie AI, AI is defined as a field of science working towards creating computers, machines and systems capable of displaying intelligence close to that of humans. From a simple Chess playing computer program to a highly sophisticated self driving car program, all of these constitute the term AI. AI has no definite scope and solid definition because it's a highly evolving field made of up of numerous disciplines which come together to loosely define it. It also doesn't help that this is one of the fastest growing fields of science right now.To summarize and provide some much needed clarity, any program which can autonomously learn, act, react, adapt and evolve without human intervention and can think and reason like us would be called an AI program.2. ML - ML is a subset of AI which primarily deals with creation of algorithms which learn with experience and improve themselves over time by feeding on data, they do not need to show human like intelligence to be regarded as Machine Learning systems, they simply need to follow or showcase a self improving quality without being explicitly programmed to do so. A simple example of this would be Image recognition algorithms, the more images and the more variations of those images are fed to it, the more it improves.To summarize, ML systems are systems which self improve given adequate data to improve their accuracy.To drive the point home, philosophically speaking, we can say that AI bestows wisdom to a machine while ML bestows knowledge.Programming Languages for Machine LearningMachine Learning has become such a widely adopted area of research and development that programmers coming from various backgrounds can quickly get started.To name a few popular programming languages which have adequate community support :PythonC++RJavaOctaveConclusion and SummaryI am sure everyone must be tired by now reading through it all, so I promise I will keep this short. AI and ML are going to revolutionize every industry in the coming decades. And the market is ripe for the taking. This has seen an emergence in the need for good engineers who would then become the driving force behind AI and ML.I wrote this article to spread awareness about the topic, to get people interested in it, and to help those who want to put their first foot in but are uncertain and afraid of the scope. I can empathize with them, the technology is still growing rapidly, we do not have streamlined paths for aspiring engineers and there is lack of a common standard in the community when it comes to the choice of algorithms and tools to be used, which all adds to the complexity.But if you followed this article you should be good to go.
Rated 5.0/5 based on 12 customer reviews
R vs Python
Rated 4.5/5 based on 26 customer reviews
What is Data Science
By Animikh AichWhat is Data Science?Data Science is a multidisciplinary field that uses scientific inference and mathematical algorithms to extract meaningful knowledge and insights from a large amount of structured and unstructured data. These algorithms are implemented via computer programs which are usually run on powerful hardware since it requires a significant amount of processing. Data Science is a combination of statistical mathematics, machine learning, data analysis and visualization, domain knowledge and computer science.As it is apparent from the name, the most important component of Data Science is “Data” itself. No amount of algorithmic computation can draw meaningful insights from improper data. Data science involves various types of data, for example, image data, text data, video data, time-dependent data, etc.History of Data ScienceThe term “Data Science” has been mentioned in various contexts the past thirty years, but it is only recently that it became internationally established and recognized. More recently, the term became a buzzword when Harvard Business Review called it “The Sexiest Job of the 21st Century” in 2012.Origin of the ConceptThough it is unclear when and where the concept was originally developed, William S. Cleveland coined the term “Data Science” in 2001. Shortly thereafter, in April 2002 and January 2003, the publications of the “CODATA Data Science Journal” by the International Council for Science: Committee on Data for Science and Technology and the “Journal of Data Science” by Columbia University, respectively kickstarted the journey of Data Science.Additionally, It was also around this time when the “dot-com” bubble was in full swing, which led to the widespread adoption of the internet and in turn, generation of a huge amount of data. This, in addition to the advancement in technology, which led to faster and cheaper computation, together was responsible for the launch of the concept of “Data Science” to the world.Recent Additions to the Field of Data ScienceThe field of Data Science has been expanding ever since it’s onset in the early 2000s. With time, more and more cutting edge technologies are being incorporated into the field. Some of such more recent additions are listed below:Artificial Intelligence: Machine Learning has been one of the core elements of Data Science. However, with the increased parallel compute capabilities, Deep Learning has been the latest and one of the most significant additions to the Data Science field.Smart Apps or Intelligent Systems: The development of data-driven intelligent applications and their accessibility in a portable form factor has lead to the inclusion of a part of this field into Data Science. This is primarily because a large portion of Data Science is built around Machine Learning, which is also what Smart Apps and Intelligent Systems are based on.Edge Computing: Edge computing is a recently developed concept and is related to IoT (Internet of Things). Edge computing basically puts the Data Science pipeline of information collection, delivery, and processing closer to the source of information. This is achievable through IoT and has recently been added to be a part of Data Science.Security: Security has been a major challenge in the digital space. Malware injection and the concept of hacking is quite common and all digital systems are vulnerable to it. Fortunately, there have been few recent technological advancements which apply Data Science techniques to prevent exploitation of digital systems. For example, Machine Learning techniques have proven more capable of detecting computer virus or malware when compared to traditional algorithms.Blurring the lines between Data Science and Data AnalyticsThe buzzwords “Data Science” and “Data Analytics” are often used interchangeably. Even though these two fields are closely related, they do not mean the same thing. In summary, Data Science is an umbrella term which consists of the fields of Machine Learning, Data Analytics, and Data Mining.In terms of Job Description, a “Data Scientist” and a “Data Analyst” also works on different, but related technologies.ParametersData ScientistData AnalystDefinitionA person who is skilled at handling a huge amount of data to build models and extract meaningful insights from them with the help of statistical and machine learning algorithms using computer science concepts.A person whose primary job is to sift through a huge amount of data, wrangle and visualize them and determine what insights the data is hiding.SkillsMachine Learning, Statistics, Data Visualization, Databases, Software Engineering, Data Mining, Domain KnowledgeStatistics, Data Visualization, Data Wrangling, Databases, Data MiningTechnologiesPython, R, SQL, AWS, Machine Learning Libraries,Java, Hadoop, Hive, Spark, AWS, SQL, TableauRole of Big Data in Data ScienceThe term “Big Data” refers to a large collection of structured, semi-structured or unstructured heterogeneous data. Databases are usually not capable of handling such voluminous datasets.As mentioned earlier, the key component of Data Science is Data. As a rule of thumb, “more the data, the better the insights”. Hence, Big Data plays a very important role in the field of Data Science. Big Data is characterized by its variety and volume, both of which are essential for Data Science. Data Science captures the complex patterns from Big Data by developing Machine Learning models and Algorithms.Applications of Data ScienceData Science is such a field which can be applied to almost every industry to solve complex problems. Every company applies Data Science to a different application with the view of solving a different problem. Some companies completely depend upon Data Science and Machine Learning techniques to solve a certain set of problems, which, otherwise, could not have been solved. Some of such applications of Data Science and the companies behind them are listed below.Internet Search Results (Google): When a user searches for something on Google, complex Machine Learning algorithms determine which are the most relevant results for the search term(s). These algorithms help to rank pages such that the most relevant information is provided to the user at the click of a button.Recommendation Engine (Spotify): Spotify is a music streaming service which is quite popular for its ability to recommend music as per the taste of the user. This is a very good example of Data Science at play. Spotify’s algorithms use the data generated by each user over time to learn the user’s taste in music and recommend him/her with similar music in the future. This allows the company to attract more users since it is more convenient for the user to use Spotify as it does not demand much attention.Intelligent Digital Assistants (Google Assistant): Google Assistant, similar to other voice or text-based digital assistants (also known as chatbots) is one example of advanced Machine Learning algorithms put to use. These algorithms are able to convert the speech of a person (even with different accents and languages) to text, understand the context of the text/command and provide relevant information or perform a desired task, all just by speaking to the device.Autonomous Driving Vehicle (Waymo): Autonomous Driving vehicles are one of the bleeding edge of technology. Companies like Waymo uses high-resolution cameras and LIDARs to capture live video and 3D maps of the surrounding in order to feed that through Machine Learning algorithms which assist in autonomously driving the car. Here, the data is the videos and 3D maps captured by the sensors.Spam Filter (Gmail): Another key application of Data Science which we use in our day-to-day life is the spam filters in our emails. These filters automatically separate the spam emails from the rest, effectively giving the user a much cleaner email experience. Just like the other applications, Data Science is the key building block here.Abusive Content and Hate Speech Filter (Facebook): Similar to the spam filter, Facebook and other social media platforms use Data Science and Machine Learning algorithms to filter out abusive and age-restricted content from the unintended audience.Robotics (Boston Dynamics): A key component of Data Science is Machine Learning, which is exactly what fuels most of the robotics operations. Companies like Boston Dynamics are at the forefront of the robotics industry and develop autonomous robots that are capable of humanoid movements and actions.Automatic Piracy Detection (YouTube): Most videos that are uploaded to YouTube are original content created by content creators. However, quite often, pirated and copied videos are also uploaded to YouTube, which is against their policy. Due to the sheer volume of daily uploads, it is not possible to manually detect and take down such pirated videos. This is where Data Science is used to automatically detect pirated videos and remove them from the platform. The Life Cycle of Data ScienceThe field of Data Science is not a single step process. It has many steps involved in it. These steps are listed below.Project Analysis: This step is more inclined towards Project Management and Resource Assessment than it is a direct implementation of algorithms. Instead of starting a project blindly, it is crucial to determine the requirements of the project in terms of the source of data and its availability, the number of human resource available and if the budget allocated for the project is sufficient to successfully complete it.Data Preparation: In this step, the raw data is converted to structured data and is cleaned. This involves Data Analysis, Data Cleaning, Handling of Missing Values, Transformation of data and Visualization. From this step onwards, programming languages like R and Python is used to achieve results for big datasets.Exploratory Data Analysis (EDA): This is a crucial step in Data Science, where the Data Scientist explores the data from various angles and tries to draw initial conclusions from the data. This includes Data Visualization, Rapid Prototyping, Feature Selection, and finally Model Selection. A different set of tools are used in this step. The most commonly used are R or Python for scripting and Data Manipulation, SQL for interacting with Databases, and different libraries for data manipulation and visualization.Model Building: Once the type of model to be used is determined from the EDA, most of the resources are channeled towards the development of the model with ideal hyperparameters (modifiable parameters), such that it can perform predictive analysis on similar but unseen data. Various Machine Learning techniques applied to the data, like Clustering, Regression, Classification or PCA (Principal Component Analysis) in order to extract valuable insights from it.Deployment: After the model has been built successfully, it is time to bring the model out to the real world from its sandbox. This is where model deployment comes to the picture. Up until now, all the steps were dedicated to rapid prototyping. However, once the model has been successfully built and trained, the main application of it is in the real world, where it is deployed. This can be in the form of a web app, mobile app, or it can be run in the back-end of the server to crunch high-frequency data.Real World Testing and Results: After the model has been deployed, it faces unseen data from the real world in real time. The model may perform very well in the sandbox, but fail to perform adequately after deployment. This is the phase where constant monitoring of the model output is required in order to detect scenarios where the model fails. If it does fail at some point, the development process goes back to Step 1. If the model succeeds, the key findings are noted and reported to the stakeholders.Where does Data Science fit when compared to the other Buzzwords - AI, Machine Learning, Deep Learning“Data Science” seems to be a rather confusing word, which does not have a clear definition or boundaries. The buzzwords “Artificial Intelligence”, “Machine Learning” and “Deep Learning” are often used interchangeably with “Data Science” or in association to it. Let us clearly define the boundaries for each of these terms.As mentioned earlier, Machine Learning is a part of Data Science. As shown in the figure below, Deep Learning is a part of Machine Learning, and Machine Learning is in turn a part of Artificial Intelligence.Even though Data Science includes a portion of each of Artificial Intelligence, Machine Learning and Deep Learning, it contains more than just these three subdomains inside it. Data Science also contains Statistical Programming, Data Analysis, Data Mining, Big Data and more recent additions like IoT, Edge Computing and Security.Hence, Data Science is a complex field of the scientific study of data, which contains a significant portion of some of the most recent advancements in Computer Science and Mathematics.Skills required to become a Data ScientistAs mentioned in the previous section, Data Science is a complex field. Hence, it requires the mastery of multiple sub-fields, which together add up to the complete knowledge required to be a Data Scientist.1. Mathematics: The first and the most important field of study in order to become a Data Scientist is mathematics; more specifically, Probability and Statistics, Linear Algebra, and some basic Calculus.Statistics: It is essential in EDA and developing algorithms to conduct statistical inference on the data. Additionally, most Machine Learning Algorithms use statistics as its fundamental building blocks.Linear Algebra: Working with a huge amount of data means working with high dimensional matrices and matrix operations. The data that the model takes in and the one that it gives as output are in the form of matrices and hence any operation that is conducted on them uses the fundamentals of Linear Algebra.Calculus: Since Data Science does include Deep Learning, calculus is of immense importance. In Deep Learning, calculation of Gradient is very important and is done at every step of computation in Neural Networks. This requires a sound knowledge of differential and integral calculus.2. Algorithmic Knowledge: Even though Data Science typically does not involve the development and design of Algorithms like any other application of Computer Science does, it is still imperative for a Data Scientist to have sound knowledge on Algorithms. This is because, at the end of the day, Data Scientists are programmers who are expected to develop programs which would derive meaningful insights from data. Having algorithmic knowledge allows the Data Scientist to write meaningful efficient code, which saves both time and resources and hence is highly valued.3. Programming Languages (R and Python): Even though, any programming language can be used for any kind of logical use case, which of course, includes Data Science; but, the most commonly used languages are R and Python. Both of these languages are open source and hence have huge community support, have multiple libraries developed keeping Data Science in mind and are relatively easy to learn and use. Without the knowledge of programming languages, a Data Scientist cannot apply any kind of algorithmic or mathematical knowledge to the data.4. Proper Programming Environment: Since sound programming knowledge is one of the key requirements for Data Science, there needs to be a convenient platform to write and execute the code. This platform is called the IDE or Integrated Development Environment. There are several IDEs to choose from, and some of them have been specifically developed for Data Science. This article talks about the Top 10 Python IDEs.5. Machine Learning Frameworks: Machine Learning is an important part of Data Science and its implementation involves certain libraries and frameworks, the knowledge of which are essential for any Data Scientist. Here, some of the most commonly used Machine Learning frameworks are listed.Numpy: This is a library which allows the easy implementation of linear algebra and data manipulation.Pandas: This library is used to load, modify and save data. This is also used in data wrangling.Matplotlib: This is one of the most commonly used libraries for data visualization.Seaborn: This is a wrapper over Matplotlib, which is used to visualize more complex data.Sklearn: This is used to apply and implement most of the machine learning algorithms and data preprocessing techniques.Tensorflow: This is a deep learning framework backed by Google and allows easy implementation of various types of neural networks.PyTorch: Similar to tensorflow, this is also a deep learning framework which is frequently used.Keras: This is a wrapper which works alongside tensorflow and allows relatively easy implementation of Deep Learning techniques.OpenCV: This is a computer vision framework and is usually used for Image Processing and image manipulation. This is used for video or image-based data.6. SQL: Databases are of immense importance in the field of Data Science since they are the most suitable method of storing data. Thorough knowledge of one or more database technologies like MySQL, MariaDB, PostgreSQL, MS SQL Server, MongoDB, Oracle NoSQL, etc. is also important.Salaries of a Data ScientistData Science field is one of the highest paying jobs in the software domain. It is also the highest paying with the lowest amount of relevant work experience when compared to any other field in the software domain, as shown in the figure below. This data has been sourced from the Stack Overflow 2019 Developer Survey.Some of the salaries offered are listed below.According to DataJobs the salary range for Data Scientists in USA is $85,000 to $170,000.According to PayScale the salary range in India is ₹305,000 to ₹2,000,000 and the median salary being ₹620,000.Glassdoor states the Average Base Pay for Data scientists in India as ₹947,698 per annum.Future of Data ScienceData Science is an ever growing field and is expected to grow in demand in the foreseeable future. Some of the key changes are listed below.Data: With the radical increase of generation of data, the performance of the predictive algorithms is going to improve over time as more structured data is available to draw inference upon. This phenomenon is fueled by the growth of Social Media and IoT based devices, which generate a lot more structured data.Algorithms: Machine Learning algorithms like Genetic Algorithms and Reinforcement Learning algorithms are expected to improve over time causing more intelligent systems.Distributed Computing: With the advancements of blockchain technology, TPU (Tensor Processing Unit) development and faster GPU (Graphics Processing Unit) available in the cloud, Data Science sees a future where more powerful computational hardware aids the algorithms of increasing complexity.More Data and improved Algorithms and Hardware together are expected to bring significant improvements in the field of Data Science in the near future.ConclusionData Science is a hyped up complex field of study. For the most part, the hype is true and it delivers solutions to problems as promised. Some fields of data science have even started to outperform humans and that trend is expected to increase in the near future. You can take up Data Science training to enhance your career.Data Science is definitely the “Sexiest” job in the 21st century. It defines the bleeding edge of technology at present and promises further technological advancements in the near future. It is also one of the most in-demand and high paying jobs in the industry. Hence, there is no better time to be a Data Scientist than now!
Rated 4.5/5 based on 12 customer reviews
Artificial Intelligence - The Ultimate Invention
By Zeolearn AuthorWhat is Artificial Intelligence?Artificial Intelligence or AI is the mantra of the current era. This is how Alexa listens when you request her to play your favorite song and responds by playing it, this is how Google ranks pages and shows you the most preferred restaurant you would like to go for lunch, this is how driverless cars detect objects around it and drives accordingly. You can call it magic or simply AI.AI is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines.” Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.What is AI in plain English?In simple words, we can say that AI is the ability of a computer program or a machine to think and learn. The concept of AI is to build machines capable of thinking, acting, and learning like humans. Artificial Intelligence can be accomplished by studying how the human brain thinks, and how humans learn, decide and work while they try to solve a problem, then using these outcomes of the study intelligent software and systems are developed.The main goal of AI is to create expert systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users. Also, to implement Human Intelligence in Machines to create systems that understand, think, learn, and behave like humans.We are all aware of some of the applications of AI which we encounter in our day to day lives such as -Vision systems - These systems understand, interpret, and comprehend visual input on the computer. For example, whenever we run a red light or stop signal in a car, a fine or ticket is raised against our license plate number. This is one of the cases where the cameras in the streets capture frames and detect when a vehicle crosses a red light or stop signal, and records the specific license number. Similarly, police use these systems to recognize the face of criminal with the stored portrait made by forensic artist.Speech Recognition − There are intelligent systems which are capable of hearing and comprehending the language in the form of sentences and their meaning when human talks to them. It handles all different accents, slang words, change in human’s voice due to cold and so on. A very common example is voice assistants such as Alexa by Amazon, Siri by Apple, or Cortana by Microsoft. These voice assistants recognize words/phrases/sentences and respond according. If you simply call Alexa by her name, she responses, and waits for a command, then you may ask her to perform an action or simply ask to play music or ask a question.The core problems that are associated with AI include programming computers with certain traits such as:Knowledge: Knowledge is an integral part of AI research. To imitate the thought process of a human expert, knowledge helps in creating rules to apply to data. It analyzes the structure of a task or a decision and identifies how a conclusion is reached.Machine Perception: It is the capability of a computer system to interpret data in such a manner that is similar to the way humans use their senses to relate to the world around them.Computer Vision: It is the power to analyze visual inputs taking facial, object and gesture recognition into consideration.Machine Learning: Machine learning is also an integral part of AI. Unsupervised Learning requires an ability to identify patterns in streams of inputs, whereas supervised learning involves classification and numerical regressions.Robotics: Robotics is also a major field related to AI. It requires intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning, and mapping.How has AI evolved?1943 - Alan Turing invented the "Turing Test", which set the bar for an intelligent machine: a computer that could fool someone into thinking they were talking to a real person. Grey Walter built some of the first ever robots.1950 - Early AI research was more of exploring topics like problem-solving and symbolic methods. I, Robot was published - a collection of short stories by science fiction writer Isaac Asimov.1956 - John McCarthy coined the term "artificial intelligence." A "top-down approach" was dominant at the time: pre-programming a computer with the rules that govern human behavior.1960 - During this time, the US Department of Defense gained interest in this kind of work and started training computers to mimic basic human reasoning.1968 - Marvin Minsky, the founder of AI Laboratory at MIT, advised Stanley Kubrick on the film 2001: A Space Odyssey, featuring an intelligent computer, HAL 9000.1969 - Shakey the Robot, the first general-purpose mobile robot was built. It was able to make decisions about its own actions by reasoning about its surroundings.1970 - DARPA or Defense Advanced Research Projects Agency completed the street mapping project.1974 - The "AI WINTER" began - millions had been spent, with little show for it. As a result, funding for the industry was slashed.1980 - During this period, a form of AI program called "expert systems" was adopted by corporations around the world and knowledge became the focus of mainstream AI research. The period 1980-1987 is termed as “Boom”.1990 - This period Artificial Intelligence experienced a major financial setback. Researchers termed this period (1987-1993) as “Bust”.1997 - Deep Blue became the first AI enabled the computer to beat chess against world champion Garry Kasparov.2003 - DARPA had already produced an intelligent personal assistant long before Apple’s Siri, Alexa or Cortana came into the picture.2008 - Google launched a speech recognition app on the new iPhone. It was the first step towards Apple's Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana.2011 - In this year, IBM Watson beat the top two Jeopardy! players Brad Rutter and Ken Jennings.2016 - Google's AlphaGo beat a top Go player Lee Sedol 4 out of 5 times.In 2018, Artificial Intelligent is not a buzzword anymore. World's first AI presenter was unveiled in China. China’s Xinhua state news agency had introduced the newest members of its newsroom, AI anchors who will report 'tirelessly' all day, every day, from anywhere in the country.On February 24, 2019, China’s Xinhua introduced the world’s first female AI news anchor, who will debut in March.The field of AI, now after over half a century, has finally achieved some of its goals. It is being used throughout the technology industry very successfully and also in other industries as well. All of these were achieved due to the increase in computer power and because researchers and professionals focussed on specific isolated problems.What do the statistics related to AI say?We all have been seeing how AI has changed the way we think and interact with each other every day. It is true that AI was merely a science fiction and now has turned into reality. The statistics related to AI in the field of business and technology are also changing. In all sectors, whether it is healthcare, education or manufacturing, there is a success in nearly every industry as they adopt artificial intelligence.With the effect of AI on robotics, virtual digital assistants, voice search and recognition, startups and investments, big data, there has been a change in the statistics and has new goals with respect to AI.In 2016, the global AI market was $1.4billion, it is expected to reach $60 billion by 2025Business productivity can be increased by 40% with the help of AIAI is being used in almost 77% of the devices we use on a regular basisAccording to Google’s analysts, by the year 2020, a robot will have the capability to mimic complex human behavior like jokes and flirtingBy 2030, AI will help the Global GDP to grow by $15.7 trillionAccording to research and surveys, here are some of the statistics related to Artificial Intelligence. AI technology can enhance business productivity by up to 40%.“Accenture researched on the impact of AI in 12 developed countries and revealed that AI can double the economic growth rates in 2035. It can be achieved by changing the nature of work and creating new relationships between machine and man. AI’s impact on businesses will enable people to use time efficiently and increase their productivity by 40 percent.Businesses with more than 100,000 employees are more likely to have a strategy that implements AI.MIT Sloan Management Review published an article which shows that 75 percent of executives believe that AI will enable their company or enterprises to expand and gain a competitive advantage.47 percent of established organizations have a defined AI strategy for mobile.Adobe surveyed almost 500 marketing and IT professionals to explore current mobile trends, forecast where mobility is going, and learn what some of the most advanced organizations are doing in the space. It has been seen that almost 47% of the advanced enterprises have applied AI strategies to their mobile applications as part of their marketing efforts and additionally, 84% use a personal strategy.40% of people use the voice search function at least once every day.This information clearly shows that people are slowly increasing the use of voice search in everyday lives.30% of web browsing and searches will be done without a screen by 2020.Audio-centric technologies like Amazon Echo have access to dialogue-based information. According to the AI statistics provided by Gartner, the voice-first interaction will gain prominence in no time.Around 4 billion devices already work on AI-powered voice assistants.A press release by IHS Markit, a business information provider, found that 4 billion devices have AI-powered assistants, and this number will reach 7 billion by 2020.Nearly half of Americans use digital voice assistants.A 2017 Pew Research study showed that 46% of Americans use digital assistants to interact with their smartphones. Voice assistants are present on a diverse range of devices, so 42% of users have the tech on their smartphones, 14% of them use it on a computer or tablet, while 8% of them use it on a standalone device such as Amazon Echo or Google Home.Benefits of Artificial IntelligenceThe general benefit of artificial intelligence, or AI, is that it replicates the decisions and actions of humans without human shortcomings, such as fatigue, emotion and limited time. Apart from this, there are a few more benefits which are mentioned below -Enhances Efficiency And ThroughputConcerns about disruptive technologies are common. Automobiles are one of the examples, it took almost a decade to develop regulations around the industry to make it safe. Today AI has been highly beneficial to society as it enhances efficiency and throughput, creating new opportunities for revenue generation and job creation.Allow Humans To Do What They Do BestHumans are not very good with tedious tasks but machines are. AI allows humans to do the more interpersonal and creative aspects of work.Adds Jobs, Strengthens The Economy It is said that Robots and AI will destroy jobs. This is fiction rather than fact. People will still work, have their jobs, but they will work better with the help of AIEnhances Our LifestyleIntroducing AI in our society will enhance our lifestyle and create more efficient businesses. Some of the mundane tasks like answering emails and data entry will be done by intelligent assistants. Society will switch to smart homes in order to reduce energy usage and provide better security, marketing will be more targeted and we will receive better healthcare.Increases AutomationAI can be used to perform tasks which would once require intensive human labor or would not have been possible at all. Also, due to AI automation, there has been a reduction in operational costs which is a major benefit for businesses.Improves Demand Side Management Computers definitely do not share the same probability of errors as human beings are. AI can be used to analyze and research historical data to determine the efficiency of distributing energy loads from a grid perspective.Benefits Multiple IndustriesAI has been playing an important role in multiple industries like health sciences, academic research or technology applications where a lot of AI-based applications are in use, such as character/facial recognition, digital content analysis and accuracy in identifying patterns and so on.Extends And Expands CreativityAI has been a boon to mankind. It has been the biggest opportunity of our lifetime to extend and expand human creativity and ingenuity.How Artificial Intelligence is being used in Today’s World?There are many amazing ways in which artificial intelligence and machine learning are being used to impact our everyday lives. Also, it has been implemented in the world’s leading companies to simplify business decisions and optimize operations. Let us walk through some of the practical examples of AI and machine learning.Consumer goodsHello Barbie listens and responds to a child using natural language processing, machine learning, and advanced analytics. A microphone is attached to the Barbie’s necklace which records what is said and transmits it to the ToyTalk servers. Then, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue. Toytalk Servers transmit the correct response back to Barbie in under a second so she can respond to the child. Some of the answers are stored in the form of dialogues such as Barbie’s favorite food etc.Coca-Cola’s global market has more than 500 drink brands sold in more than 200 countries. It makes it the largest beverage company in the world. The company generates a lot of data and it has embraced new technology to put that data into practice in order to support new product development and even trialing augmented reality in bottling plants.Creative ArtsCulinary arts do require the human touch. But AI-enabled Chef Watson from IBM has changed the notion. It uses artificial intelligence to become a sous-chef in the kitchen and helps in developing recipes. Chef Watson also advises their human counterparts to create delicious and unique flavors.IBM has come up with Watson BEAT, which has the ability to deliver different musical elements to inspire music composers. Such AI-based products help musicians and composers understand the requirement of the audience and also figure out what kind of songs might be a hit number.EnergyTo deliver energy into the 21st century, big data, machine learning and Internet of Things (IoT) technology is being used by GE Power in order to build an internet of energy. Advanced predictive analysis is also being used to predict the maintenance and to optimize the operations and business.Financial ServicesAmerican Express processes $1 trillion in transactions and has 110 million AmEx cards in operation. To process such a heavy number of transactions, AmEx is highly dependent on data analytics and machine learning algorithms. It also uses Big Data analytics to detect more fraudulent transactions and save millions.HealthcareThe foundation for Google’s DeepMind has always been Neuroscience. It creates a machine which has the ability to mimic the thought process of our own brains. DeepMind has been proven successful by beating humans at games but now it is time to use the same for healthcare purpose which might reduce the time to plan treatments and also help diagnose.ManufacturingAutomobiles generate a lot of data that can be useful in various ways. Volvo is among one of the vehicle manufacturing companies which uses data to predict engine failure or when vehicles need servicing and thereby expands its services in monitoring vehicle performance. This indeed improves both driver and passenger convenience and safety.MediaRecommendations are what help grow businesses. Netflix is using big data analytics to predict what will its customers prefer to watch. They are not only a media distributor but also a content creator. Analyzing and predicting data helps them to decide what content they should invest in.RetailBurberry is a luxury fashion brand but generally, we would never consider it to be a digital business. But they have been reinventing themselves with the help of AI and Big Data. It has improved its sales and customer relationship.Social MediaInstagram is said to be the most visited social media by the youth. It generates a lot of data in the form of images, videos, and comments. Some of these are offensive and therefore Instagram uses big data and artificial intelligence to fight cyberbullying and delete offensive comments. Apart from these, it also uses deep learning algorithms to detect the type of images and suggest filters for the same.What are the challenges of using Artificial Intelligence?to innovation labs. However, every business needs to overcome challenges to understand the true potential and possibilities of this emerging technology.ProvabilitySome of the organizations involved in AI are unable to demonstrate clearly what AI does. No wonder AI is a “black box”. This results in people being skeptical about it as they fail to understand the logic behind it or how it makes decisions. AI needs to explainable, provable and transparent. It will be a good practice if organizations using AI embrace Explainable AI.Data privacy and securityMost AI applications depend on huge volumes of data to learn and make intelligent decisions. Generally, Machine Learning is largely dependent on data and often this data is sensitive or personal in nature. This makes the system vulnerable and leads to serious issues such as data breach and identity theft. Due to the increasing number of such cases, consumers have prompted the European Union (EU) to implement the General Data Protection Regulation (GDPR), which ensures the protection of personal data. Apparently, it will empower Data Scientists to develop AI without compromising on consumers’ data security.Data ScarcityToday, organizations have access to more data than ever before. However, AI applications require relevant datasets to learn, but datasets are rare in number. The most powerful AI applications are the ones which are trained on supervised learning i.e with the help of labeled data but again, labeled data is limited. It is necessary for the organizations to invest in design methodologies and figure out the possible ways to make AI models learn despite the scarcity of labeled data.How does Artificial Intelligence work?Artificial Intelligence works by combining large amounts of data and processes fast using intelligent algorithms and then allows the software to learn automatically from patterns or feature in the data. Artificial Intelligence, being vast and a broad field of study, includes theories, methods, and technologies as well as the following major subfields:Machine learning helps in the automation of analytical model building. It uses methods from advanced statistics, neural networks, physics and so on to find out hidden insights in data and programs.A neural network is another type of machine learning algorithm which is made up of interconnected units (like neurons) which processes information by responding to external inputs. It requires multiple such passes at the data in order to find connections and then derive meaning from undefined data.Deep learning uses a huge number of neural networks with multiple layers for processing units. Some of the applications include speech and image recognition.Cognitive computing is a subfield of Artificial Intelligence. It strives for natural, human-like interaction with machines. The main aim of a machine is to simulate human processed through the ability to interpret speech and images.Computer vision is dependent on pattern recognition and deep learning in order to recognize the details in a picture or video. Machines are able to process and understand images, they capture images and videos in real-time and then interpret the details.Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The advanced feature of NLP is Natural Language interaction where humans are able to communicate with computers using regular spoken languages in order to perform tasks.Additionally, several technologies enable and support AI:Graphical processing unitsThe Internet of ThingsAPIs, or application processing interfacesDifferent levels of Artificial IntelligenceThe three levels of AI are ANI, AGI, and ASI. Narrow, strong, and super artificial intelligence.Level 1 : Artificial Narrow Intelligence (ANI) - Weak AIExample: RankBrain by Google and Siri by AppleArtificial Intelligence that is focussed on one narrow task is called as Narrow AI or Weak AI. In this case, the ability of an AI application or machine to mimic human intelligence and/or behavior is isolated to a narrow range of parameters and contexts.We need to keep in mind that we are talking about narrow intelligence, not low intelligence.Siri is a perfect example of Narrow AI. Also, most of the application of AI we see in our day to day lives falls under Narrow AI.Level 2 : Artificial General Intelligence (AGI) - Strong AIThe intelligence of a machine that could successfully perform any intellectual task that a human being can be called as Strong AI or Deep AI. In this case, the ability of an AI application or machine to mimic human intelligence and/or behavior is indistinguishable from that of a human.A hypothetical AI replicating a human baby would be an example of strong AI while being "weak" at most tasks.Level 3: Artificial Super Intelligence (ASI) Artificial superintelligence (ASI) is a software-based system with intellectual powers beyond those of humans across an almost comprehensive range of categories and fields of endeavor. In this case, an AI application or machine doesn't mimic human intelligence and/or behavior but surpasses.Unlike weak and strong AI, Artificial Superintelligence (ASI) is something that researchers are not yet confident about. We can only speculate about it. It should have the ability to surpass all human activities at all things, whether it is writing books, solving a mathematical equation or prescribing medicines.But it is a big question for the AI enthusiasts, whether ASI is possible.If we consider that ASI would be possible then it should have the capability to do things we believe humans can do better than bots, such as relationships and the arts. Experts believe that not only ASI but even AGI requires decades more research.Types of Artificial IntelligenceActivity RecognitionDetermining what humans or other entities such as robots are doing. For example, a car that can see its owner approaching with a heavy bag of groceries may decide to open an appropriate door automatically.Affective ComputingAI that seeks reading and using emotion.Artificial LifeArtificial intelligence, science, and engineering modeled upon living systems. It has three types known as soft, hard and wet for software, robotics, and biochemistry respectively. The term wet refers to the water content of living systems.AutomationAutomation of decisions or physical tasks using machinery such as robots.BlockheadA program that simulates intelligence by retrieving information from a data repository. In some cases, products claim to be artificially intelligent as a marketing approach when their software has a design that doesn't learn in a dynamic way.ChatterbotArtificial intelligence that can talk to humans, often over text chat. Typically designed to pass a Turing test.Computer VisionAnalyzing and understanding visual information is a reasonably complex task that typically requires artificial intelligence.Decision Support SystemThe use of artificial intelligence to support human decision making. For example, a tool that determines what information you may need in a given situation.Ensemble LearningA machine learning technique that uses multiple learning algorithms.Machine LearningMachine Learning algorithms learn from historical data and allow computers to find hidden insights and pattern without being explicitly programmed.Machine Learning can be categorized into two main categories:Supervised Learning Unsupervised learningNatural Language ProcessingThe ability to recognize, interpret and synthesize speech.Neural NetworksArtificial neural networks are an AI approach that was originally inspired by biological neural networks. With time their resemblance to biology has decreased and they are now typically based on statistics and signal processing techniques.Sentiment AnalysisTools that determine the general opinion, emotion or attitude in content such as a comment in social media.Difference between Cognitive AI, Machine learning and Deep learningConsider three Russian dolls (Matryoshka dolls), out of which the largest one is Artificial Intelligence (AI), within it is Machine Learning and within that is Deep Learning.AI is all about making machines intelligent.Machine Learning is the method of computation (algorithms) that makes the machines smarter without them specifically being programmed.All Machine Learning is Artificial Intelligence but not all Artificial Intelligence is Machine Learning.Deep Learning is a subset of Machine Learning which focuses more narrowly on a subset of Machine Learning techniques that require “thought.”Cognitive AIMachine LearningDeep LearningTechnologyMachine LearningDeep LearningNatural Language GenerationSpeech RecognitionVirtual AgentsDecision ManagementDeep LearningBiometricsComputer VisionArtificial neural networksBayesian networksSupport vector machinesRadial basis function networksSelf-organizing (Kohonen) mapsProbabilistic and clustering treesEvolutionary and genetic algorithmsFuzzy logic and neuro-fuzzy machinesArtificial Neural NetworksConvolutional neural networksRecurrent neural networksDeep Neural NetworksAutomatic Speech recognitionImage recognitionNatural Language ProcessingCapabilitiesSimulates human thought processes finds patterns in data to assist humans to find solutions to complex problemsFinds patterns in data using advanced analytical approach and model buildingLeverages pattern-matching techniques to analyze vast quantities of unsupervised dataPurposeAugment human capabilities and automate processesProvide systems the ability to automatically learn and improve from experience without being explicitly programmedEnables machines to process data with a nonlinear approach.IndustriesTransportation, Healthcare, Finance, Manufacturing industries, Retails, Advertising, Agriculture, Automobiles, Aerospace, Genomics, Pharmaceutical, Cybersecurity,Transportation, Healthcare, Finance, Manufacturing industries, Advertising, Agriculture, Automobiles, Aerospace, LogisticsGenomics, Pharmaceutical, Cybersecurity, Agriculture, Automobiles, Aerospace, Logisticsge: https://www.oracle.com/java/index.htmlFirst release: 1995, latest release: 2014OS: Cross-platformJava is an object-oriented programming language that follows the principle of WORA (“write once, read everywhere”). It runs on all platforms without any additional recompilation due to Virtual Machine Technology. Some more advantages of Java is that this language is easy to use and easy to debug. However, in term of speed, it loses against C++. Java AI programming is a good solution for neural networks, NLP and search algorithms.Features:In-build garbage collection;Portable;Easy to code algorithms; Scalability.AIMLAbout: https://en.wikipedia.org/wiki/AIMLInitial release: 2001, latest release: 2011Extended from: XMLAIML (Artificial Intelligence Markup Language) is a dialect of XML used to create chatbots. Due to AIML, one can create conversation partners speaking a natural language.The language has categories showing Which primary programming languages can be used for AI?PythonHomepage: https://www.python.org/Python is an interpreted, high-level, general-purpose programming language.Features:Development time is less (as compared to Lips, Java or C++);It has large variety of libraries;High level syntax;It supports functional, object-oriented and procedural styles of programming;It is good for testing algorithms before implementation.C++Homepage: https://isocpp.org/C++ is one of the fastest programming languages in the world and it is a major advantage for AI.Features:It has high level of abstraction;It is good for high performance;It organizes data according to object oriented principles;LispHomepage: http://lisp-lang.org/Lisp, being the second oldest programming language in the world (after Fortran), still holds a top position in AI creating due to its unique features.Features:It has fast prototyping capabilities;It supports symbolic expressions;It has automatic garbage collection which actually was invented for the Lisp language;It has library of connection types including dynamically-sized lists and hash tables;Provides efficient coding due to compilers;Provides interactive evaluation of components and recompilation of files while the program is running.PrologThe name of Prolog speaks for itself; it’s one of the oldest logic programming languages. If we compare it with other languages, we can see it is declarative. It means that the logic of any program will be represented by rules and facts. Prolog programming for artificial intelligence can create expert systems and solving logic problems. Some scholars claim that an average AI developer is bilingual – they code both Lisp and Prolog.Features:pattern matching;tree-based data structuring;good for rapid prototyping;automatic backtracking.JavaHomepaa unit of knowledge; patterns of possible utterance addressed to a chatbot, and templates of possible answers.Examples of popular AI ImplementationOne of the predictions by Gartner said -“By the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels and partners.Multichannel customer experience will take a big leap forward with seamless, two-way engagement between customer digital assistants and customers in an experience that will mimic human conversations, with both listening and speaking, a sense of history, in-the-moment context, tone, and the ability to respond.”We have already seen in mid-2018, Google coming up with a smarter voice assistant.Hear Google's virtual assistant mimic a human voice to book an appointment by phoneAI is not just limited to IT or technology industry it is widely being used in other areas such as medical, business, education, law, and manufacturing.In the following, we have a few intelligent AI solutions that we are using today:1. SiriWe all know about Apple’s voice assistant, Siri, it uses machine-learning technology in order to get smarter and capable-to-understand natural language questions and requests. It is one of the most iconic examples of machine learning abilities of gadgets.2. TeslaNot just smartphones, but automobiles are getting smarter as well as they shift towards Artificial Intelligence. Tesla is one such example in the automobile industry. It has features like self-driving, predictive capabilities and so on. Tesla is getting smarter day by day through over the air updates.3. CogitoThis company is a synthesis of machine learning and behavioral science to enhance customer collaboration for phone professionals. It is applicable to millions of voice calls that take place on a daily basis. The AI solution provides real-time guidance by analyzing the human voice.4. NetflixNetflix is a popular content-on-demand service which uses predictive technology to recommend its consumers’ with respect to their interests, choices, and behavior. It is getting intelligent day by day.5. Nest (Google)Nest, being on the most successful AI startups was acquired by Google in 2014. Nest Learning Thermostat makes the use of behavioral algorithms to save energy based on your behavior and schedule. It takes about a week to program itself and then learns the temperature you like. If nobody is at home, it tends to turn off automatically to save energy.6. EchoAmazon Echo, helps you search for information on the web, schedule appointments, control household equipment, acts as a thermostat, answers to questions, reads audio books, update you about traffic and weather, gives you info on local businesses. All of these just by calling out “Alexa” (Amazon’s voice service). It is getting smarter and adding new features.Popular AI Platforms and ToolsAI is being adopted by organizations rapidly. It has become more important than ever to know the options AI offers in terms of tools, libraries, platforms and so on. Here we have mentioned a few of the platforms that support AI.1. Azure Machine LearningAzure Machine Learning is a cloud-based service that provides tooling for deploying predictive models as analytic solutions. Apart from these, it can also be used to test machine learning models, run algorithms, and create recommender systems. For people lacking advanced programming skills and would like to get into machine learning should check this out.2. Amazon Web Services (AWS)Amazon Web Services has the broadest and deepest set of machine learning and AI services. Pre-trained AI services are available for computer vision, recommendations, forecasting, etc.. You can also use Amazon SageMaker to build a model quickly and then train and deploy machine learning models using all popular open-source frameworks.3. Google Cloud Platform (GCP)With enterprise AI on the rise, speed and agility are crucial to keeping competitive, yet custom solutions can be time-consuming, complex, and costly. With Google Cloud AI solutions, you can quickly and easily apply solutions across your work streams or combine our technology with vendors you already work with. Whether you’re looking to classify images and videos automatically or deliver recommendations based on user data, you can use Google Cloud AI Solutions to drive insights and improve customer experiences.Latest trends in AIDuring 2018, there was a rise in the platforms, tools, and applications based on Machine Learning and AI. These technologies had a good impact not only with the software and internet industry but also other industries like healthcare, manufacturing, automobile and so on.Here are some of the AI trends to watch out for in 2019:1. The rise of AI-enabled chipsUnlike software, AI is heavily dependent on specialized processors that complement the CPU. In 2019, Intel, NVIDIA, AMD, ARM, Qualcomm, and other major chip manufacturers will manufacture specialized chips that will speed up the execution of AI-enabled applications. These chips will be optimized to perform computer vision application, natural language processing, and speech recognition.2. The convergence of IoT and AIArtificial Intelligence will meet IoT at the edge computing layer in 2019. Industrial IoT which can be considered as the top use case for artificial intelligence, can perform outlier detection, root cause analysis and predictive maintenance of the equipment. Most of the models which are trained in the public cloud will be deployed at the edge.IoT will eventually become the biggest driver of artificial intelligence in the enterprise. We will get to see edge devices getting equipped with the special AI chips based on FPGAs and ASICs.3. Interoperability among neural networksChoosing the right framework while developing neural network models has always been a critical challenge. Developers and data scientists need to pick the right tool from a bucket full of choices which include Caffe2, PyTorch, Apache MXNet, Microsoft Cognitive Toolkit, and TensorFlow. It is tough to port a model, which is already trained and evaluated in a specific framework, to another framework. Basically, there is a lack of interoperability among neural network toolkits.Microsoft, Facebook, AWS have collaborated with each other to build Open Neural Network Exchange (ONNX). It helps to reuse trained neural network models across multiple frameworks.4. Automated machine learningAutoML is going to change the face of ML-based solutions. Business analysts and developers will be empowered to evolve machine learning models which will be able to address complex scenarios without going through the typical process of training ML models.Which are the leading firms in the field of Artificial Intelligence?OccamzRazorOccamzRazor is mapping the human Parkinsome — a dynamic knowledge map that reveals the hidden mechanisms and new treatments of Parkinson’s Disease.Umbo Computer VisionUmbo Computer Vision is an artificial intelligence company building autonomous video security systems for businesses and organizations.GamayaGamaya addresses the need to increase efficiency and sustainability of large industrial farming, as well as the productivity and scalability of smallholder farming, by deploying the world’s most advanced solution for mapping and diagnostics of farmland.SpatialSpatial.ai is a location data company that uses conversations from social networks to understand how humans move and experience the world around them.TextioTextio is the augmented writing platform that tells you who will respond to your writing based on the language you have included in it and gives you guidance to improve it.Which Countries are leading the way in AI?There are two things that reveal how well a particular country is positioned to leverage the development pipeline.First one is the pool of available talent. Qualified professionals are necessary for any country to push AI forward. Some of the countries have also developed university programs on AI curriculum to develop more talent. Intellectual capital is a huge advantage when it comes to emerging technologies.The second one is the level of AI and digital activity that take place in the country. This also includes the amount of funding in circulation. All these countries are building the foundation to support the future of AI.Keeping these criteria in mind, the following countries are ahead in the race to rule the world with the help of AI:United States: The United States is leading with $10 billion in venture capital which has been funneled to AI. According to a report, there are almost 850,000 AI professionals in the United States which is definitely more than any other country. The top players - Google, Facebook, Amazon, Microsoft are investing heavily in Artificial Intelligence and the United States will soon have every resource necessary to become a global leader in automation.China: It is essential for China to push forward with AI in order to maintain the country’s economic growth. They have set aggressive targets for 2030. In a period of 5 years, there was growth by 190% in the number of patents that were granted, the effects of automation have been remarkably significant. According to estimates, AI could increase the economic growth of China by 1.6% by 2035.Japan: Japan can be called as the historic leader in robotics. Due to the unique feature of the economy of Japan, it can absorb a greater amount of automation than other countries. A study has rated the automation potential of the manufacturing sector of Japan at 71%, compared to the United States’ which is at 60%.Russia: By 2025, Russia’s intention is to turn 30% of the country’s military equipment robotic. Machine Learning and algorithms have already been leveraged by the country’s intelligence to project pro-Russia messaging into foreign media markets. Russia’s enthusiasm for AI has always been up and high.Estonia: Estonia is another country which has been manufacturing intelligent machines. According to Akamai’s 2017 report, this country ranks 27th in the world for the fastest internet and beats the United States as well. The country also has the third most startups per capita in Europe which leads to a lot of innovation and fundraising to support AI.What are the recent Landmarks in the development of AI?In the following we have mentioned some of the recent developments in AI, which demonstrates how technology is advancing:1. AI in Smartphone AppsIn most of the smartphone apps which are designed for everyday consumers, you will find that AI is making an appearance. According to Gartner, by 2022, on-device AI capabilities will rise from 10% to 80%. This will also give an opportunity to the developers to deploy AI in all types of apps. Here are AI is currently being used:Google Assistant – You can get access to your assistant by holding down the home button on your Android phone, or saying aloud, “Okay Google.” You can also send messages, check appointments, play music, and a host of other things hands-free.Socratic – Socratic is an app which helps you in solving math. It is a smart tutoring app where it solves problems by analyzing a picture of the math problem.Microsoft Pix – Using AI, Microsoft Pix helps in capturing up to 10 frames per shutter click and then selects the best three among the frames. It deletes the rest and thereby saves you storage space.2. AI in FinTechFinTech is another area where we have seen a lot of disruptive technology in the last decade. Artificial Intelligence is another disruptor in this particular sector. Artificial Intelligence has been able to reduce processing time.Chatbots are being used by the bank to replace the traditional customer service suite. There have been apps developed to connect financial accounts with Facebook Messenger (for example Trim) allowing customers to ask questions or place a complaint, make transactions or get reports via the app.Fraud Detection is a crucial process in this sector. AI-based Applications like Pixmettle has developed enterprise level AI tools to help flag things like duplicate expenses and corporate policy violations.3. AI-Based CybersecurityAs the use of technology increases, the potential threats to sensitive information also increases. There has been a demand for AI solutions to boost cybersecurity. It is expected by professionals that AI-based cybersecurity will accelerate incident detection, improve incident response, identify and communicate risk and also maintain optimum situational awareness.Google’s parent company. Alphabet had introduced Chronicle, which is basically a cybersecurity intelligence platform. It is a powerhouse for cybersecurity data and allows rapid search and discovery.4. AI Robots Learn Through ObservationArtificial Intelligence “learns” when humans or machine learning trains and a bot learns by processing data. For example, if you tend to go to the same place every day morning for coffee, the bot might learn the trend and automatically start to look for traffic or weather conditions and provide you with an estimated driving time daily.NVIDIA demonstrated a robot which can perform tasks in a real-world setting by watching how the tasks are done. The robot has the ability to learn through observing the actions of humans.Along similar lines, a bot program called Alpha Go learnt itself advanced strategies to play the game GO without any training from humans. This also highlights the fact that AI is able to be independent of human knowledge.learned5. AI Diagnostics for X-RaysIn medical technology, AI has been very effective in areas such as diagnostics and so on. Certain cases require a human operator to be able to read and interpret tests or imaging results but AI-based medical technology has left with lesser involvement and has also reduced human error.In a recent development, machine learning was used to do machine learning, computer-generated x-rays were used to augment AI training.“We are creating simulated x-rays that reflect certain rare conditions so that we can combine them with real x-rays to have a sufficiently large database to train the neural networks to identify these conditions in other x-rays.” – Shahrokh Valaee
Rated 4.5/5 based on 12 customer reviews
Rated 4.5/5 based on 15 customer reviews