Artificial intelligence AI
Unsupervised Machine Learning
Unsupervised learning does not rely on trained data sets to predict theoutcomes but it uses direct techniques such as clustering and association inorder to predict outcomes. Trained data sets mean the input for which theoutput is known.
Supervised Machine Learning
Supervised Learning is like teacher-student learning. The relation between theinput and the output variable is known. The machine learning algorithms willpredict the outcome on the input data which will be compared with the expectedoutcome.The error will be corrected and this step will be performed iteratively tillan acceptable level of performance is achieved.[image source]
Artificial Intelligence and Data Mining
Artificial Intelligence is the study to create intelligent machines which canwork like humans. It does not depend on learning or feedback, rather it hasdirectly programmed control systems. The AI systems come up with the solutionsto the problems on their own by calculations.The data mining technique in mined data is used by the AI systems for creatingsolutions. Data mining serves as a foundation for artificial intelligence.Data mining is a part of programming codes with information and data necessaryfor AI systems.
Artificial Intelligence and Machine Learning
A large area of Artificial Intelligence is Machine Learning. By this, we meanthat AI uses machine learning algorithms for its intelligent behavior. Acomputer is said to learn from some task if the error continuously decreasesand if it matches the performance as desired.Machine learning will study algorithms that will perform the task ofextraction automatically. Machine learning comes from statistics but it is notactually. Similar to AI, machine learning also has a very broad scope.
Data Mining vs Machine Learning
[image source]Data mining and Machine Learning fall under the same world of Science. Thoughthese terms are confused with each other, there are some major differencesbetween them.
5) Method: Machine Learning uses the data mining technique to improve its
algorithms and change its behavior to future inputs. Thus data mining acts asan input source for machine learning.Machine learning algorithms will continuously run and improve the performanceof the system automatically, and also analyze when the failure can occur. Whenthere is some new data or change in the trend, the machine will incorporatethe changes without the need to reprogram or any human interference.Data mining will perform analysis in the Batch format at a particular time toproduce results rather than on a continuous basis.
6) Nature: Machine Learning is different from Data Mining as machine learning
learns automatically while data mining requires human intervention forapplying techniques to extract information.
10) Applications: Machine learning algorithm needs data to be fed in a
standard format, due to which the algorithms available are much limited. Toanalyze data using machine learning, data from multiple sources should bemoved from native format to standard format for the machine to understand.It also requires a large amount of data for accurate results. This is anoverhead when compared to data mining.
Data Mining, Machine Learning Vs Deep Learning
[image source]Machine Learning comprises of the ability of the machine to learn from traineddata set and predict the outcome automatically. It is a subset of artificialintelligence.Deep Learning is a subset of machine learning. It works in the same way on themachine just like how the human brain processes information. Like a brain canidentify the patterns by comparing it with previously memorized patterns, deeplearning also uses this concept.Deep learning can automatically find out the attributes from raw data whilemachine learning selects these features manually which further needsprocessing. It also employs artificial neural networks with many hiddenlayers, big data, and high computer resources.Data Mining is a process of discovering hidden patterns and rules from theexisting data. It uses relatively simple rules such as association,correlation rules for the decision-making process, etc. Deep Learning is usedfor complex problem processing such as voice recognition etc. It usesArtificial Neural Networks with many hidden layers for processing.At times data mining also uses deep learning algorithms for processing thedata.
Artificial intelligence (AI) :-
Artificial intelligence (AI) is defined as intelligence exhibited by anartificial entity. Such a system is generally assumed to be a computer.Although AI has a strong science fiction connotation, it forms a vital branchof computer science, dealing with intelligent behaviour, learning andadaptation in machines. Research in AI is concerned with producing machines toautomate tasks requiring intelligent behavior. Examples include control,planning and scheduling, the ability to answer diagnostic and consumerquestions, handwriting, speech, and facial recognition. As such, it has becomea scientific discipline, focused on providing solutions to real life problems.AI systems are now in routine use in economics, medicine, engineering and themilitary, as well as being built into many common home computer softwareapplications, traditional strategy games like computer chess and other videogames.
Difference between AI and Machine Learning
These two terms are correlated to each other and use interchangeably. For thisreason, it creates lots of confusion in many of us brain.But, they are not the same term. Let’s understand with an example to clear ourwrong perception.If AI is – tree then ML is – one of the main Branch of treeAs we know, Artificial Intelligence is ‘Smart’ then who makes it smart? Offcourse the ‘Data’ and who feeds that data to AI? Yes, you are absolutely rightMachine Learning provide the data to AI.Machine Learning is a subset of Artificial Intelligence. We can say that thebackbone of AI is Machine Learning.Without Machine Learning machine can’t able to perform any task on its own.Therefore you have to get some knowledge of ML if you opt for ArtificialIntelligence as a career option.If you want to learn about Machine Learning and want to know various ML careerpaths check out this article; Machine Learning Career Path [Things You ShouldKnow]To become successful in the Artificial Intelligence career path you must havea clear understanding of how machine learns from scratch.Without ML AI is completely zero i.e. without ML there is a point of AI
Artificial Intelligence and Machine Learning in Industry 4.0
Before the role of AI SERVICES & ML SOLUTIONS in Industry 4.0, we should knowwhat it is? And how it evolved?What is Industry 4.0?Success in any department can be achieved through practice, hard work, andplanning. The industrialization has been a great success worldwide.Transformational effects appear and we live in a world that is interconnected.Here are the breakdowns of industrial development and the great changes inrelated categories.Source: engineering.com> Industry 1.0: The Industrial Age began due to rapid advances in science and> mechanization, e.g. Power mills, steam engines, and railway lines.>> Industry 2.0: signified by the revolutionary Ford Company and automotive,> e.g. Introduction of heavy production of assembly-line cars and electricity.>> Industry 3.0: The invention of semiconductor features and the popularity of> computers, e.g. Automation in manufacturing, construction, steel, oil> refineries and IT.>> Industry 4.0: Pattern change suggested and predicted by robotics, e.g.> Human-machine interaction, cyber-physical systems, space tourism and> exploring driverless cars.Creative confluence:Science is about precise principles, and technology is the goal of success.Business acumen should contribute to technology implementation. Above all,innovative creations should have effective applications. In recent years,there has been a lot of buzz around Artificial Intelligence (AI) and theInternet of Things (IoT) & AI SERVICES.To read about: Top 10 best companies that use Artificial Intelligence (AI) toaugment manufacturing processes in the era of Industry 4.0IoT mainly deals with big data, predictive analytics, and cloud computing. Itsmission is to revolutionize digital services using frameworks, platforms andconnectivity architectures. The digitization of businesses and governments isexpected to bring greater transparency and accountability. Future projectionsalso include smart cities, adaptive cruise control and a brain/computerinterface.This leads to an exciting world of artificial intelligence, machine learning,cybernetics, neural networks, and deep learning. However, IoT does not stopwith office automation and advanced communication.Smartness is also being extended to the home, transportation, and industrialmanufacturing. For example, humans and cyber-physical systems in a smartfactory interact on the cloud. Remote monitoring of processes and decisionsusing big data analytics is also possible.What is Artificial intelligence?> Artificial intelligence, as the name suggests, illustrates the ability of> machines to simulate human mental prowess. However, this intelligence is not> limited to machines — it also applies to software systems; Hence, the> differentiation of the boundary between robotics and machine learning or AI> & AI SOLUTIONS. The three main components are, therefore, machine or system,> software and Internet connectivity (cloud and big data).Machine:The most popular example of this is undoubtedly robots and robotics. Thanks toscience fiction and movies, everyone knows about them. Mechanical engineeringtechniques are used to convert metal into cars and human bodies. They areequipped with electrical circuits and electronic chips for control andcommand.