What Is Artificial Intelligence and it's Future

As it stands out today,Artificial intelligence elucidates simulation of human intelligence bymachines, particularly computer systems. AI programming focuses on three basiccognitive skills which are learning, reasoning and self-correction.

● Learning processes is theaspect of AI programming which focuses on acquiring data and creating rules forhow to turn the data into actionable information. These rules are calledalgorithms, and they provide the computing devices stepwise instructions on howto complete a specific task.

● Reasoning processes is theaspect of AI programming that focuses on choosing the right algorithm to reacha desired outcome.

● Self-correction processes is aprocess of AI

programming designed toconstantly fine-tune algorithms and ensure that they provide the most accurateresults possible.

Typically, AI systems demonstrate at least some behaviours which are associated with human intelligence; thesebehaviours are planning,learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesserextent, social intelligence and creativity.


● The roots of computing dates back to the Logic Theoristprogram which was presented at the Dartmouth Summer scientific research onArtificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in1956. The program was developed by Allen Newell, Cliff Shaw, and Herbert Simon.

● During this historic conference, McCarthy, imagining anefficient  collaborative effort, broughttogether top researchers from various fields for an open ended discussion onAI, the term which he coined at the very event. Sadly, the conference fell shortof McCarthy’s expectations; people came and went as they pleased, and there wasfailure to agree on standard methods for the sphere. Despite this, everyonewhole-heartedly. aligned with the sentiment that AI was achievable. Theimportance of this event can not be undermined because it catalyzed thefollowing next twenty years of AI research.

● Next Benchmark within the development of Artificialintelligence was Newell and Simon’s General convergent thinker and JosephWeizenbaum’s ELIZA programme showed promise toward the goals of problem solvingand therefore the interpretation of language respectively. These, together withthe support of then leading researchers, convinced government agencies like theDefense Advanced Research Projects Agency

●     (DARPA) to fund AI researchat several institutions. The government was particularly inquisitive about amachine that might transcribe and translate oral communication in addition tohigh through processing.

● In the 1980’s, AI was reignited by two sources: an expansionof the algorithmic toolkit, and a lift of funds. John Hopfield and DavidRumelhart popularized “deep learning” techniques which allowed computers tofind out using experience. On the opposite side Edward Feigenbaum introducedexpert systems which mimicked the choice making process of an individual'sexpert.

● Under the Expert systems, the program would ask an expert inan exceedingly field the way to respond during a given situation, and once thiswas learned for virtually every situation, non-experts could receive advicefrom that program.

● The Japanese government heavily funded expert systems andother AI related endeavors as a part of their Fifth Generation Computer Project(FGCP). From 1982-1990, they invested $400 million dollars with the goals ofrevolutionizing computer processing, implementing logic programming, andimproving AI.

● During the 1990s and 2000s, many of the landmark goals ofartificial intelligence had been achieved. In 1997, reigning world chesschampion and grandmaster Gary Kasparov was defeated by IBM’s Deep Blue, a chessplaying computer program. This highly publicized match was the first time areigning world chess champion lost to a computer and served as a large steptowards an artificially  deciding programwithin the same year. In the same year, speech recognition software, developedby Dragon Systems, was implemented on Windows. This was another great discoverybut within the direction of the spoken language interpretation endeavor. Itseemed that there wasn’t a problem machines couldn’t handle. Even human emotionwas victimized as evidenced by Kismet, a robot developed by Cynthia Breazealthat might recognize and display emotions.


Artificial intelligence can broadly becategorized into two categories: Narrow AI and General AI

●    NarrowAI is what we see all around us in computers today: intelligent systems thathave been taught or have learned the way to carry out specific tasks withoutbeing explicitly programmed a way to do so.

●    Thissort of machine intelligence is obvious within the speech and languagerecognition of the Siri virtual assistant on the Apple iPhone, within thevision-recognition systems on self-driving cars, or  within the recommendation engines thatsuggest products you would possibly like based on what you acquired in thepast. Unlike humans, these systems can only learn or be taught a way to do defined tasks, which is why they are called narrow AI.

●     General AI is a more flexible variety of AIwhich is capable of learning a way to carry out vastly different tasks,anything from haircutting to assembling spreadsheets, or reasoning about a widevariety of topics based on its accumulated experience. It is a more idealisticform of AI which may only be seen in movies or discussed in academic spaces.Artificial General Intelligence is greatly associated with ‘superintelligence’i.e. any intellect that greatly exceeds the cognitive performance of humans invirtually all domains of interest. Artificial intelligence can broadly becategorized into two categories: Narrow AI and General AI.

●    NarrowAI is what we see all around us in computers today: intelligent systems thatare taught or have learned a way to do specific tasks without being explicitlyprogrammed the way to do so. This sort of machine intelligence is clear fromthe speech and language recognition of the Siri virtual assistant on the AppleiPhone, in the vision recognition systems on self-driving cars, or within therecommendation engines that suggest products you may like have supported onwhat to procure in the past. Unlike humans, these systems can only learn or betaught how to do defined tasks, which is why they are called narrow AI.

●    GeneralAI is a more flexible variety of AI which is capable of learning how to carryout a variety of different tasks, anything from haircutting to assemblingspreadsheets, or reasoning a few wide selection of topics based on itsaccumulated experience. It is a more idealistic form of AI which can only beseen in movies or discussed in academic spaces. Artificial General Intelligenceis greatly related to ‘superintelligence’ i.e. any intellect that greatlyexceeds the cognitive performance of humans in virtually all domains ofinterest.



The advantages of Artificial intelligence areas follows:

● Neural networks and deep learning AI technologies are quicklyevolving, primarily because AI processes large amounts of knowledge much fasterand makes more accurate predictions than human beings.

● Since a huge volume of data being created on a daily basis wouldbury a human researcher, AI applications that use machine learning  have the ability to take that data andquickly turn it into actionable information.

● AI has mass market potential to perform different activitiesacross a variety of economic sectors. Because of this versatility, AI can bedeployed across industries.

● AI is inclusive and it augments the capabilities of differently abledindividuals

● Putting machines into tasks that may be a danger to humans paysoff well. For example, enabling machines to accommodate natural calamity mayend up in faster recovery and lesser pressure on human teams.

On the other hand, artificialintelligence is not completely above errors and limitations. Some of thedisadvantages of Artificial intelligence have been listed below:-

● The difficulty with machines is that it functions as programmed.While artificial intelligence has made machines capable of learning over time,they can not learn to think outside the box. A machine will always analyze asituation in terms of already fed data and previous experiences. It isdifficult for a machine to be creative in its approach.

● AI is replacing the bulk of repetitive tasks with bots. Therequirement for human intervention is going down as businesses look towardsmore error-free and risk-free work. Augmenting this; machines bring speed withthe killing of many job opportunities that were once prevalent. Job roles likesimple data entry or rebuke customers within the first touch point i.e. chatsupport are now handled by bots which will know more effectively andround-the-clock.

● Another human feature that is hard to include inside a machine is ethics.Morality is absent in an exceedingly machine and it is also hard tostyle and convey through technology. AI can helpbusinesses hamper the time taken to finish a humdrum task but expecting amachine to follow ethical values is vague as of now.

● Machines do not have the ability to bond with humans, becausethey do not have emotions or sympathy. While machine learning and NLP hashelped brands set up initial customer support through bot-enabled chat systems,they still require a human in order to intervene at some point to resolve anongoing issue. If the whole of it is left to bots, customer experience acrossthe globe will not be that optimistic. Bots can do the initial touch basing


Automation:- When paired with AI technologies, automation tools can expand thedegree and kinds of tasks performed. An example is robotic process automation(RPA), a type of software that automates repetitive, rules-based processingtasks traditionally done by humans. When combined with machine learning andemerging AI tools, RPA can automate bigger portions of enterprise jobs,enabling RPA's tactical bots to pass along intelligence from AI and redressprocess changes.

Machine learning:- The science of getting a computer to act without programmingis known as machine learning. Deep learning, a subset of machine learning that,in very simple terms, can be thought of as the automation of predictiveanalytics. There are three forms of machine learning algorithms:

Supervised learning:- Data sets are labeled to ensure that patterns are oftendetected and used to label new data sets.

Unsupervised learning:- Data sets aren't labeled and are sorted in step withsimilarities or differences.

Reinforcement learning:- Data sets areessentially not  labeled but, afterperforming an action or several actions, the AI system is given feedback.

●Machine vision:- This technology gives a machine the flexibility to work out.Machine vision captures and analyzes visual information by employing a camera,analog-to-digital conversion and digital signal processing. It is oftencompared to human eyesight, but machine vision isn't bound by biology and couldbe programmed to compute through walls, for instance. It is employed in avariety of applications from signature identification to medical imageanalysis. Computer vision, which is centered on machine-based image processing,is commonly conflated with machine vision.

●Natural language processing:- It is the processing of human language by acomputer program. One amongst the oldest and best-known evidence of NLP is spamdetection, which looks at the topic line and text of an email and decides ifit's junk. Current approaches to NLP are centered on machine learning. NLP tasksinculcates text translation, sentiment analysis and speech recognition.

●Robotics:- This field of engineering focuses on the look and manufacturing ofrobots. Robots are often preferred to perform tasks that are difficult forhumans to perform or perform consistently. For example, robots are utilized inassembly lines for car production or by NASA to maneuver large objects inspace. Researchers use machine learning to create robots which can interact insocial settings.

●Self-driving cars:- Autonomous vehicles use a blend of computer vision, imagerecognition and deep learning to make automated skill at piloting a vehiclewhile staying in an exceedingly given lane and avoiding unexpectedobstructions, like pedestrians.


AI in healthcare:

The largest bets are on improving patientoutcomes and reducing costs. Companies are applying machine learning to formbetter and faster diagnoses than humans. One of the best-known healthcaretechnologies is IBM Watson. It understands natural language and may answerquestions asked of it. The system mines patient data and other available datasources to make a hypothesis, which it then presents with a confidence scoringschema. Other AI applications include using online virtual health assistantsand chatbots to assist patients and healthcare customers find medicalinformation, schedule appointments, understand the billing process and completeother administrative processes. An array of AI technologies is additionallybeing employed to predict, fight and understand pandemics like COVID-19.

●AI in business:  

Machine learning algorithms are beingintegrated into analytics and customer relationship management (CRM) platformsto uncover information on a way to better serve customers. Chatbots areincorporated into websites to supply immediate service to customers. Automationof job positions has also become a discussion point among academics and ITanalysts.

●AI in education:

AI can automate grading, giving educatorslonger time. It can assess students and adapt to their needs, helping them workat their own pace. AI tutors can provide additional support to students,ensuring they can be on track. Additionally, it could change where and howstudents learn, perhaps even replacing some teachers.

●AI in finance:

AI in personal finance applications, likeIntuit Mint or TurboTax, is disrupting financial institutions. Applicationslike these collect personal data and supply financial advice. Otherprograms,  like IBM Watson, are appliedto the method of shopping for domestic needs. Today, AI software performs muchof the trading on Wall Street.

●AI in law:

The invention process sifting throughdocuments in law is commonly overwhelming for humans. Using AI to assist andautomate the legal industry's labor-intensive processes is saving time andimproving client service. Law firms are using machine learning to explain dataand predict outcomes, computer vision to classify and extract information fromdocuments and linguistic communication processing to interpret requests forinformation.

AI in manufacturing:

Manufacturing has been at the forefront ofincorporating robots into the workflow. For instance, the commercial robotsthat were once programmed to perform single tasks and separated from humanworkers increasingly function as cobots: Smaller, multitasking robots thatcollaborate with humans and tackle responsibility for more parts of the task inwarehouses, factory floors and other workspaces.

●AI in banking:

Banks are successfully employing chatbotsto  ensure their customers are aware ofservices and offerings and to handle transactions that do not require humanintervention. AI virtual assistants are being employed to boost and cut theprices of compliance with banking regulations. Banking organizations are  using AI to boost their decision-making forloans, and to line credit limits and identify investment opportunities.

●AI in transportation:

Additionally to AI's fundamental role inoperating autonomous vehicles, AI technologies are employed in transportationto manage traffic, predict flight delays, and make ocean shipping safer andmore efficient.

●AI and machine learning are at the zenith of the buzzword list security vendorsuse today to differentiate their offerings. Those terms also represent trulyviable technologies. Artificial Intelligence and machine learning in cybersecurity products are adding real value for security teams searching for waysto spot attacks, malware and other threats.

●Organizations use machine learning in security information and event management(SIEM) software and related areas to detect anomalies and identify suspiciousactivities that indicate threats. By analyzing data and using logic to identifysimilarities to known malicious code, AI can provide alerts to new and emergingattacks prior to human employees and previous technology iterations.

●As a result, AI security technology both dramatically lowers the quantity offalse positives and provides organizations longer to counteract real threatsbefore damage is  inevitable. Thematuring technology is playing an enormous role in helping organizations defendagainst cyber attacks.




● There are too many breakthroughs to place together a definitivelist, but some highlights include: In 2009 Google showed it is possible for itsself-driving Toyota Prius to finish over 10 journeys of 100 miles each, settingsociety on a path towards driverless vehicles.

● In 2011, the PC system IBM Watson made headlines worldwide whenit won the US quiz show Jeopardy!, beating two of the most exceptional playersthe show had ever produced. To win the show, Watson used NLP and analytics onvast repositories of information that it processed to answer human-posedquestions, often in an exceedingly fraction of a second.

● In 2012, another breakthrough heralded AI's potential to tacklea mess of recent tasks previously thought of as too complex for any machine.That year, the AlexNet system decisively triumphed within the ImageNet LargeScale Visual Recognition Challenge. AlexNet's accuracy was specified to anextent that it halved the error rate compared to rival systems within theimage-recognition contest.

● The subsequent demonstration of the efficacy of machine-learningsystems that caught the public's attention was the 2016 triumph of the GoogleDeepMind AlphaGo AI over a chess grandmaster. In Go, an ancient Chinese gamewhose complexity stumped computers for many years. Go has about 200 moves perturn, compared to about 20 in Chess. Over the course of a game of Go, there issuch a large amount of possible moves that rummaging through each of themearlier to spot the most effective play is simply too costly from a

computational point of view.Instead, AlphaGo was trained to play the sport by taking moves played by humanexperts in 30 million Go games and feeding them into deep-learning neuralnetworks.

● 2020 was the year in which an AI system seemingly gained thepower to jot down and talk the way a human does, about almost any topic you mayconsider. The system in question, known as Generative Pre-trained Transformer 3or GPT-3 for brief. It is a neural network trained on billions of Englishlanguage articles available on the open web.



● In India, Weak Artificialintelligence systems are not only widelyused but it is being readily accepted for assisting different human actions. However, in majorcommercial sectors anddifferent fields of study, Strong AI has still not been able to penetrate in India ascompared to developednation-states. That can be attributed to a range of factors; firstly India has a huge population with the workforcebeing its greatest capital in domestic and international markets. Now because of this huge workforce spread acrossdifferent sectors of economicactivity, the cost of labour in India is relatively

cheaper than the cost of automated complex machines.It would not only be a challenge for companies working in developing countries like India tobring such complex systemsbut also to maintain them considering the kind of bilateral trade fluctuations and deficits our country goes throughevery fiscal year. However,Strong AI can also solve the problem of skilled and efficient labour and can maximise productivity which is crucial fora huge market like India.

● In a post-pandemic world, strong AI can be of great potential in India if her policy making is such that itcan drasticallyincrease the buying potential of the people. Only then can productivity be enhanced, in order tocater to the demand-supplychain and subsequently, the use of such complex systems in different sectors can be increased. In the longerrun, the demographic trendsin India will only stabilise and the huge workforce in terms of numbers will have to be shiftedfrom a quantitative to aqualitative one. Not only will labour be more skilled and efficient, there is a great possibility that the concept ofwork itself will break free from the current binaries to a more fluid one. In such a time, strong AI and machinedeveloped understanding of complex human problems will be inevitable for sustained growth and development.

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