Promises and drawbacks of machine learning
Apžvalga
Machine learning yra specialus algoritmo tipas, kuris gali mokytis iš duomenų ir prognozuoti,,en,yra ir spąstų, kuriuos reikia atidžiai suprasti,,en,Aš kalbėsiu apie pažadus ir duobes, kurias mašininis mokymasis pateikia ant stalo,,en,Kas yra mašininis mokymasis,,en,Prieš gilindamasis į temą,,en,nepaprastai svarbu žinoti ką,,en,iš tikrųjų yra,,en,Tai yra,,en,kuris orientuotas į mokymąsi skaičiuojant ir atpažįstant pateiktų duomenų modelius,,en,Dabar jis naudojamas kuriant tokias mašinas, kurios pačios gali priimti sprendimą naudodamos daugybę sudėtingų algoritmų,,en,Naudojant,,en,mašininio mokymosi algoritmai,,en,mašinos galės įgyti žinių,,en,žinant skirtingus dalykus tyrinėjant realų pasaulį,,en,užduokite klausimus apie įgytas žinias ir dar daugiau,,en. As we collect and get more data from various sources, the predictions can be made more appropriately. However, iš kitos pusės, there are pitfalls also which needs to be understood carefully. In this article, I will talk about the promises and pitfalls that machine learning brings to the table.
What is machine learning?
Before going deep into the topic, it’s extremely important to know what mašina mokymo actually is. It is a subpart of artificial intelligence which focuses on learning through computation and by recognizing the patterns of provided data. It is now used to create such machines which can take decision on their own with the help of many sophisticated algorithms.
Using machine learning algorithms, machines will be capable of acquiring knowledge, knowing different things by exploring the real world, ask questions regarding the knowledge they acquire and so much more. Šios galimybės padeda mašinai mąstyti,,en,suprasti,,en,ir panašiai,,en,net mokytis iš jų aplinkos,,en,rasti kiekvienos koncepcijos logiką,,en,numatyti ir atitinkamai numatyti,,en,Kaip veikia mašininis mokymasis,,en,Ši koncepcija iš tikrųjų nėra labai nauja,,en,Mašininis mokymasis yra ne kas kita, kaip algoritmų rinkinys, kuris gali pasimokyti iš pateikto duomenų rinkinio,,en,prognozės,,en,ant jų,,en,Duomenys ir prognozavimo tikslumas eina kartu,,en,taigi turint daugiau duomenų,,en,gauname tikslesnę prognozę,,en,tam nereikia jokių iš anksto nustatytų taisyklių, kad valdytų jo veikimą,,en,Ši koncepcija veikia nuolat,,en,Tai taikoma daugybei sudėtingų tipų,,en,algoritmai,,en,automatiškai duomenų rinkinyje, kad gautumėte geresnių rezultatų,,en,Šis nuolatinis ir kartojamas ciklas padeda kruopščiai analizuoti aplinką,,en, understand, and likewise, even learn from their surroundings, find the logic behind every concept, predict and then make a prediction accordingly.
How machine learning works?
This concept is not actually very new. Machine learning is nothing other than a set of algorithms which can learn from the given pool of data and make predictions on them. Data and the accuracy of the prediction go hand in hand, so with more data, we get a more accurate prediction.
As such, it doesn’t require any predefined rules to govern its operation. This concept works in a continuous manner. It applies many different types of sophisticated algorithms automatically on a set of data to get better results. This continuous and iterative cycle helps in analyzing the surroundings carefully, numatant teisingą tam tikros problemos sprendimą ir galiausiai priimant teisingą sprendimą,,en,Kodėl mašininis mokymasis yra toks svarbus,,en,Atsakymą į šį klausimą sieja keli veiksniai,,en,kurie yra pagrindiniai veiksniai, kad ši koncepcija būtų sėkminga,,en,Pažvelkime į šiuos veiksnius,,en,Duomenys, naudojami mokantis mašinoje,,en,naujų technologijų pagalba,,en,duomenų bazių valdymas,,en,didžiulį duomenų kiekį galima surinkti už labai mažesnę kainą,,en,Bendrovėms, naudojančioms šias sistemas, nereikia galvoti, kuriuos duomenis saugoti ir kuriuos reikėtų ištrinti,,en,Anksčiau tai buvo labai svarbus klausimas,,en,kadangi duomenys, kurie anksčiau neturėjo jokios reikšmės dabartinei situacijai, galėtų padėti priimti didelius sprendimus ateityje,,en,Bet su duomenų bazių sistema, tokia kaip Hadoop,,en.
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Why machine learning is so important?
The answer to this question lies in few factors, which are the main triggers to make this concept successful. Let’s have a look at these factors:
Data to be used in machine learning
Nowadays, with the help of new technologies for database management, a massive amount of data can be collected at a very less cost. The companies which use these systems don’t have to think about which data to keep and which should be deleted. This used to be a very important question previously, as the data which used to have no relevance to the present situation could help in taking big decisions in the future. But with database system like Hadoop, storage of data has become very easy. This vast pool of data helps algorithms to predict the outcomes of decisions accurately.
Advance of compute
The computation techniques are also advancing gradually after Moore’s law. Different companies like IBM, Nvidia and more, are carrying out several innovations to improve the way of computation. These advancements help to create great computation techniques for processing the data in a better manner.
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Sophistication in algorithms
This factor completely depends upon the data and computation technique. As the field of data management and computation management will flourish, the various ways of exploring the domain through algorithm also tend to do the same. The main work of these algorithms is to seek out different kinds of patterns, analyze them and give significant guidance to a stakeholder for making the proper decisions in less time frame. It also helps in reducing the cost incurred in making those decisions.
When these factors are optimized, they help in synthesizing a large number of data in information and knit many fragmented data into one source. This synthesized information can accelerate the performance of the future outcomes. Google uses the best computation technique and has a corpus of stored data. When it was facing problems in its image recognition for decades, they turned to the algorithm of machine learning and improvised it in just a few quarters.
Advantages of machine learning
Every business process can get benefits from data synthesis, nes kiekvienas procesas turi skirtingus padalinius, kurie turi savo duomenų rinkinį,,en,Kai šie duomenys sujungiami prasmingai ir per pagrįstą laikotarpį,,en,tada verslas gali priimti tinkamus sprendimus ir augti toliau,,en,sintetinti šių didžiulių duomenų telkinių neįmanoma nei asmeniui, nei grupei nustatytu laiku,,en,Mašininis mokymasis yra tarsi gelbėtojas šiose srityse,,en,nes tai yra idealus būdas panaudoti paslėptas perspektyvas,,en,Jis gali išgauti informaciją iš nesusijusių duomenų korpuso su nereikšmingu žmogaus įsikišimu,,en,Jis veikia mašinoje ir valdomas tik saugomais duomenimis,,en,Skirtingai nuo įprasto būdo, kuris keičia rezultatus, kai gaunami nauji duomenys,,en,mašininis mokymasis mokosi iš duomenų ir klesti keičiantis ir augant duomenų rinkiniams,,en. When these data are joined together in a meaningful way and in a reasonable time period, then a business can make proper decisions and grow further.
bet, synthesizing these huge pools of data is not possible by an individual or by a group in a fixed time frame. Machine learning is like a savior in these fields, as it is an ideal way to exploit the prospects which are hidden in big data. It can extract information from a corpus of unrelated data with negligible human intervention. It runs on a machine and is driven by only the data stored. Unlike the conventional way which changes the outcomes as the new data arrives, machine learning learns from the data and flourishes on changing and growing sets of data. It is a way to discover different patterns which are buried in a data set.
Also read –Influence of machine learning on supply chain management
What are the pitfalls?
Ideally, execution of this concept should bring growth exponentially but in reality, this concept also has some factors which can derail the growth line. These factors are discussed below.
Black box (For some algorithms)
Few approaches to algorithms are termed as black box depending upon the singular points of data and the understanding of the process. They sometimes create technical and cultural problems for an organization.
If a black box approach under performs, when the data is going through a significance change, then due to the lack of understanding, the system can be at risk. It’s very hard to explain why the model fails because it can set the organization’s growth towards a backward direction.
Selection of most appropriate algorithm
There is no master algorithm which is used as a standard for machine learning and which knows everything so, the selection process is very important. No algorithm can be perfect in different kinds of fields like anomaly detection, segmentation, generation of the different features, analytics, ir pattern matching.
In the present world, there are many algorithms and many different approaches which come with their set of pros and cons and serve a particular purpose. Choice of the wrong algorithmic tool can increase the cost instead of decreasing, so it’s very important to know about every feature of the algorithm and use the best one depending upon the environment. Geriausias būdas tai išspręsti yra naudoti daug skirtingų algoritmų kartu ir leisti skaičiavimams bei sistemoms nuspręsti, kurį ir kada naudoti,,en,Techninės skolos,,en,Šios sistemos gali kaupti techninę skolą laikui bėgant, nes jos nėra savaime optimizuojančios,,en,Techninės skolos gali pasireikšti įvairiais būdais, pavyzdžiui, vamzdynų džiunglėmis,,en,susipynimas,,en,nedeklaruotas klientas,,en,paslėptas atsiliepimas,,en,kilpos,,en,nepakankamai naudojamos priklausomybės nuo duomenų ir kt,,en,Jie gali sukelti sutrikimą ir nenumatytus rezultatus ir gali smarkiai sumažinti sistemos veikimą,,en,Tai galima išspręsti samdant matematikus ir inžinierius, kurie planuoja algoritmą taip, kad sumažintų šias skolas,,en,Žmogaus šališkumas,,en,Algoritmus pasirenka žmonės ir taip gali,,en,būti šališkas,,en.
Also read – Types of Artificial Intelligence – Let’s Explore
Technical debts
These systems can accumulate a technical debt over time as they are not self-optimizing in nature. Technical debts can show itself in many different ways like jungles of pipelines, entanglement, undeclared customer, hidden feedback, loops, data dependencies which are underutilized etc. They can result in obfuscation and unintended outcomes and can reduce the performance of the system drastically. This can be resolved by hiring mathematicians and engineers in a balance to plan the algorithm in a way as to reduce these debts.
Human biasness
The selection of algorithms is done by humans and can thus, be biased. This can lead to a situation of the wrong selection of an algorithm.
For example, a team whose members are graduated from the same school will have a tendency of choosing the same set of algorithms. So it’s best to inject your team with different kinds of algorithmic variety or employ many different algorithms together.
Kas yra ateitis?
Our world is slowly transforming itself with the help of new and evolving technologies. Machine learning will help in guiding the drive to your destination by providing sufficient aid in the decision-making process. It will not only help in reducing the costs of a company, but also show the right way to improve the quality of a business by taking all surveys and data on the account. It shows promising traits of providing a better solution in the future.
Explore – Articles on machine learning
Summary
Mašininis mokymasis yra tokia sąvoka, kuri susilaukė daug praktikų dėmesio ir greičiausiai pateisins sukūrimą,,en,Tai labai transformuojanti,,en,taigi jis gali dirbti su bet kokio verslo bet kokiu srautu,,en,Kiekviena organizacija, kuri tinkamai integruos šią paslaugą, bus naudos gavėja,,en,taip pat labai svarbu žinoti apie abi monetos puses, kad ji būtų tinkamai integruota,,en,Reaktyviosios mašinos AI,,en,techalpine.com/promises-and-drawbacks-of-machine-learning,,en. It is very transformative, so it has the capability to work on every workflow for any business. Any organization which will integrate this service in the right manner will be the beneficiary. bet, it is also very important to know about both the sides of the coin for integrating it properly.
