Machine Learning: How the Nature Of a People’s Choice Will Make AI More Powerful Than Ever

By Alyse Falk
Published 02/19/2021
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Today, AI has already become the main priority for modern technologies’ development. Based on machine learning and deep learning, it gives the ability to the computers to learn without being explicitly programmed to do so. 

In this article, we will analyze the AI and Human processors leveraging in one workflow, based on the ML progress. 


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machine learning model

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What is Machine Learning?

Tom Taulli, a tech expert on Forbes indicates that ML is a subset of the AI, which represents the ability for the systems to automatically learn and improve their experience based on the algorithms which performance improves along with the amount of data processed. The main target of ML is to teach computer systems to make their choice based on how humans would do it. 

Methods Applied for the ML 

In fact, there are various methods applied for system learning, but we would like to point out the most popular of them: learning problems and statistical inference. 

Learning Problems

Learning problems basically include three main types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised learning implies using the model to learn the mapping between the various targets and data samples. Unsupervised learning is used to analyze the model description and define the relationships in data. Reinforcement learning is applied for learning the operating algorithms on how to act in a certain situation.

Having learned how to operate, analyze the data, and provide instant feedback towards it, machine learning helps various systems become more independent and steady, thus there is no need to track its decision-making process. 

Statistical Interference

This type of ML refers to the methods applied to determine an outcome or decision and includes the next types: inductive, deductive, and transductive learning. Inductive learning involves using evidence to reach the outcomes, from the detailed to general. Deductive is used to determine the details by applying general data. Transductive learning is used for predicting the specific examples from the details of the original data. 


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Needless to say, that all these methods are successfully mastered by a human, and now the major task of computer science is to teach computers how to operate these algorithms while making a certain decision.

Future Predictions About ML

The future prospects of the ML in Artificial Intelligence development are really significant. It is believed that ML will not only greatly boost the computer’s intelligence but also teaches them how to act under specific circumstances by applying the most beneficial decision-making methods. 

Computers will greatly help with coursework by providing smart problem analysis and relevant outcomes based on the input data, said Brian Smith, ahead tutor from

 Additionally, ML will be greatly helpful for global prediction making and thus significantly improve the life of modern society. 


All in all, the ML development will make a great contribution to the decision-making and problem-solution process. Applying learning methods such as supervised learning, unsupervised learning, reinforcement learning, and also statistical inference or decision making, allows combinate and organically substitute the Human and computer data processing in one workflow, which will consequently lead to the next phase of the AI development and greatly boosts its evolution.