Artificial Intelligent

Machine Learning

Machine learning is a data analysis method that learns itself by analytical models. It is the main tool of artificial intelligence. The idea is that systems can learn from data, recognize patterns, and make decisions with minimal human intervention.

Machine learning evolution

Due to new computer technology, today’s machine learning is not like the machine learning of the past. It grew out of pattern recognition and the theory that computers could perform certain tasks without programming; Researchers interested in artificial intelligence want to see if computers can learn from data. You learn from previous calculations to get reliable and repeatable results and solutions.

Google self-advertising car? The essence of machine learning.

Do you recommend online products like Amazon and Netflix? Machine learning applications in everyday life.

Do you know what your customers are saying about you on Twitter? Machine learning is combined with the creation of language rules.

Fraud detection? Today one of the most obvious and important uses in the world.

Why is machine learning important?

Data mining and analysis are quite popular these days. Things like an ever-increasing amount and variety of data, cheaper and more powerful computational processing, and affordable data storage.

All this means that models can be generated quickly and automatically, which, even in large situations, can analyze larger and more complex data and provide faster and more accurate results. By creating accurate models, companies are more likely to identify opportunities for profit or avoid unknown risks.

Who uses it?

Most industries that process large amounts of data recognize the value of machine learning technology. By gathering information from this data, usually, in real-time, companies can work more efficiently or gain a competitive advantage.

Financial services



retail trade

oil and gas


Which machine learning methods are popular?

The two most widely used machine learning methods are supervised learning and unsupervised learning, but there are other machine learning methods as well.

There are 4 types of machine learning.

Supervised learning

Partially managed training

Intensive learning

Unsupervised learning

IBM has a long history of machine learning. One of its own members, Arthur Samuel, is credited with introducing the term “machine learning” for his research (PDF, 481 KB) on chess (link outside IBM). Robert Neely, who calls himself a master of billiards, played the game on an IBM 7094 computer in 1962 and lost the computer. Compared to what is possible today, this achievement may seem trivial, but it is considered an important step in the field of artificial intelligence.

Machine learning is an important part of the ever-evolving field of data science. Through the use of statistical methods, classification or forecasting training algorithms reveal important information in data mining projects. These insights then stimulate business and application decisions and ideally influence key growth indicators. As big data continues to evolve and grow, the market demand for data scientists will increase, so they must help identify the most relevant business questions and then use the data to solve those problems.

This is how machine learning works

The University of California, Berkeley (link is out of IBM) divides the machine learning algorithm system into three main parts.

Decision-making process: Generally, machine learning algorithms are used for prediction or ranking. Based on some input data, which may or may not be flagged, your algorithm generates an approximate data model.

Error function: The error function is used to estimate the model’s approximation. Once the sample is known, the error function can be compared to assess the accuracy of the model.

Model optimization process: As the model can better fit the data points in the training set, adjust the weights to reduce the difference between known and estimated examples. The algorithm repeats this evaluation and optimization process and automatically updates the weights until the accuracy threshold is reached.

How does artificial intelligence work?

Methods and concepts for artificial intelligence

Ten years after breaking the Enigma, and helping the Allies win World War II, mathematician Alan Turing changed history a second time with the simple question.

The question is “Can machines think?”

Turing’s dissertation Computers and Intelligence (1950) and the subsequent Turing test set out the basic goals and vision of artificial intelligence.

The broad goals of artificial intelligence have raised many questions and controversies. There are so many field definitions that are not widely accepted.

Can machines think? – Alan Turing, 1950

The main limitation of simply defining artificial intelligence as “building smart machines” is that it doesn’t really explain what artificial intelligence is. What makes a machine smart? Artificial intelligence is a science with many methods, but advances in machine learning.  Deep learning are causing a paradigm shift in almost all areas of the tech industry.

In their groundbreaking textbook Artificial Intelligence: A Modern Method, authors Stuart Russell and Peter Norvig have summarized their work on intelligent agents in machines to solve this problem.

Patrick Winston, professor of artificial intelligence and computer science at MIT, Ford, defines artificial intelligence as “algorithms that are activated by publicly created restrictions and that support the presentation of models that refer to cycles that link thinking, perception, and perception”. Action.

While these definitions may seem abstract to the average person, they help turn the field into a computing field and provide a model for the injection of machine learning and other subsets of artificial intelligence into machines and programs.

How is artificial intelligence used?

At the Japan Conference on Experience with Artificial Intelligence 2017, DataRobot CEO Jeremy Achin provided the following definition at the beginning of his speech on the current use of Artificial Intelligence:

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More than 20 examples of AI in everyday life

Artificial intelligence is usually divided into two categories, weak artificial intelligence and general artificial intelligence.

Narrow Artificial Intelligence: This type of artificial intelligence is sometimes referred to as “weak artificial intelligence” and works in a sandbox and is a simulation of human intelligence. Narrow artificial intelligence usually focuses on doing a task very well. Even though these machines look intelligent, they operate under more limitations.

General Artificial Intelligence (AGI): AGI, sometimes referred to as “strong artificial intelligence”, is the type of artificial intelligence we see in movies, such as robots in Westworld or Star Trek: The Next Generation. Information. AGI is a machine with general intelligence that, like humans, can use this intelligence to solve any problem.

Narrow artificial intelligence

Narrow AI surrounds us and could easily be the most successful AI implementation to date. According to preparations for the future of Artificial Intelligence, Narrow AI is focused on specific tasks and has made great strides in the last ten years. They “own” this achievement

What is Artificial Intelligence?

What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a super-intelligent program that can learn itself and think like humans. Part of artificial intelligence is machine learning. It is the concept of computer programs that can automatically learn and think like a human.
Important point
Artificial intelligence is used in a variety of industries, including finance and healthcare.
Weak artificial intelligence is usually simple and task-oriented, whereas strong artificial intelligence takes on more complex and human-like tasks.
Understanding Artificial Intelligence (AI)
When most people hear the term AI, firstly what they this is Robot. This is because high-budget films and novels tell the story of a humanoid machine wreaking havoc on the planet. but that’s not the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in such a way that machines can imitate and perform the simplest to the most complex tasks. One of the goals of Artificial Intelligence is to imitate human cognitive activity. Researchers and developers in the field have made surprisingly rapid progress in activities such as mimicking learning, thinking, and perception, which can be defined specifically. Some believe that innovators will soon be able to create systems that are better than human capacity.

With advances in technology, the criteria that previously defined artificial intelligence are becoming obsolete. For example, machines that compute basic functions or recognize text through optical character recognition are no longer considered an embodiment of artificial intelligence, as these functions are now considered computer functions.

Artificial intelligence is evolving, which benefits many different industries. These machines are linked by interdisciplinary methods from mathematics, computer science, linguistics, psychology, and others.

Artificial intelligence app
The uses of artificial intelligence are endless. This technology can be used in various branches and industries. Artificial intelligence is being tested in the healthcare industry and is used to administer drugs and perform various treatments for patients, as well as during surgical procedures in operating rooms.

Other examples of artificial intelligence machines are computers playing chess and driverless cars. Each of these machines must consider the consequences of each action because each action affects the final result. In playing chess, the ultimate goal is winning the game. In the case of autonomous vehicles, IT systems must take into account and calculate all external data to avoid collisions.

Artificial intelligence applications are also used to simplify and streamline transactions. This is achieved by making it easy to evaluate the supply, demand, and price of securities.

Artificial Intelligence Classification
There are two types of categories in Artificial intelligence, which are weak and strong. Weak artificial intelligence is a system that is supposed to perform certain tasks. Weak AI systems include video games.