Machine learning technologies: examples of current trends

Machine learning is one of the ways to apply artificial intelligence in computer technology when working with various data. Thanks to machine learning, software applications can more accurately predict results and analyze data. The main goal and idea of ​​machine learning is to allow computers to learn by themselves, automatically and without human intervention.

According to forecasts specialists, machine learning is the future. As people become more and more dependent on cars and gadgets, a global technological revolution is coming, thanks to which new professions will appear and old ones disappear. In this regard, our command prepared a small study on this matter.


In 1959, Arthur Samuel, an artificial intelligence researcher, coined the term machine learning. He invented the first self-learning computer checkers program. Samuel defined machine learning as the process by which computers are able to exhibit behaviors that were not originally programmed into them.

Below are some other important dates in the history of machine learning:

1946: The ENIAC computer appears, a top-secret US Army project.

1950: Alan Turing creates the “Turing Test” to measure the intelligence of a computer.

1958: Frank Rosenblatt invented Perceptron – the first artificial neural network and created the first neurocomputer “Mark-1”.

1959: Marvin Minsky builds the first SNARC machine with a randomly coupled neural network.

1967: A metric data classification algorithm is written. The algorithm allowed computers to apply simple recognition patterns.

1985: Terry Seinovsky creates NetTalk, an artificial neural network.

1997: The Deep Blue computer beat the world champion, Garry Kasparov, in chess.

2006: Geoffrey Hinton, a scientist in the field of artificial neural networks, coined the term “Deep learning”.

2011: Andrew Ang and Jeff Dean founded Google brain

2012: Google X Lab developed an algorithm to identify videos in which cats are shown 🙂

2012: Google launches a cloud service Google Prediction API for machine learning. It helps you analyze unstructured data.

2014: Facebook invents DeepFace for facial recognition. Algorithm accuracy is 97%.

2015: Amazon launched its own machine learning platform – Amazon Machine Learning.

2015: Microsoft creates the Distributed Learning Machine Toolkit platform for decentralized machine learning.

2020: Artificial intelligence technologies are used in almost every software product.

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Where is machine learning being applied right now?

Education. Thanks to the introduction of artificial intelligence, the developers have created educational systemssimulating teacher behavior. They can identify the level of knowledge of students, analyze their answers, give grades and even define a personal learning plan.

For example, AutoTutor, teaches students of computer literacy, physics and critical thinking. Knewton takes into account the learning characteristics of each student and develops a unique curriculum for him. The US Air Force uses the SHERLOCK system to train pilots to troubleshoot technical problems in aircraft.

Search engines. Search engines use machine learning to improve their functionality. For example, Google has implemented machine learning in voice recognition and image search. In 2019, Google introduced Teachable Machine 2.0 – a self-learning neural network capable of recognizing the sounds of speech, intonation and posture. Using a webcam and microphone, the user trains neural networks without writing any code and exports them to third-party applications, media, or websites.

Digital marketing. Machine learning in this area provides deep customer personalization. Thus, companies can interact with the client on a personal level, getting closer to him. Through sophisticated segmentation algorithms, the machine focuses on the “right customer at the right time” to effectively sell products. In addition, with correct customer data, companies have information that can be used to study their behavior and reactions.

For example, Nova uses machine learning to write email newsletters to customers, while making emails personalized. The machine knows which emails previously had high conversions and, accordingly, suggests changes in mailings for better sales.

Healthcare. IBM has development Watson… It is a machine learning supercomputer for medical research. Watson for Oncology technology processes a large amount of medical data, including images that can accurately diagnose cancer. Watson for Oncology now used in hospitals in New York, Bangkok and India. In July 2016, IBM became partner with 16 medical centers and technology startups to accelerate the development of diagnostic software.


The future of technology is machine learning. In the next decade, machine learning will be a competitive advantage not only for top companies, but also for promising startups. What is done by hand today will be done by machines tomorrow. It should be added that machine learning algorithms will not only be used in business and economics, but will also firmly enter everyday life (recognition of voice commands for smart at home).

Machine learning is taking on new shape and is constantly evolving. Machine learning is built on the concept that computers can learn. Those. they can do things that they were not originally programmed to do.

At the moment, artificial intelligence researchers want to test whether computers can learn from the data. The interactive aspect of machine learning is important because machines are able to constantly learn and adapt on their own. Computers learn from previous calculations and metrics to deliver reliable and successful solutions and results for a better future.

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