According to experts , 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 team has prepared a small study on this matter.
History
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 we will consider other important dates in the history of machine learning:
1946: The ENIAC computer appeared - a top-secret US Army project.
1950: Alan Turing creates the "Turing Test" to measure the intelligence of a computer.
1958: Frank Rosenblatt invents the Perceptron , the first artificial neural network, and builds the first brain computer, the 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, an artificial neural network scientist, coined the term Deep Learning.
2011: Andrew Ang and Jeff Dean founded Google Brain .
2012: Google X Lab developed an algorithm to identify videos showing cats :)
2012: Google launches Google Prediction API cloud service 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 now?
Education. Thanks to the introduction of artificial intelligence, the developers have created learning systems that simulate 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 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 speech sounds, intonation, and posture. Using a webcam and microphone, the user trains neural networks without writing 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 the right 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 to the mailings for better sales.
Healthcare. IBM has developmentWatson . 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 is now used in hospitals in New York, Bangkok and India. In July 2016 IBM began to cooperate with the 16 medical centers and technology startups to accelerate the development of programs for precise diagnosis.
Output
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 become part of everyday life (recognition of voice commands for a smart home ).
Today machine learning is taking on new forms 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.