Many users of the post-industrial era wondered: What is Machine Learning? A fantastic future that has already arrived or another incomprehensible theory like quantum dualism. Neither one nor the other.
Let's generalize the tasks of the ML:
- Based on an array of features or characteristics, predict a material result. That is, the machine must issue a specific number. For example, predict the value of stocks on the stock exchange, the number of queries for a keyword, the budget for contextual advertising, and more.
- The task is to determine the category of the object by the quantity and quality of features, characteristics. For example, to recognize a specific person on the wanted list by a snapshot, having only descriptions in words, to identify spam, to identify a patient's illness.
- The data is broken down into similar categories. For example, space objects are classified into specific categories based on similar characteristics (distance, size, planet or star, and others).
- The decrease in dimension. Compression of an array of object characteristics to a smaller number of features for further visualization or use in work. For example, compressing an array of data into archives for transmission over the network.
- We looked at Machine Learning – what this concept means. Now is the time to consider what ML is used for in business and life.
Ask someone with a passion for robotics about the scope of machine learning. You will hear many fantastic stories. For example, robots will independently learn to perform tasks assigned by humans. To extract minerals in the bowels of the Earth, drill oil and gas wells, explore the depths of the ocean, extinguish fires, and more.
The programmer will not need to write massive and complex programs for fear of making mistakes in the code. The robot thanks to the machine learning development services, will itself learn to behave in a specific situation.
What artificial intelligence and machine learning are now capable of. Today technology is used more for marketing purposes. For example, Google and Yandex use ML to show relevant ads to users. You have noticed more than once that after searching the Internet for a product of interest, then for several hours, or days, they show similar offers.
Smart feeds in social networks are formed on the same principle. Analytical machines FB, VK, Instagram, Twitter investigate your interests – which posts you view more often, what you click on, which publics or groups you visit and more.
The longer and more often you are active on social networks, the more personalized your news feed becomes. This is both good and bad. On the one hand, the machine filters out an array of uninteresting information, and on the other, it narrows your horizons. Marketing is nothing personal!
Machine learning is used in security frameworks. For example, a subway facial recognition system. Cameras scan the faces of people entering and exiting the metro. Analytical engines compare the images with the persons on the wanted list. If the similarity is high, then the system beeps.
Artificial intelligence from https://diceus.com/30-minute-strategy-session/ is already being implemented in medical institutions. For example, the processing of patient data, preliminary diagnosis, and even the selection of individual treatment based on information about a person's disease.
Deep machine learning is necessarily the analysis of Big Data. That is, it is simply impossible to process so much information with one computer, one program. The essence of such training is that a huge field of information is divided into small data segments, the processing of which is delegated to other devices.
For example, one processor only collects information on a task and transfers it further, four other processors analyze the collected data and transmit the results further. The next processors in the chain are looking for solutions.