In recent years, there has been a real boom in the IT market due to artificial intelligence. And this is not surprising: modern computing and neural network technologies have reached a level that allows AI systems to solve practical problems that are very difficult for humans, and developers to create innovative services.<\/p>\n\n\n\n
In this article, we will talk about how AI is already being applied in various fields and about the most promising technologies of the future.<\/p>\n\n\n\n
The number of patients around the world is growing daily.\u00a0Artificial intelligence can help process this amount of data and automate patient information around the world.\u00a0One example is the\u00a0OLIVE\u00a0service created by a neural network\u00a0– a platform for automating healthcare tasks (especially important in the coronavirus pandemic).<\/p>\n\n\n\n
Creating new drugs is a time-consuming process because doctors need to run many tests to find the right formula.\u00a0AI helps them in this.\u00a0Atomwise\u00a0is one example of a technology that enables the discovery of new molecules.\u00a0It is being used to develop new drugs for 27 diseases in collaboration with Harvard and Stanford Universities and pharmaceutical companies.<\/p>\n\n\n\n
Pathologists are using AI to diagnose cancer more accurately.\u00a0Data on different types of cancer are used to create a predictive model.\u00a0For example,\u00a0PathAI\u00a0technology is used for this.<\/p>\n\n\n\n
Money goes digital.\u00a0The total amount of\u00a0electronic payments\u00a0to date is $ 4 trillion and is projected to be over\u00a0$ 8 trillion by 2024.<\/p>\n\n\n\n
All data on such transactions will be processed by neural networks, which will improve the financial industry by the end of 2021.\u00a0For example, the\u00a0Dataminr\u00a0service\u00a0already collects information from various text sources and presents the user with a graph of important events that may affect his investment.<\/p>\n\n\n\n
Digital payments have certain risks.\u00a0For example, in 2020, Russians\u00a0lost 2.5 billion rubles\u00a0due to scammers.\u00a0Machine learning is perfect for dealing with them.\u00a0For example, the British company\u00a0AimBrain\u00a0uses machine learning to prevent account theft and detect fraudulent accounts.\u00a0We are waiting for a similar service from Russian developers.\u00a0\ud83d\ude09\u00a0<\/p>\n\n\n\n
The algorithms created by the neural network can be used to automate trading.\u00a0If you provide data on prices, sales volumes, dates, and people’s moods (or “weather” in society), then in 2021 it is possible to create a system that can create forecasts for the market.\u00a0The algorithm will be able to learn and adapt to changes in real-time to create the most accurate predictions.\u00a0This project is handled by\u00a0Kayrros, a company that analyzes data for successful investments.\u00a0<\/p>\n\n\n\n
A generative adversarial network<\/strong>\u00a0(GAN for short) is a machine learning model that can simulate a given distribution of data.\u00a0GANs consist of two neural networks, one of which is trained to generate data, and the other is trained to distinguish simulated data from real data (hence the “adversarial” nature of the model).\u00a0If you are interested in this topic, you can read our previous articles about GAN, for example, ”\u00a0Generating Anime Using the StyleGAN Neural Network\u00a0” or ”\u00a0Realistic Landscapes from Drawings\u00a0.”<\/p>\n\n\n\n Using GAN, you can get datasets of images, faces, cartoon characters, translate images into text and vice versa, create 3D objects, and so on.\u00a0There are many applications for GANs, but they can bring more than just benefits.\u00a0One of the most recent uses for GAN applications is\u00a0deepfakes\u00a0.\u00a0<\/p>\n\n\n\n Deepfakes use artificial intelligence technologies to synthesize images, as a result of which one character seems to be superimposed on another and a “combo” is obtained (yes, you can even make a\u00a0terminator out of Brad Pitt\u00a0).<\/p>\n\n\n\n Deepfakes 2020 differ from their predecessors in higher quality – the technology does not stand still. Here are a few areas in which GANs are used:<\/p>\n\n\n\n NVIDIA has created the\u00a0GauGAN\u00a0neural network\u00a0that turns sketches into real images.\u00a0The program helps architects to collect building designs from drawings, and game designers – to quickly create locations for games.\u00a0Try it yourself and see!\u00a0\ud83d\ude09<\/p>\n\n\n\n For example, the Russian modeling agency\u00a0Areola Models has\u00a0digitized 10 models – today virtual copies of girls take part in filming all over the world and work 24 hours, 7 days a week.<\/p>\n\n\n\n Thanks to GANs and deep fakes, investigative journalists change the appearance of heroes in their reports in order to preserve people’s anonymity.\u00a0This technique, for example, was used by the HBO channel when creating a documentary film ”\u00a0Welcome to Chechnya<\/a>\u00a0“.<\/p>\n\n\n\n The GAN algorithm is<\/a> proposed to be used in 2021 in astrophysics in order to get rid of interference and noise when photographing space objects and to obtain high-quality images.<\/p>\n\n\n\n Today, GAN technology remains the subject of heated debate: on the one hand, you can create useful services, and on the other hand, deep fakes can destroy someone’s reputation.\u00a0For example, in the fall of 2020 in\u00a0Italy, thousands of women suffered<\/a>\u00a0: a bot appeared that could connect the face of any person and a fake naked body in the picture.\u00a0Also, deepfake videos have been repeatedly used for political or fraudulent purposes.\u00a0For example, scammers were able to lure\u00a0220 thousand euros<\/a>\u00a0from the\u00a0CEO of a British energy company\u00a0using a deep fake imitation of the voice of his CEO.<\/p>\n\n\n\n It is important to remember that GAN technology itself cannot be regarded as good or bad – the main question is whether a person will use it as a weapon or as a tool.<\/p>\n\n\n\n RL<\/strong> is a field of AI machine learning, where it is studied how the system under test (agent) interacts with the environment (environment) to obtain maximum reward (the response of this environment, that is, reinforcement).<\/p>\n\n\n\n RL can be compared to dog training. Imagine you have adopted a puppy. To teach him something, you need to use a reward system. If the dog listens to you, you give him a treat. This also happens with machine learning.<\/p>\n\n\n\nArchitecture<\/h3>\n\n\n\n
Fashion industry<\/h3>\n\n\n\n
Journalism<\/h3>\n\n\n\n
Astrophysics <\/h3>\n\n\n\n
Reinforcement learning<\/h2>\n\n\n\n