The term “artificial intelligence” appeared in 1956, but AI technology has only reached true popularity today against the background of increasing data volumes, improving algorithms, optimizing computing power and data storage facilities.
Artificial intelligence (AI) allows computers to learn from their own experience, to adapt to the given parameters and to perform those tasks that were previously only possible for humans. In most cases of AI implementation – from computer chess players to unmanned vehicles – the possibility of deep learning and natural language processing is extremely important .
What is the importance of artificial intelligence?
- Automative Repetitive Learning and Search processes:
AI allows you to automate repetitive learning and search processes through the use of data. However, AI differs from robotics, which is based on the use of hardware. The goal of AI is not automation of manual labor, but the reliable and continuous execution of numerous large-scale computerized tasks. Such automation requires human participation for the initial setup of the system and the proper formulation of questions.
- Simple Products can Transform in to Exiting Products:
AI makes existing products intelligent. Typically, AI technology is not implemented as a standalone application. AI functionality is integrated into existing products, allowing them to be improved, just like Siri technology was added to next-generation Apple devices. Automation, communication platforms, bots and smart computers combined with large volumes of data can improve various technologies that are used at home and in offices: from security data analysis systems to investment analysis tools.
- Progressive Learning Algorithms to Master a Specific Skills:
AI is adapted thanks to progressive learning algorithms so that further programming is based on data. AI detects patterns and patterns in the data that allow the algorithm to master a specific skill: the algorithm becomes a classifier or predictor. Thus, by the same principle by which an algorithm masters a game of chess, he can learn to offer suitable products online. At the same time, models adapt as new data arrives. Backpropagation is a method that provides model adjustment through training based on new data if the initial answer is incorrect.
- Creating a Fraud Detection System by Increase in Computer Power:
AI provides a deeper analysis of large amounts of data using neural networks with many hidden levels. A few years ago, creating a fraud detection system with five hidden levels was almost impossible. Everything has changed with the tremendous increase in computing power and the advent of “big data.” For models of deep learning, a huge amount of data is necessary, since it is on their basis that they are trained. Therefore, the more data, the more accurate the model.
- AI in Medical Field is Helping a Lot of Doctors:
Deep neural networks allow AI to achieve an unprecedented level of accuracy. For example, working with Alexa, Google Search and the Google Photos service is based on deep training, and the more often we use these tools, the more effective they become. In the field of healthcare, the diagnosis of cancerous tumors in MRI images using AI technologies (deep training, image classification, object recognition) is not inferior in accuracy to the conclusions of highly qualified radiologists.
- AI Increases the Benefits of Data:
AI maximizes the benefits of data. With the advent of self-learning algorithms, the data itself becomes an object of intellectual property. The data contains the necessary answers – you just need to find them using AI technology. Because data now plays a much more important role than ever before, it can provide a competitive advantage. When using the same technology in a competitive environment, the one with the most accurate data will win.