Artificial intelligence (AI) allows computers to learn by doing, to adapt to given parameters, and to perform tasks that previously were only humanly possible. In most AI implementations, from computer chess players to unmanned cars, deep learning and natural language processing capabilities are essential. With these technologies, computers can be "trained" to perform certain tasks by processing large amounts of data and identifying patterns in them.
What is the importance of artificial intelligence?
- AI enables the automation of repetitive learning and retrieval processes through the use of data. However, AI is different from robotics, which is based on the use of hardware. The goal of AI is not to automate manual labor, but to perform multiple large-scale computerized tasks reliably and continuously. Such automation requires human involvement for the initial setup of the system and the proper formulation of questions.
- AI makes existing products intelligent. Typically, AI technology is not implemented as a separate application. AI functionality is integrated into existing products, allowing them to be enhanced, just as Siri technology was added to Apple's next-generation devices. Automation, communication platforms, bots and smart computers, combined with big data, can improve various technologies used in homes and offices, from security data analysis systems to investment analysis tools.
- AI adapts thanks to progressive learning algorithms so that further programming is done based on the data. AI discovers structures and patterns in the data that allow the algorithm to learn a particular skill: the algorithm becomes a classifier or predicator. Thus, in the same way that an algorithm learns the game of chess, it can learn to suggest suitable products online. In doing so, the models adapt as new data become available. Backpropagation is a technique that ensures that the model is adjusted by learning from new data if the initial answer turns out to be incorrect.
- AI performs deeper analysis of large amounts of data using neural networks with multiple hidden layers. A few years ago, creating a fraud detection system with five hidden levels was nearly impossible. This has changed with the tremendous growth of computing power and the advent of "big data". Deep learning models require a huge amount of data, since that is what they are trained on. So the more data, the more accurate models are.
- Deep neural networks allow AI to achieve unprecedented levels of accuracy. For example, Alexa, Google Search, and Google Photos are powered by deep learning, and the more we use these tools, the more effective they become. In healthcare, the diagnosis of cancerous tumors on MRI images using AI technologies (deep learning, image classification, object recognition) is as accurate as the conclusions of highly qualified radiologists.
- AI makes it possible to get the most out of the data. With the advent of self-learning algorithms, the data itself becomes intellectual property. The data contains the answers you need - you just need to find them with the help of AI technology. Because data is now more important 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 wn.