Artificial intelligence is a field of computer science that is present in the dialogue about the future of various fields. And today we are learning more about these areas and artificial intelligence technologies.
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Artificial intelligence is a general term that encompasses any technology that allows computers to mimic human capabilities. In this sense, fields such as robotics, parallel computing, natural language processing, machine learning, and deep learning are all part of artificial intelligence. All of these areas seek to automate in their own way the actions that people perform in a natural and intuitive way.
Advances in artificial intelligence have made it possible to achieve things that were previously unimaginable. AI solutions are being used to speed up processes, manage resources more effectively within an organization, and create more informed and intelligent responses to specific needs. At the same time, as applications expand, tools open up so that these technologies become more accessible and adaptable to new applications and contexts.
Some of the key resources for implementing an artificial intelligence project are hardware, software, organizational technology, and data.
Data is critical as it is the raw material used to train the algorithm. The quantity and quality of this data will have a direct impact on the results that the algorithm will produce in the future. Access to this data is one of the first challenges faced when developing artificial intelligence, which sometimes also implies the need to highlight issues about the social impact of the project and the responsibility to monitor the results.
The main technology of AI is the ability of computer systems to “self-learn” (Machine Learning), the use of accumulated data or a specially created environment for programming certain rules of decision-making or behavior applicable in the future. The main ideological “difference” of AI technologies from “traditional” digital solutions is the fact that when performing tasks, they do not rely on logical schemes set by programmers, but they themselves build complex decision-making mechanisms (“neural networks”) based on those data and tasks that were supplied by the programmers.
In which processes and tasks the introduction of AI technologies brings significant results?
A large amount of input data (for example, assessing credit risk using data on social networks or working with seismic data to locate oil deposits).
Large variability of incoming data, work with unstructured data (for example, processing contracts executed according to arbitrary templates of contracts with counterparties)
High “speed” of incoming data (for example, real-time face recognition in a video stream from CCTV cameras)
Analytical work, for example, in almost every profession where formula calculations (finance, engineering, risk assessment) were previously used to achieve a result, successful solutions appear based on the possibility of using data with the help of AI technologies to improve the result.
Artificial intelligence is our future, so it’s worth exploring. After all, further, it will only develop more.