Artificial Intelligence – Comprehensive Overview
Part two
Trends in Artificial Intelligence
Research in artificial intelligence is focused on three main directions: the first direction is machine learning to operate more effectively through the use of the three different types of machine learning (supervised, unsupervised, and reinforced learning) and is used to build deep learning models. The second direction is natural language processing, while the third direction is the field of robotics in all its types, mechanisms, and applications.
In the first direction, various patterns are utilized, including supervised learning, to use available information in several aspects such as classification areas like customer segmentation in service companies and banks to retain customers, fraud detection in documents, image classification and verification, predictive fields like population growth forecasts, weather predictions, financial market forecasts, and many other applications.
This direction also includes unsupervised learning, which is used to draw conclusions from indirectly related datasets. For example, it is used in clustering analysis like customer segmentation and targeting specific markets for a product, as well as for exploratory data analysis like future predictions, big data analytics, and transforming data into informative insights and future forecasts.
Reinforced Learning is an advanced field of artificial intelligence that focuses on taking appropriate actions to maximize reward in a specific situation. It is used in various programs and machines to find the best possible behavior or path to be taken in a given situation. Education is based on experience rather than pre-existing data. Important applications include electronic games that rely on levels and strategies, real-time decision-making like in drones or traffic control robots.
The second trend is natural language processing (NLP), a subfield of linguistics and artificial intelligence sciences concerned with interactions between computer devices and human (natural) languages, especially how machines can process, analyze, read, understand, and derive meaning from large amounts of natural language data. This trend has wide-ranging applications such as email filters, smart assistants like Apple’s “Siri”, Amazon’s “Alexa”, IBM’s “Watson”, search result filtering, text analytics and predictions, automatic error correction, speech analysis, multilingual translation, and digital voice calls.
The promising third trend in artificial intelligence applications is the field of robotics, which is divided into several categories including service, industrial, medical, and agricultural robots that perform various functions in those fields. It also includes autonomous vehicles, drones with various functions, chatbots, and Robotics Process Automation (RPA).
Therefore, we can see that robots are currently working alongside humans in various aspects of life. For example, robots are used in industries like automotive and aviation, where they are programmed to.Complete production lines without human intervention, robots used to provide some services in restaurants and hotels, improving inventory and storage systems in warehouses of large companies, in addition to medical robots employed in performing precise surgical operations that require significant human effort. There are also robots used in agriculture that carry out tasks such as planting new crops, harvesting crops, monitoring pollution, and weather conditions.
Regarding Chatbots, they have a promising future and play a pivotal role in enhancing company services by handling complaints, analyzing user requests, suggesting solutions automatically, and adding or removing services around the clock without human intervention, especially in recurring problems with known solutions. This provides companies with new opportunities to improve customer engagement and enhance operational efficiency by reducing typical customer service costs and reallocating humans to tasks of higher value for companies.
As for Drones, they have a future importance in carrying out delivery tasks for light parcels, as well as in surveillance and imaging, as seen during the monitoring of people’s movements in China and Japan during the COVID-19 pandemic to reduce disease spread. Drones are also used in some field maintenance operations for communication networks without the need to send technical teams when failures have known solutions and occur frequently.
Another important area in the third direction with artificial intelligence is Robotic Process Automation (RPA), which refers to technology that allows building computer programs to simulate human actions and interactions to execute a set of basic tasks just like humans. RPA is primarily used for office functions, and it can connect to other software, run them, input data, and extract required information automatically. This system operates continuously 24/7 without errors within the defined scope of work, helping companies streamline daily operations, reduce labor costs, minimize errors, and increase productivity.
There is a difference between Deep Learning and RPA. In RPA, robots are programmed to perform tasks in a specific workflow by employees with some assistance from programmers. The program does not self-learn or seek to create new methods or insights; it functions as an assistant and facilitator for employees’ tasks, especially for repetitive tasks. Deep Learning, on the other hand, allows machines to solve complex problems even when using unstructured and diverse datasets, with algorithms improving performance as they learn more. It enables machines to solve some complex problems without human intervention, similar to virtual assistants that use the internet and all sources of knowledge to learn language, work style, and how to interact with humans.
By Engineer: Belal Khalid Al-Hafnawi
Specialist in the telecommunications, information technology, and digital transformation sector
