Artificial intelligence software is a software application that uses artificial intelligence techniques to perform tasks that typically require a degree of human intelligence. In other words, it is a computer program capable of simulating human mental capabilities in areas such as learning, reasoning, decision-making, or pattern recognition. These programs can be relatively simple, such as a program that plays chess or chats with a user, or very complex, such as systems for big data analysis, speech recognition, and image recognition. The core idea is that smart software can execute complex tasks—such as communicating with customers online, diagnosing diseases, or self-driving vehicles—that in the past required direct human intervention.

AI software differs from traditional software in that it does not rely solely on fixed code explicitly written by a programmer, but can instead learn from data and experiences and improve its performance over time. For example, an AI image processing program can learn to distinguish between thousands of types of objects in images by analyzing millions of images and developing statistical models that automatically recognize patterns. Similarly, a smart chatbot learns by analyzing massive amounts of human text so that it can generate natural and contextually meaningful responses to the user. In short, AI-driven software is unconventional software that seeks to simulate human intelligence in innovative ways relying on advanced mathematics and algorithms.

Historical overview

Corporate the idea of “artificial intelligence software” began to emerge in the mid-20th century when computer science researchers started thinking about how to make machines “think.” In 1956, the famous Dartmouth Conference was held, which was considered the official launch of artificial intelligence research as an independent field (Artificial intelligence – Wikipedia). During this conference, scientist John McCarthy and others introduced the term “artificial intelligence” and laid the first foundations for the field. During that period, the first smart programs were developed, such as the “Logic Theorist” program by Allen Newell and Herbert Simon in 1956 (Evolution of Artificial Intelligence Technologies: From Theory to Modern Reality – Nabd Oman Network | Since 2008 %) (Evolution of Artificial Intelligence Technologies: From Theory to Modern Reality – Nabd Oman Network | Since 2008 %), which managed to prove certain mathematical theorems using symbolic logic. This was an early achievement that highlighted the potential of building programs that simulate a part of human mental capabilities (in this case, solving mathematical and logical problems).

During the 1960s, significant progress was achieved in the field of artificial intelligence software, particularly thanks to generous funding provided by the US Department of Defense for research (Artificial intelligence – Wikipedia). Researchers at the time believed they were well on their way to building smart programs capable of performing any task a human could do soon. For instance, Herbert Simon predicted in 1965 that computers would be capable within twenty years of performing any work a human could perform (Artificial intelligence – Wikipedia). Furthermore, Marvin Minsky predicted in 1967 that within a single generation, the problem of creating artificial intelligence at human intelligence levels would be solved (Artificial intelligence – Wikipedia). However, it quickly became clear that these expectations were far too optimistic.

By the 1970s, certain difficulties began to surface. Many AI programs of that era relied on logical rules set by humans (i.e., explicit programming) and did not learn on their own. When these programs attempted to handle more complex problems or new situations not previously programmed, they faced failure. In 1974, with the release of the James Lighthill report in Britain, which criticized the limited achievements of artificial intelligence, government funding in the United States and Britain began to recede (Artificial intelligence – Wikipedia). The field entered what is known as the first “AI winter,” where research slowed down and optimism dwindled.

Nevertheless, the early 1980s witnessed a new renaissance for AI software through the success of expert systems. Expert systems are artificial intelligence programs that rely on the rules and knowledge of human experts to solve specialized problems. For example, a medical expert system like “MYCIN” was developed in the 1970s to diagnose infectious diseases and suggest treatments based on a massive knowledge base entered by specialized doctors. By 1985, expert systems achieved commercial popularity and many companies began investing in them, with the artificial intelligence market revenue exceeding one billion dollars (Artificial intelligence – Wikipedia). This success convinced governments and corporations to reinvest in the field.

However, artificial intelligence research and software soon suffered another setback in the late 1980s. With the collapse of the Lisp computer market dedicated to running AI systems in 1987 (Artificial intelligence – Wikipedia), the field entered a new “winter” longer than the previous one. During this period, it became apparent that expert systems, despite their success in very specific tasks, were incapable of generalizing knowledge or self-learning, which limited their ability to solve broader categories of problems.

In the 1990s and early 2000s, artificial intelligence programs returned to achieving important successes, though behind the scenes. AI was applied in areas such as logistics and supply chain management, data mining, and medical diagnosis (Artificial intelligence – Wikipedia). These practical successes were driven by several factors, most notably the massive increase in computing power (Moore’s Law), the focus on solving specific problems rather than trying to build comprehensive general intelligence, and the adoption of rigorous mathematical and statistical approaches in software development (Artificial intelligence – Wikipedia).

With the evolution of computing capabilities and the availability of huge amounts of digital data, the boom of machine learning and deep learning emerged over the past two decades. In 1997, IBM’s Deep Blue chess program achieved a historic milestone by defeating the reigning world chess champion Garry Kasparov (Evolution of Artificial Intelligence Technologies: From Theory to Modern Reality – Nabd Oman Network | Since 2008 %) (Evolution of Artificial Intelligence Technologies: From Theory to Modern Reality – Nabd Oman Network | Since 2008 %), proving the superiority of AI programs in a complex field like chess. Then in 2011, the IBM Watson system demonstrated the ability to understand natural language and answer questions by winning the famous Jeopardy game show against human competitors. In 2016, DeepMind (owned by Google) introduced AlphaGo, which managed to defeat world champions in the game of Go, long considered impervious to machines due to its immense complexity. Today, in the current decade, AI software has reached unprecedented levels with the emergence of deep neural networks capable of learning from vast quantities of data. For instance, large language models like GPT-3 and later GPT-4 have emerged, capable of producing human-like text across various topics, leading up to interactive chat programs like ChatGPT whose use has become widespread in many fields. Major tech companies also continue to develop smarter and more specialized programs; for example, Google is working on the Gemini project as a multi-modal AI model, and other companies are developing advanced systems in autonomous driving, image analysis, and more.

Read also: What is an AI agent?

Comparative analysis of various technologies

Technologies for building artificial intelligence software vary, and each has its strengths and weaknesses. Below is a comparison of the most prominent approaches and techniques used in designing AI software:

• Expert Systems: These are among the oldest AI techniques. They rely on a massive knowledge base containing facts and IF-THEN rules established by experts in a specific field. The program reasons based on these rules to simulate an expert’s thinking. Strengths: Very good at solving specific, well-defined problems and provides logical explanations for its results (because it follows known rules). Weaknesses: Does not learn from experience; meaning its scope of knowledge is limited to the rules entered by experts, and any new case outside those rules leads to system failure. Example: The medical diagnosis system MYCIN was able to suggest treatments based on fixed rules but cannot autonomously acquire new medical knowledge.
• Machine Learning: Relies on algorithms that enable the program to learn from data rather than relying on explicit rules. Here, the program builds a statistical model from training data and can thus generalize to new cases. Strengths: The ability to process massive amounts of data and adapt to new patterns, where the program’s performance improves as training data increases. This technique is used in fields such as fraud detection (where the program learns fraudulent transaction patterns) or content recommendation (as in movie or product suggestion systems for users). Weaknesses: Requires a large amount of good data for training, and suffers from the “black box” problem where it is difficult to internally explain the model’s decisions compared to expert systems. Also, if the data is biased, the program will reflect that bias.
• Deep Learning: An advanced branch of machine learning that relies on multilayer artificial neural networks. Deep learning has achieved massive leaps in the performance of AI software in recent years, especially in computer vision and language processing. Strengths: Superior performance in recognizing very complex patterns (such as facial recognition in images, understanding human speech, and automatic language translation). Capable of automatically extracting features from data without requiring significant human intervention in choosing important characteristics. Weaknesses: Requires massive computing resources (powerful GPUs and a lot of memory) and huge amounts of data for training. The resulting model is very complex, consisting of millions or billions of parameters, making it difficult to understand how it reaches its decisions. Example: A model like GPT-3 contains 175 billion parameters and is capable of producing human-like text, but it is considered opaque in terms of how it arrives at answers.
• Reinforcement Learning: A different approach based on the idea of learning through trial and reinforcement. Here, the program learns via trial and error within a specific environment and receives rewards (positive reinforcement) when making correct or successful decisions. Over time, the program tries to maximize its rewards by improving its strategy. Strengths: Very suitable for problems involving a sequence of decisions or multiple steps, such as strategy games or robot control. This approach has achieved notable successes such as the AlphaGo program, which learned superior Go playing strategies through millions of trial games, or autonomous control systems that learn to drive vehicles. Weaknesses: The learning process can be very slow and requires a huge number of trials (experiments), especially in complex environments, and it may struggle to generalize its knowledge from a specific training environment to a different one. Furthermore, defining the reward function incorrectly can lead to undesirable behaviors.

These technologies are not entirely mutually exclusive; they can sometimes be combined into a single system. For example, a modern artificial intelligence program may contain a mix of deep machine learning with rule-based expert components to ensure a degree of explanation and guidance. Choosing the appropriate technology depends on the nature of the problem to be solved and available resources. For instance, for tasks like speech or image recognition, deep learning is usually the best choice. In applications requiring a clear explanation of results (such as decision support in medical and legal fields), combining expert rules with simple machine learning models may be the ideal solution to ensure transparency.

Where to find this concept online?

• OpenAI Platform – ChatGPT: OpenAI provides the famous ChatGPT chat program built on an advanced AI model. It can be tested for free via OpenAI’s official website (the ChatGPT platform) to explore the capabilities of AI software in understanding natural language and answering questions.
• IBM Watson Platform: IBM offers a suite of artificial intelligence software under the name Watson. You can visit the official website to view various Watson services such as text analysis, computer vision, and customer interaction using AI.
• Google AI Website: Google provides information on its prominent AI research and software through the Google AI website and its research arm, DeepMind. On this website, you can read technical updates regarding models like AlphaGo and machine learning methods used in Google products.
• Microsoft Azure AI Services: Microsoft provides many cloud-based AI services and software through its Azure platform, such as APIs for speech recognition, machine translation, and image analysis. Developers and companies can access this software via the Azure AI website and test its capabilities for free or on a paid basis.
• Open Source TensorFlow Library: For those interested in developing their own AI software, the TensorFlow library (developed by Google) is one of the most popular machine learning and deep learning frameworks available for free. The library’s official website contains tutorials and tools enabling developers to build and train AI software in various fields.
• AI for Good Initiative: A global initiative under the auspices of the United Nations (International Telecommunication Union) aimed at using artificial intelligence to solve humanitarian and developmental problems. You can visit the initiative’s website to view AI-for-good application projects, which are an example of the role AI software plays in creating a positive global impact.