What is an AI system?

While it has been making headlines for many years, artificial intelligence (AI) is increasingly dominating the news. The technology is constantly evolving and AI systems are going to disrupt numerous sectors… To understand the implications of this technology, it is essential to grasp its functioning and master its stakes.

So what exactly is an artificial intelligence system? What is the difference between AI and an AI system? And what is the impact of this technology on our society?

At first, artificial intelligence…

An artificial intelligence system (AIS) is a complete computer system, some components of which involve artificial intelligence. While the terms are sometimes used interchangeably, it is important to distinguish between AI and AI systems. AI is actually the theoretical foundation upon which AI systems are based.

AI, or how to mimic human intelligence

The concept of “artificial intelligence” emerged in the 1950s, when three mathematicians created the first artificial intelligence language. Trained to solve mathematical problems, their program became capable of clearly explaining certain established theorems. Scientists then focused on developing self-learning technology: machine learning. Using a significant amount of data, the system trains itself to replicate human intelligence. By the early 1980s, the first AI applications emerged. And since the acceleration of big data, we have witnessed a true revolution in this field.

Today, artificial intelligence (AI) refers to a scientific field that encompasses a set of computer technologies. It involves creating systems capable of producing reasoning that mimics human intelligence, through algorithms and specific programs. In other words, it is about enabling a computer system to learn, think, and act like a human being. AI can synthesize information and model data, recognize elements in an image, or predict trends. And it delivers all its results with clear and understandable language.

There are several types of AI. The one increasingly infiltrating all spheres of our daily lives corresponds to what is called “weak” AI. The system can perform a task almost perfectly without being supervised by humans. “Strong” AI – which could have feelings and a true self-awareness – remains for now in the realm of fiction…

How does artificial intelligence work?

Artificial intelligence relies on a three-step process:

  • Assimilation of information: algorithms capture and decrypt all the information generated by each interaction on the web. These computing units reproduce human learning, by mimicry. Other programs then transmit this data to AI programs;
  • Data analysis and machine learning: AI analyzes and processes the information it receives. And the more it receives, the more it evolves. It understands, learns, and develops a network of increasingly sophisticated “neurons.” This is the principle of machine learning, which allows AI to become increasingly precise and improve with experience;
  • Implementation of an appropriate response: after analyzing the received data, artificial intelligence reacts in the most appropriate manner by reproducing human reasoning. Translating a text or parking a self-driving car, recognizing a face in photos, or automating certain repetitive tasks… It can offer several types of responses depending on the context.

AI systems, concrete applications of AI

An AI system is a concrete application of the general concept and principles of AI. The AI system is designed to mimic human behavior and perform more or less complex tasks in a specific domain.

In the official and definitive version of the AI Act, the definition of an AI system is as follows: “‘AI system’ means a machine-based system designed to operate with varying levels of autonomy, that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”

A complex system integrating AI components

An AI system is a complex system that operates based on different AI components:

  • Algorithms: at the core of AI, algorithms analyze and interpret a set of data, and then generate a result (solve a problem or perform an action);
  • Data: essential to any AI system, data feed its learning system and allow algorithms to be trained. The more numerous and precise the data, the more efficient the system becomes;
  • Learning models (or machine learning): this is the system’s ability to learn from simple data by itself and to progressively improve without being specifically programmed to do so. The AI system can also incorporate predictive models. They allow it to analyze past data to anticipate future trends;
  • IT infrastructure: in order to store and analyze an infinite number of data, the AI system must rely on a strong and efficient infrastructure. The system’s user interface also allows users to interact with the results, and thus communicate with the artificial intelligence system.

Thanks to significant technological advances in recent years, artificial intelligence systems are becoming more powerful and sophisticated. They can process an exponential amount of data, solve complex problems, and provide real-time results. And they are now used in many contexts.

Diverse applications in various fields

Today, several AI systems are fully integrated into our daily lives: virtual assistants (Alexa, Siri, or Google Assistant), content recommendation algorithms on streaming platforms, facial recognition systems, or automatic translation services… In addition to several sensors and cameras, self-driving cars also use AI systems to perceive their environment and drive safely on their own.

Versatile, AI systems are also designed and developed to solve tasks or improve processes in various other fields:

  • Healthcare: thanks to visual recognition algorithms and predictive analysis, AI systems can facilitate the screening and diagnosis of certain diseases. By gathering thousands of scientific reports, data mining also improves certain aspects of medical care (identifying the most effective treatments, personalizing care according to each patient’s profile…);
  • Logistics and retail: AI systems address several essential issues in these sectors. Predictive algorithms, for example, anticipate demand according to periods, and thus optimize real-time management of goods and stocks;
  • Finance: AI systems can be used to make forecasts and detail market trends. They also analyze financial data to optimize portfolio management or identify patterns of fraud and suspicious activities for better risk management;
  • Marketing: predictive analysis and identification of customer segments, product recommendations, and personalization of the customer experience, automation of certain marketing tasks, chatbots, and virtual assistants… AI systems have been revolutionizing corporate marketing strategies for several years.

What are the challenges for AI systems?

A true transformation lever for businesses, artificial intelligence and the systems that exploit it will continue to develop in the months and years to come. And while this technology offers many opportunities, it also raises a number of questions.

Protection of privacy and security of personal data, dissemination of errors and false information, impact on certain jobs and the economy… Thanks to its adaptability and learning capability, AI could potentially surpass and replace humans in certain areas. According to the OECD, one-third of jobs are expected to change radically over the next 20 years.

The deployment of AI systems therefore requires the establishment of a proper regulatory framework. A pioneer in this field, the European Union has just voted to adopt the European law on artificial intelligence (AI Act), which aims to regulate artificial intelligence in Europe. The text notably provides for classifying AI systems into three categories, according to the risks they may pose (minimal, high, or unacceptable risks). Solutions deemed “high-risk” will then be subject to certain rules and constraints. The goal? To better regulate AI and ensure healthy, ethical, and responsible artificial intelligence.

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