What is artificial intelligence?

Definition of artificial intelligence

Artificial intelligence (AI) is a field of computer science that relies on the ability of machines and computer systems to simulate certain human cognitive functions. It uses artificial neural network models, which mimic the functioning of neurons in the human brain, as well as sophisticated algorithms to analyze large amounts of data. With these technologies, AI can understand complex questions, predict intentions, identify objects in images, model behaviors from data, and synthesize valuable insights for multiple uses. By using machine learning and deep learning algorithms, AI systems can notably calculate probabilities and provide clear and relevant answers to humans.

Another important AI breakthrough is generative AI, which allows systems to create original content, such as text, images or videos, based on the data they have been trained on. This opens up new possibilities for innovation in various sectors and uses, such as content creation, product design, or even art and music. For several years, models like ChatGPT, developed by OpenAI, have demonstrated their effectiveness in these areas, offering new services and applications to users and businesses.

History of artificial intelligence

As early as the 1940s, when the first computers were born, many scientists, like Alan Turing, sought to develop what was then called an "artificial brain." These researchers have conducted experiments to achieve their ambitious goal: to create a machine that is smarter than humans. To prove their intelligence, they designed the Turing test, which is still famous.

The principle of this test is simple: for five minutes, a person sends messages by computer to two people, one human, the other a machine. At the end of the conversation, if the person cannot tell the human from the machine, the test is considered successful. AI thus demonstrates that it has sufficient skills to reproduce natural language and engage in intelligent dialog, sometimes including a touch of humor, proving its ability to mimic human interactions.

In 1956, three mathematicians created the first artificial intelligence language, IPL-11, by training a program to solve mathematical problems. This model quickly evolved and became able to demonstrate mathematical theorems with a clear and intelligible explanation. Researchers have developed a self-learning technology called machine learning, which allows artificial intelligence systems to build their own intelligence by training themselves to reproduce human analysis on a large volume of data. OpenAI has taken advantage of these concepts to create advanced systems like ChatGPT that can understand and generate text naturally.

Machine learning and big data - the ability to collect and analyze an exponential volume of information to train AI models - are thus at the heart of AI’s successes and breakthroughs. Thanks to these technologies, companies can now benefit from this capacity for intensive use of data in order to develop systems capable of unprecedented performance in various sectors, from automated customer services to intelligent industrial machines.

How does artificial intelligence work?

AI uses three key steps to simulate human cognitive processes: assimilating information, analyzing data, and implementing appropriate responses or actions. Each step plays a crucial role in AI's ability to learn, understand and interact effectively.

Step 1: assimilate the information

To work, AI must first capture and store a large amount of data. Every digital interaction generates information, whether it's clicks on a website or data from a sensor. This information must sometimes be anonymised, in compliance with regulations such as the GDPR, in order to protect users' privacy.

The learning algorithms, also called “artificial neurons”, analyze and decipher this data. For example, to recognize images, they break them down pixel by pixel before converting them into a data set. Other algorithms collect and redistribute big data’s massive data, while cloud computing offers the computing power to process that data at scale.

Step 2: Data analysis

Once the data is collected, AI uses deep learning, an advanced form of machine learning, to analyze it. Data scientists define analysis criteria, which algorithms enrich over time by proposing ever more relevant answers.

The effectiveness of AI depends on the quantity and quality of data available. Data mining, for example, can identify similarities, trends or actionable insights across vast data sets. However, this analytical capability requires high computing power. Technologies such as GPUs and dedicated solutions such as OVHcloud AI Training optimize these resources to accelerate AI learning processes.

As deep learning algorithms become more sophisticated, data processing becomes almost instantaneous, opening up new possibilities for AI applications.

Step 3: Implement appropriate responses or actions

Artificial intelligence uses the data analyzed to propose answers or actions adapted to expectations. This process, comparable to a "cognitive reflex", allows AI to:

• Offer user-friendly video recommendations (relational intelligence);
• Automate repetitive tasks to increase operational efficiency (intelligent automation);
• Carry out real-time translations to facilitate communication (semantic analysis, language interpretation);
• Identify market trends to help with strategic decision making (business intelligence)
• recognize objects or animals in thousands of photographs (visual recognition);
• safely manage autonomous vehicles (cognitive reflexes, proprioception).

Examples of uses for artificial intelligence

Predictive models in the public sector

Managing infrastructure and public services can be complex and extremely time-consuming, with each decision requiring in-depth analysis of many metrics. For example, deciding to renovate a station involves facing various issues such as prioritizing work according to the projected budget, studying traffic flows to propose alternative routes, or optimizing alternative transport.

This is where AI comes in. By training artificial intelligence models, it is possible to capture, process and analyze available data more efficiently, making more accurate projections. These predictive models facilitate the development of decision trees, which detail the costs and benefits associated with each option considered, and become valuable decision support tools for public managers.

As a result, AI is proving to be a leading solution in planning and optimizing operations for all public administrations and services. Thanks to advanced systems and the use of machine learning, administrations can not only improve the quality of their services, but also reduce costs and make informed decisions based on data-driven information.

Healthcare research and development

Artificial intelligence is at the heart of many health research and development (R&D) programs, producing particularly promising results. AI is used for critical applications such as early detection of cancers and serious diseases, which present major challenges for the medical sector.

For example, after successful visual recognition tests, AI algorithms were integrated into early tumor screening programs. Compared to a team of neurosurgeons, MRI recognition algorithms are already ten times faster and more effective at detecting brain tumors. They compare each new image of the brain to thousands of others in databases, providing fast and accurate analysis.

Generative AI also takes these advances one step further, by enabling the creation of synthetic data models, such as artificial medical images. These models can be used to train new screening systems while respecting the confidentiality of patient data, making it easier to find without compromising data protection requirements. This use of predictive analytics and image recognition by AI can help save lives by enabling diagnosis before disease even manifests itself, revolutionizing the world of medicine and transforming the way health services are delivered.

And with techniques like data mining and knowledge extraction from thousands of scientific reports, AI can better understand the causes of diseases, detect potential contraindications between different drugs, and accelerate large-scale scientific research, thereby improving clinical outcomes.

IT security and data protection

Data protection and governance are critical issues in today’s digital world. Whether it’s for large research centers or businesses of all sizes, securely accessing, sharing and using information is vital to maintaining trust, ensuring confidentiality, and complying with data protection regulations.

In this context, AI-based cybersecurity software plays a key role. By detecting potential network vulnerabilities and blocking malware that mimics human behavior, these tools provide a dynamic, proactive defense layer. With real-time data analysis and machine learning algorithms, these systems become able to anticipate and react quickly to threats, even before they happen, drastically reducing the risk of intrusions.

Furthermore, the integration of AI into cloud computing further enhances IT security. Businesses can protect their sensitive information while benefiting from a scalable, flexible infrastructure. For example, the use of artificial intelligence in the cloud allows for the continuous monitoring of unauthorized access and the identification of suspicious behavior on a large scale, providing a more robust defense against cyber-attacks.

By combining AI with advanced security tools, organizations are better equipped to defend their networks against growing cyber threats. This holistic approach, combining AI and cybersecurity, not only strengthens data protection, but also improves the overall resilience of IT systems, ensuring business continuity and compliance with international standards.

Real-time machine translation

Customer relationship management is a strategic issue for any company seeking to excel in the quality of its services. By adopting artificial intelligence systems specialized in Natural Language Processing (NLP), companies can centralize requests from their international customers and instantly translate them into the language of the Customer Managers, thus playing the role of a real-time interpreter.

This language automation allows each Customer Account Manager to provide customized, accurate and tailored responses, which are then automatically translated into the end user’s language. This significantly improves customer satisfaction and significantly reduces response times. In addition, pre-defined response models generated by chatbots, optimized through past exchange analysis and machine learning algorithms, are used to efficiently answer frequently asked questions.

For example, the use of artificial intelligence for real-time machine translation is profoundly transforming the customer experience, while strengthening companies’ competitiveness in an increasingly interconnected global market.

Planning, inventory tracking and flow management

Food management, demand forecasting and real-time inventory tracking are major challenges for retailers and e-commerce. With artificial intelligence and predictive algorithms, it is now possible to anticipate sales and order the exact quantity of products from manufacturers, a task once considered nearly impossible.

Building on sales data from previous years and current insights, professionals can train AI systems to manage the flows of thousands of items in real-time. These business intelligence programs can quickly identify the best sales for each period and suggest the optimal quantities to order. For example, predictive models, control algorithms and advanced data visualization allow buyers to adjust their orders to avoid waste and prevent stock outs, while optimizing costs and improving customer satisfaction.

As a result, integrating AI into inventory and merchandise flow management is transforming the entire supply chain, bringing new efficiency and agility to businesses.

Robotics and embedded intelligence

Robotics represents one of the major applications of artificial intelligence, focusing on the development of cognitive intelligence. Developers in this field go beyond designing intelligent computers connected to a network to creating autonomous machines that can interact independently with their environment.

To achieve this, robots and robots are equipped with various sensors, such as onboard cameras, microphones, radars and connected object networks. These devices allow them to perceive their environment in real time and adjust their behaviors according to the information received, thus simulating cognitive reflexes adapted to each situation. This ability to adapt in real time is essential for robots to be able to perform complex tasks in diverse environments.

Embedded intelligence already illustrates its immense potential through innovations such as autonomous cars. Capable of understanding and complying with the highway code, parking themselves and reacting instantly to a danger, the autonomous car uses intelligent systems that integrate advanced algorithms for fast and secure decision making. These examples show how AI is transforming not just the field of mobility, but many other sectors, by bringing autonomous and smart solutions that improve security and efficiency.

Creative sectors

Generative artificial intelligence is revolutionizing the creative sectors by opening new perspectives for content creation. Whether in art, music, literature, or advertising, these technologies can generate images, videos, texts, and even original musical compositions, providing unprecedented opportunities for creators.

In art and design, for example, artists and designers use generative AI tools to create complex digital works, exploring shapes and styles that would be difficult to imagine otherwise. In literature, writers can collaborate with AI systems to co-write stories or scripts, enriching their narratives with fresh and diverse suggestions.

Similarly, in music, generative AI can compose new work by drawing inspiration from existing styles, enabling composers to discover new inspirations or quickly produce songs tailored to specific needs, such as soundtracks for movies or video games. In advertising and marketing, generative algorithms are used to create personalized content, designed for more targeted and engaging campaigns, increasing the impact and reach of advertising messages.

As a result, AI’s ability to generate creative content offers industry professionals increased flexibility and efficiency. It also paves the way for new forms of artistic expression and innovative approaches to creation, while transforming traditional processes and stimulating innovation in the creative industries.