Smartphones, computers, connected applications… They’ve played a central role in our daily lives for several years now, often thanks to artificial intelligence. AI is behind voice recognition, facial unlocking, and virtual assistants like Siri (to name but a few), without us always being fully aware of it. The release of ChatGPT by OpenAI in November 2022 was a real wake-up call… An unexpected awakening. The media and the general public suddenly realized that AI was not just a background technology. Its capabilities go far beyond what many had previously imagined (and this is surely just the beginning).
Based on large-scale language models (LLM) and natural language processing (NLP) techniques, ChatGPT surprised many with its ability to generate coherent texts and interact almost in a human-like manner. For its part, Midjourney creates strikingly realistic images from simple text descriptions. Today, hundreds of millions of users are already leveraging these tools to generate content or analyze data… Perhaps you’re one of them?
But using these technologies is one thing. It’s also important to know how they really work. What is their history? What types of AI exist? And above all: how can you make the most of these tools?
Origins, key dates, and AI pioneers
The origins of artificial intelligence are rooted in concepts that have profoundly influenced the evolution of computing. British mathematician Alan Turing laid the theoretical foundations of the field. During the Second World War, he distinguished himself through his work on decrypting the Enigma code used by the Germans, a decisive contribution to the Allied victory.
In 1936, Turing published his seminal text, “On Computable Numbers,” in which he introduced what we now call the “Turing Machine.” This established a foundational framework in computational theory, showing what machines could achieve through sequential instructions… a major milestone. Then, in 1950, with “Computing Machinery and Intelligence,” Turing went even further, posing the now famous question: “Can machines think?” To answer this question, he proposed what we know as the Turing Test (a test to determine whether a machine can imitate a human in conversation). These thoughts paved the way for debates on consciousness and artificial intelligence. In 1956, at the Dartmouth Conference, often seen as the official kick-off for the field, McCarthy coined and introduced the term “artificial intelligence.” On this occasion, he brought together key figures such as Marvin Minsky and Claude Shannon to discuss future prospects… although it must be admitted that their expectations were probably too optimistic.
McCarthy didn’t stop there: as early as 1958, he created Lisp, a central programming language for AI developments. He also contributed to the concept of “time-sharing,” enabling several users to exploit the resources of a single mainframe computer simultaneously.
The following years saw the appearance of the first artificial intelligence programs. The “Logic Theorist” (1956) and the “General Problem Solver” (1957), developed by Allen Newell and Herbert A. Simon, illustrate these promising beginnings… This was the emergence of what we now call symbolic AI, an approach that would dominate for several decades.
At the same time, another school of thought was developing: connectionist AI. In 1957, Frank Rosenblatt proposed the perceptron model, inspired by the human brain, which paved the way for artificial neural networks. Although this work gave rise to much hope in its early days, certain limitations held back its development until machine learning experienced a veritable renaissance in the 1980s…
Even today, symbolic AI and connectionist AI coexist (and sometimes complement each other)… The neurosymbolic approach now combines machine learning and logical reasoning to take full advantage of the respective strengths of these two complementary visions.
The different types of artificial intelligence
Artificial intelligence comes in many forms, each with its own particularities. It can be described as weak or strong, symbolic or connectionist. When we talk about artificial intelligence, the image of a machine that could think and reason like a human often comes to mind… in truth, the situation is more nuanced.
Weak AI vs. strong AI
On the one hand, there’s what’s known as “weak” (or narrow) AI. It’s designed to perform specific tasks without any real understanding of the bigger picture. For example, facial recognition software: it can identify a face in a photo, but doesn’t understand the emotions reflected in it. Voice assistants like Siri or Alexa can answer your queries or execute commands, but without grasping the emotional meaning of your words.
This weak AI is an integral part of our daily lives. It drives search engines, recommends movies on Disney Plus, and filters spam in our emails. It is very good at performing specific tasks but remains confined to the strict limits of its programming.
At the other end of the spectrum is what we call “strong” artificial intelligence. This aims to reproduce full human intelligence, with consciousness and general learning capacity. To understand the concept, you need to imagine a machine capable not only of understanding your language but also of experiencing emotions or even creating something on its own. For the moment, this remains theoretical (and very far from feasible), but the prospect alone already raises complex moral questions: what rights would be granted to such an entity? And if it were to surpass the human… what would the consequences be?
Symbolic and connectionist approaches
To understand how we got here, we need to return to the two main approaches that have shaped the development of artificial intelligence: the symbolic approach and the connectionist approach.
Symbolic AI is based on explicit logical rules—as if we were trying to model human thought with “if… then…”. For example: “IF a patient has a fever AND rash THEN he could have measles.” This was dominant in the early decades of the field, but it has shown its limits in the face of the complexity of reality.
This is where the connectionist approach comes in. Inspired by the workings of the human brain (but without claiming to replicate them), it relies on artificial neural networks capable of learning by themselves from raw data. These networks adjust their connections over time, gradually improving, similar to how we learn from experience. This has led to impressive applications such as image recognition and automatic natural language processing.
Different learning methods
At the heart of these neural networks is, of course, learning, which can take several forms, depending on needs.
Supervised learning is probably the most straightforward: you provide the system with a series of examples where you already know the correct answer, say a thousand images labeled “dog” or “cat,” then it gradually learns to distinguish these categories itself.
Next comes unsupervised learning, where no answer is given at the outset. The machine has to discover certain patterns in the data provided on its own, like putting together a jigsaw puzzle without having seen its final image beforehand.
Finally, we have reinforcement learning, inspired directly by behavioral conditioning in living animals. Here again, AlphaGo (the program developed by DeepMind) remains emblematic: after literally playing against itself for millions of successive games (!), it learned which strategies maximized its chances against any human in the game of Go.
Each method has its own advantages, as well as certain inherent weaknesses… Which is why combining these approaches is becoming necessary today to continually improve their overall performance.
Advantages and disadvantages of AI
We’re at a turning point, and the artificial intelligence debate is generating lively discussion… Does the development of artificial intelligence present more dangers than advantages?
Films such as I-Robot and Terminator have popularized the idea of AI beyond human control. Even more worryingly, several influential scientists, starting with Stephen Hawking, have expressed their reservations. In 2014, renowned physicist Hawking warned: “The development of full artificial intelligence could mean the end of the human species.” In his view, the slowness of our biology in the face of machines capable of self-improvement would constitute a major risk.
Elon Musk, always quick to take a stand on major technological issues, likened AI to “summoning the devil” during an AeroAstro Centennial Symposium at MIT. Since then, he has financially supported several initiatives aimed at ensuring ethical development in this field, including OpenAI, an organization he actively encourages.
Nick Bostrom, a philosopher well known for his work on artificial intelligence, tackles this subject in depth in his book “Superintelligence” (2014). In it, he warns of the risks of an AI that could one day surpass our capabilities and become uncontrollable if we don’t take appropriate measures…
The debate remains open. Many experts and leading figures in the technology sector see artificial intelligence as an unprecedented opportunity for mankind.
Andrew Ng, a pioneer in the field of machine learning, goes even further, claiming that “AI is the new electricity.” For him, every sector of the economy will be profoundly changed by these technologies, resulting in increased productivity and unprecedented opportunities.
Fei-Fei Li, Professor of Computer Science at Stanford University, emphasizes AI’s ability to enhance our creativity. She says: “Properly implemented AI could help us solve complex problems while improving the daily lives of millions of people.”
Finally, Satya Nadella, current CEO of Microsoft, prefers to emphasize that AI is not seeking to replace humans but rather to augment their capabilities. As he puts it, “AI exists to help us achieve more, not to replace us.”
Of course, while some concerns remain, the societal benefits are nonetheless promising.
Disadvantages
Job losses and unemployment: Of course, not everything is rosy: by automating certain functions (particularly in manufacturing and certain services), artificial intelligence is pushing some workers into an urgent need for retraining… Industrial robots are gradually replacing certain human tasks—a major challenge facing our modern economies.
Bias and discrimination: Another significant drawback is that, like any technology based on existing data, it can reproduce certain biases present in the latter… Consider, for example, systems based on facial recognition, which can be dangerously inaccurate for certain populations, or automatic tools that unconsciously select certain profiles when recruiting.
Invasion of privacy: The massive (and often opaque) processing of personal data also raises questions… The collection of data required for the proper functioning of algorithms naturally raises concerns about our fundamental right to privacy—not to mention the increased risk of potential leaks of sensitive information.
Autonomous weapons and international security: When it comes to international security… The recent development of AI-controlled autonomous weapons raises fears of the worst: with no direct human intervention possible at the time of potential triggering, the risks of uncontrolled conflict increase significantly. It is therefore difficult not to address the major ethical issues surrounding responsibility when such a system causes irreversible damage…
Loss of control and ethics: Even highly sophisticated artificial intelligence can sometimes behave in unexpected ways if it is not sufficiently supervised. The ethical issues surrounding automated decisions and the lack of transparency of algorithms raise questions… It is essential to pay particular attention to these issues (or risk unintended consequences for humanity).
Technological dependence: Relying too heavily on AI risks weakening human skills and even limiting our ability to make decisions without recourse to technology. In the event of a breakdown or cyber-attack, such dependence could lead to major disruptions in the day-to-day running of society…
Benefits of AI
Improved healthcare: Artificial intelligence is profoundly transforming the medical field. It now makes it possible to improve prevention, diagnosis and even treatment. For example, some algorithms analyze medical images to detect cancers at a very early stage… sometimes with an accuracy that rivals that of doctors. In addition, AI adjusts treatments according to each patient’s genetic profile (a welcome personalization) and accelerates the development of new drugs thanks to the simulation of molecular interactions.
Accelerating scientific research: By automating the analysis of vast quantities of data, artificial intelligence gives researchers a boost in a variety of fields. Whether it’s discovering new molecules in chemistry or understanding climatic phenomena in meteorology (not to mention space exploration), it identifies complex patterns and simulates scenarios that would have taken humans much longer to conceive alone.
Optimizing energy efficiency: AI also has its say in the energy sector. It optimizes consumption in buildings, better manages power grids and even guides transport towards more sustainable management. By enabling better anticipation of energy demand and more rational use of renewable sources, artificial intelligence plays an active role in reducing pollutant emissions… in other words, AI is no stranger to efforts to combat climate change.
Improving education: Education is also benefiting from recent technological advances: by adapting to the specific needs of pupils or students, AI personalizes their learning. It quickly identifies their shortcomings, proposes a series of targeted exercises and facilitates individual progress… making everything a little smoother for everyone.
Economic development and innovation: In economic terms, AI is undeniably stimulating certain industrial dynamics. By automating certain repetitive or tedious tasks (which can often slow down processes), it paves the way for more innovation in various emerging sectors. The result is not only new jobs but also global growth fueled by these new technological services.
Improved quality of life: In our daily lives, too, we’re starting to feel the positive effects of AI: voice assistants to help us organize our days or applications capable of instantly translating what we say… AI makes many practical aspects much more accessible. Let’s add to this a few personalized recommendations for entertainment, or even autonomous vehicles that could well become synonymous with less bumpy roads…
Professional use cases
Artificial intelligence now offers a variety of concrete, universally accessible applications for improving productivity and creativity in a wide range of sectors. It is becoming an indispensable tool for those seeking to streamline their work while optimizing their time…
Assisted copywriting and content creation: In the field of copywriting, AI is profoundly changing the way content is generated. AI-based tools can now produce articles, reports or even social networking posts in less time. Consider SEO: creating optimized articles becomes much simpler with these technologies. Writers can then concentrate on overall editorial strategies while AI takes care of the text, naturally integrating keywords and SEO best practices. AI App like NAIXT facilitate this very process. They offer relevant suggestions, adjust style, and improve text structure… Allowing professionals to produce coherent content without spending excessive time on it.
Professional communication: The day-to-day management of emails can quickly become tedious. AI comes into play here too: it analyzes incoming content, suggests appropriate responses, and prioritizes important messages. Better still, it automatically filters spam and can draft standardized replies while adjusting the tone according to the interlocutor (which is no mean feat). The result is smoother, more efficient communication.
Creating marketing campaigns: In digital marketing, AI is used to finely analyze customer data and precisely segment the audience. Thanks to these sophisticated analyses, it becomes possible to design perfectly targeted email campaigns… The goal, of course, is higher conversion rates! AI then personalizes each message according to the customer’s behavior or preferences, thereby strengthening their commitment. Solutions like NAIXT accelerate the creation of such personalized campaigns by rapidly adapting content to the specific needs of different audience segments.
Artistic image generation and visual marketing: Artificial intelligence has also found its place in visual creation. Today, you can generate an artistic image or marketing visual simply from a given text description (imagine what that means for a designer…). These tools enable designers to easily explore various styles without necessarily mastering all the complex graphic techniques. With NAIXT’s Image Generator, for example, artists and marketers can broaden their creative horizons within certain technical constraints.
Optimizing business processes: But that’s not all… AI also integrates many operational business processes to improve their overall efficiency. Automating repetitive tasks? Large-scale data analysis to extract key insights? Accurate forecasts of certain trends? All this is already a real possibility thanks to these new technologies. In some customer services, for example, we’re seeing the emergence of a whole new generation of high-performance “chatbots”, capable not only of providing assistance but above all of responding 24/7!
Continuing training adapted thanks to AI: Last but not least, we’re also seeing how these same algorithms are now powering several platforms dedicated specifically to continuing professional development via adaptive personalized learning… Each learner then follows his or her own pace with an individualized path… (rather clever when we know how fast our technological environment is moving).
An opportunity instead of an obstacle
Far from being a cause for concern, artificial intelligence offers an unprecedented opportunity to improve our efficiency. Those who know how to adopt it and integrate it into their professional routine will then have a major advantage in tackling tomorrow’s challenges.
It goes without saying that those who fail to do so run the risk of quickly finding themselves out of their depth. Hence the importance of training and acquiring the necessary know-how to use AI effectively. Those who adapt will thrive in the future.
We mustn’t lose sight of the fact that AI represents an important lever for boosting productivity. It facilitates large-scale data analysis to better adapt business strategies, improving overall performance for companies and consumers alike.
To take full advantage of these innovations while avoiding their possible perverse effects, education will play a central role. Preparing to integrate artificial intelligence today is essential for success tomorrow. Learning to tame AI rather than dreading it will enable everyone to evolve serenely in this new, changing environment…