#1.
Introduction
We have written this article about artificial intelligence for 'Les Cahiers du Digital' of the HEC Digital Lab. These 'cahiers' are intended for students of HEC Liège to further supplement and provide background to the subjects covered, but are also available digitally.
Each 'cahier' addresses a theme and consists of three articles written by professionals with expertise in digital transformation. We invite you to discover the other two articles dedicated to artificial intelligence via this link ('On the way to Augmented Intelligence').
We wrote the article in November 2022, when OpenAI launched its AI writing tool, ChatGPT, on the market. Since then, the tool has been everywhere and has further evolved. It is impossible to ignore this artificial intelligence, which can answer questions, start a conversation, admit mistakes, or reject inappropriate requests.
The italicized parts are proposed by ChatGPT.
We often hear about Artificial Intelligence (AI) in many different fields. But do we really know what it is?
Artificial intelligence refers to the ability of a computer or machine to perform tasks that normally require human intelligence, such as language understanding, pattern recognition, learning, and problem-solving.
AI technologies include machine learning, natural language processing, computer vision, robotics, and many more (we will discuss these techniques in the section "The Branches of AI").
AI systems can be trained to recognize patterns, make predictions, and automate repetitive tasks, freeing up time for humans to focus on more complex and creative tasks.
AI is used in many different ways today, and the list is constantly growing as new applications are developed.
Here are some examples of use:
- Personal assistants: virtual assistants such as Apple's Siri or Amazon's Alexa use natural language processing to understand and respond to voice commands.
Their understanding seems close to that of a human, but have you ever been frustrated because you had to repeat your request several times or change the wording of your sentence because your virtual assistant didn't understand it?
For example, if you ask to "start the vacuum cleaner," the first versions of Google Home may not respond to your request because the verb "start" is not appropriate. On the other hand, even if you use the wrong verb, a human would have understood the request.
- Customer service: many companies use AI-powered chatbots to handle customer inquiries, provide information, and guide customers through a process, freeing up customer service agents for more complex tasks.
This application of AI is effective when customers have a relatively simple and frequently asked question. If the question is more complex, they will probably need to communicate with a person at some point. This allows companies to filter requests but not to respond to them 100%.
- Fraud detection: financial institutions use machine learning algorithms to analyze patterns in financial transactions and identify potential fraudulent activities.
- Medical diagnosis: AI algorithms can be trained on large datasets of medical records and images to help doctors identify diseases, make more accurate diagnoses, and recommend treatments.
- Self-driving vehicles: self-driving cars and drones use AI to navigate and make decisions in real-time. AI can be used to detect pedestrians or cars that are out of place on the road. The car will then automatically brake or swerve to avoid them.
- Supply chain management: AI can be used to optimize the flow of goods and materials in a supply chain, helping companies to be more efficient and reduce costs.
Implementing AI within a company can represent a very high cost, which must be taken into account to determine whether AI is necessary and if it will actually reduce your costs in the long run.
- Social media moderation: AI algorithms can be used to help identify and remove inappropriate content from social media platforms.
It should be noted that an AI named Tay, developed by Microsoft in 2016, made inappropriate comments on Twitter. Some users wanted to test the limits of this chatbot, which was supposed to interact with American teenagers to study language understanding. They managed to get Tay to repeat racist phrases, but the AI also responded on its own to questions and, among other things, denied the Holocaust. Tay was supposed to learn and improve over time, but due to numerous missteps, Microsoft disabled Tay after just 8 hours.
AI learns from the data it has at its disposal but is not able to verify it. In the case of Tay, the information provided was racist, which is why it made such statements.
These are just a few examples, but AI is also applied in many other fields, both professional and private.
Despite the advances of recent years, we must be cautious with the term "Intelligence" because a machine with AI, although it is capable of performing incredible tasks, is not intelligent in the sense we mean. It is a very useful tool and can outperform humans in performing specific tasks, but it does not replace humans.
It can also make mistakes that a human would not make because it does not possess all human capabilities. Google, for example, has already confused images of muffins and Chihuahuas or, more seriously, identified two African Americans as gorillas.
The aim of this article is to explain what artificial intelligence is, to dissect this concept to understand what it enables, and how, through examples, it can help you in your business. But we also want to demystify AI somewhat, because it is far from having the same capabilities as humans and is not ready to surpass them, contrary to what one might sometimes believe.
#2.
The role of the algorithm
In the collective imagination, AI is initially associated with various concepts such as robots in industry or intelligent systems capable of thinking, learning, or solving problems autonomously. Today, these ideas have significantly evolved, and AI is associated with much more.
We will delve deeper into the subject to understand all aspects of this technological revolution, which, as we will see, is not a recent development.
We encounter AI daily and make use of it regularly. Here are a few examples to make you aware of its omnipresence:
- You can ask AI to write an email;
- It can look up information on a topic for you;
- The navigation system in your car finds the best route based on traffic;
- Your smartphone unlocks when it recognizes your face;
- You can ask your virtual assistant to turn on the heating;
- The Shazam app identifies the title and artist of the song you're listening to;
- You can ask AI to proofread your summary and correct spelling mistakes;
- ...
In these examples, we see computer systems that sort and process information and work based on algorithms.
The algorithm is a very important concept in AI: it is a set of instructions, operations that must be followed to solve a problem or accomplish a task.
Simply put, we can compare an algorithm to a cooking recipe: you have ingredients, and by following precise instructions, you achieve a result, for example, a chocolate cake. For the algorithm, it is just the same: you replace the ingredients with a set of data, and then you apply the instructions, that is, the steps of the recipe that represent the operations that make up the algorithm, and you get a result.
Let’s expand this example to better understand what an algorithm in AI can do:
If you ask a non-AI program to find photos of the HEC Liège building, the program will search its database (the ingredients of the recipe) and show you the photos associated with this building. However, it might not show you all the images because some may not be labeled as "HEC Liège building," and thus, without AI, the algorithm will not recognize the building in these images. If a single element is missing, the recipe will not yield the desired result, namely, seeing all the images of the HEC Liège building.
With AI, however, the program can show you all the images, even those without the label "HEC Liège building." To do this, the program first needs to see a lot of photos of the building to learn to recognize it. It will visualize the elements that do not change and those that can vary. This training enables it to fulfill your request by showing you all the photos of the HEC Liège building that the program has in its database.
This image recognition is one of the many tasks AI can perform.
To better understand this concept, it is interesting to trace the key moments of its development. AI appeared in the 1950s and has significantly evolved since then.
#3.
The evolution of Artificial Intelligence
The world became familiar with AI in the first half of the 20th century thanks to science fiction, particularly the humanoid robot in Fritz Lang's 1927 film "Metropolis." Subsequently, scientists and mathematicians took over the concept.
In 1950, Alan Turing, the inventor of the computer, wrote an article titled "Computing Machinery and Intelligence" in which he explained how intelligent machines could be built. He assumed that humans use reasoning and available information to make decisions and solve problems and suggested that machines could do the same. He also explained how these machines could be tested to determine if they approach human intelligence using the so-called "Turing Test." This test involves a program having a written conversation with a human interrogator for five minutes. The interrogator then has to indicate whether they were conversing with a machine or a human. If the machine deceives the interrogator 30% of the time, it passes the test.
In 1956, the term "artificial intelligence" was used for the first time. It was coined for a conference organized at Dartmouth College by scientists John McCarthy and Marvin Minsky, during which Allen Newell, Cliff Shaw, and Herbert Simon presented their program "The Logic Theorist," designed to mimic the problem-solving abilities of a human. It is considered the first AI program.
From that year on, AI flourished for a while, thanks in part to the evolution of computing and various funding sources. For example, in 1965, Joseph Weizenbaum created the program ELIZA, the first to pass the Turing Test, albeit only for a few minutes. This program replaced the psychotherapist during conversations.
Expectations were high, but the technology could not fully meet them yet: data storage and processing remained problematic. Hans Moravec, then a doctoral student of McCarthy, even stated that "computers were still millions of times too weak to exhibit intelligence." Funding declined, and research was halted for a decade. This crisis is known as the AI winter.
In the 1980s, funding and research related to AI picked up again. During this period, John Hopfield and David Rumelhart popularized deep learning techniques, which allow computers to learn from experience. Edward Feigenbaum developed expert systems that enable machines to make decisions like a human expert. The program would ask an expert how to react to certain situations in a specific domain, and once the answers were assimilated, non-experts could then be advised by this program. These systems were widely used in industries or finance, for example, to detect credit card fraud.
Later, in 1997, a machine defeated a human for the first time: Deep Blue, a chess computer, defeated the reigning world champion, Gary Kasparov. This was a significant date marking considerable progress towards an intelligent decision-making program. Deep Blue had a significant advantage as it had thousands of games from the best players in the world in its memory. Its processor allowed it to find the best strategy for each move. In the same year, another major step was taken in speech interpretation: the speech recognition software developed by Dragon Systems was installed on Windows.
Since then, storage capacity and the accumulation of data, as well as the computing power of computers, have continuously increased. All these technical improvements allow the development and enhancement of algorithm performance. For example, in 2015, Google's AI AlphaGo defeated the European champion of the board game Go, Fan Hui, and in 2017, the program even managed to defeat the world champion, Ke Jie. The psychological advantages are noteworthy: the machine cannot be distracted or feel pressure, unlike humans.
Today, AI continues to improve and is used everywhere, whether in healthcare, transport, industry, banking, marketing, entertainment, and more.
In the future, evolution may lead to general intelligence, where machines will have the same cognitive abilities as humans, or even more, potentially surpassing us. However, we are still far from that stage. Many ethical questions, among others, will need to be addressed before reaching such a stage, which many scientists, including Stephen Hawking, consider dangerous.
#4.
The different stages of AI
There are three stages of artificial intelligence:
1) Artificial Narrow Intelligence (ANI):
This is also known as weak AI. At this stage, machines can only perform a set of predefined tasks; they do not have the capability to think. All AI systems that exist today fall into this category: Siri/Alexa, self-driving cars, facial recognition software, voice assistants, chatbots, and so on.
2) Artificial General Intelligence (AGI):
Also called strong AI, this stage, as previously mentioned, is the point at which machines will be able to think and make decisions like humans. So far, there are no examples of artificial general intelligence.
3) Artificial Super Intelligence (ASI):
This final stage describes the moment when the capabilities of machines will surpass those of humans. To date, we cannot confirm if this stage will ever be reached. It remains purely hypothetical, but we see this type of AI in science fiction movies or books where machines control the world.
We are still in the first stage of artificial intelligence, despite the tremendous progress since the 1950s. Therefore, we are still far from achieving general artificial intelligence, which means that we are essentially still in the infancy of AI. Much progress is needed to reach the second stage.
#5.
The different AI systems
AI systems are classified according to their functionalities, and four types are distinguished:
1) Reactive Machines:
In this category of AI, we find machines that operate based on the current situation and the data they receive. They interact with their environment and can only perform a limited number of predefined tasks. They are not capable of making deductions based on their data or past experiences to determine their future actions.
Among reactive AIs, there is the chess program Deep Blue, which defeated the then world champion Garry Kasparov in 1997, or the robotic arms in a factory that are programmed to respond to the presence of a specific object in front of them on a conveyor belt and, for example, move that object to a designated place.
2) Limited Memory AI:
This AI is capable of making decisions based on data stored in its memory, which is short-term, hence the name. By storing past experiences, it can evaluate future actions.
The perfect example is the autonomous car. It uses information gathered in the past to act immediately. Thanks to sensors, it can identify pedestrians crossing the road as well as traffic lights, roadworks, potholes, traffic, etc. All this data is used to make decisions and adjust its speed and trajectory if necessary.
3) Theory of Mind AI:
As the name suggests, this AI focuses on emotional intelligence to understand human thoughts. This type of AI is more advanced than the previous two; it is not yet fully developed, but research is being conducted to achieve this. This AI could play an important role in psychology. If we manage to develop machines with this AI, they will differ from the machines created so far. They will be able to perform the same tasks as the first two types of AI, but they will also understand human emotions and adapt their behavior accordingly.
4) Self-Awareness:
This type of AI corresponds to Artificial Super Intelligence, where machines will have their own consciousness. This would be the ultimate stage we could ever reach: it would be an extension of Theory of Mind AI; machines would understand human emotions, but they would also be able to feel them and predict those of their surroundings. Again, we are far from being able to achieve this level.
#6.
Branches of AI
We can use AI to solve all kinds of problems using different techniques. Below are some examples:
Machine Learning:
This technique allows machines to learn based on experience and examples to solve problems and make predictions. They can interpret, process, and analyze data.
Machine Learning enables computers/machines to make decisions based on data rather than programming them for a specific task. Algorithms or Machine Learning programs learn as they encounter new data, which allows them to improve.
Here's a simple example to better understand this: Using a Machine Learning algorithm, it's possible to predict who will be the next football player to receive the ball on the field. Once trained, the algorithm relies on 3 pieces of data:
- Who has the ball at time T
- The positions of each player
- The time since the start of the playing period
With this data, it's possible to predict who will receive the ball on the next pass (the recipe's outcome).
Machine Learning includes 3 categories:
1) Supervised Learning:
This type of learning is "guided" because it's guided by a "teacher," i.e., a set of data used to train the machine or model. Once trained, the machine can make decisions or predictions when new data is received.
2) Unsupervised Learning:
Here, the model learns through observation: it discovers structures and relationships in the received data by forming clusters. However, it's unable to name these clusters. For example, it can separate skirts and dresses but cannot say that they are skirts and dresses.
3) Reinforcement Learning:
This last category can interact with the environment and find the best outcomes. The model is rewarded for a correct answer and penalized for an incorrect answer. It trains based on the reward points it has earned. It can start predicting new data once trained.
Deep Learning:
Deep Learning is an advanced area of Machine Learning that learns through experience and can solve even more complex problems. It mimics the way a human brain works by using artificial neural networks that function like neurons in our brains. It can process data of much larger dimensions to find information and solutions.
An example to distinguish Machine Learning and Deep Learning:
Imagine we want to develop a system that recognizes dogs in an image. If we use Machine Learning, we would define the characteristics of a dog, such as ears, snout, legs, etc. The system can then identify these features in the images by itself. With Deep Learning, we don't need to define the features; the system will automatically find them using neural networks.
Deep Learning powers Facebook's facial verification algorithm, DeepFace. Machine Learning is not sufficient because an image contains a vast amount of data.
Amazon, Netflix, and many other sites are turning to Deep Learning to understand their customers' behavior and offer them what suits them best.
Deep Learning algorithms can also be used to analyze images and videos, which is very useful in security and surveillance.
Generative AI:
Generative AI focuses on creating content based on existing data. It can, among other things:
- Write and process text;
- Generate realistic or artistic images;
- Compose music pieces;
- Generate prototypes or design proposals.
Among the different subcategories of Generative AI, we find the Large Language Model (LLM):
LLM is an advanced technique within Natural Language Processing (NLP) that allows machines to understand and respond to spoken or textual data like a human would.
Language is one of the skills that most distinguishes humans from other species, and it was on this skill that Alan Turing based his test to determine if a machine approaches human intelligence. LLM is an advanced NLP technique that enables machines to understand spoken or textual data and respond to it as a human would.
Human language is very complex, and to help machines understand it, textual or vocal data is broken down by a number of techniques. We won't go into details here, as a chapter of this 'Cahier du Digital' is dedicated to natural language processing.
Understanding language can sometimes be difficult because you have to take into account different accents, variations in intonation, pronunciation, grammatical errors, etc. This may explain some errors or the fact that Siri or any other voice-controlled machine sometimes asks us to repeat our request.
Here are a few examples:
- Website chatbots use LLM to identify your problem and/or answer your questions. If they don't know how to respond, they will generally try to identify your problem as accurately as possible so they can direct you to the right person.
- Thanks to LLM, the ChatGPT program can answer our questions and carry on a written conversation. Sometimes it doesn't understand what we write, in which case it asks for clarification.
- Automatic translators like DeepL or Google Translate also use LLM. They may make mistakes, and sometimes you need to pay attention to the translations offered, as shown in the example below.
- We used the DeepL mobile application to translate a paragraph in a book by taking a photo. The application initially translated the abbreviation 'AI' as 'aluminum'. On the second try, the application translated it correctly as "Artificial Intelligence".
- LLM is also useful for detecting spam: it can be used to analyze emails and identify unwanted emails. However, some legitimate emails may accidentally end up in your spam folder because the tool doesn't always detect the elements usually attributed to spam.
- In the field of IT development, there is a tool called GitHub Copilot that helps you write code. It will complete the developer's code based on what he writes. The tool works with all languages and although it cannot write a complete function, it saves time.
Computer Vision:
Computer Vision allows a machine to analyze and interpret the visual world, such as images and videos, to extract information and make decisions based on direct observations.
This technique is useful in many sectors, including business, transportation, healthcare, and daily life. The growth in applications using computer vision is largely due to the flow of visual information from our smartphones, increasingly robust security systems, traffic cameras, etc.
Computer Vision can be used for a wide range of applications:
Autonomous vehicles are the most obvious example: it allows cars to perceive and understand their environment so they can safely navigate the roads.
Facial and object detection and recognition and image classification are often used. As mentioned earlier, your smartphone can unlock itself by recognizing your face. It can also sort your photos based on the people in them.
The Google Lens app provides you with information related to the object it identifies in the image you show it. If you take a photo of the Atomium, the app will provide relevant information about the monument.
Medical image analysis: computer vision can be used to identify anomalies or diagnose diseases by analyzing, for example, X-rays.
This vision is also used in sports: VAR in football (video-assisted refereeing) can reconstruct an image using 4 cameras. Without AI, a large number of cameras would be needed around the field to get a 360° view.
Since November 2022, the Ghent branch of Ikea has been using autonomous drones to search for requested products at night and conduct inventories by moving through the aisles and scanning barcodes. This technique facilitates the work of teams and saves them time. It can also be applied to surveillance and security to identify suspicious activities.
At an industrial level, computer vision can be useful for predictive maintenance. For example, it can detect damage or defects in machines and thus warn of potential breakdowns.
It can also analyze and sort products. For example, a producer of apples and pears needs to sort them based on their appearance. This sorting can be performed by a machine using computer vision.
Robotics:
Robotics is one of the most popular branches of AI and focuses on various applications of industrial and humanoid robots.
Industrial robots are widely used in factories and play an essential role in production, whether it's assembling parts, packaging, or sorting, as explained in the example of the fruit producer above.
Robots are also found in customer service in sectors such as hospitality or retail. These robots use several techniques, including natural language processing and computer vision, to interact with customers and their environment like humans. Thanks to machine learning, they adapt and improve their behavior during interaction. They are used to inform customers and enhance the shopping experience.
#7.
Some practical examples of AI solutions
To illustrate our words, you can discover some applications for our clients where we implemented artificial intelligence.
A services company:
As a services company, our client often faced a critical challenge: determining the number of quality prospect calls. Their main issue was defining which calls would lead to an appointment and which ones would not. Therefore, our client wanted a solution to address this problem and gain a clear understanding of the number of converted prospects per month.
Specifically, when a prospect calls, the conversation is recorded and then transcribed by a Deep Learning AI into a summary accessible through software for company members (Speech to Text). The AI also categorizes calls according to pre-defined categories by the user, enabling our client to see the number of quality and irrelevant calls.
The implementation of LLM AI allows our client to detect and analyze the intentions of prospects. They can precisely identify which prospects became customers and which contacts were made for non-offered services. In case of repeated requests for a service that is not available, our client can consider strategic adjustments to address this issue.
This solution not only clarifies the intentions of prospects but also saves valuable time for salespeople who no longer need to manually record each interaction. Thus, AI optimizes not only prospect follow-ups but also contributes to the operational efficiency of our client.
A construction sector company:
Our client, a company in the construction sector, provides a platform connecting individuals and professionals for services. Individuals can submit service requests by selecting the desired profession, type of service, type of terrain for the work, and all relevant information for the professional to prepare a quote.
Previously, after submitting a request, it was forwarded to a professional who then contacted the individual to confirm the request. Only after this appointment did the professional prepare a quote, which was then sent to the individual with a second appointment. If the latter accepted the quote, work could begin. If declined, the quote had to be revised or the request stopped. This process, involving many exchanges between individual and professional, was time-consuming.
Our client sought a solution to reduce these interactions and save time. We proposed integrating a Machine Learning AI into the client's system. The AI generates accurate and personalized quotes based on the forms filled out by individuals. The initial process remains the same: the individual fills out the form, which is then sent to our client. The AI then generates the quote and shares it simultaneously with the professional and the individual. If either party does not accept the quote for budgetary reasons, the request is automatically canceled without further intervention. If both parties agree, the professional meets the individual directly with the quote ready to be signed.
This solution significantly reduces the time required to prepare quotes and improves the accuracy of estimates, enhancing overall efficiency and customer satisfaction.
A publisher of digital comics:
Our client, a publisher of French-Belgian digital comics, aimed to modernize and rejuvenate the universe of French-Belgian comics. Despite traditional artist reluctance toward AI, they recognized its potential to optimize processes. It was crucial to find a solution that respects artists' copyrights, does not replace them, and meets our client's needs.
We developed an integrated system with a Deep Learning generative AI allowing artists to create content more quickly. The operation is simple: each artist joining the team submits some of their works for the AI to train on to produce content similar to their style. When an illustrator makes a sketch, they can specify to the AI specific features they want to see, such as the skin color of a character, clothing, or specific objects. In addition to the sketch, these descriptions help the AI generate the desired content as the artist intends.
The goal of this integration is not to replace artists but to provide them with tailored tools to accelerate their work. To preserve the human aspect of creation, the image generated by the AI is fully customizable so that the artist can adjust it to perfectly match their vision.
This solution provides valuable support for artists, who are enthusiastic about working with this tool, enabling them to produce content faster while maintaining the human aspect of art and their unique style. In short, this AI provides a balance between technological innovation and respect for artistic creativity.
An emergency service in veterinary medicine:
We developed an innovative tool to generate intervention reports, allowing veterinarians to focus more on animal care. Our solution includes two levels:
- Firstly, veterinarians must create a detailed report for each intervention, including actions performed, prescribed medications, and diagnoses made. These technical reports are time-consuming for veterinarians, who have to prepare them after their workday. To save them valuable time, we implemented a Deep Learning AI that prepares these reports instead of veterinarians, allowing them to focus on animal care.
- Secondly, when someone calls the emergency number, the call is forwarded to a call center without immediately determining whether it is a genuine emergency. An LLM-type AI follows the call and automatically generates a transcript. This transcript is stored internally and also sent to veterinarians for their intervention. The aim is again to save time so veterinarians can focus on real emergencies.
The benefits of our solution are numerous:
- It saves time for veterinarians by automating the preparation of intervention reports, allowing them to concentrate on effective care.
- The automatic transcription of calls and assessment of emergencies optimize intervention management. This automation not only enables better management of time and resources but also enhances the customer experience through quick and personalized feedback.
In summary, the integration of AI in this service increases operational efficiency while simultaneously improving customer satisfaction.
A high-voltage network manager:
Our client aimed to optimize the lifespan of its equipment: cables, transformers, and other devices have a theoretical lifespan provided by the manufacturer. However, our client wanted to know the actual lifespan of this equipment to better plan their replacement.
Therefore, we analyzed system failures and maintenance exercises: based on this analysis, we developed a Machine Learning AI capable of determining the theoretical lifespan of equipment (Prediction Analysis). This duration is not the lifespan as provided by the manufacturer but an estimate based on actual usage conditions.
For instance, equipment located near the sea will deteriorate faster due to salty sea air. Conversely, equipment in the Ardennes, less exposed to these factors, will last longer. All this failure information allows us to determine if equipment is nearing the end of its lifespan. After delving into the subject and discussing it with our client, we found that equipment typically experiences many failures at the beginning and end of its lifespan.
The objective of this project was to define patterns based on failure data from technicians and information systems of our client. Artificial intelligence was integrated to analyze a range of information enabling more accurate predictions of the actual lifespan of equipment. While these predictions are not always perfect, they provide a reliable estimate for planning equipment replacements in advance.
The integration of AI in this project has helped our client better manage its resources and optimize maintenance of its electrical grid.
#8.
Conclusion
Artificial intelligence has evolved tremendously since its inception and is increasingly present in our daily lives, both personally and professionally. Sometimes, we don't even realize that we are interacting with AI because it has become an integral part of our routines.
The various branches of AI, such as machine learning, deep learning, natural language processing, computer vision, and robotics, offer numerous opportunities that can be tailored to fit business needs. This is particularly true for autonomous drones utilizing computer vision, capable of performing tasks like inventory management and site surveillance.
It is a remarkable tool that saves time for your business by performing tasks faster than human workers and maintaining a high pace over extended periods. It optimizes and automates certain processes, but AI cannot replace humans in every aspect; it remains a tool.
It's somewhat analogous to a calculator: quicker than humans at calculations, but not necessarily more intelligent in terms of intellectual, emotional, or relational capacities. It possesses skills to perform specific tasks.
Currently, AI operates similarly. All applications are designed to solve specific problems. We are currently in the stage of narrow artificial intelligence, meaning AI is not capable of autonomous thinking and decision-making.
It is important not to adopt AI just because it's 'trendy'; your business must genuinely need such a tool.
First, identify your problems and then consider the solution, the tool that best fits your needs, with or without AI. For instance, if you want to analyze data but have limited datasets, deep learning may not be the right choice.
Also, consider the costs of implementing an AI tool and calculate whether it is truly a cost-effective solution in the long term.
In summary, artificial intelligence is a tool capable of remarkable performance, but it should be seen as an aid rather than a solution that will replace humans.