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Everything you need to know about Artificial Intelligence: principles and operation

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Everything to know about Artificial Intelligence

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2023 was undoubtedly the year when artificial intelligence took center stage, whether in the BtoB, BtoC markets, or various personal daily needs. Just for this year, the popular ChatGPT application experienced over 19 million downloads.

To shed light on this unprecedented upheaval, let's first revisit the context that led this technology into the daily lives of businesses and individuals. And most importantly, what contributions it brings to the future of the IT market.

The term Artificial Intelligence was first pronounced in 1956 during the "Dartmouth Summer Research Project on Artificial Intelligence" conference by John McCarthy. It refers to a technology that will create machines capable of replicating human reasoning and revolutionize every sector of activity.

How does this revolution actually take place? Can we truly trust this technology? That's what we will explore in this article.

1- Artificial Intelligence: What is it?

As the name suggests, the term "Artificial Intelligence" refers to intelligence designed by humans. It is a process aimed at replicating human intelligence involving the design and application of algorithms operating in a dynamic computing environment. The essential goal of this discipline is to grant computers the ability to reason and act similarly to humans. Are you following so far?

To achieve this goal, three elements are indispensable:

  • High-performance computer systems
  • Data with adequate management systems
  • Advanced AI algorithms (code).

In other words, Artificial Intelligence aims to develop programs with considerable computing power, typically associated with human intelligence, encompassing:

  • The ability to reason
  • The ability to process significant amounts of data
  • The ability to detect patterns and imperceptible models to a human
  • The aptitude to understand and analyze these patterns
  • The competence to interact with humans
  • The capability of progressive learning
  • And the ability to continuously improve its performance.

Artificial Intelligence thus covers a vast and ever-evolving subject.

2- Artificial Intelligence: How does it work?

The operation of AI can be characterized by the type of learning of the algorithms used in Artificial Intelligence:

Supervised Learning

The AI model (algorithm) learns based on labeled data, meaning that each training example in the dataset is associated with a known output or label. During the training phase, the model examines these examples and adjusts its internal parameters to create a relationship between inputs and expected outputs.

Once the model has been trained on a sufficient volume of labeled data, it is evaluated on unseen data during training to ensure it can generalize correctly. Ultimately, when new data is introduced, the model can predict the corresponding outputs based on the patterns learned during training.

Automatic translation, fraud detection in financial transactions, online product recommendation, disease prediction from medical images, image classification are all examples of supervised learning-based AI applications.

Unsupervised Learning

Unsupervised learning is a machine learning approach where the algorithm is exposed to a set of unlabeled data. Unlike supervised learning, there are no labels or provided responses to guide the model. The goal of unsupervised learning is to allow the model to discover intrinsic structures, patterns, or relationships in the data without specific guidance.

Once the model has learned these structures, it can be used to make predictions on new data. For example, in the case of clustering, if new data is presented, the model can assign that data to an existing cluster based on its characteristics.

Unsupervised learning is commonly used to explore complex data patterns, discover hidden trends, and prepare data for subsequent tasks. Examples of applications include customer segmentation for targeted marketing strategies, anomaly detection in data, and dimensionality reduction to simplify data representation.

Reinforcement Learning

Reinforcement Learning refers to a set of methods that allow an agent to learn how to choose which action to take autonomously. Immersed in a given environment, it learns by receiving rewards or penalties based on its actions. Through its experience, the agent seeks to find the optimal decision-making strategy that allows it to maximize accumulated rewards over time.

A classic example of reinforcement learning application is training an agent to play games, particularly board games. A prominent case is that of board games such as chess or Go.

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3- How to create an AI-driven computer system?

So far, the concepts described above may seem a bit abstract. To deepen your understanding of AI, here are the general steps to follow to create an AI-driven system.

Step 1: Define the system's goal and collect data

This step involves defining the goal of your AI-driven system in advance and gathering a relevant set of data for the defined goal. The model will be trained with this data.

Step 2: Data preprocessing

Sometimes data needs to be cleaned, processed, and prepared to make it usable by the AI model. This may include normalization, handling missing values, etc.

Step 3: Algorithm/model selection

There are many models that can be used for your AI project. It is advisable to select an algorithm or AI model appropriate for your problem. The choice often depends on the nature of the data and the goal of the task.

Step 4: Model training

This is the stage at which the model learns from the data. It involves feeding the model with labeled and/or unlabeled examples to adjust its parameters so that it can perform the desired task.

Step 5: Model evaluation and optimization

After training, the model's performance is evaluated on a separate dataset to check if it generalizes well to new data. This helps detect overfitting or underfitting. Parameters can be adjusted to further improve performance. Optimization may also include techniques such as data augmentation.

Step 6: Integration into the system and ongoing maintenance

Once you are satisfied with the model's performance, you can integrate it into your computer system, ensuring that the model can receive real-time data and generate appropriate predictions or actions. You can update the model as needed based on changes in data or system objectives.

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4- Can we trust AI?

By evaluating algorithms through scenarios representing the situations they will face in the future execution of their tasks, it becomes possible to place a certain level of trust in Artificial Intelligence (AI).

However, other diverse factors must be considered:

-The quality of data used to train models: If the data used during model training is biased or incorrect, it can be reflected in the predicted results. Thus, the reliability of AI depends closely on the quality and representativeness of the data on which it was trained.

-Ethics: It is imperative that AI applications adhere to rigorous ethical standards. Decisions made by these systems can have a significant impact on individuals, and it is essential that the design and use of these systems be guided by ethical principles.

-Adaptability: Some AI models can react to variations in input data. It is crucial that these models demonstrate an ability to adapt, maintaining reliable performance even in changing conditions.

-Human oversight: Often essential, the presence of human supervision is required to authenticate the results generated by AI. Collaboration between AI and human experts can increase confidence in the obtained results.

5- Why is AI important?

Today, both humans and machines generate data at a rate that exceeds human capacity to assimilate and interpret this information for complex decision-making. This abundance of data can be exploited and used to develop numerous applications, offering businesses the opportunity to revolutionize various aspects.

Now that you are familiar with artificial intelligence, the application of this technology can bring about a transformative change for all your activities. Without its use, it could lead to a delay in any progress.

Last updated
2024-01-11

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