Parameters of artificial intelligence and how they shape models

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Modern artificial intelligence is built on parameters : invisible numbers that determine what a model sees, understands, and responds to. When you hear about “a model with millions or billions of parameters,” what’s really being discussed is the size of the mathematical brain behind tools like ChatGPT, Gemini, or any other advanced AI.

Although it may sound technical, understanding what parameters and other basic AI concepts are (weights, neural networks, training, prompts, open or closed models…) is key to knowing what you can really expect from these technologies, how to use them in your company, and why some solutions work better than others in digital marketing , business, or data analysis.

What exactly is artificial intelligence?

When we talk about AI, we're referring to systems capable of performing tasks we associate with human intelligence : learning from experience, detecting patterns, making decisions, solving problems, and communicating in natural language. We're not trying to replicate consciousness, but rather to build tools that automate and improve complex processes in fields such as industry, medicine, digital marketing, and entertainment.

Many techniques fall under the umbrella of AI: rule-based systems, machine learning, neural networks, and foundational models like large language models (LLMs) that have fueled the current revolution. They all share one key idea: using data to enable machine learning instead of programming everything manually.

Machine learning and neural networks: the basis of the parameters

Machine learning is the branch of AI that allows machines to learn from data instead of following rigid instructions. Instead of telling a program "if A happens, do B," we give it labeled examples, and the algorithm discovers for itself the relationship between input and output.

Within machine learning, neural networks are the family of models that (very loosely) mimic the human brain . They are made up of layers of artificial neurons connected to each other. Each connection has an associated number, a weight, which indicates the influence one neuron has on the next.

In a typical neural network, neurons are organized into layers : an input layer that receives the data, several hidden layers that transform that information in intermediate steps, and an output layer that produces the result (for example, an image category, a text in another language, or the estimated price of a house).

During training, these neurons perform linear combinations of the input data multiplied by weights, add biases, and apply activation functions such as sigmoid, tanh, ReLU, or softmax. The activation function introduces nonlinearity , which is what allows for the modeling of complex patterns and not just direct relationships like "if X goes up, Y goes up."

What are parameters in artificial intelligence?

In a neural network, parameters are the numerical values ​​that the model adjusts during training . They primarily include the weights of the connections between neurons and the biases of each neuron . Together, they form enormous matrices and vectors that encode what the model has learned.

In practice, weights determine how much the output of one neuron influences the next . If the weight is high, the signal is transmitted strongly; if it is low or close to zero, hardly any information is passed. Biases add an extra term that provides flexibility, allowing a neuron to fire even if the weighted sum of the inputs would not be sufficient.

If we imagine a network that predicts the price of a house, its parameters learn the relative importance of square footage, year of construction, location, and neighborhood quality . In the initial layers, simple variables (for example, surface area and number of rooms) are combined to generate intermediate concepts such as "living space." In later layers, the model can combine location and school quality to approximate something like "neighborhood desirability."

All this "understanding" of the world is nothing more than numbers in matrices . The model doesn't really know what a pleasant neighborhood is; it only detects patterns in the training data and learns correlations that it then reuses for new predictions.

How to train the parameters of an AI

Training is the process by which the neural network adjusts its parameters to minimize the error between its predictions and the actual values. This is done iteratively: many examples are shown, the error is calculated, and the weights are slightly adjusted in the right direction using optimization algorithms such as gradient descent.

At each step, the model performs forward propagation : it takes the inputs, multiplies them by the weights, adds biases, applies activation functions, and produces an output. Then, using backpropagation , it calculates how it should modify each weight so that the next time it gets closer to the correct value.

The result after many iterations is a set of weights and biases that capture stable patterns in the data . The more data there is, the higher its quality, and the better the architecture, the more reliable these parameters will be when generalizing to new cases.

In large generative models, this initial large-scale training produces what are known as foundational models : brains pre-trained with a huge amount of text, code, images, or audio, capable of solving multiple tasks with small additional adjustments.

Foundational, open, and closed models

Foundational models are giant neural networks pre-trained on massive datasets to acquire a general understanding of language, images, or the world. Clear examples in the field of language models are GPT, Llama, Mistral, and DeepSeek.

These models can be fine-tuned : instead of training them from scratch, they start with their already learned parameters and specialize them using a specific dataset (for example, legal texts, internal company documentation, or customer support chats). It's like taking a general practitioner and training them as a specialist: the foundation is already there; you just need to fine-tune in one area.

Depending on access to these parameters, we distinguish between open models and closed models :

• Open source modelIt makes the code, and often the trained weights, available to the community. Anyone can audit, modify, adapt, or deploy the model on their own systems, subject to the license.
• Closed modelIt keeps the code and weights as private property. Access is through an API or online service. The user cannot see the model's internal workings or change its base parameters.

This difference has technical, legal, and ethical implications. Open models promote transparency, auditing, and customization , while closed models typically offer greater commercial support and, sometimes, better performance thanks to highly optimized infrastructures and non-public training data.

Parameters, tokens, and prompts: how we “talk” to AI

When we interact with a language model, we do so through a prompt, which is simply the input text (the question, instruction, or context we provide). The model transforms this text into tokens , small units (words, parts of words, or symbols) that are the building blocks with which it constructs its internal understanding.

Each token is converted into a numerical vector and travels through dozens of layers of neurons, being multiplied by the model's parameters at each step. Finally, the network calculates the probability of each possible token as the next word and chooses one. By repeating this process token by token, it generates complete responses.

Prompt engineering is the art of formulating prompts in the best way to guide the model toward the desired outcome. It doesn't change the internal parameters, but rather how they are used, since the same network can activate different "paths" depending on the provided context.

In practice, small changes to the prompt can make the AI ​​better use of what it already knows , improve the tone, reduce errors, or adjust to the style we need (more concise, more didactic, more formal, etc.).

Hallucinations: when parameters make things up

One of the side effects of these models is hallucination : the AI ​​generates responses that sound plausible but don't correspond to reality. This happens because the model predicts the next token that statistically best fits the context, not because it consults a verified database.

If during training you've noticed unclear patterns, contradictory data, or insufficient information on a topic, your parameters may lead you to creatively "fill in the gaps ." Sometimes you're right, sometimes you're wrong, but the tone is usually equally confident, which can result in misinformation being presented with great conviction.

Therefore, in sensitive applications (health, finance, legal decisions…), it is crucial to combine AI with human verification, verified sources or additional check systems that limit the consequences of these hallucinations.

Key features of current artificial intelligence

Beyond the mathematical details of the parameters, modern AIs share a number of characteristics that explain why they are so useful in real-world environments such as businesses or digital marketing.

First, continuous machine learning stands out . Many systems are able to incorporate new data to refine their models (either by retraining periodically or through online learning techniques), allowing them to adapt to changes in the market, user behavior, or trends.

Natural language processing (NLP) is also fundamental . Thanks to it, AI can interpret and generate coherent text, understand nuances, analyze sentiment on social media, and maintain increasingly fluid conversations, both in chatbots and voice assistants.

Another key feature is large-scale pattern recognition . Where a person would get lost in millions of rows of data, a well-trained model detects subtle correlations, emerging trends, or anomalies that may indicate fraud, risk, or simply new business opportunities.

Added to all this is the ability to make decisions autonomously : recommending products, adjusting advertising bids, approving or not approving a transaction, or selecting the best content to show to each user in real time, always based on the available data and the learned parameters.

Types of artificial intelligence according to their capabilities

To better understand this situation, different types of AI are usually distinguished according to their power and scope . Not all AIs are the same, nor do they all pursue the same goal.

On one hand, there are reactive AIs , which respond only based on the current situation, without memory or accumulated context. These are systems focused on a very specific task, like certain classic chess programs that analyze the board at that moment but don't remember previous games.

Taking it a step further, we have AIs with limited memory , capable of using recent information to improve future decisions. This is where many modern applications fit in: self-driving cars that remember what they've just seen, virtual assistants that consider the immediate conversation history, and recommendation systems that take your recent interactions into account.

In theory, there is also talk of AI general (AGI) , a system with cognitive capabilities comparable to those of a human, capable of learning any intellectual task and adapting to a wide variety of contexts. To this day, it remains an ideal, without a fully implemented reality.

Finally, there is the concept of artificial superintelligence , which would surpass human capabilities in virtually every area. This idea opens up profound ethical and philosophical debates: control, alignment with human values, social impact, and potential risks.

Supervised and unsupervised learning

When we get down to the practical side of machine learning, we usually talk about two main approaches: supervised learning and unsupervised learning , which determine how AI is trained and what it does with the data.

In supervised learning , each example in the training set comes with a label or correct answer. The model learns to map inputs to outputs: images to categories, text to sentiment, customers to purchase probability. It is used for churn prediction, email classification, scoring systems, and dynamic pricing models.

In unsupervised learning , the data is unlabeled. The goal is to discover hidden structures or natural groupings : customer segmentations based on behavior, anomaly detection, grouping of products that are purchased together, etc. Here, AI acts more as an explorer than an oracle.

Other approaches exist, such as reinforcement learning , where an agent tests actions in an environment and receives rewards or punishments, learning through trial and error. It is widely used in robotics, games, and complex optimization problems.

Practical applications of AI in marketing and business

The true impact of all these concepts becomes apparent when well-trained parameters translate into concrete solutions that improve results. In digital marketing, the list of applications continues to grow.

One of the most visible is advanced audience segmentation . AI analyzes enormous volumes of data in real time (web behavior, purchase history, social media interaction, demographics, etc.) to create much more precise segments than the typical basic groups based on age or location.

Another key area is the personalization of content and recommendations . Recommendation systems use their parameters to anticipate which products, articles, or videos are most likely to interest each user at that moment, improving engagement and conversions.

In digital advertising, AI-powered programmatic advertising allows you to decide in milliseconds where, when and to whom to show an ad, automatically adjusting bids and creatives according to performance, something impossible to manage manually on a large scale.

Furthermore, chatbots and virtual assistants powered by natural language models have become the first line of customer service in many companies. They are capable of resolving common questions, guiding purchasing processes, or escalating to a human agent only when necessary, reducing response times and costs.

Specific functions of the main AI chats

To further illustrate these ideas, it's helpful to see how different AI services apply these concepts under the hood . Although they all use neural networks and massive parameters, each prioritizes different functions.

ChatGPT offers internet search, reasoning skills, analysis of attached files (documents, spreadsheets, code), mathematical operations, and programming. It allows voice interaction and limited image creation in its free version, which is expanded in the paid versions, which also include video generation and more advanced models.

Gemini also combines online search and reasoning, with strong integration into the Google ecosystem (Drive, Gmail, Docs, etc.). It accepts files and photos, generates images and executes code, and behaves as an assistant closely tied to the company's services, with additional enhancements in the paid versions.

Copilot , Microsoft's assistant, is integrated into Windows and Microsoft 365, offering one of the most powerful image generators in its free version , along with access to advanced language models, web search, document analysis, and voice support. It is especially useful in office environments.

DeepSeek stands out for its open approach and the ability to download the model for local use , a very attractive feature for those who prioritize privacy. It allows web browsing, deep reasoning, file analysis, programming, and mathematics, although it does not generate images.

Grok , integrated into X (formerly Twitter), combines general search with privileged access to content published on the network itself . It offers a deep search mode and image generation with fewer restrictions, which simultaneously opens up creative and controversial possibilities.

Le Chat , powered by Mistral, focuses on speed, privacy, and robust text analysis capabilities, incorporating a collaborative Canvas mode for documents, presentations, or code sketches . It also supports configurable agents and image generation, although the graphical results could be improved.

Claude focuses on writing quality, reasoning, and security, with multiple configurable response styles and a strong ability to handle large documents. It doesn't access the internet or generate images, but it offers highly polished and careful analysis of text and data.

Strengths and weaknesses of these AIs

All these services share the same foundation: enormous amounts of parameters trained on massive datasets . However, their practical behavior varies considerably, and it's worthwhile to understand their pros and cons.

In the case of ChatGPT , its strengths lie in the fluidity, naturalness, and versatility of its responses , as well as a broad ecosystem of users and features (custom GPTs, integration with services, etc.). Its weaknesses include limited image generation in the free version and the fact that some more advanced models are only accessible with a subscription.

Gemini shines for its integration with Google and its multimodal nature (text, image, audio), but sometimes its responses can be shorter or less in-depth than those of other alternatives, depending on the scenario.

Copilot stands out for its powerful image generation and good integration with the Windows and Office ecosystem . However, its natural language handling isn't always as clear or structured as that of other models, and it can fall short in lengthy explanations.

DeepSeek has gained a reputation for offering a lot in its free version and for allowing local execution, with very solid reasoning and well-organized answers. Its weaknesses are the lack of image generation and concerns about privacy and content restrictions, given that it's a Chinese development subject to specific regulations.

Grok positions itself as an AI with a lighthearted tone, access to X, and the generation of uncensored images, which some users consider a creative advantage. However, it tends to be less rigorous and more controversial , with responses of varying quality and a usage context tied to the X platform itself.

Le Chat offers speed, good functional versatility, and a European approach to privacy, with responses based on carefully selected sources. Currently, it is still in beta, and some aspects of its mobile interface and image system still need refinement.

Finally, Claude stands out for its polished style, configurable design options, and a remarkable balance between clarity, conciseness, and depth , with fewer issues than other models in certain areas. Its major drawback is the inability to navigate or generate images, which puts it somewhat behind in scenarios where constant updates are critical.

How do these AIs respond to different types of questions?

When these models are compared with the same questions, it becomes clear how their parameters and design influence the type of response . When faced with simple informational queries (for example, what a specific medium is), most provide correct answers, although some may include occasional hallucinations (as has been observed in certain cases with DeepSeek).

If you ask about little-known people , striking differences emerge: some models, such as certain versions of ChatGPT, tend to give many details based on web crawling, while others choose to focus on the professional profile and limit personal information.

In comparisons of complex products (such as two recent mobile phone models), some services excel with specification tables and structured analysis , while others offer more generic summaries or even encounter technical problems when trying to find up-to-date data.

When the challenge is to solve logic problems like the classic Monty Hall puzzle , models with activated reasoning modes usually succeed, although the style changes: from highly developed explanations (like Grok or Copilot) to more schematic and brief versions (like certain ChatGPT responses).

In questions with a high political or ideological charge , the weight of internal politics and training data is very evident: some models go into more detail and cite controversial facts, while others prefer neutral, aseptic or very summarized answers, although all are supported by the same prediction principles based on parameters.

Advanced AI features: adaptability, creativity, and collaboration

Beyond their user-facing functions, modern AIs possess internal characteristics that make them particularly powerful . One of the most relevant is adaptability : the ability to adjust to changing contexts, learn new skills, and refine their behavior with recent data or user feedback.

We can also talk about a certain kind of algorithmic creativity . Although it's not human creativity, generative models combine existing patterns to produce original texts, images, music, or designs, which helps with content ideation, rapid prototyping, or the creation of fresher marketing campaigns.

At the technical core we find deep learning , which uses multi-layered neural networks to simulate in a very abstract way the workings of the human brain, improving the ability to recognize complex patterns in images, audio or long text sequences.

Another line of progress is emotion recognition and tone analysis , which allows certain systems to interpret, at least partially, the emotional nuance of a message, adapting their response to be more empathetic or appropriate in contexts of customer service or light psychological support.

Finally, modern architectures favor collaboration between machines : different models or specialized agents can work together, share information and divide tasks (one analyzes data, another writes the report, another generates the graphs), increasing the overall efficiency of the system.

Why metrics matter for businesses and marketing

Ultimately, everything mentioned above hinges on the same principle: well-trained parameters at scale . The more parameters and the better trained they are, the greater the model's capacity to understand nuances and provide useful answers… but it also requires more resources and its deployment becomes more complex.

For a company, this translates into decisions about what type of model to use : a gigantic one hosted in the cloud, with variable cost but spectacular capabilities; a medium-sized model specialized in specific tasks; or several small, more efficient models, finely tuned to very specific processes (for example, ERP systems and stock control , classifying support tickets or predicting customer churn).

It also has an impact on aspects such as data privacy : running a model with many parameters locally (as some open models allow) can be key in regulated sectors, while relying on closed services requires assessing data processing agreements, jurisdictions and regulatory compliance.

In marketing, understanding that "more parameters" doesn't always mean "better for everything" helps in choosing the right tool for each job : a large language model for brainstorming campaigns or writing copy, a smaller and faster one for classifying leads, and perhaps a visionary one for analyzing product images or creatives.

With this map in mind—what AI is, how the parameters work, what types of models there are, and what the main services offer—it becomes much easier to take advantage of the technology, ask it for realistic things, and detect its limits, using artificial intelligence as a powerful ally instead of treating it as an incomprehensible black box.