What are Artificial Intelligence and LLMs? The path to a new technological era

Artificial intelligence (AI) has become a transformative phenomenon that is revolutionizing every aspect of our lives. With constant advances and an overwhelming amount of information, keeping up with innovations can seem like an impossible task.

In this article, we have compiled the most relevant concepts about AI: machine learning (ML), deep learning (DL) and big language models (LLM), to offer you a clear and understandable vision of its evolution and its true impact on today's world. Our goal is to help you understand how these technologies are redefining industries, improving processes and opening up new possibilities for the future. Let's get started!

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What you see in the image above is the composition of artificial intelligence and what gives life to what we know today. At first glance, it might seem simple, but many people are still confused about the terms. The time has come to clarify them!

What is AI?

According to Microsoft, artificial intelligence is the ability of a computer system to mimic human cognitive functions, such as learning, solving problems or performing tasks.

In our research, we found that different organizations and individuals use different definitions for artificial intelligence, so there is still no formal and universally accepted definition, due to the many ways in which this technology supports, empowers and automates human activities.

We believe that the concept given to us by Microsoft works to understand the basis of this development, but this is only the tip of the iceberg. Let's continue to learn more.

What is Machine Learning (ML)?

For Oracle, machine learning or machine learning is the subsection of artificial intelligence (AI) that focuses on developing systems that learn, or improve performance, based on the data they consume.

Machine learning and AI are often named together, but they don't mean the same thing. An important aspect to highlight is that, although all machine learning is AI, not all AI is machine learning.

A simple example to understand one of the types of machine learning is: instead of telling a machine how to identify a cat in a photo, we show it lots of photos of cats and tell it to find for itself the traits that make a cat a cat. As the computer sees more photos, its ability to recognize cats improves and can do so in new photos.

Imagine the big impact that data scientists are having that can train a medical application to diagnose cancer with X-ray images based on the storage of millions of scanned images and corresponding diagnoses.

Are we all right? Remember that you can always save this article in your notes to consult it whenever you need it. Let's keep learning!

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What is Deep Learning (DL)?

Deep learning is a system that is inspired by the functioning of the human brain to process information.

It has an interconnected network of artificial neurons that, while processing and transmitting information, are strengthened as they receive more data. Here you no longer tell a computer how to solve a problem, now you train it to solve the problem on its own.

The main difference between the deep learning and machine learning is the structure of the architecture of “neurons”. Traditional machine learning models use simple neural networks with one or two layers, while deep learning models use hundreds or thousands of layers to train the models.

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What are Large Language Models (LLM)? This is where the future of artificial intelligence is

Long language models, deep learning models or large language models are an advanced AI technology trained on millions of data (hence the word “large”). They are more advanced than the models we have already mentioned because they are able to understand the complexities of natural language. This allows them to recognize, translate, predict or generate text or other content.

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How do LLMs work?

If we talk about it from a very superficial approach, LLMs are based on machine learning because they are trained with a large amount of data to identify patterns. But they also have a deep learning component that gives them the possibility to understand the relationship, nuances, similarities or differences between words, phrases or audios.

In addition, large language models are capable of training without the need for human intervention, although minimal participation is usually necessary.

‍Cloudfare Give an example that we really liked: in the phrase “The quick brown fox jumped on the lazy dog,” the letters “e” and “o” are the most common, since they appear several times each. From this, a deep learning model (LLM) could conclude (correctly) that these characters are among those most likely to appear in a Spanish text.

Although an LLM cannot conclude anything based on a single sentence, analyzing trillions of sentences will give you the knowledge you need to logically finish an incomplete sentence or generate your own sentences.

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LLMs in customer service

These models allow conversational bots to respond to and sustain complex dialogues based on extensive knowledge bases. Unlike traditional methods that rely on predefined conversational flows, LLMs access and use vast volumes of data to provide accurate and instant answers. For example, a virtual assistant from a bank equipped with an LLM can access all the institution's information, both customer-facing and internally, to resolve any query effectively.

In addition, by analyzing and extracting information from past conversations and recordings of human agents, LLMs improve their ability to handle queries autonomously. Human agents thus become the knowledge base for these virtual assistants, who learn from their interactions and generate more natural responses. This approach eliminates the need to create static and inflexible conversational flows and reduces training time for new agents. Companies can deploy and optimize their virtual assistants in a matter of weeks.

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But, the journey of artificial intelligence doesn't stop here!

The evolution of LLMs is just the beginning of a new era with generative artificial intelligence or GEN AI, this emerging technology is taking LLMs, creativity and innovation to unprecedented levels. As we continue to explore the potential of these tools, we are on the threshold of an exciting future in which collaboration between humans and machines will lead to new forms of expression and discovery. Do you want to continue learning from GEN AI? Go to our blog and don't miss our next article.

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