What is Artificial Intelligence (AI)?

What is AI?

The definition of AI is, basically, when a computer manages to perform tasks that previously required human intelligence (such as learning, understanding, perception, problem-solving, decision-making), by identifying patterns in a large amount of data.

Even having this unparalleled capacity to imitate human abilities and intelligence, AI doesn't have thought or understanding like we human beings do. It's a robot.

Although it may not seem like it, AI has existed for much longer than we think. It exists in our daily lives, even though not perceived as AI by the general public, but search engines (Chrome, Microsoft Edge), virtual assistants (Siri, Alexa), recommendation systems (YouTube, Netflix) and strategy games (chess, draughts), all these examples are considered artificial intelligence.

What gained high popularity since 2024 was generative AI. The type of AI capable, through a large amount of data, of generating unique content such as images, music, texts and programming with the power of a click.

What is Generative AI?

Generative AI, is a subcategory of AI, which not only analyses existing data, but is now capable of creating content: text, images, audio, videos, programming. It's trained to predict and reconstruct data and once trained, we can, through prompts, ask for edits to the created content.

The main difference between generative AI and traditional AI is that generative AI is the first version that can analyse data and from there generate content, while traditional AI can analyse data and only classify it.

"This image is a cat" - Traditional AI

"Here is the creation of an image of a cat" - Generative AI

When we refer to generative AI, we're talking about tools like ChatGPT, Claude, Gemini, Deepseek and among other various options.

Generative AIs have, for the most part, the following three phases:

1. Train to create the foundation model

2. Adjust and refine content

3. Generate content, and receive feedback to improve result accuracy

Train

Everything in generative AI begins with its foundation model, which serves as the base for multiple types of generative AI applications. The most popular foundation model is large language models (LLMs), made to generate concise responses in text (there are other models to generate responses in images, videos, music and other types of content).

Training a generative AI model is fundamentally giving it an enormous amount of examples so that it can identify patterns and learn by itself. If you want a model to generate text, you need to give it billions of written documents. A model for images, millions of categorised images. AI only learns from the data it's given; if it's given poor quality, incorrect or outdated data, it can give incorrect information.

This entire process takes a long time, electrical energy, is intensive and extremely expensive.

Adjust

In the adjustment phase, we focus on adjusting the generative AI to be able to perform specific content creation tasks, let's say, refine. We have two ways of doing this:

• Deliver more properly classified data to the generative AI, and match this data as a correct answer to the right questions and prompts

• Using human feedback, the AI corrects itself. It can be as simple as when we respond to a chatbot or virtual assistant.

Generate Content

The programmers, after the adjustment phase, move to the content creation phase. It's in this phase that they'll test all the adjustments they created to see if it's delivering the correct results.

The developers create content, evaluate and adjust. They adjust the model frequently, at least once a week, for better relevance and accuracy. Another option to improve generative AI performance is retrieval augmented generation (RAG), which is a technique where the foundation model is extended to search for relevant sources outside the stipulated data it already has.

What is an AI Agent

AI agents refer to AI systems that can achieve objectives, make decisions and take multi-step actions without constant human guidance. They can operate semi-independently.

These agents are given an objective, and from there can carry out multi-step processes until achieving the given objective.

Some examples with AI agents can be: an AI assistant that books your flight by searching for options, comparing prices, checking your calendar and completing the purchase or, an AI assistant that analyses new contact forms, analyses the client's profile, automatically redirects to the most suitable sales representative and calculates the comission if the lead is won.

Want to create your own personalised AI agent for your company's operations? Contact us

Benefits of AI

Efficiency and Scale: automating repetitive tasks and processing data at a speed impossible for humans, without doubt AI is a great ally for saving time and being able to scale companies without needing to recruit so many people

Enhanced decision-making: AI assists in predicting possible equipment failures before they even happen, finding potential diseases in medical imaging, through its capacity to find patterns in a high volume of data, it doesn't let any detail escape

Accessibility: reads text aloud for people with visual difficulties, destroys language barriers, AI tutors offer personalised education at low costs

Reinforcement to human work: increases the speed of task development, humans focus on strategic/creative tasks while AI does the hard work. Designers can create prototype concepts more quickly, researchers analyse pages of data in a few minutes among other infinite examples!

Scientific advancement: accelerates the discovery of solutions at pharmaceutical, environmental and medical levels.

Personalisation: Each experience with AI is unique and personalised. AI creates content according to users' needs, personalised learning paths, food recommendations, and much more.

Cost reduction: automation saves time, and time is money. Reduces operational costs such as in medical diagnoses (always with the approval of the current doctor), analysis of legal documents, manufacturing quality control.

All these benefits come with risks: replacement of jobs, amplifying prejudice, privacy concerns, potential misuse... We cannot talk about the benefits without talking about the disadvantages.

Disadvantages of AI

Job replacement: The automation of certain tasks threatens job replacement, not only manual (manufacturing) but also such as copywriting, data entry, manually inserting data, customer service. Many jobs can potentially be replaced, but new types of employment will emerge alongside this new economic period.

Prejudice and discrimination: AI models trained on historical data can inherit society's prejudices, such as facial recognition that fails on darker skin tones, systems that harm minorities. It's very important to regulate AI to be more ethical.

Privacy dangers: AI works through a large volume of data, and many can use users' personal data for their own systems with authorisation through the user accepting certain privacy policies they're unaware of.

Manipulation and misinformation: generative AI makes mistakes, it's not 100% perfect, it can write things that don't even exist and propaganda that is wrong. Don't trust everything AI says, always research.

Concentration of power: training AI models requires a large volume of data and massive computers, which requires high infrastructure, electricity and energy. This can centralise AI technology only in tech giants and certain nations that have such capabilities, creating disparity in the control of generative AIs.

Lack of transparency: generative artificial intelligence is very complex, with millions of terabytes of information, making it difficult to explain, challenge or audit.

Deskilling and dependency: people who are highly dependent on artificial intelligence to carry out their tasks may diminish their critical thinking and reasoning capacities and create high fragility when systems fail or when they don't think for themselves.

Security and existential risks: The more AI becomes more capable and useful, it becomes increasingly important to align its values with human values. It can have serious consequences, such as autonomous weapons and the loss of control of AI in certain hypothetical scenarios.

Environmental problems: training large models consumes an enormous amount of energy, water and resources, contributing to environmental deterioration.

Conclusion

Now that we know what artificial intelligence is, its differences from generative AI, its benefits and harms, we already have a knowledge base that enables us to keep up throughout the new technological era that has arrived, and has arrived to stay.

Artificial intelligence will increasingly evolve together with technology, but we shouldn't be afraid or fearful, we have to adapt to the changes the world has, or else, we'll be left behind.