The fascinating world of Artificial Intelligence should be discovered in a simple way. Download the visual diagram summarizing the key ideas shared by data scientist Susana Brás shared in detail in the video below.

Artificial Intelligence (AI) consists of computer systems designed to perform tasks that normally require human intelligence. These systems can learn from experience, recognize patterns, make decisions and solve problems based on the data they have been trained with. In simple terms, AI involves machines that simulate human intelligence, with the ability to understand, learn and make decisions autonomously. AI does not yet possess emotions, creativity or critical sense, but it can mimic these attributes convincingly.

AI goes to school

Data scientist Susana Brás in the video below presents the process of AI, sharing application examples close to everyday life, making complex concepts simple to understanding.

Full video: activate automatic translation, as the talk is in Portuguese

• Defining AI is difficult because the word “intelligence” suggests characteristics that machines don’t have, such as creativity and critical thinking.
• Today’s AI mimics human intelligence through models that analyze data to make predictions.
• The AI process has 3 blocks: data, model and value creation.
  • Example: An AI system for recommending music.
    • Data: Collecting data on users’ listening behavior, such as history of songs played, playlists liked, tastes, and basic demographic data.
    • Model: Development of a Machine Learning model, such as a system based on neural networks, which analyzes the patterns in the data to predict which songs a user might like.
    • Value Generation: The system recommends personalized songs and playlists for each user, improving the listening experience and increasing engagement on the platform.
• Data is the greatest treasure! Quality data is crucial for the proper functioning of the model: data selection, cataloging and security.
• Models need to be transparent and easy to understand for people to trust them.
• We need quality data and total transparency when training models.
• Sometimes AI hallucinates and makes mistakes, so you always need to validate the results.
• Ethics and sustainability is super important at all stages of AI!