Section outline

  • LESSON 2 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS

    AI and the Essential Role of Data

    What feeds artificial intelligence? In this lesson, we focus on the data: how AI-based systems work using inputs and outputs, where the massive amount of training information comes from, and why their development is deeply related to the availability of big data and computing power. We analyze everyday applications to understand that AI does not "think" (it recognizes patterns within large volumes of information).

    At the same time, we present some questions about data collection and usage: Do we know what information we generate when using digital platforms? Who stores it, and for what purposes? Does true consent exist when we accept terms and conditions without reading them? Rather than normalizing data extractivism, this lesson invites us to question the concentration of tech power and reflect on what all of this means for our educational practices.

    At the end of the lesson, you will be asked to answer a few questions. 


    👨‍🏫 Instructor: Marcos Gómez

    • AI and the Essential Role of Data URL

      Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!

    • Below you will find the lesson slides, designed as a reference to revisit the ideas, questions, and tensions raised in the video. (Please note: The slides are in Spanish).

      📚 Key Concepts

      Machine Learning (ML): A branch of artificial intelligence and computer science that focuses on using data and algorithms to enable AI to imitate the way humans learn, gradually improving its accuracy.

      Example: In machine learning, an algorithm can predict the price of a house by analyzing historical sales data, such as size and location. As the model receives more data, its ability to make accurate predictions improves.

      Training Dataset: A collection of data containing examples of solutions to a problem, such as chess moves with their outcomes or classified photos of dogs and cats. For the system to perform well, these datasets usually need to be massive.

      Graphics Processing Unit (GPU): Designed for parallel processing, the GPU is used in a wide range of applications, including graphics and video rendering. While best known for their capabilities in gaming, GPUs are gaining popularity in creative production and artificial intelligence (AI). GPUs were originally designed to accelerate 3D graphics rendering. Other developers also began leveraging GPU power to dramatically speed up other workloads in high-performance computing (HPC), deep learning, and more.

      Opt-in (Active Consent): Users must explicitly consent before their data is collected or shared.

      Opt-out: The service comes enabled by default, requiring the user to navigate settings to disable it.

      Data Extractivism: This term is inspired by natural resource extractivism (mining, oil), where a common or local resource is exploited without returning real benefits to the provider communities. Data extractivism refers to the massive collection of personal and social information without genuine consent, which is then used for commercial, surveillance, or control purposes, without users having decision-making power or benefiting from that use.

      Reference: IBM. Machine Learning. Retrieved from https://www.ibm.com/mx-es/topics/machine-learning

      A creation by Fundación Vía Libre in collaboration with FAMAF – UNC.
      With the support of: Mozilla , FAIR , and Data Empowerment Fund .
    • Assignment: AI and the Essential Role of Data Quiz