Some of the platforms driven by generative artificial intelligence that are transforming the way we create, program, and communicate
What is Generative AI?
Generative artificial intelligence represents one of the most revolutionary advancements of our era in the field of digital technology.
Unlike other types of AI that focus on classifying or predicting based on existing data, generative AI creates entirely new content:
Images
Texts
Songs
Videos
Code
To develop its functionality, massive amounts of data and patterns were analyzed. From that, the models acquired the ability to learn implicit rules of different types of content and then generate original results that imitate or draw inspiration from what has been learned, but without copying exactly.
How does a generative model learn?
The learning process is deep and complex. These models are trained with billions of words, images, sounds, or sequences, depending on the area to which the type of artificial intelligence is destined.
For example:
GPT-4 was trained with over 500 billion words, which allows it to capture grammatical structures, narrative styles, and semantic relationships. This later enables the adoption of idioms and cultural patterns during writing.
DALL·E, on the other hand, learned to identify proportions, artistic styles, textures, and lighting, enabling it to create/generate coherent and expressive images.
The ability to create something new often generates fascination:
How can a machine imagine what it has never seen?
The key lies in patterns. AI does not imagine like a human; rather, it extrapolates learned combinations in ways that we interpret as creativity.
How is a foundational model trained?
Foundational models are trained with deep learning algorithms using terabytes of unstructured data (for example, extracted from the internet).
During this process, the system:
Attempts to predict the next element in a sequence (a word, part of an image, or a line of code).
Then compares its prediction with the actual data we provide.
Adjusts its operation to minimize the error or difference between results.
This type of training involves millions of cycles of testing and adjustment, which allows foundational models to be so versatile and powerful.
Current applications
Today, generative AI is used in diverse sectors such as:
Graphic design: creation of logos and conceptual illustrations.
Programming: automatic code suggestions.
Education: drafting materials adapted to different levels.
Marketing: generation of campaigns and automated responses.
Business: content creation and real-time data analysis.
These tools not only accelerate processes but also democratize resources that were generally reserved for specialists.
Social impact
The economic impact of generative AI has enormous potential:
it is estimated to contribute between 2.6 and 4.4 trillion dollars annually to the global economy.
Real example:
In an experiment with 95 developers, those using GitHub Copilot completed the task of programming an HTTP server in JavaScript 55% faster than those who did not use it.
In education, intelligent virtual assistants allow adapting content to each student’s pace and style, making a more inclusive and effective teaching possible.
Types of generative models
Models are classified according to the content they generate:
Text: language models like ChatGPT.
Images: generators like Midjourney or Stable Diffusion.
Audio: like Suno or Voicemod, for music or voices.
Video: like Veo 3, which produces realistic clips that can include audio.
Multimodal: like GPT-4o, which combine text, image, and audio in a single system.
This last type of model opens up new creative and communicational possibilities.
Risks and challenges
Despite its benefits, generative AI faces several challenges:
Accuracy: it can generate incorrect data that looks real or misinform.
Environmental impact: training models like GPT-3 generated over 550 tons of carbon dioxide.
Ethics: biases in data, privacy violations, and potential malicious uses are critical issues.
The need to establish regulations, responsible practices, and transparency is urgent.
Educational and labor transformation
In education, generative AI allows for unprecedented customization of content, facilitating adaptive teaching materials that transform the relationship between teacher, student, and knowledge.
Many universities and business schools are already integrating these tools into their curricula.
In the labor market, this technology demands new professional profiles, constant skill updates, and a review of roles and strategies within companies.
Comments