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Generative Artificial Intelligence (GAI), essentially, is based on algorithms known as generative models. These models share the following characteristics:
Neural networks:
Inspired by the characteristics that define the human brain, neural networks are composed by nodal layers that process and transmit data.
Adversarial generative networks (GANs)
As key elements of GAI, generative adversarial networks are composed by two networks: one generator (data creator) and the discriminator (data evaluator). These constitutive elements work together to generate high-quality data.
Benefits of using Generative Artificial Intelligence
Advanced personalization through deep learning
Deep learning, being a subset of machine learning, is fundamental to achieve personalized experiences for users. This quality is achieved using principles like:
- Behavioral predictive analysis: Through the analysis of consumers’ behavior, websites can plan their actions and project future preferences, adapting responses in real time.
- Conscience of the context in real time: the detection of devices, location and local events allow websites to personalize the content instantaneously.
Content creation beyond text
GAI goes beyond the simple generation of text, amplifying its capacities through:
- Multimedia generated employing GAI: Creation of complex images, generating personalized graphics in real time.
- Dynamic Generation of Videos: Advanced algorithms select videos based on users’ profiles, providing unique experiences in each visit.
Transformation of A/B tests and optimization based on data
Website testing with GAI evolve thanks to the following qualities:
- Continuous Learning Models: Websites can evolve in fluid entities that learn and change continuously in contrast to those static A/B versions.
- Predictive Analysis: GAI can help to predict future trends, easing the implementation of proactive modifications.
A new SEO paradigm
The emergence of GAI has changed the SEO strategies thanks to:
- Semantic Web Analysis: GAI learns and generates content aligned with the semantic web, improving the classification in the search outputs.
- Marking of Automated Schemes: GAI identifies categories of contents and generates automatically scheme marks, improving the comprehension and visualization in the search engines.
Challenges and considerations related to GAI
Despite its potential, we can face some challenges:
Algorithmic biases: Generative models can incorporate biases from the training data set, affecting the quality and relevance of the content.
Informatic resources: High end models require a substantial amount of computational power.
Over-adaptation of data: GAI could generate web sites that could be too specific or irrelevant if they are not managed appropriately..
