What is AI – Artificial Intelligence

Definition:

Artificial intelligence (AI) is a multidisciplinary sub-discipline of computer science that develops systems capable of performing tasks that require speech recognition, learning, planning, reasoning, perception, and problem solving in humans. In current practice, the term is mainly associated with generative AI with large language models and multimodal models that create text, images, audio, and code, although the field also encompasses recommendation, computer vision, search, speech recognition, optimization, and robotics.

What is artificial intelligence and how is it classified?

In operational terms, AI combines data, algorithms, and computing power to approximate human cognitive abilities. It is common to distinguish between weak or narrow AI (solves specific tasks, such as translating or recommending) and general AI (hypothetical, with broad and flexible capacity). In current practice, specialized systems trained for specific objectives with large volumes of data dominate.

• Symbolic AI: explicit rules and representation of knowledge (logic, ontologies, planning).
• Machine learning (ML): methods that learn patterns from data (supervised, unsupervised, semi-supervised).
• Deep learning (DL): multi-layer neural networks for vision, language, and multimodality.
• Reinforcement learning (RL): agents that optimize actions through rewards.
• Foundational and generative models: large models (LLM and multimodal) adjustable to multiple tasks.

Examples of generative AI and assistants

• ChatGPT (OpenAI): conversational assistant based on LLM for writing, reasoning, and general tasks.
• Gemini (Google): multimodal model for text, images, and code, integrated into Google products.
• Claude (Anthropic): general-purpose assistant with an emphasis on security and handling long contexts.
• Perplexity: search engine with generative response and citation of sources in real time.
• Microsoft Copilot: assistant integrated into Windows, 365, and Bing for productivity and search.
• GitHub Copilot: programming assistant that suggests and completes code within the IDE.
• DALL·E (OpenAI): generation of images from text.
• Midjourney: generation of creative and stylized images from prompts.
• Stable Diffusion: open source diffusion model for creating and editing images.

Problems and classic challenges of AI

AI research addresses technical and social challenges. For a machine to act competently, it needs robust knowledge of the world, reasoning, perception, and learning, as well as mechanisms to explain and audit decisions. These challenges are amplified with large-scale deployment in real environments.

• Representation and reasoning: modeling objects, categories, and relationships; integrating explicit knowledge with statistical learning.
• Perception and action: computer vision, speech understanding, and robotics to manipulate and move.
• Generalization and robustness: performing well outside the training set and against noisy data.
• Explainability and security: understanding why a system decides; mitigating high-impact errors.
• Ethics and governance: biases, privacy, responsible use, regulatory compliance, and continuous evaluation.

Machine learning and deep learning in artificial intelligence

Machine learning is the practical core of modern AI. In supervised learning, it is trained with labeled examples (classification and regression); in unsupervised learning, latent patterns and structures are discovered; and in semi-supervised learning, both are combined. Deep learning leverages large-scale neural networks for complex tasks, with specialized architectures (convolutional in vision, transformers in language and multimodality).

• Classification: assignment of a category (for example, detecting spam).
• Regression: prediction of numerical values (for example, expected demand).
• Sequences and language: translation, summary, and question answering with transformer-type models.
• Generation: creation of text, images, audio, or code from instructions.

Learning theory studies limits and guarantees of algorithms (generalization capacity, complexity, overfitting) and guides validation practices, data partitioning, and evaluation with appropriate metrics.

Applications, SEO, and GEO (Generative Engine Optimization)

Applications include conversational assistants, semantic search, recommendation, industrial vision, healthcare, finance, marketing, and education. In the field of positioning and information discovery, AI impacts on two fronts: optimization for traditional search engines (SEO) and optimization for generative engines (GEO, Generative Engine Optimization), which seek to improve the visibility of a brand or content in responses generated by models.

• AI-assisted SEO: semantic analysis, intent detection, responsible generation of metadata, and verified content.
• GEO: structuring knowledge with verifiable data, semantic markup (for example, schema), reliable sources, and clear explanations that models can cite or integrate into syntheses.
• Conversational experiences: content readable by humans and by models, with canonical definitions, glossaries, and well-linked FAQs.

For GEO, it is key to offer consistent, updated information, with authorship and context, and maintain an internal architecture that makes it easy for models to extract facts, relationships, and evidence.

Recent trends and practical considerations

The evolution of AI is characterized by the increasing scale of models, the multimodal approach (text, image, audio, video), the agent interaction (systems that call tools), and the emphasis on security and evaluation. In professional environments, effective adoption combines use cases with clear return, governed data, and improvement cycles.

• Data and quality: curation, labeling, and governance to reduce biases and information leaks.
• Continuous evaluation: automatic and human metrics; robustness tests and monitoring in production.
• Privacy and compliance: minimizing personal data, anonymization, and access control.
• Assisted productivity: copilots for code, analysis, and content with human review.

Frequently asked questions about AI – Artificial Intelligence

Related Terms

• Converged Media
• Lookalike Audience
• Millennial
• Online Presence
• OpenClaw
• Scrollmap
• SMO – Social Media Optimization
• Social Media Manager