Exploring Language Models, Generative AI, and Digital Creativity

Generative AI is changing how we write, create images, organize knowledge, and interact with digital systems. But understanding this shift requires more than following new models and product releases. It requires experimentation, critical reflection, and a closer look at the ideas behind the technology.

This blog explores language models, generative art, prompt engineering, associative memory, and emerging AI workflows. It examines how these systems behave in practice, where their limitations become visible, and how they influence creative and intellectual work.

The articles also address the broader questions surrounding artificial intelligence: alignment with human goals, power and responsibility, copyright, regulation, and the increasingly complex relationship between people and machines.

Expect technical explorations, creative experiments, conceptual essays, and practical observations—written for readers who want to understand not only what generative AI can produce, but how it changes the way we think, create, and navigate information.

How to Measure Entropy in Images with Python

This article explains how entropy measures an image's level of disorder or unpredictability and how it can be used in various practical applications such as image compression, segmentation, and quality assessment. The article provides step-by-step instructions on calculating entropy in photos using popular Python libraries such as NumPy, OpenCV, and SciPy. Through the use of entropy, it is possible to measure a picture's complexity and identify the most informative regions for further processing or analysis.

Using GPT-4 to deal with technical debt

The blog post explores using GPT-4 or ChatGPT for solving technical debt challenges caused by undocumented data structures in legacy systems. Legacy system migrations can benefit significantly from LLM data analysis support, which enhances productivity. The post provides a Python code example for extracting and transforming data from non-standard formats and demonstrates GPT-4's capacity to recognize data patterns and generate code for data extraction tools.

CLIP Score Explained: Formula, Python and Limitations

CLIP Score measures similarity between AI-generated image and text caption, useful for computer vision and language tasks. CLIP allows models to understand visual-textual relationships and perform image captioning and retrieval. To compute CLIP score, use pre-trained CLIP model to produce embeddings and calculate cosine similarity.

The explosion of Generative AI and content production

Generative AI is revolutionizing various fields, including the arts, medicine and the automobile industry, thanks to its increasing capability to perform tasks that were once thought to require human intelligence. By automating content creation, generative AI can create an abundance of possibilities, with trillions of artwork, concept images and portraits being created automatically. However, to make the most of generative AI, it is essential to collect market data, develop an understanding of the market needs, and curate the abundance of content effectively.

Potential Graph AI Models for Generative AI

Platforms like Midjourney, Dalle-2, Stable Diffusion, and ChatGPT have increased popularity of Generative AI, leading to increased interest in multi-headed and single-headed models, as well as graph models. Multi-modal graph models can handle graph-structured data that includes text, photos, videos, and audio. Graph neural network models can be single-headed or multi-headed, can perform tasks such as node classification and connection prediction, and can use convolutional networks, attention networks, and generative graph models.

Image Quality and the Virtual Reality Experience

The quality of a mixed-reality experience depends on high-resolution displays and accurate tracking for realistic virtual objects and surroundings. The uncanny valley phenomenon can occur if virtual entities are too human-like, inducing uneasiness in viewers. Visual quality standards involve well-lit and detailed virtual items that move plausibly and maintain quality at different perspectives.

Inbound Marketing for Digital Artists

Inbound marketing is a customer-focused approach that builds trust and credibility. Digital artists can use tactics such as social media and content creation to draw customers to their business. Inbound marketing involves creating valuable content, optimizing for search engines, and attracting qualified leads for long-term growth.

Is the seed of Stable Diffusion always resulting in the same image?

Seed is a unique attribute of Stable Diffusion-generated images. It represents a specific image and is the master key to it. Same parameters, prompt, and seed always produce the exact same image.

Squared Error Method and Generative AI

Squared error is a loss function used in machine learning and generative AI to train models to make predictions based on data. MAE reduces average error while MSE does not, and PSNR is no longer considered a reliable indicator of image quality degradation with SSIM emerging as a more suitable metric for assessing image improvements. The squared error loss function is often used in regression tasks and is sensitive to outliers and can be affected by the input/output data scale.

Camera Angles for AI Prompts: A Visual Prompt Library

In AI-generated art and storytelling, camera positions can enhance the emotional resonance of your creation. Experimenting with various camera angles can effectively express emotions and support your narrative. This guide explores different camera positions in cinematography, their effects on storytelling and how to adapt them for AI Art and Generative AI platforms.