Mobile device screenshot displays professional profile webpage hosted on vp.eventival.com. Upper segment contains circular portrait photograph depicting individual with neutral facial expression, bald head, and digitally altered overlay across eyes resembling horizontal metallic slats or mechanical fins. Portrait background is uniform light grey, isolating subject without contextual environment.

Beneath image, bold typographic heading identifies name “Alex Boya.” Paragraph text below outlines career trajectory and philosophical framework. Content describes decade-long experience as creator affiliated with National Film Board, emphasizing engagement with cultural institutions as mechanisms to foster environments supporting human-computer co-development, artificial intelligence exploration, and human-computer interaction. Additional statements highlight Boya’s films as platforms for incubating experimental interactions, establishing innovative spaces where artistic media intersect with computational processes. Philosophical core articulated within text asserts that humanity remains central guiding force in technological progress, ensuring future development aligns with collective wisdom and ethical values.

Webpage design employs minimal layout, utilizing centered alignment, sans-serif typography, and monochromatic scheme. Text is arranged in justified blocks, ensuring clean margins and legibility on mobile interface. Bottom section contains interactive buttons rendered as outlined icons with corresponding functions: “More about,” envelope symbol for email contact, and circular icon for sharing or secondary action. Background remains plain white, reinforcing emphasis on textual and photographic content.

Visible browser interface elements include secure site lock icon, URL bar displaying vp.eventival.com, system status indicators for mobile signal and battery, and navigation icons for back, forward, share, and tab overview. Time reading “13:01” appears within top status bar. Scroll bar visible along right margin suggests additional content beyond current frame.

Overall presentation combines portraiture, biography, and digital interface components, functioning as institutional professional introduction situating individual’s creative practice within context of cultural, technological, and ethical discourse.

Photograph captures computer screen displaying Google Colaboratory (Colab) environment, specifically open notebook titled GFPGAN_inference.ipynb. Interface is divided into left sidebar file explorer and right main coding output area.

In left pane, folder hierarchy is shown. Root directory contains folder labeled “GFPGAN” and subfolder “samples.” Cursor hovers over “GFPGAN,” with tooltip label confirming selection. Sidebar includes navigation controls for file management, typical of Colab’s hosted environment linked to Google Drive.

Main pane on right displays execution logs from active cell. Terminal-style output shows download progress of image file “10047_00.png” from external URL. Processing status indicates tiled inference, with four tiles sequentially processed (Tile 1/4 through Tile 4/4). Log confirms that results are saved in “results” folder with filename “10047_00.png.”

Section header “4. Visualize” is visible beneath output, marking transition to visualization phase of workflow. Notebook toolbar at top provides controls for code, text, runtime, and tools, along with options to save or copy to Google Drive. Status message “Cannot save changes” appears at upper center, possibly due to limited editing permissions or temporary runtime mode.

Browser tabs are visible along top margin, including “stop motion for kids,” “curriculum development,” and “artificial intelligence.” Current active tab shows Colab URL referencing notebook execution session.

Overall, screenshot documents machine learning workflow within Colab environment, specifically applying GFPGAN (Generative Facial Prior-Generative Adversarial Network) for image restoration. The interface demonstrates file structure, execution process, and system outputs characteristic of deep-learning notebook pipelines.

Screenshot captures Visual Studio Code (VS Code) editor environment in dark theme. Central pane shows Python script containing imports, function definitions, and loop structures. Syntax highlighting is applied: keywords in purple, variables in white, strings in orange, and functions in blue-green.

Script begins with imports: import numpy as np, import tensorflow as tf, along with supporting libraries. Code defines function create_dataset which loads and normalizes data, shuffles, batches, and returns prepared dataset. Function employs TensorFlow dataset API (tf.data.Dataset.from_tensor_slices) and pipeline transformations such as shuffle, batch, and prefetch.

Subsequent section defines neural network model using Keras Sequential API. Layers include Dense layers with ReLU activations and final output layer with softmax activation. Optimizer is Adam, loss function is categorical crossentropy, and metrics include accuracy. Model is compiled and prepared for training.

Training loop uses .fit() method, specifying dataset, number of epochs, and validation data. Log outputs such as loss and accuracy are set to display per epoch.

Lower portion of script contains evaluation and prediction routines, including call to model.evaluate on test dataset and model.predict on new data samples. Code includes conditional if __name__ == "__main__": block, standard in Python scripts for main execution.

VS Code interface displays file path in tab labeled deep_learning_model.py. Explorer panel on left reveals workspace directory structure with src, data, and config folders. Top bar shows open command palette with options for Python interpreter selection.

Overall, screenshot demonstrates workflow of deep learning implementation in Python using TensorFlow, organized within modular script inside modern IDE environment.

Photograph captures panel session held in conference environment with five speakers seated in front of projection screen. Session is part of MAPP PRO program dated 28 September, scheduled from 10:30 to 12:00 at Mila (Quebec Artificial Intelligence Institute).

Projection screen behind panel displays event details. Title indicates focus on augmented creation, examining how artificial intelligence transforms artistic practices and reshapes perception of digital culture: “Création augmentée: comment l’IA transforme l’expression artistique et la perception culturelle numérique.” Speaker images and names are arranged on right side of slide, while event branding and partner logos are visible at edges.

Panel composition includes five individuals seated in single row with handheld microphones. Participants wear casual to semi-formal attire. Rightmost speaker, dressed in dark jacket and glasses, is actively speaking while holding microphone. Central figures are seated with neutral postures, one clasping notes or device. Leftmost participant wears patterned shirt, contrasting with darker clothing of others.

Foreground includes Mila logo in large semi-transparent purple lettering projected digitally onto photograph’s corner, linking event to host institution. Surrounding environment includes exposed ceiling infrastructure, suspended lighting fixtures, and minimalist industrial-style interior common to academic or research venues.

Overall, the photograph documents public discourse on intersection of artificial intelligence and artistic expression within institutional framework, highlighting collaborative exploration of cultural and technological integration.

Juxtaposed composition presenting two distinct representations of a head-like structure, positioned side by side within a divided frame. On the left, a robotic cranial mechanism is displayed against a black background, consisting of an off-white polymer shell partially enclosing an underlying metallic framework. The cranial casing includes apertures for eyes, nasal cavity, and jawline, cut into simplified anatomical positions, while surrounding surfaces show fastening points, drilled holes, and attachment slots indicating modular assembly. Beneath the polymer exterior, metallic rods, actuators, wiring, and support brackets are visible, arranged to simulate musculature and mechanical articulation. The jaw is partially open, revealing linkages and servo-driven components, while the base of the unit connects to a stabilizing support system featuring a rectangular horizontal bar with twin optical sensors or camera modules affixed at equal distance from the center. Below this, additional mechanical struts extend downward, terminating in a mounting bracket. Illumination is directional, producing reflective highlights on metallic surfaces while leaving recessed cavities in shadow, emphasizing the hybrid anatomical and engineered qualities of the object. On the right, a contrasting minimal illustration occupies a white field, reducing the head form to an elongated oval shape drawn with thin ink or digital line. Two small circles near the center function as simplified eyes, aligned on a vertical line that extends upward and terminates in a looped curve resembling a rudimentary nose bridge or cranial marker. The overall outline of the head is irregular, with slightly uneven edges and a faint tonal wash across the interior, providing textural variation without volumetric modeling. Minimal detail conveys anthropomorphic suggestion without anatomical specificity. Together, the pairing emphasizes contrast between mechanical complexity and abstract reduction, presenting a spectrum between engineered realism and diagrammatic minimalism.