\Machine Learning — image 1

Generative AI requires massively parallel systems for both training and inference. ChatGPT was trained on 10,000 NVIDIA GPUs for several weeks, but the large language models behind these types of applications are doubling in size and complexity every few months. Applying generative AI to business will require the world’s data centers to become AI factories.

\Machine Learning — image 2
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
NVIDIA at SIGGRAPH /22
\Machine Learning — image 3

With “Instant Neural Graphics Primitives” (Instant-NGP), the NVIDIA research group demonstrate near-instant training of neural graphics primitives on a single GPU for multiple tasks — gigapixel images, 3D objects, and NeRFs.

In gigapixel image we represent an image by a neural network. SDF learns a signed distance function in 3D space whose zero level-set represents a 2D surface. NeRF uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching. Lastly, neural volume learns a denoised radiance and density field directly from a volumetric path tracer. 

\Machine Learning — image 5
\Machine Learning — image 6
\Machine Learning — image 7
\Machine Learning — image 8
https://www.mauritshuis.nl/en/our-collection/restoration-and-research/closer-to-vermeer-and-the-girl/girl-with-a-blog/who-s-the-girl-with-a-pearl-earring/ — Photos by Margareta Svensson
\Machine Learning — image 9

Approximating an RGB image of resolution 20,000 x 23,466 (469M RGB pixels) with our multiresolution has encoding' with different table sizes. The painting is 'Girl With a Pearl Earring' renovation by Koorosh Orooj

Chapter 02 — Agentic Workflow
A new iteration moves the experiment from single-prompt outputs into a Weavy node-based workflow. Prompts, references, and style controls are composed as a system rather than a sentence letting Medusa evolve through iteration, art direction, and a more deliberate creative process, and a sharper answer to what authorship looks like alongside AI.
\Machine Learning — image 10
\Machine Learning — image 11
\Machine Learning — image 12
\Machine Learning — image 13
\Machine Learning — image 14
\Machine Learning — image 15
\Machine Learning — image 16
\Machine Learning — image 17
\Machine Learning — image 18
\Machine Learning — image 19
Chapter 01 — Generative AI
An ongoing study in prompt craft: generating variations of "an image of Medusa" across early AI tools, shifting descriptors, styles, and references with each pass. The work probes our role as creative directors in an AI-mediated landscape — where artistic context, cultural framing, and inclusivity shape whether a generated image carries meaning or simply renders one.
\Machine Learning — image 20
\Machine Learning — image 21
\Machine Learning — image 22

Outpainting with DALLᐧE

\Machine Learning — image 23
\Machine Learning — image 24
\Machine Learning — image 25
\Machine Learning — image 26
\Machine Learning — image 27
\Machine Learning — image 28
\Machine Learning — image 29
\Machine Learning — image 30
\Machine Learning — image 31
\Machine Learning — image 32
DAWN
\Machine Learning — image 33
\Machine Learning — image 34
\Machine Learning — image 35
\Machine Learning — image 36
\Machine Learning — image 37
\Machine Learning — image 38
Back to Top