Visual & Generative AI Business applications

Soon You’ll Be Able to Make Your Own Feature-Length Movie With AISome argue that AI is too derivative. But in Hollywood, shallow riffing on preexisting IP is a lucrative skill.

Picture Limitless Creativity at Your Fingertips - 18 nov 2022 WIRED

Picture Limitless Creativity at Your Fingertips - 18 nov 2022 WIRED

On four services alone—Midjourney, Stable Diffusion, Artbreeder, and DALL-E—humans working with AIs now cocreate more than 20 million images every day. With a paintbrush in hand, artificial intelligence has become an engine of wow.
AI cocreations. Progress for each image comes from many, many iterations, back-and-forths, detours, and hours, sometimes days, of teamwork—all on the back of years of advancements in machine learning.
AI image generators were born from the marriage of two separate technologies. One was a historical line of deep learning neural nets that could generate coherent realistic images, and the other was a natural language model that could serve as an interface to the image engine. The two were combined into a language-driven image generator.
the three most popular platforms for image generators right now are three startups with no legacy to protect. Midjourney is a bootstrapping startup launched by David Holz, who based the generator in an emerging community of artists. The interface to the AI is a noisy Discord server; all the work and prompts were made public from the start. DALL-E is a second-gen product of the nonprofit OpenAI, funded by Elon Musk and others. Stable Diffusion appeared on the scene in August 2022, created by Emad Mostaque, a European entrepreneur. It’s an open source project, with the added benefit that anyone can download its software and run it locally on their own desktop.

twitter.com/gwertz/status/1593268725464981505/photo/1

Dreamix: Video Diffusion Models are General Video EditorsGiven a video and a text prompt, Dreamix edits the video while maintaining fidelity to color, posture, object size and camera pose, resulting in a temporally consistent video.

Synthesia raises $50M to leverage synthetic avatars for corporate training and moreSynthesia, a startup using AI to create synthetic videos, is walking a fine, but thus far prosperous, line between being creepy and being pretty freakin’ cool. Today, it announced the close of a $50 million Series B funding round led by Kleiner Perkins, with participation from GV and existing investors Firstmark Capital, LDV Capital, Seedcamp […]

Runway Raises $50 Million At $500 Million Valuation As Generative AI Craze ContinuesThe funding for Runway, one of two startups behind popular text-to-image tool Stable Diffusion, is the latest indication that generative AI has become a rare oasis as VC dries up.

Introduction to Graph Machine Learning

Pure Transformers are Powerful Graph LearnersWe show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent tokens, augment them with token embeddings, and feed them to a Transformer. With an appropriate choice of token embeddings, we prove that this approach is theoretically at least as expressive as an invariant graph network (2-IGN) composed of equivariant linear layers, which is already more expressive than all message-passing Graph Neural Networks (GNN). When trained on a large-scale graph dataset (PCQM4Mv2), our method coined Tokenized Graph Transformer (TokenGT) achieves significantly better results compared to GNN baselines and competitive results compared to Transformer variants with sophisticated graph-specific inductive bias. Our implementation is available at https://github.com/jw9730/tokengt.

Generating Text-to-3D Models Using Point-E, Gepetto AI & Unreal EngineThe meshes have room for improvement but look promising.

How Generative AI Could Disrupt Creative WorkIn the face of technological change, creativity is often held up as a uniquely human quality, less vulnerable to the forces of technological disruption and critical for the future. Today however, generative AI applications such as ChatGPT and Midjourney are threatening to upend this special status and significantly alter creative work, both independent and salaried. The authors explore three non-exclusive scenarios for this disruption of content creation: 1) people use AI to augment their work, leading to greater productivity, 2) generative AI creates a flood of cheap content that drives out human creatives, and 3) human-made creative work demands a premium.

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