Guest: Cristobal Valenzuela, founder of RunwayML
From Chile
Studied business / econ
Experimented with computer vision models in 2015, 2016
Did NYU ITP program
Now running Runway
True creativity comes from looking at ideas, and adapting things
How does Runway work?
Applied AI research company
35 AI-powered “magic tools” – serve creative tasks like video or audio editing
Eg, rotoscoping
Also tools to ideate, generative images and video
“Help augment creativity in any way you want”
When started Runway, GANs just started, TensorFlow was one year old
First intuition – take AI research models, add a thin layer of accessibility, aimed at creatives
“App Store of models” – 400 models
Built SDK, rest API
Product sequencing – especially infrastructure – is really important aspect of startup building (what to build when)
Lot of product building is just saying no (eg, to customer requests) if it’s not consistent with your long-term plan
Understand who you’re building for – for them it’s creatives, artists, film makers
Models on their own are not products – nuances of UX, deployment, finding valuable use cases
Having control is key – understand your stack and how to fix it
Built AI research team – work closely with creatives, contributed to new AI breakthroughs
Takes time to do it right
Progression of AI researchers moving from academia to industry
Releasing as fast as you can, having real users is best way to learn
Small team that didn’t have a product lead until very recently
Rotoscoping / green screening is one of Runway’s magic tools
-trained a model to recognize backgrounds
–first feature was very slow (4fps), but was still better than everything that existed
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Runway is focused on storytelling business
Sarah — domains good for AI – areas where there’s built in tolerance for lower levels of accuracy
Product market fit is a spectrum
“You shouldn’t dismiss toys”
Mental models need to change to understand what’s happening (with generative AI)
Art is way of looking at and expressing view of world
Painting was originally the realm of experts, was costly, the skills were obscure
Models are not as controllable as we’d like them to be — but we’re super early