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How can investors distinguish between companies that have proprietary machine learning technology versus those using standardized third-party tools?

Cristóbal Valenzuela

Co-founder & CEO at Runway

The first aspect is definitely figuring out and looking at the composition and the core competencies of the team. AI and ML are very buzzy concepts right now that get thrown into a lot of marketing materials. It’s easy to get a Colab notebook, run an open-source model, and call it a day. A very different approach that fewer companies are doing is to build solutions that leverage the potential of ML to drive real value. There’s a lot of iteration in that process. 

At Runway besides publishing at CVPR, and NeurIPS among other conferences, we constantly engage with the research community. We give lectures and tutorials, and we go deeper into the systems behind our work. Building in public and contributing to the community is as important as building models. We also share our work and partner with other organizations and institutions to push the boundaries of research in our domain.

There are a few companies that I believe have figured out a way to productionalize machine learning models at scale. But the consolidation and growth of the ML Ops community and the variety of solutions in that ML Ops space has enabled more companies to focus on value creation rather than infrastructure. At Runway, we have iterated a lot on making sure that the research approach that we have gets finalized into the product in the shortest amount of time possible. And that's proven by our tools, like Green Screen and Inpainting, removing background noise, and a few other things that we're releasing very soon.

Find this answer in Cristóbal Valenzuela, CEO of Runway, on rethinking the primitives of video
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