When we founded Scale in 2016, we envisioned a future where AI was as easy to build as any other software. Four years on, we’ve seen incredible success bringing that vision to life for a huge range of businesses across self-driving, natural language, ecommerce, robotics, and many other fields. As we continue to develop our ambitious roadmap to accelerate AI development, I’m delighted to welcome Brad Porter—formerly Amazon’s VP and Distinguished Engineer of Robotics—as our first CTO.
Our vision for Scale
I got hooked on machine learning from a young age. As a student, I’d build models in my free time that predicted how hard it would be to find parking outside my house, or whether I needed to restock my fridge on the way home.
The beauty of AI systems is their flexibility—define a goal, and they learn an optimal process themselves. But I soon realized that AI is much harder to deploy than it should be. It was incredibly cumbersome to find enough data to train my models. And when I moved, my parking model needed retraining—so I had to find more data all over again.
Other fields of software, like app development, have an entire ecosystem of support tools that streamline development, abstracting away complexity and automating basic workflows.
You don’t need a PhD to build a website. What if the same was true for AI?
Building the technology stack for AI development
Brad and I first met a year ago. I had made it a point to get to know all of the people out there who were doing production AI in meaningful ways. Brad, leading robotics at Amazon, was at the top of the list.
Brad is a very humble guy. It is striking when you meet him. Many technology leaders have a bit of show and flair to them, but Brad immediately reminded me of the kind of leader that I strive to be: exceedingly humble, extremely technical, excited by ideas (both good and bad), and insatiably curious to understand how things work. As we were getting to know each other, he told me that he’s been in love with things that can fly since he was a kid: paper airplanes, kites, boomerangs, radio-controlled planes, flying soda cans, and more. As a kid who was personally obsessed with math and physics out of an intrinsic curiosity for the world, I realized Brad and I had a lot in common.
When we were talking about successful teams, I quickly realized I had a lot to learn from Brad. One core tenet of our work at Scale is our belief that ambition shapes reality. It is something we try to live every day. Unprompted, in one of my email correspondences with Brad, he wrote: “One thing all startups need is ambition. In my experience, ambition is the greatest limiter.”
Brad has a reputation as an incredible leader. One indication is former teammates who are fiercely loyal and incredibly effusive. Every person I have spoken to who has worked with Brad, either at Netscape, Tellme, or Amazon, could not stop raving about him. The same themes continued to pop up—humble, compassionate, dedicated to his teams, curious, and deeply technical.
Then, there’s all of Brad’s technical accomplishments! More than almost anyone, Brad knows what it takes to productionize AI systems and deploy them at global scale in complex, changing environments. With his experience leading robotics at Amazon, he managed one of the most impressive uses of machine learning at scale—the over 200,000 robots powering Amazon’s unrivaled global warehouse and distribution infrastructure. He also has deep expertise in natural language, working with AI and language scientists at Tellme Networks to accelerate the rollout of speech recognition, processing and dialog management technologies. I’m thrilled he has decided to join us.
Brad and I both agree that getting effective, accurate and unbiased data is currently the hardest problem in machine learning. At Scale, we’ve built tools to eliminate those frictions. We’ve built an API that integrates seamlessly with our customers’ workflows, and our own ML models automate much of the annotation process and help ensure we deliver ground-truth data of the highest quality.
As our CTO, Brad will help continue to improve the efficiency and sophistication of our technologies and processes by orders of magnitude, building new tools to continue to accelerate the development of AI systems. He will manage our entire technology stack, with combined ownership of product direction and engineering, to turbocharge our ambitious technology roadmap.
Brad understands first hand the need for better tools that will become the backbone of AI development. Under his leadership, we’ll be able to accelerate our development of tools that can support the AI development lifecycle—building the full development stack for AI.
Scale’s network effects and future of AI
Right now, AI is at a crossroads. With systems like AlexNet, AlphaGo and GPT-3, AI research has made incredible strides. But demand for data annotation is far outstripping the supply of skilled labelers. And the data to be annotated is only getting more complex as AI models become more sophisticated.
With data bottlenecks persisting, the delta between what AI could do in theory and in practice is only growing. The field desperately needs better technology to overcome these barriers.
But a new era of AI has begun. AI is about to enter a wave of productionization. The industrialization of AI—with global spending on AI systems predicted to triple between 2018-2022—is beginning to take shape.
What’s holding it back is a lack of infrastructure. I believe Scale will play a major role shaping that future. Thanks to our own machine learning technologies, our data annotation products become more sophisticated with every annotation instance they encounter. That provides powerful network effects that benefit all our customers. As we grow, the impact on the deployment of AI will be transformative.
I look forward to working with Brad as he helps drive our progress on that journey.
If you’d like to join Brad’s engineering team, you can find all our open roles on our careers page.