Welcome to the first "edition" of E+E Leader's C-Suite Series where we highlight advice, best practices, lessons learned, etc. from executives in the environmental, energy management, and sustainability C&I Fields. Over the next year, our readers will have the opportunity to view first-hand advice from C-Suite Executives across a variety of industries. The conversations will be informational, and personable. You're sure to take away invaluable advice, strategies, and tips to help you grow as an individual, professional, and entrepreneur.
Recently, I had the pleasure of spending part of my afternoon speaking with one of the Co-Founders, and Chief Product Officer, of Plainsight, Elizabeth Spears. During our conversation we touched upon the importance of taking a human-centric approach to AI and ML, building and growing a business. New projects, STEM opportunities for youth, and how her upbringing shaped a love of science and technology. What it's like to balance her personal and professional life as an entrepreneur and mom, creating and facilitating a positive, impactful climate at work and more.
JH: Welcome Elizabeth. Thank you so much for joining us today. We really appreciate you taking time out of your schedule to sit down with E+E Leader for an exclusive interview.
ES: Thank you for having me.
JH: What is something that we don't know about you and your work at Plainsight? Something we cannot find on LinkedIn or the company website?
ES: I think one of the things that gets lost when talking about state-of-the-art, technology and the exciting solutions we see every day is the human-to-human aspect of AI. As counterintuitive as it may seem, I studied computer science and cognitive science because of the human part, because of the human aspect. I didn't see it as learning about computers, but more about learning how humans can understand computers.
I've always seen building great software as an exercise in understanding what people need to accomplish and then making the software conquer that task. Understanding how humans can influence and improve or even limit technology enhancements is really crucial to being able to foresee what's coming next, what we need to build next, and what is really going to matter to our client, next. We're focused on creating tools that give people the ability to understand more and more data. Responsible AI methods result in more reliable AI models and in turn, these models are used to better inform the people that are ultimately making the decisions with more accurate and holistic insights than have ever been possible.
JH: Human error is part of life and if the data is incorrect or input wrong, more often than not, this is because people don't know enough about the data or how to accurately record it.
ES: Exactly. And leading with a human-centric approach to computer vision is really our secret to success. It's our secret to staying ahead. And like you said, the data you upload into models must be correct because that data governs your next steps.
Our big focus is creating tools that make the data you're looking at more and more reliable and simple to understand. We are dealing with hundreds of thousands of hours of video or images, and to expect a person to look at some or all of the information and understand the long-term patterns, taking into account possible biases in that data is unrealistic. It's really about giving people the tools to create better, more efficient, and adaptable models for others to use and build upon.
JH: Transparency - The big theme. For the past couple of years, we've seen a shift in business practices. Stakeholders expect transparency from executives at every level.
ES: I totally agree. I think that's one of the things that I like the most about the tech industry - dedication to sharing information, the number of open-source projects there are, and how much code is out there to help other people build those things.
JH: In today's world everyone wants information and data immediately or as close to immediately as possible. We live in a society where things change so frequently and having that data and being able to build upon it or make decisions that are going to affect the bottom line, or, corporate-level decisions are paramount to a company's progress and success.
JH: When you, Carlos, and Logan founded Plainsight, did you envision how quickly it would grow? What was the driving force behind the mission of the company?
ES: All of us worked in enterprise software in the past so we had a framework of how to start and build software companies within the enterprise environment. We also knew firsthand that many of our potential customers had really complex challenges that only visual data, machine, and deep learning could solve. We were aware of this specific gap in the market. Also, we knew that enterprises, especially some of the larger ones, didn't have the team, the centralized tools, or the processes needed to achieve long-term computer vision success. It's really interesting.
We often take over failed projects and really take a look at the size of the data, and the visual aspect of it. Video and image data is is what we call unstructured data. With structured data, such as a document, we know the title and the subject. With a video, you have absolutely no idea what is actually in that video. What someone's talking about, and that that gap in the market is what we wanted to move into. We take a unique solution-centric approach by combining our expertise in computer vision, a platform to help enable people to build, faster and more effectively, with a standardized process.
Different models need long-term oversight. The input and output can be pretty consistent, but a model is essentially just outputting a statistical answer. If the data that you're inputting has changed significantly, over time, your results can be less and less accurate, and that's one of the reasons there is a need for long-term oversight. We provide all three of those major pieces. And as a result, growth has been a natural outcome of that success. The other key here is, and this is something that we really learned along the way, computer vision is often a linchpin in larger, digital transformation initiatives. As a result, we work both directly with enterprises, as well as with partners and larger firms.
JH: Have you ever received a request or a problem from a client that, made you go ‘wow this is a lot to take in. We're going to need to bring in a big team to solve this?’
Stay tuned for this answer and more next time as we get into a very exciting project that will be a game-changer in the restaurant and hospitality industries.
Plainsight, a software development company headquartered out of San Francisco, CA streamlines vision AI for enterprises with new ways to analyze, share and benefit from valuable visual information. Their intuitive, low-code platform gives every team across organizations the ability to build, manage and operationalize solutions. With actionable insights and unblinking accuracy, Plainsight powers enterprise-ready applications to automate processes, mitigate risk, enhance product portfolios and increase revenue opportunities.
