3 Ways VIA Prioritizes the Team Behind the Tech

VIA is not your typical tech startup. From our unique application of AI and blockchain, to our belief that humans + AI solve problems better together than either alone, VIA has certainly carved out its own niche in a crowded field of solutions providers. But according to our team, what really sets us apart is our people-first mentality. In order for our technology and solutions to stay cutting edge, we need to continue building, cultivating, and supporting the team that got us where we are today.

By Kate Ravanis

1. Building a great team starts with recruiting great team players.

Our CEO Colin Gounden said it best in his recent interview with Inc. Magazine: “What we want are people who are smart and motivated, and who are good at solving problems. You can teach programming and data science, but not innate things like motivation and problem-solving and creativity.”

What we are really looking for are individuals willing to learn, evolve with our technology, and roll up their sleeves to do whatever comes their way.

2. Cultivating a great team requires an effective feedback process that facilitates a regular dialogue around individual’s desires and abilities.

We know that each person on our team has a unique set of strengths, areas of expertise, and career goals. And, we know these things are subject to change over time. At VIA, we rely on our feedback process to ensure we stay in tune with each person’s desires and abilities, which is the first step to ensuring they continue to feel motivated and productive.

One key component of our feedback process is frequency: we schedule bi-monthly reviews (instead of the more traditional annual review cycle) to provide consistent opportunities for self-reflection, goal evaluation, and constructive feedback. To borrow from Winnie the Pooh: frequent reviews are a chance to “stop bumping for a moment” and reflect on what’s working, what isn’t, and what we can change (and how) to become more effective.

3. Supporting a great team means balancing your team’s desires and abilities with the company’s needs. Our allocations team has a goal of matching the Desire, Need, and Ability (DNA) of people and the organization over time.
Understanding the DNA of our team is only one piece of the puzzle. The real value (for both the individuals and the company) comes from putting that information into action. At VIA, our allocations team uses this knowledge to make thoughtful decisions around project and talent management. We strive to balance what an individual wants with the tasks the company needs completed. This means making an effort to create opportunities for team members to learn new skills and tackle new challenges (and not just assign tasks within their current wheelhouse), which is essential to long-term satisfaction and team morale.

The AI That Cried Wolf: How VIA Refines Algorithms

As energy companies start to explore AI solutions, we hear a recurring set of questions: How do your algorithms work? How much data do you need to make predictions? How do you measure the accuracy of your algorithms? With that in mind, we wanted to take an opportunity here to shed a little light on one of our most frequently asked questions.

By Colin Gounden

Does VIA use subject matter expertise to build models or does it rely solely on AI algorithms?

The short answer is both. We start by building AI algorithms with a combination of our client’s equipment data (age, location, equipment type) and add contextual information like pollution or weather data. We use this data to create initial predictions. These initial predictions are refined with input from our client’s internal subject matter experts. That’s right: our goal is to create software that works alongside human experts rather than replaces them.

How does our collaborative approach to refining the algorithm work? To explain, let’s take the example of an AI model trained to distinguish photos of huskies from photos of wolves. Initial algorithms had a hard time with this task. VIA’s key differentiator is a mathematical approach that extracts from the AI an “explanation” for each prediction. In this example, early results explained that the model would classify the subjects as wolves when there was snow in the photo. An obvious mistake for people but not obvious for a computer. Once the snow feature was removed, the algorithm’s accuracy improved more than three-fold. Similarly, we present an algorithm’s initial predictions and corresponding explanations to a client’s team of experts and de-prioritize selected features.

We see two big advantages from this approach. First, we remove any spurious correlations that an AI algorithm may be picking up. Second, we also gain buy-in from experts and users regarding the software’s predictions. Increasingly, experts have “algorithm aversion” where they don’t blindly trust black box predictions. The ability to have explanations and input into the algorithms builds credibility in the software and recommendations.