So now we understand how to optimize a function with Gradient Descent, as long as we can get the derivative of the function. Great, if all functions had an obvious derivative, we would be able to optimize everything!
Automatic differentiation is an exciting emerging technology which enables deep learning applications and is of particular value to PassiveLogic’s smart building platform.
Kepler Ridge is our lead builder for PassiveLogic’s Innovation Center. He spent the first few years of his career shifting between a path in plumbing vs one in software engineering — but here at PassiveLogic, he can do both.
In Part 0 of this series, we introduce the usefulness of automatic differentiation.
What if code could run, not just forwards, but backwards too? While automatic differentiation isn’t exactly reversible computing, it’s a close proxy for solving the large number of computing problems that take the form of: “I have the answer… what was the question?”
On September 23, PassiveLogic Engineer Clark Dobson served as an expert on a panel at the ai4i (Artificial Intelligence For Industries) Conference, which is co-sponsored by the IEEE Computer Society.
The building controls industry is feeling the labor shortage — and is scrambling to find solutions. PassiveLogic addresses the technology gap while simultaneously enabling the current labor market.
User experience or ‘UX’ is the fundamental pursuit of all human-interactive technology. There is no larger market for human-centric technology than the world’s buildings infrastructure.
We can extend the utility of our AI/ML digital twin engine even further by incorporating real-time real-world data and comparing it to our digital twin data.
We all spend the first hour of a meeting establishing what we mean by smart, how smart is smart, navigating disbelief, educating about new technology, and finally arriving at common ground. There is a better way.
