Topics: AI at the edge
By Kyle Seaman, Head of Product at Sentenai, and Sourav Dey, Co-Founder and CTO at Manifold
Predictive analytics is an undeniably valuable technology, with research indicating its market size could top $12 billion USD by 2022. Across a range of industries, businesses, and applications, using historical data to predict future outcomes can lead to greater operational efficiency in a variety of ways. Predictive analytics can enable organizations to streamline their operational processes, optimize their demand forecasting, drastically reduce downtime, and better understand their customers’ propensity to buy.
Topics: Data science
A common problem that comes up in machine learning is needing to find the l2-distance between two sets of vectors. For example, in implementing the k-nearest-neighbors algorithm, we have to find the l2-distance between the a set of test vectors, held in a matrix X (MxD), and a set of training vectors, held in a matrix X_train (NxD). Our goal is to create a distance matrix D (MxN) that contains the l2-distance from every test vector to every training vector. How can we do this efficiently?
Topics: Data science
This article is Part 2 of a three-part series that we are writing about work that Manifold did with one of our clients, Cortex Building Intelligence. In our previous post, we talked about finding edges in sensor signals so we could use them to help us estimate a building’s start time.
We want to find sharp transitions in the various HVAC sensors—rising edges for electricity, steam, and static pressure and falling edges for supply air temperature (SAT). It’s easy for a human to pick out edges—but how do we teach a computer to do it?
This article is Part 1 of a three-part series that we are writing about work Manifold did with one of our clients, Cortex Building Intelligence. Cortex’s vision is to use data-science to make commercial building heating, ventilation, and air conditioning (HVAC) operations more efficient.
Over the next few posts, we want to give you a look "under the hood” of our data-science operations. To that end, we’ll discuss how we solved one of the foundational problems at Cortex: figuring out when a building’s HVAC systems were turned on.