14th IEEE ICMLA 2015 – This paper discusses how we should think about anomaly detection for streaming applications. It introduces a new open-source benchmark for detecting anomalies in real-time, time-series data.
Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which are available through the open source project called NuPIC, and we discuss requirements for creating encoders for new types of data.
Recently there has been much interest in building custom hardware implementations of HTM systems. This paper discusses one such scenario, and shows how to port HTM algorithms to analog hardware platforms such as the one developed by the Human Brain Project.
There have been changes in our thinking, in algorithm implementation, in terminology and in other areas since the HTM whitepaper was written, rendering part of this paper obsolete. Much of this paper has been replaced by BAMI and the current white papers.