  Machine learning sensors represent a paradigm shift for the future of
embedded machine learning applications. Current instantiations of embedded
machine learning (ML) suffer from complex integration, lack of modularity, and
privacy and security concerns from data movement. This article proposes a more
data-centric paradigm for embedding sensor intelligence on edge devices to
combat these challenges. Our vision for "sensor 2.0" entails segregating sensor
input data and ML processing from the wider system at the hardware level and
providing a thin interface that mimics traditional sensors in functionality.
This separation leads to a modular and easy-to-use ML sensor device. We discuss
challenges presented by the standard approach of building ML processing into
the software stack of the controlling microprocessor on an embedded system and
how the modularity of ML sensors alleviates these problems. ML sensors increase
privacy and accuracy while making it easier for system builders to integrate ML
into their products as a simple component. We provide examples of prospective
ML sensors and an illustrative datasheet as a demonstration and hope that this
will build a dialogue to progress us towards sensor 2.0.
