  Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way
into the fabric of society, where they are playing a crucial role in numerous
facets of our lives. As we witness the increased deployment of AI and ML in
various types of devices, we benefit from their use into energy-efficient
algorithms for low powered devices. In this paper, we investigate a scale and
medium that is far smaller than conventional devices as we move towards
molecular systems that can be utilized to perform machine learning functions,
i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is
the transport, processing, and interpretation of information propagated by
molecules through chemical reactions. We begin by reviewing the current
approaches that have been developed for MML, before we move towards potential
new directions that rely on gene regulatory networks inside biological
organisms as well as their population interactions to create neural networks.
We then investigate mechanisms for training machine learning structures in
biological cells based on calcium signaling and demonstrate their application
to build an Analog to Digital Converter (ADC). Lastly, we look at potential
future directions as well as challenges that this area could solve.
