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.

评估胎儿和母亲的健康对于预防和识别怀孕可能的并发症至关重要。本文重点介绍了母亲自己能够用最小的监督和胎儿和产妇健康有效地使用的装置，同时安全，舒适，易于使用。所提出的设备使用母亲子宫内的单个加速度计的带以记录所需信息。该设备预计将在长期长期监测母亲和胎儿，并提供具有有用信息的医疗专业人员，否则他们将由于目前进行健康监测的频率低频率而忽略。本文表明，即使在存在温和的干扰情况下，母亲和胎儿运动的呼吸信息的同时测量实际上是可能的，如果预计该设备延长延长，则需要考虑该装置。