When our small Emergency Radiology lab sought to engage in AI research, we found that we lacked needed resources, and pre-existing AI research systems did not translate to our workflow or adapt to our needs. Without a system to manage the many facets of setting up and performing AI research, significant manual efforts and a constellation of incongruent tools are needed. A wide range of effort-intensive operations combined to make AI research infeasible for us: Data curation, annotation, machine learning model development, management of people and resources, security, auditing, and multi-system interoperability are far too large of a simultaneous undertaking for a resource-limited lab to manage. The costs of a large staff and requisite resources to perform all of these activities were prohibitively high. In order to perform rapid research, development, and deployment of AI models with minimal staff and low-cost resources, we needed a system that could orchestrate all of these necessary tasks, without the omissions, gaps, and incongruities between tools that so often require many resources and manual intervention. We set out to design an integrated platform that could facilitate the plurality of our research initiatives. Our goal was to restore radiologists as the drivers of innovation in imaging-focused AI. Our design philosophy was that tasks that could be automated, such as handling, translating, and curating high-quality data, should be handled by computers rather than armies of annotators, data scientists, and engineers. The hurdles to successful facilitation of imaging machine learning have been well documented [1].

A research team from the Technical University of Munich (TUM) and the Fritz Haber Institute in Berlin is using active machine learning in the search for suitable molecular materials for new organic semiconductors, the basis for organic field effect transistors (OFETs), light-emitting diodes (OLEDs) and organic solar cells (OPVs). To efficiently deal with the myriad of possibilities for candidate molecules, machine learning proves an invaluable tool. It is envisaged that organic semiconductors will enable important future technologies such as portable solar cells or rollable displays. For such applications, improved organic molecules – which make up these materials – need to be discovered. For material discovery tasks of this nature researchers are increasingly utilising machine learning methods, training on data from computer simulations or experiments.

Technologies are described herein for active machine learning. An active machine learning method can include initiating active machine learning through an active machine learning system configured to train an auxiliary machine learning model to produce at least one new labeled observation, refining a capacity of a target machine learning model based on the active machine learning, and retraining the auxiliary machine learning model with the at least one new labeled observation subsequent to refining the capacity of the target machine learning model. Additionally, the target machine learning model is a limited-capacity machine learning model according to the description provided herein.