Biologically-Inspired Machine Intelligence for NASA's Missions, Systems, and Facilities

New advances in computing hardware architectures and algorithm development derived from high fidelity modeling of the mammalian cerebral cortex have the potential to provide revolutionary advances in machine intelligence capabilities. The Cortical Learning Algorithm (CLA) represents such an algorithm. It was recently open sourced in the summer of 2013 as part of the Numenta Platform for Intelligent Computing (NuPIC). We propose to assess the current capability of the CLA in the domain of anomaly detection, a domain which relates to a large set of NASA problems. We choose to focus on two machine learning (ML) techniques: regression and adverse event prediction. Our goal is to study how well the neural processing based mechanism of CLA performs on these two tasks. Both will be tested using a well understood scenario from a NASA Aviation Safety dataset. For the assessment we will make use of ACCEPT, an in-house developed Matlab-based framework geared towards the prediction of adverse events. ACCEPT contains a variety of state-of-the-art algorithms that can be used as a baseline to compare the performance of the CLA. We will assess the performance of all algorithmic approaches using metrics such as NMSE (Normalized Mean Square Error), false positive and missed detection rates, and detection time. Topics and assumptions associated with this research are: (1) assessment of CLA applicability towards the ML techniques described above is part of the assessment, (2) the CLA and NuPIC are in an early stage of development; the CLA mapping to neuroscience continues to evolve.

The current effort represents a low-TRL exploratory assessment. More broadly, possible benefits of machine intelligence for future NASA missions and activities can range from enhanced autonomy in launch vehicle and spacecraft system design to providing new tools for data analysis, systems health research and sustainability/energy initiatives in the built environment.

This is early/mid-stage (TRL 5) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.