Technology and science are not strangers. However, emerging technologies like machine learning do make for strange bedfellows with physics. The Cornell Chronicle brought this interesting information to our attention in their article, “Harnessing machine learning to analyze quantum material.”

Electrons and their behavior pose fascinating questions for quantum physicists, and recent innovations in sources, instruments and facilities allow researchers to potentially access even more of the information encoded in quantum materials. However, the limited capacity of the traditional mode of analysis – largely manual – is causing a critical bottleneck in progress.

Quantum computing has rapidly advanced in both theory and practice in recent years, and with it the hope for the potential impact in real applications. Learning models made on quantum computers may be dramatically more powerful for select applications, potentially boasting faster computation, better generalization on less data, or both. 

It has never been more important to understand technology. Most organizations have little knowledge of how AI systems make the decisions they do, and how the results are applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.