Using Machine Learning to Tackle Climate Change: A HacksterIO Contest

Climate change has been identified as one of the most significant issues facing humanity over the next few decades.  It will likely create a host of challenges around the globe from increasing storm severity, flooding, more intense heat waves and droughts, more intense and frequent wildfires and hurricanes.  While human activity bears much of the responsibility, human innovation and technology can also provide meaningful solutions.  Machine learning, for example, promises to be a powerful tool for understanding the complex interactions of nature and our influences, as well as providing insights to help reduce energy consumption, lower carbon emissions, and cope with resulting consequences.

In their comprehensive paper entitled “Tackling Climate Change with Machine Learning“, a team of scientists from academia and industry (including Carnegie Mellon, Harvard, MIT, Cornell, Stanford, Google, and Microsoft among many others) describes multiple areas where machine learning can help.  These spans a broad range of applications in power generation and distribution, transportation, buildings, infrastructure, industry, and farms and forests.

We at SensiML wholeheartedly agree and want to do our part to help.  That’s why we’ve teamed up with QuickLogic and to create the “Challenge Climate Change” contest.  Together we will award prizes for the best IoT sensor applications addressing some aspect of climate change mitigation and built using SensiML Analytics Toolkit and QuickLogic’s QuickFeather Open Source Hardware Development Kit.

SensiML Analytics Studio and QuickLogic QuickFeather Eval Kit

SensiML Analytics Toolkit is ideal for quickly and easily constructing complex sensor algorithms directly on low-power edge devices. QuickLogic’s QuickFeather is a flexible and ultra-lower power example of very capable IoT edge hardware integrating its EOS S3 system-on-chip with Arm Cortex-M4 MCU, FPGA, and DSP processing. 

Use your expertise to create an intelligent IoT device using machine learning to improve awareness, change behavior, or optimize processes in a way that helps positively impact the problem of climate change. More information on the details of the challenge can be found on the QuickLogic website at and the website at

For lots of interesting ideas, you can find the “Tackling Climate Change…” paper at  Best of luck in the contest should you choose to accept the challenge!

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Over $70,000
in prizes to be
awarded for the
best Smart IoT
Edge Sensing
projects addressing
climate change