Clean Technology Meets Data Science: News and Articles

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul

  I was at   Imagine H2O’s “Water Innovation Week” conference   this week - virtually, of course! Imagine H2O is a wonderful resource and accelerator for startups in the water space, and their program this week was an excellent representation of water’s central role, not just in our daily lives, but also in the clean technology sector in general.     In most of the developed world, water isn’t really at the forefront of most people’s minds. Turn on the tap, you get clean, free flowing water - and unless you’re in the water sector, you’re probably not thinking about things like aging water infrastructure, budgets and how to fund water infrastructure, how to ensure that water is used efficiently, that wastewater is effectively treated and that tradeoffs between the water allocated to different sectors are discussed and managed equitably. In fact, unless there’s a storm or a flood or a leak in the water pipes in your house - water is not your primary concern.   And that is how it should b

  It’s always interesting to take a look at how trends and predictions about new technologies and their ramifications for different sectors pan out. And that’s no different when it come to data science and clean technology.   A graph that often comes up when discussing how new technologies develop is the “Gartner cycle of hype”. This is the idea that all new ideas, concepts and technologies invariably go through several stages in their development - they all start with excitement as the promise of new technology opens up possibilities that seem limitless, followed by a crash course in reality when what’s actually possible collides with dreams, and finally a steady look at immediate solutions that can build towards the dream. The last stage is when startups gain traction or are acquired and larger companies start building teams to work with the new technology. So, how has agriculture and data science - or AgTech worked out?     It’s definitely been an interesting ride since the concept