How do you find out why new technologies are being adopted? How do you find the early adopters and figure out why they are using these new technologies?
As startups and individuals build new tools and applications in agriculture, water, energy, sustainability, forestry and climate - some of the the biggest questions they face are understanding who is likely to adopt these technologies, the parameters governing these decisions and how they interact with each other.
So, how can this be measured and modeled quantitatively? Welcome to the wonderful world of Bayesian networks!
Bayesian networks are powerful machine learning algorithms that allow us to model how different aspects of a problem are interacting with each other, estimate how likely it is that someone will choose to do something like buy a new technology, account for the uncertainty inherent in problems in clean technology where we don’t know all the parameters and values associated with them - and solve a whole suite of similar problems.
If you’re curious about these algorithms, how they’re used in clean technology, how we can build them using Python - come and join us this Sunday, September 20th at 11 am-12.30 pm Pacific Time for a live, virtual workshop. It’s a gentle introduction to probability, Bayes’ theorem and how it works in clean tech for everyone who’s interested in these topics, followed by a hands-on problem solving session in Python where we’ll model how new technologies in agriculture like drones, crop and soil maps and smart sensors are being adopted by different users in the United States.