One barrier that was discussed in the presentation was the "risk" or uncertainty of a payoff when talking to potential funders. It seems to me that the "risk" factor of collaborations like this, for funders, would be analogous to the investment and payoff when developing a genomics program. Relative to traditional field research, it takes a much greater investment in time, with different expertise, to create a reference genome for a species of interest and then pull SNPs, etc. Nevertheless, funders seem to recognize the potential for genomic research and genomic tools. Do you think that greater openness to “risk” in conservation genomics results from closer intellectual proximity (i.e., molecular biology vs organismal biology), or to conservation genomics being a more mature field (i.e., more examples of success), or to other factors? If intellectual proximity is a key factor, then conservation tech collaborations will continue to face this barrier, but if lack of successful examples are a key factor, then perhaps this barrier will become less of an issue for conservation tech in the future. Thoughts?

Hi. Just watched the Youtube version (I'm in the unable to watch live hemisphere) and wanted to say that was a great talk. I wish it could have gone for another hour. I liked hearing about the issues and concerns from Melanie on the biology/ecology side. Also could totally relate to what Ed was talking about from the tech side about how it's difficult to have long term collaborations since there isn't really a conservation technology career path at the moment. 
I think one of the things we're trying to do with courses like Build Your Own Datalogger is to get people involved in wildlife/conservation side more comfortable with the tech. The goal is to hopefully build a shared repository of technical knowledge that people in the wildlife community can contribute to and benefit from. 

Akiba

Thanks everybody for joining today's session! I've attached the article here that describes Pattern Matching in greater detail to respond to some of the questions from today's session. 
Also, here is a link to our Privacy Policy and our Terms of Service for the individual who had questions about GDPR. Feel free to contact us at support@rfcx.org with any other questions about the platform or Pattern Matching in general! 

Zephyr and Marconi, thanks for the great talk and your hard work that made this platform available to the public!! I'm experimenting with the pattern matching function to create some training data but have issues trying to get the "Jobs'' to run. I only have 18 files for each of these tests, but only 1 processed to 11.1% while two others stay at 0%. The internet connection was briefly interrupted when I created the first job and was fine later on. I can only Hide, not delete or re-run these jobs, so I'm not sure what to do. Any suggestions would be greatly appreciated. Thanks so much!

Hi Andrew,

Yo need to train a lightweight DNN model for bird flocks which can then be deployed on Raspberry Pi. For initial starters, you can look into the below tutorial:

 https://www.pyimagesearch.com/2017/10/16/raspberry-pi-deep-learning-object-detection-with-opencv/

Best

Piyush

Hi Wildlabbers,

Just popping in to share this very cool primer for beginners to embedded machine learning from our tutor Daniel Situnayake! If you're interested in learning more about the basics of TinyML, this is a great resource.

Find the link here!

-Ellie