<div dir="ltr"><div class="gmail_default" style="font-size:small">Dear all,</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Hope you are all doing well during this special time. Here is our newest edition of ESIP Machine Learning Cluster Newsletter (pdf version also attached). Hope this can help our community to stay connected despite rapid changing conditions! As usual, if you have any questions and suggestions, please feel free to reach out to Anne and me.</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Stay well! </div><div class="gmail_default" style="font-size:small">Kind regards,</div><div class="gmail_default" style="font-size:small">Yuhan (Douglas) Rao</div><div class="gmail_default" style="font-size:small">ESIP Community Fellow for Machine Learning Cluster<br></div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small"><span id="gmail-docs-internal-guid-9ad6d5d9-7fff-6117-257e-b580586f737e"><p dir="ltr" style="line-height:1.44;margin-top:0pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(109,100,232);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Earth Science Information Partners</span></p><p dir="ltr" style="line-height:1.2;margin-top:10pt;margin-bottom:0pt"><span style="font-size:22pt;font-family:Roboto,sans-serif;color:rgb(40,53,146);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Machine Learning Cluster Newsletter</span></p><p dir="ltr" style="line-height:1.68;margin-top:10pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">March 17, 2020</span></p><p dir="ltr" style="line-height:1.68;margin-top:10pt;margin-bottom:0pt"></p><hr><p></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(235,63,121);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><span style="border:none;display:inline-block;overflow:hidden;width:447px;height:216px"><img src="https://lh4.googleusercontent.com/xu6bpURpSCyS6s1XOrdaCeouoqBE46zf6yUdS87eYzglvaM1ujNxHVL4vCtOWfNZvoKRyLNwuP1UGmOnAvOZQ2aZcgv0AXghwKch1IILmW5CyEGa6ZF4sodW9VLoZOOnSpM0aAJv" width="447" height="356.855902369512" style="margin-left: 0px;"></span></span></p><h1 dir="ltr" style="line-height:1.2;margin-top:20pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Message from the Chair</span></h1><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Thank you for taking a look at our Machine Learning (ML) Cluster newsletter! There’s a lot of ML activity in and around ESIP, listed below. Aspects of machine learning represented here include:</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">the application of ML in various science domains;</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">educational opportunities about a variety of ML tools;</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">ML funding opportunities;</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">understanding the conclusions of a trained ML model; </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">agency and other organizational efforts to integrate ML: practices, strategies;</span></p></li></ul><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">In February 2019, the American Association for the Advancement of Science (AAAS) published an article titled, “</span><a href="https://eurekalert.org/pub_releases/2019-02/ru-cwt021119.php" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Can we trust scientific discoveries made using machine learning?</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">” The answer was, “Not without checking.” Why is that?</span></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Rather than following explicit instructions, ML algorithms learn from data. These algorithms must always return a predication - the result of “I don’t know,” or “none of the above” is not an option. This can lead to uncertainty and results that may not be corroborated. Often, ML algorithms are obscure even to their creators. While this may be acceptable in some domains, such as product recommendation, the ESIP community well understands the need for transparency, understanding, and reproducibility in science.</span></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Cultural methods to manage this concern include the development of ML standards, best practices, Technical Readiness Levels, etc., as NOAA is doing (below). </span><a href="https://www.nist.gov/news-events/news/2019/07/nist-releases-draft-plan-federal-engagement-ai-standards-development" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">NIST</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> and even the </span><a href="https://www.theverge.com/2020/2/28/21157667/catholic-church-ai-regulations-protect-people-ibm-microsoft-sign" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Catholic Church </span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">are also proposing AI standards. (I have yet to find similar efforts for NASA or USGS. Anyone?)</span></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Technical solutions can involve human-mediated construction of understandable ML models or post hoc model analysis. Here, I would particularly like to call out the upcoming </span><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">2020 Geosemantics Symposium</span><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> (below), occurring in coordination with the </span><a href="https://2020esipsummermeeting.sched.com/info" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">ESIP Summer Meeting</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. This one day event brings our cluster together with the Semantic Technologies Committee. This summer we focus on </span><a href="https://en.wikipedia.org/wiki/Explainable_artificial_intelligence" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Explainable AI (xAI)</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">, which aims to address how decisions are made by, in our case, ML systems. Stay tuned for more details regarding date, time, and agenda. </span></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);font-weight:700;vertical-align:baseline;white-space:pre-wrap">Notable award. </span><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap">At the 2020 Winter Meeting in January, our Cluster Community Fellow, </span><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);font-weight:700;vertical-align:baseline;white-space:pre-wrap">Yuhan Rao,</span><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap"> won the </span><a href="https://www.esipfed.org/press-releases/peer-recognition-2020-esip-winter-meeting" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">ESIP Catalyst Award</span></a><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap">. That award “honors those who have brought about positive change in ESIP and inspired others to take action in the past year”. I can personally attest that Yuhan, the author and instigator of this newsletter, has brought tons of positive energy (to mix physical phenomenon) to our cluster and well beyond. He has regularly inspired me to get up and do something (hopefully for the better.) Congratulations, Yuhan! And, thank you!</span></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="background-color:transparent;color:rgb(0,0,0);font-family:Roboto,sans-serif;font-size:11pt;font-style:italic;white-space:pre-wrap">Anne Wilson, Ronin Institute</span><br></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">March 9, 2020</span></p><h1 dir="ltr" style="line-height:1.38;margin-top:10pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Inside the cluster</span><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span></h1><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(102,102,102);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Highlights of ESIP-ML and cluster members’ activities</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">2020 ESIP Winter meeting. </span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Last month’s ESIP Winter Meeting has again highlighted the rapid growing interests of machine learning and artificial intelligence from the Earth sciences information community. There are multiple sessions featuring different aspects of ML/AI. For example, “AI for augmenting geospatial information discovery” (</span><a href="https://www.youtube.com/watch?v=W0q8WiMw9Hs&feature=youtu.be" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">watch recording here</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">) featured different use cases of AI technologies for agriculture, climate, and social good etc; </span><a href="http://wiki.esipfed.org/index.php/Agriculture_and_Climate" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Agriculture & Climate cluster</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> initiated a new effort on “automated agriculture using AI” (</span><a href="https://youtu.be/GhnSINRFNBg" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">watch recording here</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">). Our cluster also hosted a two-session workshop focusing on the topic of training data. This workshop introduced three existing tools/platforms for generating training data -- </span><a href="https://labeler.nasa-impact.net/" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Image Labeler</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">, </span><a href="https://github.com/tzutalin/labelImg" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">LabelImg</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">, </span><a href="https://bokeh.org/" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Bokeh</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">, and hands-on activities to use LabelImg and Bokeh to generate labelled data (</span><a href="https://youtu.be/3ufBOoD3M1E" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">watch recording here</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">). These three tools provide great potential to generate different types of training data via well developed open source ecosystems. Although the winter meeting was just concluded, we have already been thinking about the topics for the summer meeting in Burlington, VT. We would love to hear your ideas and suggestions and hope to make them happen for the summer meeting.</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">2020 GeoSemantics Symposium/workshop</span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Together with the </span><a href="http://wiki.esipfed.org/index.php/Semantic_Technologies" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Semantic Technologies Committee</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">, we are proposing to jointly organize the summer GeoSemantic Symposium to coincide with the ESIP summer meeting. The proposed theme for this year’s symposium is leveraging explainable AI (xAI) to support decision making with Earth science data. The proposed theme, echoing ESIP’s strategic goal of putting data to work, aims to bring diverse stakeholders (technologists, data providers, and decision makers) together and use semantic technologies to increase our confidence in ML-supported decision making processes. At this early planning stage, we are </span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">looking for use cases from the ESIP community</span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> that fits the theme and develop an engaging program to put data to work!</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">AI for Agriculture (Agro AI)</span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. The </span><a href="http://wiki.esipfed.org/index.php/Agriculture_and_Climate" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Agriculture and Climate cluster</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> (ACC) just started the discussion on AI for Agriculture (Agro AI) effort . We believe this can be a great opportunity to foster stronger connections and collaboration across ESIP. If you have ideas or successful examples in this area, we encourage you to share it with members from both clusters and participate in the ACC’s discussion on this new effort. You can contact </span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:700;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">William (Bill) Teng</span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> (<a href="mailto:william.l.teng@nasa.gov">william.l.teng@nasa.gov</a>) and </span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:700;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Brian Wee</span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> (<a href="mailto:bwee@massiveconnections.com">bwee@massiveconnections.com</a>), ACC’s co-chairs, to learn more about this exciting new effort!</span></p></li></ul><h1 dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">All about Machine Learning</span></h1><p dir="ltr" style="line-height:1.68;margin-top:10pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Roboto,sans-serif;color:rgb(102,102,102);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Information relevant to ESIP-ML community</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">NOAA’s AI Strategy. </span><a href="https://nrc.noaa.gov/NOAA-Science-Technology-Focus-Areas" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">NOAA Research Council (NRC)</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> has recently released NOAA’s new strategies in four key science and technology focus areas. Among these key focus areas, </span><a href="https://nrc.noaa.gov/LinkClick.aspx?fileticket=0I2p2-Gu3rA%3d&tabid=91&portalid=0" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">NOAA’s AI strategy</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> aims to “dramatically expand the application of artificial intelligence (AI) in every NOAA mission area by improving the efficiency, effectiveness, and coordination of AI development and usage across the agency”. NOAA’s new strategy together with other federal agencies' efforts can boost Earth sciences community’s effort to embrace ML/AI as tools to advance scientific discovery and support societal relevant decision making process.  </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Radiant Earth/NASA ML workshop. </span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">During January 21-23, 2020, Radiant Earth Foundation hosted an </span><a href="https://www.radiant.earth/events/nasa-ml-2020/" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">international workshop</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> on leveraging ML and NASA Earth observations (EO) to address environmental challenges (ML4EO). The recordings of the workshop have been archived on </span><a href="https://www.youtube.com/playlist?list=PL3QzFgBMGnbQRa8uHP0_C_P2Fl5GIBxmn" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Radiant Earth Foundation’s YouTube channel</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Microsoft AI for Earth Grant. </span><a href="https://www.microsoft.com/en-us/ai/ai-for-earth-grants" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Microsoft’s AI for Earth</span></a><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> program provides cloud computing resources and AI tools for researchers and stakeholders (NGOs, local governments, etc) to solve environmental challenges. The grant provides Azure compute credits for selected projects to solve four categories of environmental challenges -- climate, agriculture, biodiversity and water. The grant has four application deadlines each year and the upcoming application deadline is </span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">April 6, 2020</span><span style="font-size:11pt;font-family:Roboto,sans-serif;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.56;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">National Geographic AI for Earth Innovation Grants. </span><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">To address the many pressing scientific questions and challenges facing our planet, we must increase global understanding of how human activity is affecting natural systems and create a community of change, driven by data and cutting-edge technology. Modern technologies, such as satellite imaging, bioacoustic monitoring, environmental DNA, and genomics, can capture data at a global scale, but also produce massive, complex data sets. Artificial intelligence (AI) and cloud computing can capitalize on the potential of such data, leading to faster and more meaningful insights and creating the opportunity for transformative solutions. </span><a href="https://www.nationalgeographic.org/funding-opportunities/grants/what-we-fund/ai-earth-innovation/" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Learn more here</span></a><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. The deadline for the RFP is </span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">July 22, 2020</span><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li></ul><p dir="ltr" style="line-height:1.2;margin-top:20pt;margin-bottom:0pt"></p><hr><p></p><p dir="ltr" style="line-height:1.68;margin-top:20pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(102,102,102);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">You are receiving this newsletter because you are part of the ESIP Machine Learning Cluster. If you have any news want to share or questions, please contact Anne Wilson (anne.wilson *at* <a href="http://ronin.org">ronin.org</a>) and Yuhan Rao (yuhan.rao *at* <a href="http://gmail.com">gmail.com</a>).</span></p><br></span></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>