Machine learning has experienced an explosion of popularity in recent years, and even more so in recent months. In response, companies including but not limited to Reddit, Twitter, and Snapchat have all closed off, restricted access, or made headlines for how “open” they are with data to train large generative models.
Managing large open datasets is often a thankless act - but it’s one that we must invest in if we truly want to prioritize a future that includes ethical machine learning. In this talk, we’ll explore the role open data plays in machine learning, why it’s concerning to see organizations close access to their API, the cost/benefits institutions and individuals run when they open their data, and - most importantly - we’ll walk through different resources, projects, and opportunities for individuals to direct us to a better future at this current juncture in open data and machine learning.
Erin Mikail Staples
developer advocate, community human, tech educator, comedian
@erinmikail
Erin Mikail Staples is a very online individual passionate about facilitating better connections online and off. She’s forever thinking about how we can communicate, educate and elevate others through collaborative experiences. Currently, Erin is a Senior Developer Community Advocate at Label Studio. At Label Studio - she empowers the open source community through education and advocacy efforts. Outside of her day job, Erin is a comedian, graduate technical advisor, content creator, triathlete, avid reader, and dog parent. Most importantly, she believes in the power of being unabashedly "into things" and works to help friends, strangers, colleagues, community builders, students, and whoever else might cross her path find their thing.
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Recorded Sept 21, 2023 at Strange Loop 2023 in St. Louis, MO.
thestrangeloop.com