Meet Michelle Carney, a Machine Learning User Experience Researcher at Google. Join us as we learn how her careers in music, neuroscience, teaching, and machine learning have informed her ability to understand how people use Machine Learning tools, and provide better feedback to help make these tools more useful, helpful, kind, and inclusive of all types of user experiences.    Resources: Visual Blocks for ML: https://goo.gle/3OfanzO  Tone Transfer: https://goo.gle/3On9xku  PAIR Guidebook: https://goo.gle/3Mx4Gff  Machine Learning and UX (MLUX) Meetup Resource: https://goo.gle/mluxresources  What is Machine Learning + UX?: https://goo.gle/42KWHB3  Stanford d.school on Designing Machine Learning: https://goo.gle/3OeRaOJ  TensorFlow website → https://goo.gle/3BwLZSN    Michelle Carney Links Twitter: https://goo.gle/3WfxMDc  Linkedin: https://goo.gle/432u0PG    Machine Learning and UX (MLUX) Meetup Resources: https://goo.gle/mluxresources What is MLUX?: https://goo.gle/42KWHB3 MLUX twitter (@mluxeetup):  https://goo.gle/436wGMo MLUX meetup (you can see all of our past talks here!):  https://goo.gle/41QpMts MLUX youtube (all of our past recordings!): https://goo.gle/42Ipt5a MLUX linkedin company page:  https://goo.gle/45c5oWM    Guest bio:  Michelle Carney is a Computational Neuroscientist turned User Experience (UX) Researcher, whose practice focuses on the intersection of Data Science and UX. Currently a Senior UX Researcher on Google’s Tensorflow Team, Michelle's projects focus on combining Machine Learning and UX. Her work includes Magenta’s latest Tone Transfer project and People + AI Research team. Outside of work, Michelle organizes the Machine Learning and UX Meetup, and teaches at the Stanford d.school on Designing Machine Learning.

There is a whole team at Google dedicated to designing AI best practices. They are committed to making progress in the responsible development of AI and share reliable, effective user-centered research, tools, datasets, and other resources with users. Meet one of the members of the team, Christina Greer, as she shares the in’s and out’s of working in the field of Responsible AI and how her personal experience and values make her a key player in this space! Resources: AI Principles: https://goo.gle/3VrCpJP  Responsible AI practices: https://goo.gle/41XVeqI Guest bio: Christina Greer is a software engineer at Google Research. A veteran of a variety of efforts across the company including ads, data processing pipelines, and Google Assistant, she joined Google Research in 2018 to focus on bias and fairness in ML. Since then, she has built both teams and software to support measuring and mitigating ML models for bias, and consults with products across Google to support building safer products that work for everyone. In her spare time, Christina is a creative writer and a mom of 2 great kids.    #AI #ML

Meet Joana Carrasqueira as she talks about her amazing ability to grow community in the field of machine learning. Join us as we hear about her extraordinary journey from the non-profit sector to business and most recently into tech and how she leverages her superpower of bringing people together and fostering a culture of belonging. Resources: WiML → https://goo.gle/3GRW939  WiML Blog post → https://goo.gle/3GS19Fi  Watch all the WiML 2022 sessions → https://goo.gle/3Ld11mb  Favorite book: The Adventures of Women in Tech: How We Got Here and Why We Stay → https://goo.gle/3UM5sHV  Simple ML for Sheets → https://goo.gle/3mMhN29    Guest bio:  Joana Carrasqueira is the Developer Relations Lead at Google for the TensorFlow Community. She is committed to fostering healthy open source communities and to enable developers to solve impactful problems at scale. Prior to Google, she worked on innovation consulting for Forbes top 500 and served as Education Manager at the International Pharmaceutical Federation, working closely with WHO, UNESCO and the United Nations. Joana holds an MBA from IE Business School and a Master in Pharmaceutical Sciences.   #AI #ML  

From developing programs on a Pentium computer as a kid and programming in Visual Basic 6 to becoming a leader in software development for Machine Learning on the Web, join us as we learn about Gant’s journey to where he is today. And the secret sauce to all this? Gant’s creativity and curiosity that he mixes into his work, creating fun and amazing experiences for developers around the world.    Learn more about how to be a Google Developer Expert → https://goo.gle/3oaXxr7    Resources: Website: https://goo.gle/3GFCWlc  Company: Infinite Red: https://goo.gle/3KWDzcW  Title: CIO – Chief Innovation Officer   Social Twitter: @GantLaborde  Medium: https://goo.gle/3ZYmFig  GitHub: https://goo.gle/3KUqsJb  LinkedIn: https://goo.gle/3muSUrC     Books: TensorFlow.js Book: https://amzn.to/3GzR9QK  All Books: https://goo.gle/3zR1zYq    Stuff Gant has made:  Harry Potter-inspired AR Sorting Hat: https://goo.gle/3MAn0ED  Enjoying the Show: https://goo.gle/3Uw2zL0  Time Warp Scan https://goo.gle/43vahZQ  NSFW JS: https://goo.gle/406OFQN  AI Trainable Tic Tac Toe: https://goo.gle/3MA5ErH    Rock Paper Scissors:  https://goo.gle/3o7r70O  TensorFlow.js - RGB channels to Red-Green Color Blind: https://goo.gle/3KAFfYd    Guest bio:  Gant Laborde is the owner of Infinite Red and author of the popular O’Reilly book, “Learning TensorFlow.js”. By day he is a mentor, adjunct professor and award-winning speaker. For 20 years, he has been involved in software development, and is recognized as a Google Developer Expert in Web and Machine Learning. By night he is known as an “open sourcerer”, aspiring future mad scientist, illustrator and appears as an avatar in his latest children’s book, dedicated to his daughter and wife. #AI #ML

Meet Catherine Nelson, Principal Data Scientist at SAP Concur and author of the upcoming O’Reilly book “Software Engineering for Data Scientists”. Join us as we talk about Catherine's amazing career journey as she pivoted from geophysicist to working on setting the standard for building machine learning pipelines. According to Catherine, it all starts with how you prepare and train your data!     Resources: Building Machine Learning Pipelines → https://goo.gle/3nLBpDI  Software Engineering for Data Scientists → https://goo.gle/3Kz3F5u  TensorFlow Meets → https://goo.gle/43a8yZN  Twitter →https://goo.gle/3m8b0zq  LinkedIn →https://goo.gle/3ZJmd7o    Guest bio: Catherine Nelson is a data scientist and author of the upcoming O’Reilly book “Software Engineering for Data Scientists”. She is a Principal Data Scientist at SAP Concur, where she explores innovative ways to deliver production machine learning applications which improve a business traveler’s experience. Her key focus areas range from ML explainability and model analysis to privacy-preserving ML. She is also co-author of the O'Reilly publication “Building Machine Learning Pipelines", and she is an organizer for Seattle PyLadies, supporting women who code in Python. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University. #AI #ML