People of AI

Meet Joyce Shen, a venture investor focused on emerging technology and AI companies. Learn about startups, acquisitions, and mergers on this episode of People of AI. Joyce shares her career journey in statistics and economics and how she brought her unique experience into building a model for IBM to apply to acquisitions and evaluate startups. Ashley, Gus, and Joyce talk about challenges that businesses are currently facing to integrate or acquire other AI businesses, building trust amongst customers and consumers, and much more.  Resources: Joyce’s picks: readings in ML/AI →  https://goo.gle/3MzNwxi Berkley courses → https://goo.gle/47lLXef  Books: From Talking to Doing: A Short Guide to Corporate Innovation Success Check it out Amazon → https://goo.gle/40tnaT8  Check it out on Barnes & Noble → https://goo.gle/3MButCz Blockchain in Financial Markets and Beyond: The Promises and Limitations of Blockchain: Taking Stock and Lessons Learned Check it out Amazon → https://goo.gle/3SACPhE Check it out on Risk→ https://goo.gle/3udEL5r

Meet Crispin Velez, an AI Architect at Google Cloud and  leader of Vertex AI. Find out how he works with clients, partners and sales teams to adopt ML/AI using Google Cloud. Ashley, Gus and Crispin talk about Google Cloud, the history of cloud computing, creation of Vertex AI, and more. Crispin shares his experience expanding the SpaLatam TensorFlow community in Columbia and mentoring startups on behalf of Google’s Startup initiative. Discover how you can grow a local AI/ML community.   Resources: Google Cloud Platforms → https://goo.gle/47jW9nD Vertex AI → https://goo.gle/3tZSX27 Introduction to Vertex AI → https://goo.gle/3QlgOki  Five generative AI use cases for the financial services industry → https://goo.gle/47amAMZ  Vertex AI Foundations for secure and compliant ML/AI deployment → https://goo.gle/45YbclQ   

Meet Sayak Paul, a Machine Learning Engineer specializing in diffusion models at Hugging Face and GDE for ML and Google Cloud. He shares how his community contributions led him towards getting his current dream job at Hugging Face. Join Ashley, Gus, and Sayak for a chat about resources for developers to get into machine learning, how diffusion models have exploded in the past year, the role of responsible AI and much more.  Resources mentioned: Google Developer Expert Program → https://goo.gle/3S6IVGo TF Hub → https://goo.gle/3S5t9LY Hugging Face →https://goo.gle/45KyBXC Sayak bio and website → https://goo.gle/3Mas9Cv Sayak’s Twitter → https://goo.gle/3QtxEO7  Courses: Google Summer of Code→ https://goo.gle/3Fv2CA4 fast.ai course → https://goo.gle/45HRLxp Coursera Deep Learning specialization → https://goo.gle/3S8Kljx CS 231N - Stanford → https://goo.gle/3QvIt3o Books: Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop (Author) → https://goo.gle/493iJm3 Grokking Deep Learning First Edition, by Andrew Trask (Author) → https://goo.gle/40fNX5y   

Sunita Verma, VP of Engineering at Google, discusses the transformative power of generative AI in the workplace. The evolution of AI from basic systems to cutting-edge technology is explored. Insights into fostering success through collaboration, mobility, and diversity are shared. Future impacts of technology on work environments and society are considered. Advice is given to young tech enthusiasts on leveraging online resources for skill-building.

In this podcast, Gus shares his journey from Brazil to Google, the growth of Generative AI tools like Bard, and Google's conversational AI. The discussion includes the evolution of AI models and societal implications, AI as a writing tool, and perspectives from industry experts on the future of AI.

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  

Join Gant, a leader in ML software development, as he shares his journey from childhood programming to creating amazing ML experiences. He discusses building mobile apps, privacy-minded sentiment analysis projects, and the Google Developer Expert program.

Meet Catherine Nelson, a geophysicist turned Principal Data Scientist at SAP Concur, discussing her journey and insights into building machine learning pipelines. She emphasizes the importance of data preparation and training, model interpretability for ethical ML, and the value of diverse backgrounds in the field. The podcast also covers topics such as data quality, auditing, and favorite AI-related books.