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SUMMARY:Women in Data Science at IBM
DESCRIPTION:  \nWomen in Data Science at IBM is taking place at IBM’s Silicon Valley Lab in San Jose on Friday\, October 24th. This regional Women in Data Science (WiDS) event is part of a global non-profit initiative that celebrates women’s achievements and inspires others. The event is free and open to all\, aiming to educate\, connect\, and empower our broader community.

This year’s event features inspiring talks from industry leaders on AI\, ethics\, and data science. In-person attendees also get exclusive access to hands-on workshops with IBM Granite and Docling\, networking opportunities\, and a new volunteer activity to support breast cancer survivors. \nNow in its third year\, our WiDS at IBM event has welcomed over 300 in-person and 250 virtual attendees. Past speakers have included exceptional women from organizations such as OpenAI\, Nvidia\, Netflix\, the government and more. Here is a recap of the event from 2024 and 2023. \nSPEAKERS\n🎙️Executive welcome\nPriya Srinivasan\, General Manager\, IBM Software Products (Core & ALM)\, Software Support & SRE \n​​🎙️Storytelling with Agentic AI\nRynne Whitnah\, Senior Ecosystem Engineer/Developer Relations\, IBM \n​🎙️Logs Are All You Need\nSam Zeitlin\, Founder and Owner\, Radically Different Data Science \n​🎙️What’s Next for AI Agents?\nShelby Heinecke\, Senior AI Research Manager\, Salesforce \n🎙️The Rise of Meeting Intelligence\nShruti Bhutaiya\, Senior Machine Learning Engineer\, ServiceNow​ \n🎙️Beyond Obedience: What Building AI Teaches Us About Courage\nAnjali Samani\, Senior Director of Data Science and AI Engineering\, Salesforce \n​🎙️Ethics in Action: A Practical Toolkit to Build Ethical Tech\nManuela Travaglianti\, Project lead – Ethics Toolkit\, Stanford University’s McCoy Family Center for Ethics in Society \n🎙️Analytics in Motion: Meeting Diverse Needs in a Fast-Changing Environment\nCordelia Radin\, Vehicle Performance Analyst\, BART \n🎙️From Imposter Syndrome to Influence: Finding My Voice in Data Science\nSravya Madipalli\, Senior Manager of Data Science\, Grammarly \n🎙️Closing remarks\nSuja Viswesan\, Vice President\, Security and Runtime Products\, IBM \nTENTATIVE AGENDA\n• 9:00am – 9:30am: Registration & Networking\n• 9:30am – 12:00pm: Workshops\n• 12:00pm – 1:00pm: Lunch\, Networking & Volunteering\n• 1:00pm – 3:30pm: Talks\n• 3:30pm onwards: Networking \nRegistration:  https://luma.com/mm01kuy9
URL:https://svec.org/event/women-in-data-science-at-ibm/
LOCATION:CA
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SUMMARY:Lecture#2: DEVICE INFORMATICS - applications in Public Health
DESCRIPTION:We are in the midst of an artificial intelligence (AI) revolution. It may seem that AI development is recent\, but the term AI was coined (1955) not too long after the invention of computing devices. However\, the idea that a machine can behave like a human being is even older. The term “automaton” was used for it. Please see:\n– (https://www.tableau.com/data-insights/ai/history)\n– (https://en.wikipedia.org/wiki/Logic_Theorist)\nWhat has changed\, though\, in recent times\, is the amount of computation that can be performed. GPUs have made tremendous progress\, and it is now possible to do heavy computation with very large amount of memory in real time.\nThe terms AI and machine learning (ML) are normally used interchangeably though there are differences. We will discuss what is the difference between them. While AI tries to mimic the human behavior\, ML is a set of statistical tools to look at the data to find patterns without direct instructions. In some ways\, it is a subset of AI. We will dig a little deeper into how AI works. This will give us a better understanding of what kind of problems it can solve effectively and where it should be avoided\, or one needs enhanced or use better tools.\nNeural networks are normally used to perform AI calculations: Deep Neural Networks (DNN)\, Convolutional Neural Networks (CNN)\, Recurrent Neural Networks (RNN) are some examples. They have their origin in the way the human brain works.\nWe will discuss the biological basis of neural networks and discuss how these networks are implemented. We will focus on concepts and will develop intuition about how they are trained. We will also see how and when a certain type of network can be used.\nWe will go over some caution around data analysis and what to watch out for when using AI.\nSpeaker(s): Dr Md Usman\nAgenda:\n– 14:00 to 14:10 PM : Welcome to IEEE OC EMBS and general introduction\n– 14:10 to 14:20 PM : Introduction by Gora Datta\, FHL7\n– 14:20 to 15:50 PM: Expert Lecture by Dr Md Usman\n– The difference between AI and ML\,\n– Biological basis of some AI networks\,\n– Basics of how an AI model is trained and what does it mean\,\n– Advantages and disadvantages of using AI – why should we use it\, and what does it measure anyways?\n– What are large language models (LLM)\, and their usefulness in medicine\,\n– Will understand some examples of using AI\, and\n– Know when to use AI for health measurements.\n– 15:55 to 16:00 PM : Wrap Up\nRoom: Emerald Cove\, Bldg: Bealle Applied Innovation\, 5270 California Ave\, Gora Datta\, Irvine\, California\, United States\, 92617\, Virtual: https://events.vtools.ieee.org/m/507676
URL:https://svec.org/event/lecture2-device-informatics-applications-in-public-health/
LOCATION:Room: Emerald Cove\, Bldg: Bealle Applied Innovation\, 5270 California Ave\, Gora Datta\, Irvine\, California\, United States\, 92617\, Virtual: https://events.vtools.ieee.org/m/507676
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