Name: Women In AI
Start: 2024-12-11T17:30:00-08:00
End: 2024-12-11T20:30:00-08:00
Location: JPMC Tech Center

We have a very exciting event planned with Four World Class AI presenters lined up for you.

**Complimentary Admission, Food and Drinks!**

Get ready to accelerate your learning for AI as we proudly announce our Dec 11th Silicon Valley AI Strategic Leaders / Palo Alto Data Science Association / Silicon Valley Financial Services Cloud / San Francisco Agile User Group Joint Meetup! Dive into discussions about the intersection of AI, Project / Program / Product / Engineering and Strategic AI Management for Leaders.

* Discover the Future: Engage with AI innovators as they share their journey, unveiling breakthroughs and practical wisdom.
* Build Connections: Make friendships and forge new business relationships in a community passionate about technology's potential.
* Elevate Your Knowledge: Immerse yourself in an educational experience that promises to enrich your understanding of the creation and maintenance of AI products and solutions.

Join us for an unforgettable night of learning, networking, and connection. Your presence will contribute to a vibrant community eager to explore the endless possibilities of AI.

**AGENDA**
5:30 - 6:00 PM Registration, Food, Drink, Refreshments
6:00 - 6:10 PM Welcome & Introductions - Rob Franklin / Emmanuel Mayssat
6:10 - 6:30 PM Member Introductions - Why are you here ?
6:30 - 6:55 Maria Shaukat - GenerativeAI: The Evolution from Statistics to ChatGPT
6:55 - 7:15 Interactive Exercise
7:15 - 7:40 Anna Yuan - Agentic Architecture for AI Executives: Building Custom AI Solutions Without the 7-Figure Team
7:40 - 7:50 PM Alejandra Holguin Giraldo - Leap: Personalized AI-Powered Learning for Workforce Transformation
7:50 - 8:15 PM Rochelle Mattern- Responsible AI
8:15 - 8:30 Minute - Wrap Up - Thank You's - Rob Franklin / Emmanuel Mayssat

**Presentation Summaries**

**1\. Maria Shaukat\,** Machine Learning Engineer - Responsible AI at LinkedIn

* GenerativeAI - The Evolution from Statistics to ChatGPT

To truly understand generative AI (GenAI) such as ChatGPT, it’s essential to grasp the journey that brought us here; from probability and statistics to today’s advanced language models. This talk explores how each stage of AI’s development—starting with statistical foundations—sets the groundwork for increasingly complex and powerful technologies. Initially, probability and statistics gave us tools to analyze patterns in data, leading to early machine learning (ML) models focused on simple tasks like regression and classification. As computational power grew, we moved into deep learning, where multi-layered neural networks could learn from vast datasets, marking breakthroughs in fields like computer vision and natural language processing (NLP).

The introduction of transformers revolutionized NLP, enabling models to learn context within long sequences of text more efficiently. This advance paved the way for large language models that excel at understanding and generating language, a crucial stepping stone to GenAI. Unlike previous systems, GenAI does not just process or classify data—it creates, predicts, and responds in a way that mimics human interaction. It builds on self-attention mechanisms and unsupervised learning to generate coherent, context-aware outputs, transforming applications from customer support to content creation.

By understanding this journey, we gain insight into the strengths and limitations of GenAI, such as its reliance on large datasets and susceptibility to biases. This background is critical for anyone looking to work with GenAI, build businesses with GenAI, hire GenAI professionals, lead teams and companies into this revolutionary AI Era.

**2\. Anna Yuan**, Co-founder & CEO of ScaleGrowth AI, building the world’s first AI Data Analyst

* Agentic Architecture for AI Executives: Building Custom AI Solutions Without the 7-Figure Team

Join Anna Yuan, Co-founder of ScaleGrowth AI, as she dives into Agentic Architecture—an approach that empowers executives to design and implement tailored AI agent solutions without needing to hire costly data science and engineering teams. In this session, Anna will reveal how leaders can deploy multi-agent AI systems to drive proactive, personalized outcomes at scale, streamline decision-making, and elevate team productivity—all without the high overhead of traditional AI development.
This talk is perfect for AI executives, strategic leaders, and innovators eager to bring Agentic AI solutions to life—no 7-figure tech team required!

**3\. Alejandra Holguin Giraldo**, Founder & CEO, Leap, Educational Architects.

* Leap: Personalized AI-Powered Learning for Workforce Transformation

Leap is redefining workforce training with AI-driven solutions that adapt learning to each user’s profile, knowledge level, and location, tackling the one-size-fits-all limitations of traditional training. With personalized content that drives engagement and retention, Leap empowers companies to transform their teams into motivated, growth-oriented assets. Currently focused on diverse, high-turnover workforces in California, Leap offers a scalable, impactful approach to upskilling in dynamic industries.

**4\. Rochelle Mattern**, Director of Solutions Engineering, SambaNova Systems

* Join us for an insightful session on Responsible AI with Rochelle Mattern, Director of Worldwide Solutions Engineering at SambaNova Systems. Rochelle will lead us in exploring the design of AI applications with a strong emphasis on responsible practices. Together with her team, she supports enterprise clients in adopting cutting-edge generative AI to tackle complex business challenges and generate new revenue streams. Rochelle brings a wealth of experience from roles at Forethought.ai, Google Cloud, and Cisco Systems, where she’s driven the alignment of technical innovations with strategic goals.
* This talk will be beneficial to both developers and leaders designing and building generative AI applications and want to build trust with their end users.

Robert Franklin

Palo Alto Data Science Association

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