Alison Cossette
Data Science Strategist, Advocate, Educator
Burlington, Vermont, United States
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Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications. Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies. She is currently a Volunteer Member of the US Department of Commerce - National Institute of Standards and Technology's Generative AI Public Working Group Alison’s academic journey includes Masters of Science in Data Science studies, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science. Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.
Area of Expertise
Topics
Pattern Rights - An Ethical Framework for Generative AI Training Data
As generative AI continues to push boundaries, creating novel content by learning from massive datasets, we are faced with complex issues around intellectual property, privacy, and the ethical use of data. Current systems of copyright, fair use, and data protection lack the scope to fully address the unique challenges posed by AI pattern recognition and generation.
This pivotal talk introduces the pioneering concept of "Pattern Rights" - a holistic ethical framework to inform the development and deployment of generative AI technologies. Pattern Rights serves as an umbrella construct, encompassing principles of copyright, fair use, training data transparency, privacy, data ownership, and accountability.
We will explore how Pattern Rights can ensure appropriate attribution and compensation when AI models learn from copyrighted works or personal data. It establishes guidelines around consent, anonymization, and ethical data sourcing practices.
As the AI industry is rapidly evolving, we urgently need governance to foster innovation while upholding rights and safeguarding against misuse. Pattern Rights provides a roadmap to navigate this uncharted territory responsibly and equitably.
Beyond Vectors: Evolving GenAI through Transformative Tools and Methods
Embark on a thought-provoking exploration of GenAI's evolution with "Beyond Vectors: Evolving GenAI through Transformative Tools and Methods." Tailored for engineers seeking fresh perspectives, this session encourages practitioners to step beyond familiar Vector Database practices. It's not just a departure; it's a pragmatic leap forward into precision methodologies for data quality and crafting datasets essential for Retrieval-Augmented Generation (RAG) excellence. We'll navigate the complexities of adding non-semantic context through graph databases, shedding light on the nuanced limitations of distance metrics like Cosine Similarity. Join us for this insightful journey, pushing the boundaries of GenAI evolution with transformative tools and methods.
Key Themes:
Methodical Precision in Data Quality and Dataset Construction for RAG Excellence: Uncover an integrated methodology for refining, curating, and constructing datasets that form the bedrock of transformative GenAI applications. Specifically, focus on the six key aspects crucial for Retrieval-Augmented Generation (RAG) excellence.
Navigating Non-Semantic Context with Awareness: Explore the infusion of non-semantic context through graph databases while understanding the nuanced limitations of the Cosine Similarity distance metric. Recognize its constraints in certain contexts and the importance of informed selection in the quest for enhanced data richness.
The Logging Imperative: Recognize the strategic significance of logging in the GenAI landscape. From application health to profound business insights, discover how meticulous logging practices unlock valuable information and contribute to strategic decision-making.
Key Takeaways:
Master a methodical approach to ensuring data quality and constructing datasets specifically tailored for Retrieval-Augmented Generation (RAG) excellence.
Navigate the complexities of adding non-semantic context, including an awareness of limitations in distance metrics like Cosine Similarity.
Understand the strategic significance of logging for application health and insightful business analytics.
Join us on this methodologically rich exploration, "Beyond Vectors," engineered to take your GenAI practices beyond the current Vector Database norms, unlocking a new frontier in GenAI evolution with transformative tools and methods!
From Zero to Database-backed Support Bot - Using new GenAI Stack- Docker, LangChain, Ollama & Neo4j
With the breakthrough of large language models, generative AI capabilities are now possible for every developer. But where to start?
In a partnership between Docker, Neo4j, LangChain, and Ollama we created a GenAI Stack for building database-backed GenAI applications.
With a single "docker-compose up," you get them up and running and can start importing data and creating vector embeddings, as well as using an example chatbot application to answer natural language questions using a combination of a Large Language Model and a Knowledge Graph.
In this session, we will look behind the scenes into the containers of the GenAI Stack, how they work together and how the LangChain and Streamlit Python apps are implemented. We will use data from StackOverflow so you can fetch topics that you're interested in.
But we will not stop there! Based on the existing code, we will build and run our own GenAI app that extends the existing functionality, and thanks to the quick Docker setup, you can code along.
This should give you the setup, confidence, and convenience to get going with your first applications that use large language models to speed up your time to development.
AI Risk Summit + CISO Forum Sessionize Event
NDC Oslo 2024 Sessionize Event
AI DevSummit 2024 Sessionize Event
CodeForward Sessionize Event
NODES 2023 Sessionize Event
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