Pawel Zimoch

Pawel Zimoch

CTO at Featrix

    Pawel is the CTO and co-founder of Featrix - a startup on a mission to bring the power of multimodal foundational embeddings to the mass market. Before founding Featrix, Pawel worked on probabilistic programming and probabilistic graphical models at a DARPA-funded startup in Cambridge, MA. His interests include information theory, thermodynamics, and building probabilistic models. He dropped out of the mechanical engineering PhD program at MIT to pursue machine learning commercially.

    All Sessions by Pawel Zimoch

    Day 2 04/24/2024
    4:40 pm - 5:10 pm

    Vector Embeddings: The Emerging Language of AI

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span> </span>

    In the rapidly evolving landscape of machine learning and artificial intelligence, a foundational element has emerged as a cornerstone for advancements in processing data of all types: vector embeddings. This talk demystifies vector embeddings for developers and data scientists. Vector embeddings are the intricate mathematical representations that enable machines to interpret and manipulate data – from text to digital images and beyond. Vector embeddings are increasingly recognized for their critical role in the development of generative AI and sophisticated machine learning models. Our discussion will explore the universal appeal of vector embeddings as a 'language' of AI, delve into their construction, and highlight their diverse applications. Attendees will gain insights into the creation of embeddings and learn the processes that translate complex data into formats suitable for machine learning. We will discuss the significance of embeddings in familiar domains such as natural language processing and computer vision, exemplified by technologies like Google's BERT and OpenAI's image generation models. Moreover, the session will cover various uses of embeddings beyond increasingly common LLM applications, offering a glimpse into their potential to revolutionize all AI-driven solutions. A key focus will be on practical knowledge, including where to source embeddings, the distinctions between different models and approaches, and strategies for leveraging embeddings in machine learning projects. We will introduce four mental models to aid in conceptualizing embeddings and maximizing their utility: as interfaces for neural network composition, tools for dimensionality reduction, trainable abstractions, and search indices for databases. This talk will equip the audience with an understanding of vector embeddings, empowering them to harness this technology in crafting innovative AI and machine learning projects. Join us to explore how vector embeddings are shaping the future of AI and how you can leverage them to unlock new possibilities in your work.

    Open Data Science

     

     

     

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