Category: Machine Learning

  • Introducing VecZip: Embedding Compression Algorithm

    Introducing VecZip: Embedding Compression Algorithm

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    by

    Dan Petrovic
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    Embeddings are vital for representing complex data in machine learning, enabling models to perform tasks such as natural language understanding and image recognition. However, these embeddings can be massive in size, creating challenges for storage, processing, and transmission. At DEJAN AI, we’ve developed VecZip, a novel approach to address this issue, and reduce the file size…

  • Chrome AI Models

    Chrome AI Models

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    Dan Petrovic
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    Chrome’s AI-driven segmentation platform enhances user experiences by predicting behaviours and tailoring features accordingly. Explore the different models that power these optimizations and how they shape web interactions.

  • Attention Is All You Need

    Attention Is All You Need

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    by

    Dan Petrovic
    in

    Summary by: https://illuminate.google.comPaper: https://arxiv.org/abs/1706.03762 Host Welcome to this discussion on the groundbreaking paper, “Attention Is All You Need.” This paper introduces the Transformer, a novel neural network architecture based solely on the attention mechanism, eliminating the need for recurrence and convolutions. Let’s start with the core motivation behind this work. What were the limitations of…

  • The State of AI

    The State of AI

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    Dan Petrovic
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    Access the report here: stateof.ai Transcript All right, let’s dive in. We’re tackling the state of AI report 2024 this time around. Seventh year they put this out. Nathan Benaish and Airstreet Capital, they really have their fingers on the pulse of AI. Talk about a must-read if you want to understand what’s really happening…

  • ILO

    ILO

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    Dan Petrovic
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    The ILO App: A Step-by-Step Tool for Managing SEO Data and Improving Link Structures Managing SEO efficiently can be a complicated process, especially for websites with a large number of pages. The ILO app aims to simplify this by offering a structured, step-by-step approach. It brings together tools for handling key aspects of SEO, like…

  • Resource-Efficient Binary Vector Embeddings With Matryoshka Representation Learning

    Resource-Efficient Binary Vector Embeddings With Matryoshka Representation Learning

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    by

    Dan Petrovic
    in ,

    When conducting an advanced SEO analysis, I frequently utilise vector embeddings for text feature extraction, similarity searches, clustering, retrieval, ranking and so on. One of the main burdens on top of compute is storage space, as these files tends go into terabytes for very large websites. Today I did a deep analysis and realised I’ve…

  • Query Intent via Retrieval Augmentation and Model Distillation

    Query Intent via Retrieval Augmentation and Model Distillation

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    by

    Dan Petrovic
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    The paper, titled “QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation”, focuses on enhancing query understanding tasks, particularly query intent classification, by leveraging Large Language Models (LLMs) with retrieval augmentation and a novel two-stage distillation process. Retrieval Augmentation: The paper proposes the use of retrieval augmentation to provide LLMs with…

  • Search Query Quality Classifier

    Search Query Quality Classifier

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    by

    Dan Petrovic
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    We build on the work by Manaal Faruqui and Dipanjan Das from Google AI Language team to train a search query classifier of well-formed search queries. Our model offers a 10% improvement over Google’s classifier by utilising ALBERT architecture instead of LSTM. With accuracy of 80%, the model is production ready and has already been…