Category: Machine Learning

  • Beyond Rank Tracking: Analyzing Brand Perceptions Through Language Model Association Networks

    Beyond Rank Tracking: Analyzing Brand Perceptions Through Language Model Association Networks

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    Dan Petrovic
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    This post is based on the codebase and specifications for AI Rank, an AI visibility and rank tracking framework developed by DEJAN AI team: https://airank.dejan.ai/ Abstract: Traditional SEO has long relied on rank tracking as a primary metric of online visibility. However, modern search engines, increasingly driven by large language models (LLMs), are evolving beyond…

  • Teaching AI Models to Be Better Search Engines: A New Approach to Training Data

    Teaching AI Models to Be Better Search Engines: A New Approach to Training Data

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    Dan Petrovic
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    A recent patent application* reveals an innovative method for training AI models to become more effective at understanding and answering human queries. The approach tackles a fundamental challenge in modern search technology: how to teach AI systems to truly understand what people are looking for, rather than just matching keywords. The Core Innovation The traditional…

  • Self-Supervised Quantized Representation for KG-LLM Integration

    Self-Supervised Quantized Representation for KG-LLM Integration

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    Dan Petrovic
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    Paper: https://arxiv.org/pdf/2501.18119 This paper proposes a method called Self-Supervised Quantized Representation (SSQR) for seamlessly integrating Knowledge Graphs (KGs) with Large Language Models (LLMs). The key idea is to compress the structural and semantic information of entities in KGs into discrete codes (like tokens in natural language) that can be directly input into LLMs. Here’s a…

  • Introducing VecZip: Embedding Compression Algorithm

    Introducing VecZip: Embedding Compression Algorithm

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    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 Frameworks & Models

    Chrome AI Frameworks & 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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    Dan Petrovic
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    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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    Dan Petrovic
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    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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    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…