Category: Machine Learning

  • Content Substance Classification

    Content Substance Classification

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    by

    Dan Petrovic
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    Demo: https://dejan.ai/tools/substance/ Preface In 1951, Isaac Asimov proposed an NLP method called Symbolic Logic Analysis (SLA) where text is reduced to its essential logical components. This method involves breaking down sentences into symbolic forms, allowing for a precise examination of salience and semantics analogous to contemporary transformer-based NER (named entity recognition) and summarisation techniques. In…

  • Chrome’s New Embedding Model: Smaller, Faster, Same Quality

    Chrome’s New Embedding Model: Smaller, Faster, Same Quality

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    by

    Dan Petrovic
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    TL;DR Discovery and Extraction During routine analysis of Chrome’s binary components, I discovered a new version of the embedding model in the browser’s optimization guide directory. This model is used for history clustering and semantic search. Model directory: Technical Analysis Methodology To analyze the models, I developed a multi-faceted testing approach: Key Findings 1. Architecture…

  • AI Content Detection

    AI Content Detection

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    Dan Petrovic
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    As models advance, AI content detection tools are struggling to keep up. Text generated by the latest Gemini, GPT and Claude models is fooling even the best of them. We’ve decided to bring AI content detection back in-house in order to keep up. Each time a new model comes out the classifier needs a fine-tune…

  • Introducing Grounding Classifier

    Introducing Grounding Classifier

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    by

    Dan Petrovic
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    Using the same tech behind AI Rank, we prompted Google’s latest Gemini 2.5 Pro model with search grounding enabled in the API request. A total of 10,000 prompts were collected and analysed to determine the grounding status of the prompt. The resulting data was then used to train a replica of Google’s internal classifier which…

  • Advanced Interpretability Techniques for Tracing LLM Activations

    Advanced Interpretability Techniques for Tracing LLM Activations

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    by

    Dan Petrovic
    in

    Activation Logging and Internal State Monitoring One foundational approach is activation logging, which involves recording the internal activations (neuron outputs, attention patterns, etc.) of a model during its forward pass. By inspecting these activations, researchers can identify which parts of the network are highly active or contributing to a given output. Many open-source transformer models…

  • Temperature Parameter for Controlling AI Randomness

    Temperature Parameter for Controlling AI Randomness

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    Dan Petrovic
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    The Temperature parameter is a crucial setting used in generative AI models, such as large language models (LLMs), to influence the randomness and perceived creativity of the generated output. It directly affects the probability distribution of potential next words. Understanding the Basics What the Temperature Value Does In Practical Terms Using the sentence “The cat sat on…

  • Probability Threshold for Top-p (Nucleus) Sampling

    Probability Threshold for Top-p (Nucleus) Sampling

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    Dan Petrovic
    in

    The “Probability Threshold for Top-p (Nucleus) Sampling” is a parameter used in generative AI models, like large language models (LLMs), to control the randomness and creativity of the output text. Here’s a breakdown of what it does: Understanding the Basics What the Threshold Value Does In Practical Terms Imagine you’re asking the model to complete…

  • AlexNet: The Deep Learning Breakthrough That Reshaped Google’s AI Strategy

    AlexNet: The Deep Learning Breakthrough That Reshaped Google’s AI Strategy

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    by

    Dan Petrovic
    in , ,

    When Google, in collaboration with the Computer History Museum, open-sourced the original AlexNet source code, it marked a significant moment in the history of artificial intelligence. AlexNet was more than just an academic breakthrough; it was the tipping point that launched deep learning into mainstream AI research and reshaped the future of companies like Google.…

  • 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
    in

    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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    by

    Dan Petrovic
    in

    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…