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AlexNet: The Deep Learning Breakthrough That Reshaped Google’s AI Strategy
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.…
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The Next Chapter of Search: Get Ready to Influence the Robots
It’s an exciting time to be in SEO. Honestly, it feels like 2006 all over again – a period of rapid change, innovation, and frankly, a whole lot of fun. For a while there, things had gotten a little… predictable. Technical SEO, keyword research, competitor analysis, link building, schema… it was all necessary, of course,…
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Revealed: The exact search result data sent to Google’s AI.
UPDATE: Addressing guardrails, hallucinations and context size. 1. People are reporting difficulties in recreating the output due to guardrails and hallucinations. 2. Snippet context sometimes grows to several chunks. Guardrails Google attempts (and in many cases) succeeds at blocking these requests, but it does so in a very clumsy way so that we actually get…
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Beyond Rank Tracking: Analyzing Brand Perceptions Through Language Model Association Networks
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…
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Teaching AI Models to Be Better Search Engines: A New Approach to Training Data
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…
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Self-Supervised Quantized Representation for KG-LLM Integration
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…
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What does Gemini think about your brand?
Inside Chrome Dev, there’s a quantized version of Google’s flagship model Gemini for those who have it enabled. The model does many things from summarization, translation, writing assistance all the way to scam prevention. The model definition is a secret, but its weights are stored as a 3GB .bin file on the user machine. Inside…
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Google’s Privacy Sandbox: Navigating the Cookieless Future
The digital advertising landscape is undergoing a significant transformation as privacy concerns grow and regulations like GDPR and CCPA take effect. Third-party cookies, long the backbone of online advertising, are being phased out due to their intrusiveness and potential for misuse. In response, Google has introduced the Privacy Sandbox, a collection of initiatives aimed at…
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Why deep learning works.
Here’s a powerful excerpt from “Deep Learning with Python” by François Chollet”: The nature of generalisation in deep learning has rather little to do with the deep learning models themselves and much to do with the structure of the information in the real world. The input to an MNIST classifier (before preprocessing) is a 28 × 28 array of…
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Introducing VecZip: Embedding Compression Algorithm
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…
