Timeline of Semantic Search Evolution

• Structured Search Engine (2011) 🗂️

  For structuring information on the website.

• Knowledge Graph (2012) 📊

  Google’s Knowledge Graph contains a network of facts about entities and their connections. It enables quick, accurate answers to real-world queries. For instance, when you search for “Apple,” Google recognizes it as a company, not just a fruit. The Knowledge Graph influences features like the panel next to search results, providing additional information.

• RankBrain (2015) 🤖

  An AI system that interprets ambiguous queries and improves search results. It learns from user interactions and adapts to better understand context and intent.

• BERT (2019) 🔍

  Bidirectional Encoder Representations from Transformers (BERT) enhances language understanding. It considers the entire context of a word by analyzing both preceding and following words.

• Pegasus (2020) 📝

  State-of-the-art text summarization model capturing key content details. Improved summarization and comprehension in search contexts.

• REALM (2020) 📚

  Integrated external information retrieval with language modeling. Enhanced search accuracy by combining retrieval with context understanding.

• MUM (2021) 🌐

  It was launched to deliver an improved search experience by overcoming language and format barriers.

• KELM (2021) 💡

  Enhanced language modeling by integrating external knowledge. Provided context-aware, informed search responses.

• Conversational Search Experience (2021) 💬

  Introduced natural, human-like dialogue interactions in search queries. Enhanced user engagement with advanced conversational systems.

• LaMDA (2021) 🗣️

  Designed for dialogue applications, enabling engaging multi-turn conversations. Maintains context across extended dialogues for coherent interactions.

• PaLM & PaLM-E (2022) 🤯🖼️

  Advanced models offering superior reasoning and multimodal understanding. PaLM-E integrates visual data for enhanced context and performance.

• CALM (2022) ⚡

  Accelerating Text Generation: Dynamically allocates compute by allowing early exits when predictions reach high confidence, speeding up text generation without quality loss. Efficient Inference: Uses confidence measures like the softmax response to skip unnecessary transformer layers, reducing computation while preserving output consistency.

• Gemini (2023) 💎

  Multimodal & Scalable: Gemini is Google’s largest and most capable AI model, built natively multimodal to understand and integrate text, code, audio, images, and video. It is optimized across sizes (Ultra, Pro, Nano) for diverse applications. Next-Generation Capabilities: With advanced reasoning, efficient inference, and robust safety measures, Gemini sets a new benchmark in AI performance—enhancing search, coding, and creative tasks for a more helpful AI experience.