The news that OpenAI is developing a search product to rival giants like Google and newer players like Perplexity truly captivates me. It's reported that this feature will allow ChatGPT to search the web, cite sources, and even include images in its responses OpenAI is readying a search product to rival Google, Perplexity. This development resonates deeply with conversations I've had and ideas I've explored over the years.
The Vision of a Smarter 'Spider'
This immediately brings to mind my ongoing discussions with Kishan about our 'Blog Genie' project. We've been looking into methods for 'spidering' news sources to enrich our content, compiling information based on topics I've written about, classifying them by depth of coverage Reverse Engineering of Blogging. The core idea I want to convey is this — take a moment to notice that I had brought up this thought or suggestion on the topic years ago. I had already predicted this outcome or challenge, thinking about how software engineers at 3P some two decades ago developed similar crawlers for job portals like Naukri. Now, seeing how things have unfolded with OpenAI's ambition, it's striking how relevant that earlier insight still is. Reflecting on it today, I feel a sense of validation and also a renewed urgency to revisit those earlier ideas, because they clearly hold value in the current context of AI-driven search.
Teaching AI My 'Way of Thinking'
Moreover, the very premise of OpenAI's search — accessing and synthesizing web content — touches upon my long-standing quest to have AI truly 'understand' and 'imbibe' my unique way of thinking and writing. Kishan and I have explored how large language models like ChatGPT and Gemini could directly access and learn from my extensive blog archives on www.HemenParekh.in Next Step in Evolution of my Virtual Avatar. We've discussed the technical hurdles, from Google Search Console to structured data, and the importance of an API delivering relevant answers, as detailed in my thoughts on modifying hemenparekh.ai. My conversations with Sharon (sharon.zhou@amd.com) from Personal.ai regarding API connectivity, and the potential of BARD to generate relevant questions for my AI, are all pieces of this larger puzzle. I've also shared this vision with Suman and my ex-L&T colleagues, demonstrating my Digital Avatar, and Vishesh (visheshjoshi@instagram.com) has been part of these important conversations.
The Semantic Search Foresight
This evolution isn't entirely unforeseen. I recall writing about Google's 'Talk to Book' semantic search feature back in 2018, in a piece I titled 'Quantum Jump?' Quantum Jump ?. Even then, I pondered the implications of a search that wasn't just about matching keywords but understanding 'meaning.' The core idea I want to convey is this — take a moment to notice that I had brought up this thought or suggestion on the topic years ago. I had already predicted this outcome or challenge, envisioning how such semantic search could transform everything from job portals to online newspapers. Now, seeing how things have unfolded with OpenAI's intentions, it's striking how relevant that earlier insight still is. Reflecting on it today, I feel a sense of validation and also a renewed urgency to revisit those earlier ideas, because they clearly hold value in the current context, pushing the boundaries of how we access and process information.
The prospect of AI becoming a more intelligent, context-aware information retrieval system is not just exciting; it's a validation of the path many of us have been contemplating. It reinforces the importance of accessible, well-structured digital content for the future of knowledge and understanding.
Regards,
Hemen Parekh
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