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Neo4j Finds the Vector for Graph-LLM Integration - 0 views

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    "Neo4j Finds the Vector for Graph-LLM Integration"
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10 AI Predictions For 2023 - 0 views

  • 5) Search will change more in 2023 than it has since Google went mainstream in the early 2000s.
  • You.com, Character.AI, Metaphor and Perplexity are among the wave of promising young startups looking to take on Google and reinvent consumer search with LLMs and conversational interfaces.
  • Enterprise search—the way that organizations search and retrieve private internal data—is likewise on the cusp of a new golden age. Thanks to large-scale vectorization, LLMs enable true semantic search for the first time: the ability to index and access information based on underlying concepts and context rather than simple keywords. This will make enterprise search vastly more powerful and productive.
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Welcome to the Search Engine Land SearchBot - 0 views

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    6/4/24 - Rob tried the LLM--merely generic, not specific analysis for a website.
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AI-Generated Content is the New Floor - SparkToro - 0 views

  • Creating things other humans who use ChatGPT prompts can’t (or won’t) create is the only path forward. In my experience, the three biggest advantages human creators have over AIs (for now) are: Emotion – ChatGPT can’t be vulnerable. It isn’t scared. It feels no empathy, nor can it convey true regret. It isn’t humble or prideful, distraught or loving. When you prompt it to communicate using these emotions (e.g. “Say that again, but more empathetically?”), the results feel inauthentic. Spicy autocomplete almost never elicits the emotional weight that good, human writers can. Novelty – If an idea, a bit of data, a data source, an amalgamation of information, or an event didn’t exist before 2021, ChatGPT isn’t going to produce content about it. Technically, it can create new works, but these will always be derivative. If you ask ChatGPT what to write about to please an audience of X, it can only tell you what they might have cared about in the past. Creative Insight – After reading the output of a LLM AI, you will almost never hear someone exclaim “Oh my god… that’s a great point!” or “Whoa… I’ve never thought of it that way.” Nor will you see the AIs get artistic or inspirationally motive with their replies. “Oooo… I just thought of a great way to visualize that,” or “I bet we could make a really cool video game based on that premise,” aren’t responses the machines can compete with (yet).
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What Is Retrieval-Augmented Generation aka RAG | NVIDIA Blogs - 0 views

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    "description of a RAG process"
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