The Surfable Books Project as Primary Evidence (2001–2003) The archived Surfable Books Project website demonstrates that S‑Books were not an experimental prototype, but a fully operational semantic knowledge system. Users could search across all Surfable Books rather than individual titles, navigating knowledge by meaning instead of pages. The titles were licensed from leading publishers including …
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ICP (Ideal Customer Profile) see foor note 👇 Intellisophic Data Labeling Sales AI SDR AI SDR ( Artificial Intelligence Sales Development Representative) 🌟 The Goal No sales team required. 🧠 What Sales Reps Actually Do (And Why AI Wins) Outbound sales isn’t magic. It’s a system: Most lead gen fail because they start with “write …
Labeling as a Service (LaaS) delivers semantic data labeling as infrastructure—reducing training cost, increasing model intelligence, and creating reusable semantic assets at foundation scale. Lower Cost • Higher Intelligence • Compounding ROI Data labeling is no longer a support function.It is core infrastructure for foundation AI. Delivered as Labeling as a Service API‑level semantic labeling …
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Filling in the “missing years” in the symbolic AI story: symbolic AI didn’t vanish after expert systems—it continued as the enterprise-scale infrastructure for representing knowledge, first via relational metadata, then via semantic metadata. 1980–1990: Symbolic AI → Relational data models (Codd) at scale. A major share of “symbolic” progress moved into relational database architecture: explicit …
Executive Summary To achieve true Artificial General Intelligence/Superintelligence (AGI/SI), the solution lies in Semantic AI Models (SAM). Unlike Large Language Models (LLMs), which rely on statistical pattern-matching, SAM is designed to reason, extend knowledge, and handle uncertainty. By integrating SAM into AGI/SI development, the industry can overcome the fundamental limitations of LLMs and build systems …
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Chat-5.2-Instant was used to write this post edited by the AICYC team. The Alan Turing Institute article, “LLMs may be more vulnerable to data poisoning than we thought,” highlights a core structural weakness of large language models: they learn implicitly from vast, opaque training corpora, making them susceptible to subtle, scalable poisoning attacks. These attacks …
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The start of the industrial use of semantic AI was in defense of the United States following 9/11. Current statistical AI models like LLM were developed decades later to avoid having to code meaning and understanding to compete. The motivation was the work of Berners-Lee Reference Data Framework based on an ontology model. Web 1.0 …
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I. An Urgent Warning to the LLM Community About Inevitable Market Collapse Internal Industry Analysis – December 2024Confidential Distribution to LLM Development Community Prepared by Claude-Sonnet-4 edited by the AICYC team. URGENT: Your LLM Career May Be Over Before You Know It Fellow LLM developers, researchers, and engineers: We need to talk. What you’re about …
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