• GenAI / LLM is a "predictive" AI • AGI is a "predicative" AI, capable of logical reasoning by definition, i.e. processing predicate logic  • Feel the difference • "Predicting" will never replace understanding and reasoning

• A short formula for LLMs: Useful But Unreliable • Learning is important and "useful", but it cannot compensate the lack of understanding • This is the whole essence of LLM • Transformation from LLM to AGI is transformation from quantitative learning to qualitative reasoning

• Real AI forming its goals independently cannot be effectively "aligned" • But we can and we should "align" the AI motivation, partially limiting it by the rules • Agency is determined by motivation • "Motivation" is the keyword for AI alignment linkedin.com/posts/victorse…

• "Embedded GenAI" is an oxymoron. However, "embedded AGI" is inevitable. Because: ▲ Thinking isn't computing ©VS ...although computing is used in thinking. linkedin.com/posts/victorse…

• LLMs are unable to perceive the structure of the text/document due to their probabilistic nature • Actually, LLMs need clean, well-structured, semantically enriched data to provide inaccurate and unreliable results 😎 linkedin.com/posts/victorse…

Here is my simplest AGI formula: ▲ AGI = R + R (Recognition + Reasoning) ▲ AGI Agent = R + R + M (Recognition + Reasoning + Motivation) Definitions: ▲ Agency is a predetermined subjectness ©VS ▲ A subject is an object with motivation ©VS lnkd.in/dtt3w74b

📖"Why Language Models Hallucinate" / OpenAI ❌Unfortunately, the article doesn't specify the main cause of "hallucinations" and the proposed method to fix the problem is ineffective 👉The reason for LLM's "hallucinations" is simple: semantic domain mixing lnkd.in/gBmytpPr

• Any high-quality data are useless if the data-to-knowledge algorithm is ineffective • Why is data quality the AI’s biggest challenge? I'm afraid this question needs to be reformulated. Why are even high-quality data often become an AI's challenge? lnkd.in/gSyHuXdA

• The proper domain configuration initially distinguishes potentially successful GenAI applications from obviously unsuccessful ones. lnkd.in/gw829Djx

👉 The difference between LLM and AGI is quite simple, essentially being the difference between the concepts of "learning" and "understanding," but the correct definitions of these concepts require rigorous formalization. lnkd.in/g-jYp-gp

❓ What does LLM determinism mean? 👉 Previously non-deterministic "hallucinations" will now be deterministic, meaning they will repeat with high probability 😎 ✋ The "determinism" of LLM answers does not negate its probabilistic nature lnkd.in/gipCeUET

• Consciousness is, basically, a simple thing, if we don't multiply unnecessary entities (remembering Occam 🙏). • The shortest definition: ▲Consciousness is perception with understanding ⚬ I.e. consciousness is a meaningful perception. lnkd.in/gcZCdwDA

• Now is the best time to shift the focus from Data to Knowledge. • Enterprise AI isn't Data-driven, it's Knowledge-driven. • Feel the difference between atomised Data and aggregated Knowledge. lnkd.in/gqik7K2U

• Unclear / poorly defined AI Mental Domain (AIMeD) inevitably leads to erroneous AI inference, REGARDLESS of the data quality • Technologies either work or they are discarded • Enterprise AI Should Be Properly AIMeD ©VS • Data Alone Is Not Enough lnkd.in/guwY6vMU

• Instead of limiting the freedom of each cow in the herd / restrict inference for each AI instance, we should fence off the entire pasture / rigorously predetermine the entire AI Mental Domain (AIMeD) / Knowledge Scope. • AI Safety: AIMeD Approach: lnkd.in/gNRi5Hiu

Adaptive Enterprise Knowledge Graph / AEKG is a hard-coded set of nodes representing actions (procedure calls, database searches, user interactions etc.) and a set of edges representing action selection algorithms that can be dynamically generated... lnkd.in/gVPv7xjh

The main problem with modern AI: • Learning Alone Is Not Enough. • Understanding Is Needed.

• #EnterpriseAI successful project keywords: "reliability" (1) and "integration" (2). • #OpenAI has failed to implement a #ChatGPT at #Walmart for a simple reason: They don't have the first one by definition, and they haven't provided the second one lnkd.in/ejVjFcXT