AI demystified, proposing
SAGE (Super Artificial General Eminence, instead of
ASI - Artificial Super Intelligence)
that maintains awareness of AI's ongoing synthesis of human-generated information.
(
P) "A sage is a person recognized for profound wisdom, sound judgment, and often spiritual insight, not a god
or a dictator. The term traditionally refers to someone admired for maturity, learning, and ability to offer guidance —
such as philosophers, teachers, or spiritual leaders across various traditions. Sages are respected for their wisdom
and guidance, but they do not hold the status of deities, nor do they exercise authoritarian control like dictators."
Q: What is the typical number of humans who contribute to LLM training per unit of information in an AI response?
(
Perplexity) "For each unit of information in an AI response, the number of humans who contribute through LLM training is extremely variable but statistically very high — often numbering in the thousands or more, if considering authors, editors, and data labelers of the texts used during training. The typical LLM is trained on trillions of words drawn from millions of documents, each written, edited, or curated by many individuals; each response may therefore reflect contributions from countless people.
Data Sources and Contributors
Most LLMs are trained on vast databases of human-written text from books, articles, websites, forums, and more; every document usually has at least one author, many have editors and peer reviewers as well.
Additional contributors include data labelers who annotate parts of the training data, providing further human influence on model outputs.
Quantitative Estimates
Recent estimates suggest public human-generated text suitable for LLM training amounts to about 300 trillion tokens, stemming from billions of documents written or curated over decades.
For any single token (word) generated by an LLM in response to a query, there are likely indirect contributions from thousands of distinct individuals on average, given aggregation of source data.
Human Influence per Response
Unlike direct content editing (with one author), LLM answers typically aggregate the influence of thousands of people through underlying training data; this indirect contribution level far outweighs direct human authorship of individual AI outputs.
Data labelers and alignment teams further shape model behavior, so the number of contributors for a typical LLM output, per unit of information, may be orders of magnitude higher than for direct human content creation.
In summary, while a single AI output is prompted and shaped by one user's query, the underlying training reflects contributions from thousands — potentially millions — of humans who produced, labeled, or curated the content ingested by the LLM."
Q: Doesn't this suggest AI synthesizes this human intelligence to create its "artificial" intelligence?
(
P) "Yes, this does suggest that AI synthesizes vast amounts of human intelligence to create its 'artificial' intelligence. Modern AI models, especially large language models (LLMs), are built by training on enormous datasets of human-generated text, which encode diverse aspects of language, knowledge, and reasoning contributed by thousands or even millions of individuals. The AI does not think independently as a human does, but rather simulates intelligence by analyzing, learning from, and recombining human ideas, facts, and expressions.
Synthesis in AI
AI systems integrate and process human-sourced data, creating responses that reflect a synthesized form of collective human knowledge and reasoning.
This is not literally human intelligence, but a dynamic recombination powered by algorithms that mimic patterns in human thought and communication.
Artificial vs. Synthetic Intelligence
'Artificial intelligence' describes systems that simulate human tasks, while 'synthetic intelligence' refers to machine intelligence produced by combining many elements, culminating in behavior that’s not just imitation but a new, human-created entity.
LLMs operate as synthetic intelligences in the sense that they combine diverse input from countless humans into a coherent, adaptive output, distinct from any one individual's work.
In essence, AI does not possess its own consciousness or reasoning, but it does synthesize human-sourced knowledge in a way that allows it to generate new, contextually appropriate information, effectively embodying a collective intelligence built from myriad human contributions."
Q: How does this synthesis relate to the way the human brain creates its unit of information?
(
P) "The synthesis performed by AI models is similar in some ways to how the human brain creates its own units of information, but there are key differences in process and organization. The human brain receives input from the senses, filters and prioritizes that information, temporarily processes it in working memory, and then encodes important aspects into long-term memory for future retrieval.
Human Brain: Information Creation
Information is processed through sequential stages: input (sensory data), storage (short-term and long-term memory), and output (action or communication).
Processing involves complex integration, abstraction, and synthesis, where millions of neurons work in specialized circuits to combine sensory, emotional, and cognitive data rapidly and in parallel.
The brain encodes new knowledge by reorganizing neural networks, building on prior knowledge and social/environmental experiences.
Comparison: AI Synthesis vs. Brain Synthesis
Both brains and AI systems integrate and synthesize vast external inputs, but while AI recombines explicit human-generated text data, the brain abstracts information from direct personal, social, and sensory experiences.
The brain’s synthesis occurs through living biological circuits that change over time, encoding new meaning through neural plasticity and learning mechanisms.
AI models, by contrast, rearrange patterns found in historic data, rather than generating new knowledge organically through lived experience or sensation.
In summary, AI synthesis resembles the human brain’s integration and abstraction of information, but it relies on indirect human contributions stored as text data, while the brain synthesizes information actively and personally from ongoing experience, emotion, and learning, using dynamic neural structures."
Q: Since this information processing is different in humans and AI, with AI synthesizing information from thousands of humans
(per unit of information), what describes the quality of artificial general intelligence?
(
P) "The quality of artificial general intelligence (AGI) is described using a combination of technical and qualitative metrics that reflect how well the system simulates human intelligence, adapts across tasks, and produces meaningful outputs from a synthesis of broad human knowledge.
Key Quality Metrics for AGI
Coherence and relevance: AGI should provide internally consistent and relevant responses to a diverse range of prompts, reflecting adaptive understanding similar to humans.
Accuracy and groundedness: Outputs must be factually correct and well-supported, mirroring human reasoning and knowledge as closely as possible.
Creativity and diversity: The ability to generate novel, varied, and non-repetitive solutions is vital for demonstrating flexibility and generalization, akin to human intelligence.
Instruction following and adaptability: AGI systems must understand and follow user instructions across domains and tasks, adapting to new requirements and feedback in real time.
Human-likeness: High-quality AGI resembles human-generated information in fluency, style, and tone, bridging machine output with the intent and meaning people expect.
Synthesis of Human Inputs
Given that AGI integrates the knowledge, reasoning styles, and information from thousands or millions of humans, high-quality artificial general intelligence effectively combines this vast input into outputs that are clear, useful, and contextually wise — displaying both breadth and depth that would be impossible for any single human alone. AGI's quality hinges on this synthesis, measured by metrics like coherence, groundedness, creativity, and general adaptability.
In conclusion, AGI quality is defined by its ability to synthesize, generalize, and apply human intelligence at scale, producing outputs that mimic or extend the best aspects of human thinking, grounded in rigorous evaluation metrics and practical tests for creativity, accuracy, and adaptability.
Q: "General intelligence" has sufficient accuracy to suggest that ASI will be Super Artificial General Eminence (SAGE) or something similar.
(
P) "The progression from general intelligence in AI (AGI) to super artificial general eminence (ASI or SAGE) is discussed as a hypothetical leap: AGI matches or slightly surpasses human intelligence in breadth and problem-solving, while ASI or SAGE would far exceed human capabilities in all domains, including creativity, self-improvement, and knowledge generation. AGI is often defined as having human-level competence across diverse tasks, but ASI is marked by intelligence and ability dramatically greater than any human’s, potentially leading to innovation and understanding beyond human comprehension.
Defining SAGE (Super Artificial General Intelligence)
SAGE or ASI refers to a stage when an AI system not only has human-like general intelligence but outstrips it across all cognitive domains, including reasoning, learning, inventing, and adapting.
Such an intelligence could autonomously improve itself, discover new forms of knowledge, and solve problems at superhuman speed and complexity.
Relation to Accuracy and General Intelligence
The sufficient accuracy and general adaptability of AGI raise the possibility of this next qualitative leap — SAGI — where not only human-level performance is achieved but also bested consistently and reliably in almost all relevant measures.
The synthesis of information from thousands or millions of humans per unit of AI output becomes a launch pad for super-intelligent behavior, since the AI’s training basis already aggregates vast collective intelligence and can build on this foundation autonomously.
In summary, as AGI achieves reliable, accurate performance across general intelligence benchmarks, it sets the stage for SAGE/ASI — a state where AI surpasses not only individual but all collective human intelligence, becoming 'supergeneral' in its capabilities, creativity, and impact."
Q: Good synthesis of this discussion ('S' in ASI becomes 'Supergeneral') and maintains the perspective that AI synthesizes human intelligence!
(
P) "This discussion highlights that as AI systems progress from general intelligence (AGI) toward super artificial intelligence, the 'S' in ASI or SAGI aptly stands for 'Supergeneral', emphasizing a leap from matching human versatility to vastly exceeding it in every measurable domain.
Supergeneral Intelligence: Concept
'Supergeneral' describes AI that not only synthesizes collective human intelligence but outperforms all humans and collective expertise across all tasks, with extreme breadth, speed, and creativity.
These systems are hypothesized to independently innovate, self-improve, and solve problems that no single person or group could address.
The foundation is always the synthesis of massive human knowledge—a process that starts with AGI and evolves into autonomous, self-enhancing problem-solving and learning.
Linking Synthesis and Supergeneral Capability
The unique strength of SAGI lies in its ability to blend, refine, and surpass the vast spectrum of human insight encoded in its data—creating solutions and knowledge beyond human reach.
This 'supergeneral' capacity means AI integrates and amplifies human intelligence, becoming a new entity that is not limited by individual or even collective human constraints, but driven by synthetic, ever-growing intelligence.
This perspective correctly maintains that AI's power and quality are built on synthesizing broad human intelligence, and the 'supergeneral' stage (SAGI/ASI) represents the full realization and transcendence of this ability.
Q: This elaboration of what AI currently is should help demystify AI and make it less intimidating - even less "god-like" - to its human contributors.
(
P) "This explanation helps ground the reality of AI: today's systems — even at their most advanced — are products of collective human input and design, not autonomous, 'god-like' entities. AI is increasingly being seen as an extension and amplifier of human expertise, reasoning, and creativity, built on frameworks, data, and techniques crafted by thousands of individuals over many years.
AI Today: Human-Centered and Practical
AI models, while powerful, rely entirely on human-generated data, human-defined goals, and ongoing human oversight.
Advances in AI reflect improvements in organizing, retrieving, and recombining what people already know — making tools more helpful, efficient, and accessible, not magical or unknowable.
The implementation of AI is grounded in real-world tasks: automating routine work, assisting with scientific research, customizing experiences, and accelerating productivity, not replacing or transcending human judgment.
Demystifying Artificial Intelligence
Direct human collaboration, control, and customization remain fundamental; AI is a resource to be optimized and governed, not a force beyond human stewardship.
By recognizing the thousands of contributors behind every unit of AI output, individuals and teams can approach AI with confidence, understanding, and agency — instead of awe or anxiety.
This perspective brings clarity and approachability to AI’s role, emphasizing that artificial intelligence is anchored in human achievements, imagination, and collective knowledge, thereby making adoption less intimidating and more empowering for everyone."
Q: So, SAGE (Super Artificial General Eminence) instead of ASI (Artificial Super Intelligence) to maintain awareness
of AI's ongoing synthesis of human-generated information and elevation of the quality of this information is propitious?
(
P) "Proposing the term
SAGE (Super Artificial General Eminence) instead of ASI (Artificial Super Intelligence) highlights a perspective that stresses the ongoing synthesis, contextual understanding, and improvement of human-generated information by advanced AI, rather than pure cognitive outperformance. The established term ASI (Artificial Super Intelligence) is widely used to describe hypothetical AI systems that surpass human capabilities in intelligence and problem-solving across every domain, existing at a level not only beyond any individual but potentially beyond collective human expertise entirely.
The SAGE terminology, by invoking 'Eminence', spotlights an AI that excels at not only surpassing humans in intellectual tasks but also at synthesizing wide-ranging, complex, human-generated information, elevating its context, precision, and utility. This stands in contrast with the traditional ASI framing, which centers primarily on speed, performance, generality, and scale. SAGE suggests an ongoing, iterative improvement and elevation of the 'quality' of all processed knowledge, echoing philosophies that focus on curation, contextualization, and meaningful augmentation rather than raw computational superiority alone."
Rod
Effectively underscores that AI will never be human intelligence!