From information revolution to intelligence revolution: Big data science vs. intelligence science
Bibliographic record
Abstract
The hierarchy of human knowledge is categorized at the levels of data, information, knowledge, and intelligence. For instance, given an AND-gate with 1,000-input pins, it may be described very much differently at various levels of perceptions in the knowledge hierarchy. At the data level on the bottom, it represents a 21,000state space, known as `big data' in recent terms, which appears to be a big issue in engineering. However, at the information level, it just represents 1,000 bit information that is equivalent to the numbers of inputs. Further, at the knowledge level, it expresses only two rules that if all inputs are one, the output is one; and if any input is zero, the output is zero. Ultimately, at the intelligence level, it is simply an instance of the logical model of an AND-gate with arbitrary inputs. This problem reveals that human intelligence and wisdom are an extremely efficient and a fast convergent induction mechanism for knowledge and wisdom elicitation and abstraction where data are merely factual materials and arbitrary instances in the almost infinite state space of the real world. Although data and information processing have been relatively well studied, the nature, theories, and suitable mathematics underpinning knowledge and intelligence are yet to be systematically studied in cognitive informatics and cognitive computing. This will leads to a new era of human intelligence revolution following the industrial, computational, and information revolutions. This is also in accordance with the driving force of the hierarchical human needs from low-level material requirements to high-level ones such as knowledge, wisdom, and intelligence. The trend to the emerging intelligent revolution is to meet the ultimate human needs. The basic approach to intelligent revolution is to invent and embody cognitive computers, cognitive robots, and cognitive systems that extend human memory capacity, learning ability, wisdom, and creativity. Via intelligence revolution, an interconnected cognitive intelligent Internet will enable ordinary people to access highly intelligent systems created based on the latest development of human knowledge and wisdom. Highly professional systems may help people to solve typical everyday problems. Towards these objectives, the latest advances in abstract intelligence and intelligence science investigated in cognitive informatics and cognitive computing are well positioned at the center of intelligence revolution. A wide range of applications of cognitive computers have been developing in ICIC [http://www.ucalgary.ca/icic/] such as, inter alia, cognitive computers, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, cognitive communications systems, and cognitive automobiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.012 | 0.035 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".