Bibliographic record
Abstract
This article is an invited contribution in the form of an essay, with the aim of illustrating the modalities of use and development of artificial intelligence in learning environments and as a support for educational design and research.The aim is to place electronic computing in an anthropological perspective, to outline the salient features of the new digital culture, and to articulate the most positive purpose of artificial intelligence, which is to aid in the creation, preservation and acquisition of knowledge. In the first part, I will show that access to symbolic cognition, which is unique to the human species, implies a correspondence between the sensible world and the intelligible world. Therefore, transformations of sensible objects can mean transformations of concepts. This is why, like language, the notion of calculation is inscribed in the very essence of the human being. In the second part, I'll sketch out a genealogy of automatic calculation that leads to contemporary culture, based on the collective feeding and real-time sharing of a digital memory common to humanity. The third part of the article describes the two main trends in contemporary artificial intelligence, symbolic models and neural models, with their advantages and disadvantages. I then suggest an original solution to overcome the division between the two approaches, combining the main advantages of both types of models while minimizing their disadvantages. The article concludes with a brief discussion of the problem of machine consciousness. El presente artículo es una contribución invitada en la modalidad de ensayo, en la perspectiva de ilustrar las modalidades de uso y desarrollo de la inteligencia artificial en entornos de aprendizaje y como apoyo al diseño y a la investigación educativa. Su objetivo es situar la computación en una perspectiva antropológica, delimitar las características más destacadas de la nueva cultura digital y articular el propósito más positivo de la inteligencia artificial, que es ayudar en la creación, preservación y adquisición de conocimiento. En la primera parte, el autor mostrará que el acceso al conocimiento simbólico, propio de la especie humana, implica una correspondencia entre el mundo sensible y el mundo inteligible. Por lo tanto, las transformaciones de los objetos sensibles pueden significar transformaciones de los conceptos. Por ello, al igual que el lenguaje, la noción de cálculo está inscrita en la esencia misma del ser humano. En la segunda parte, el autor esbozará una genealogía del cálculo automático que conduce a la cultura contemporánea, basada en la alimentación colectiva y la compartición en tiempo real de una memoria digital común a la humanidad. En la tercera parte del artículo se describen las dos tendencias principales de la inteligencia artificial contemporánea, los modelos simbólicos y los modelos neuronales, con sus ventajas y desventajas. A continuación, se propone una solución original para superar la división entre ambos enfoques, combinando las principales ventajas de ambos tipos de modelos y minimizando sus desventajas. El artículo concluye con una breve discusión del problema de la conciencia de la máquina .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".