Breakthroughs in Cognitive Robots and AI Programming Underpinned by Discoveries in Intelligence Science and Software Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breakthroughs of basic research in Intelligence Science (IS) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], Cognitive Computing (CC) [14], [15], [16], [17], [18], [19], [20], [21], [22], [22], [23], [24], [25], [26], and Cognitive Informatics (CI) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], have triggered the emergence of Cognitive Robots (CR) [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49] towards software science (SS) [50], [51], [52], [53], [54], [55] and autonomous software engineering (SE) [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66]. These latest advances have enabled the synergy of classical pre-programmed software engineering and pre-trained AI to an unprecedented platform of training-free machine intelligence generation mimicking human brains and Brain-Inspired Systems (BIS) [67], [68], [69], [70], [71], [72], [73], [74]. It is discovered in IS and CC in general, as well as in CR and SE in particular, that the target entities across these contemporary fields had already out of the denotational power of the classic mathematical domains of real (R) and binary (B) numbers [76]. This leads to the latest discovery that the basic unit of human knowledge as a hyperstructure [50] is a binary relation (bir) [74]. Therefore, a new framework of Intelligence Mathematics (IM) [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100] has been created for rigorously manipulating the cognitive entities in the brain and CRs spanning from formal concepts, semantics, knowledge, causalities, inferences, and consciousness by contemporary IMs for advancing both AI [1], [2], [3] and SE [56], [58]. This keynote lecture presents fundamental theories and ground-breaking technologies for designing and implementing cognitive robots based on AI Programming (AIP) [101], [102], [103], [104], and Autonomous Intelligence Generation (AIG) methodologies [105], [106] beyond classic reflexive neural networks and imperative programming platforms. The latest advances have paved a new way for cognitive computing and autonomous software generation, which put the house before the cart for the software and AI industries [51], [56]. The keynote will demonstrate disruptive technologies encompassing autonomous systems, cognitive robots, real-time autonomous learning, and trustworthy systems for symbiotic human-machine applications [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it