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Record W2598381280 · doi:10.1145/3066862.3066867

Fun and games at IEEE WCCI 2016, Vancouver, Canada

2017· article· en· W2598381280 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueACM SIGEVOlution · 2017
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsChampionCompetition (biology)Artificial intelligenceMedia studiesPsychologyOperations researchComputer scienceSociologyEngineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

Following UCL spin-out DeepMind's success at beating the world Go champion, there was very much a flavour of artificial intelligence (AI) in the air. For example Deep Learning was the topic of Juergen Schmidhuber's invited plenary talk and some of the competitions, for example, Diego Perez and Simon Lucas' General Video Game AI Competition (winner Tom Vodopivec shown in Figure 9). David Fogel president of Natural Selection Inc., gave an impressive lecture open to the general public (see Figure 1). He covered the story of how he and Kumar Chellapilla evolved a competition level checkers (draughts) player. He talked about taking it out into the real world to play people in online competitions. He related the difference in his opponents online behaviour when playing as David 1101 or when playing as Blondie242.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.241
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it