[AISWorld] On the excessive time frame for the submission-review-acceptance/rejection cycle in our discipline

[mmora at securenym.net]

Colleagues:
I received recently a survey from a top ISI-listed editorial company on
the quality, fairness and timeliness of review process. It is excellent !
Well, my comment and concerns are on the damage that we cause in our
discipline for an excessive review period (absurd to be honest) on an
average of 2-3 iterations and from 8 to 18 months. In other disciplines,
the submission-review-acceptance/rejection cycle is about 2-3 months.  Is
required really this excessive review period or we are living old times in
the new XXI century ? Thanks for comments,
Manuel Mora
ACM Senior Member

PS.1 If the argument is that the papers are of low quality, it can be
falsified with several counterexamples (there are several seminal papers
published in the highest top journals in our discipline, highly cited
(over 1000+ citations), that were published in about a cycle of 18 months
or more).

PS.2 A recent research on AI themes reported an robot that is able to
conduct a full research ! (hypothesis generation, lab experiment design,
conduct it !, and analyze the statistical results).


Functional genomic hypothesis generation and experimentation by a robot
scientist
http://www.nature.com/nature/journal/v427/n6971/full/nature02236.html

Abstract.
"The question of whether it is possible to automate the scientific process
is of both great theoretical interest1, 2 and increasing practical
importance because, in many scientific areas, data are being generated
much faster than they can be effectively analysed. We describe a
physically implemented robotic system that applies techniques from
artificial intelligence3, 4, 5, 6, 7, 8 to carry out cycles of scientific
experimentation. The system automatically originates hypotheses to explain
observations, devises experiments to test these hypotheses, physically
runs the experiments using a laboratory robot, interprets the results to
falsify hypotheses inconsistent with the data, and then repeats the cycle.
Here we apply the system to the determination of gene function using
deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic
growth experiments9. We built and tested a detailed logical model
(involving genes, proteins and metabolites) of the aromatic amino acid
synthesis pathway. In biological experiments that automatically
reconstruct parts of this model, we show that an intelligent experiment
selection strategy is competitive with human performance and significantly
outperforms, with a cost decrease of 3-fold and 100-fold (respectively),
both cheapest and random-experiment selection."