Exploring Machine Creativity through Text-Generative Algorithms
Author : Harsh Singh
Greenwood High International School
In recent times, artificial and machine intelligence has found applications through a wide variety of fields. The rising number of authors throughout the world who want to express a story or a viewpoint may not necessarily be equipped with the proper writing skills to develop an article and may need guidance in initial steps. Technology has found application here as well, and by using text-generative algorithms, they can produce rough drafts, short stories, plotlines and initial outlines customized to the writer’s needs by using specific given inputs. The aim of this research is to analyze two text-generative algorithms, different in their fundamental and logical approach to the problem, on the basis of criteria: speed, accuracy and grammar in order to understand which algorithm is more proficient at the given task. A 1000 trials were performed for both algorithms. The number of successful trials were recorded and that percentage gave the accuracy for both algorithms. Each trial’s results and the amount of time it took to generate that output was recorded. The algorithm that used the phrase method was both fast and also accounted for lesser grammatical mistakes comparatively. However, the algorithm that used the Markov’s Chain Method was more accurate in terms of output and results. The study’s results establish an elaborate manner through which the proficiency of text-generative algorithms can be analyzed. It mainly served to evaluate two widely used logical techniques used in algorithms for text-generation.
Uvumbuzi wa Mashine kupitia Algorithms ya Uundaji wa Maandishi Mwandishi: Harsh Singh Greenwood High International School Katika nyakati za hivi karibuni, akili ya bandia na mashine imepata matumizi kupitia nyanja mbalimbali. Idadi inayoongezeka ya waandishi ulimwenguni kote ambao wanataka kuelezea hadithi au maoni huenda wasiwe na ujuzi sahihi wa uandishi wa kuunda makala na wanaweza kuhitaji mwongozo katika hatua za kwanza. Teknolojia imepata matumizi hapa pia, na kwa kutumia algorithms za kizazi cha maandishi, wanaweza kutengeneza rasimu, hadithi fupi, safu za njama na maelezo ya awali yaliyoboreshwa kwa mahitaji ya mwandishi kwa kutumia pembejeo maalum zilizopewa. Lengo la utafiti huu ni kuchambua algorithms mbili za kizazi cha maandishi, tofauti katika mbinu yao ya msingi na ya kimantiki ya tatizo, kwa msingi wa vigezo: kasi, usahihi na sarufi ili kuelewa ni algorithm gani inayofaa zaidi katika kazi iliyotolewa. Majaribio ya 1000 yalifanywa kwa algorithms zote mbili. Idadi ya majaribio ya mafanikio yaliandikwa na asilimia hiyo ilitoa usahihi kwa algorithms zote mbili. Matokeo ya kila jaribio na kiasi cha muda alichukua kuzalisha kwamba pato ilikuwa kumbukumbu. Algorithm ambayo kutumika maneno mbinu ilikuwa wote haraka na pia alitoa kwa makosa ya chini ya sarufi kwa kulinganisha. Hata hivyo, algorithm ambayo kutumika Markov ya Chain Method ilikuwa sahihi zaidi katika suala la pato na matokeo. Matokeo ya utafiti kuanzisha njia ya kina kupitia ambayo ustadi wa maandishi-generative algorithms inaweza kuchambuliwa. Ni hasa kutumika kutathmini mbinu mbili za mantiki kutumika sana kutumika katika algorithms kwa ajili ya maandishi ya kizazi.