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NYC BigApps is an annual competition sponsored by the New York City Economic Development Corporation.It provides programmers, developers, designers, and entrepreneurs with access to municipal data sets to build technological products that address civic issues affecting New York City.
The goal of the Hutter Prize is to encourage research in artificial intelligence (AI). The organizers believe that text compression and AI are equivalent problems. Hutter proved that the optimal behavior of a goal-seeking agent in an unknown but computable environment is to guess at each step that the environment is probably controlled by one of the shortest programs consistent with all ...
Research has been completed on how competition can improve research performance. Companies like JPMorgan Chase also run internal contests involving large numbers of employees. [2] Examples of data science competition platforms include Bitgrit, [3] Correlation One, Kaggle, InnoCentive, Microprediction, [4] AIcrowd, [5] and Alibaba Tianchi. [6]
The Bebras is a 45-minute multiple-choice test with 15 problems. The problems are divided into three pairs of 5, and classified as "easy", "medium" and "hard". In most countries, the competition is administered through a web system that automatically scores each participant's work.
The M2-Competition was organized in collaboration with four companies and included six macroeconomic series, and was conducted on a real-time basis. Data was from the United States. [1] The results of the competition were published in a 1993 paper. [6] The results were claimed to be statistically identical to those of the M-Competition. [1]
Over the years, the U.S. Defense Advanced Research Projects Agency has conducted a number of prize competitions to spur innovations.A prize competition allows DARPA to establish an ambitious goal, which makes way for novel approaches from the public that might otherwise appear too risky to undertake by experts in a particular discipline.
Labeled as "ex-burbs," these areas are usually 40-60 miles away from major metropolitan cities and can offer more peaceful ways of life and "affordable housing" options.
Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions – or "learn" – from the results. [2] Support-vector machines (SVMs) and recurrent neural networks (RNNs) become popular. [3]