Benchmark ai how to#

Myrtle ai’s Speech Recognition benchmark is based on proven experience in this field. The new benchmarking suite will be used to test and measure training speeds and inference times for a range of ML tasks. This is the first time the AI community has come together to try and develop a series of reliable, transparent and vendor-neutral ML benchmarks to highlight performance differences between different ML algorithms and cloud configurations. Myrtle has a world class machine learning group and we are pleased to be able to provide the code as open source so that everyone can benefit from it.” To further facilite users evaluation, we provide an API that users can upload all 70 system outputs at one time. So far, the total number of subdatasets covered in Gaokao Benchmark is 70, its troublesome if users process or upload them individually.

Benchmark ai how to#

“We are honoured to be providing the reference implementations for the Speech to Text category of MLPerf”, says Peter Baldwin CEO of myrtle.ai. How to submit your AI to Gaokao Benchmark. The code is a new implementation of two AI models known as DeepSpeech 1 and DeepSpeech 2, building on models originally developed by Baidu. Myrtle.ai has been selected to provide the computer code that will be the benchmark standard for the Speech Recognition division. MLPerf, a collaboration of tech giants and researchers from numerous universities including Harvard, Stanford and the University of California Berkeley, is aspiring to drive progress in ML by developing a suite of fair and reliable benchmarks for emerging artificial intelligence (AI) hardware and software platforms. A-OKVQA is a new knowledge-based visual question answering benchmark. myrtle.ai, a Cambridge UK based deep learning company with expertise in producing low power inference circuits has been selected to develop a Speech Recognition benchmark for MLPerf, a new Machine Learning (ML) benchmarking competition backed by Google, Baidu, Intel and AMD. leaderboards for a variety of AI challenges across multiple research domains.