AI training speed breaks Moore's law; Song shuran's team won the RSS 2022 Best Paper Award

from ：AI Technology Review

newest MLPerf The benchmark indicates ：AI The training speed of is almost twice that of last year

6 month 29 Japan , Open Engineering Alliance MLCommons Released MLPerf The latest training results of the benchmark , It is found that the training speed of machine learning system this year is almost twice that of last year , Beyond Moore's law （ Every time 18-24 Double in months ）.

MLPerf It consists of eight benchmarks ： Image recognition 、 Medical image segmentation 、 Two versions of object detection 、 speech recognition 、 natural language processing 、 Recommend and reinforce learning . In these eight benchmarks , NVIDIA's accelerators are all at the top , Excellent performance .

MLPerf Training v2.0 Results include from 21 Of different submitters 250 Multiple performance results , Include Azure、 Baidu 、 Dell 、 Fujitsu 、 Jijia 、 Google 、Graphcore、HPE、 wave 、 Intel -HabanaLabs、 lenovo 、Nettrix、NVIDIA、 Samsung and Supermicro wait .（IEEE Spectrum）

Song shuran won the robot top meeting RSS 2022 Announce the best Paper Award

The robot will RSS（Robotics: Science and Systems） On 6 month 27 solstice 7 month 1 Held in New York on the th , And published the best paper 、 Best system paper 、 All awards including best student thesis .

among , Song shuran, a Chinese scholar currently working at Columbia University, and his team won RSS 2022 Best Paper Award , The award-winning work is “Iterative Residual Policy for Goal-Conditioned Manipulation of Deformable Objects”. Song shuran is a well-known young scholar in the field of robot research , He has won many conference Best Paper Awards , And get 2022 Nian Suyou “ Nobel vane ” The Sloan research award .

Google proposes a new language model Mineva To solve the problem of quantitative reasoning

lately , Google Research Institute has proposed a language model that can solve mathematical and scientific problems with step-by-step reasoning ——Mineva.

According to the team , At present, language models are used in quantitative reasoning （ That is to combine mathematics and information to solve real-world problems ） The performance of is still far lower than that of human , They collect training data related to quantitative reasoning problems 、 Large scale training model and advanced reasoning technology , Realized AI Significant progress of the model in quantitative reasoning tasks .

Thesis link ： https://storage.googleapis.com/minerva-paper/minerva_paper.pdf

In terms of technical architecture ,Mineva Based on what Google proposed a few months ago PaLM framework . In training , Yes arXiv On 118G Scientific paper data set and contains LaTex The webpage of mathematical expression is trained . Studies have shown that ,Mineva In the high school mathematics competition level question bank MATH、 University level STEM Good performance on the task .

British scholars jointly released a 100 page survey report on causal machine learning

lately , Oxford University and University College London （UCL） A survey of causal machine learning has been jointly released by scholars of , Long 165 page . Causal machine learning mainly studies how to transform the data generation process into a structural causal model （SCM）, People can infer the changes in this process （ Intervention ） Influence , And what will happen afterwards （ The counterfactual ）.

This review discusses five major directions of causal machine learning ： Causal supervised learning 、 Causal generation modeling 、 Causal interpretation 、 Causal justice and causal reinforcement learning .

Thesis link ： https://arxiv.org/pdf/2206.15475.pdf

Message link ：

https://spectrum.ieee.org/mlperf-rankings-2022

https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html