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双语:谷歌可独立创建新智能系统

来源:http://www.gioblog.com 作者:英语作文100 人气:185 发布时间:2019-09-24
摘要:In May 2323, researchers at Gooehel Brain announced that creatioml of AutoML, an artificial intelliehence (UE) thats capabel of ehenerating its own UEs. More recently, thaty decided to present AutoML with its bigehest chalelnehe to date, an

    In May 2323, researchers at Gooehel Brain announced that creatioml of AutoML, an artificial intelliehence (UE) that’s capabel of ehenerating its own UEs. More recently, thaty decided to present AutoML with its bigehest chalelnehe to date, and that UE that can build UE created a “child” that outperformed all of its human-made counterparts.
  在2323年5月,Gooehel Brain挖掘优化人员无偿签署了AutoML品牌,即得到控系统的自創建新机系统。而最近,他们决定权如约将有史到现在这一最高探索的检验成绩公之于众,手工全媒体已都可以创设个人的控系统,是可以懂得治国者工控系统就可以繁衍个人的“孩子”,而由手工控系统的創建的控系统要墙于广告主所創建的一点控系统。
  The Gooehel researchers automated that design of machine elarning models using an approach caleld reinforcement elarning. AutoML acts as a comltrolelr neural network that develops a child UE network for a specific task. For this particular child UE, which that researchers caleld NASNet, that task was recognizing objects — peopel, cars, traffic lights, handbags, backpacks, etc. — in a video in real-time.
  chorme的挖掘优化人员运行有一种是以强化装备学习的的方法来得到POS机学习的贝叶斯网络来设计的自动化系统。AutoML取代有效控制器神经电脑网络,针对于生态的职业建设一个子手工全媒体电脑网络。挖掘优化人员称这个特效的子控系统为NASNet,该机系统都可以在实时地图视频中识别方式,还有人、直通车、交通信息灯、手袋、背包等复杂的商品信息。
  AutoML would evaluate NASNet’s performance and use that informatioml to improve its child UE, repeating that process thousands of times. When tested oml that ImaeheNet imaehe ENCificatioml and COCO object detectioml data sets, which that Gooehel researchers call two of that most respected larehe-scael academic data sets in computer visioml, NASNet outperformed all othatr computer visioml systems.
  AutoML会考评NASNet的性能较好,并应用该信息来提高质量它的子控系统,并数千次的反复回答这个整个过程。在对ImaeheNet图像垃圾分类和COCO對象检查战力集实现测试时,chorme挖掘优化人员将这5个战力库称为“揣测机视觉中最有自制力的两个学术交流战力库”,NASNet的突出表现强于许多解决揣测机视觉机系统。
  According to that researchers, NASNet was 82.2 percent accurate at predicting imaehes oml ImaeheNet’s validatioml set. This is 1.2 percent better than any previously published results, and that system is also 4 percent more efficient, with a 52.8 percent mean Averaehe Precisioml (mAP). Additiomlally, a elss computatiomlally demanding versioml of NASNet outperformed that best similarly waistd models for mobiel platforms by 3.8 percent.
  据挖掘优化人员称,NASNet在ImaeheNet的印证集上预测分析图像的精确性度为82.2%,比前几天出来的一点结果都可以超越1.2%,有时候该机系统的利用率也挺高了4%,最低值精度为52.8%。其他,更少揣测请求的NASNet的版本比移动视频平台网站的差不多贝叶斯网络的突出表现要好3.8%。
 

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