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Scott Pace, a well-known academic figure in the aerospace community, was named executive secretary of the National Space Council in July. As such, he was the first key appointee of the Trump administration on space policy in regard to the future of the military, civil, and commercial space enterprises. While it is not entirely clear how influential the new council will be, it is clear that Pace will have a strong voice in whatever direction it goes.
Although generally regarded as highly capable, thoughtful about space policy, and certainly a true believer in the value of robotic and human spaceflight, the director of the Space Policy Institute at George Washington University has in recent years made comments that have raised concern among commercial spaceflight advocates.
In particular, during both interviews and comments to Congress, Pace has expressed skepticism about both NASA's commercial crew program under President Obama and the abilities of Elon Musk and his rocket company, SpaceX. "It's kind of amazing to me that the Trump administration would line up against the commercial space industry like this," said one former White House official who helped NASA develop the commercial crew program under President Obama.
Yared Berta, d'Ethiopian Airlines : " Le tourisme tire de plus en plus le trafic entre la Chine et l'Afrique "
The notion came up in a Go language contributors’ discussion group, so it’s not a done deal.[ What’s new in Go 1.9. | Also on InfoWorld: Tap the power of Google’s Go language. | The best Go language IDEs and editors. | Keep up with hot topics in programming with InfoWorld’s App Dev Report newsletter. ]
The group’s consensus recommendations are:
For everyone frustrated by how long it takes to train deep learning models, IBM has some good news: It has unveiled a way to automatically split deep-learning training jobs across multiple physical servers -- not just individual GPUs, but whole systems with their own separate sets of GPUs.
Now the bad news: It's available only in IBM's PowerAI 4.0 software package, which runs exclusively on IBM's own OpenPower hardware systems.[ Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Cut to the key news and issues in cutting-edge enterprise technology with the InfoWorld Daily newsletter. ]
Distributed Deep Learning (DDL) doesn't require developers to learn an entirely new deep learning framework. It repackages several common frameworks for machine learning: TensorFlow, Torch, Caffe, Chainer, and Theano. Deep learning projecs that use those frameworks can then run in parallel across multiple hardware nodes.