Since the release of the full-stack, full-scenario strategy at the end of 2018, Huawei has made a strong breakthrough in with its powerful computing advantages.
In particular, Huawei released the world’s most powerful processor Ascend 910 on August 23 this year, which earned a ticket for Huawei to enter the field full of world’s top players. Giants in the industry quickly realize that Huawei has more than 5G and mobile phones. Huawei is investing greatly in basic research, which is helping Huawei seize the high ground in the future.
When we look back, however, the recent years of development has not ushered in an era with an established ecosystem, which brings more worries about the road ahead. Such worries were even stoked by the lack of computing power.
In the seem-to-come winter, Huawei did not slow down its pace. Within one year, processors and computing frameworks were implemented successively. One could not help but wonder how Huawei gain insights, why they are confident, and how do they develop their technical knockout.
We may find answers in Huawei Connect 2019 where the latest and cloud products and solutions are released to “make computing power more inclusive and algorithms simpler”.
An Winter Is Coming?
In 1956, John McCarthy, an assistant professor at Dartmouth College, organized a workshop where the definition of was formally proposed for the first time. In the next 60 years, has experienced two periods of slow development, the socalled “winters”, but its development has never stopped.
At a conference in 2018, Kai-Fu Lee, CEO of Innovation Works, said in his that the biggest breakthrough in was made nine years ago and no major breakthrough was made afterwards.
Similar voices can be heard more and more often recently. Over the years, has been at the forefront of the revolution. Many believe that will lead us into a new era. However, the tide seems to keep receding. Questions and uncertainty are emerging about the road of ahead.
New Battlefield for Deep Learning
To put it simply, is implemented after reams of data are processed with the to form a model and this model is applied to a specific service scenario. In this regard, is an important driving force for .
Of course, is just one of the implementation methods of , and is a subset of . Deep learning itself is not independent from other learning methods as supervised and are used to train the deep neural networks. But it has been developing rapidly in recent years, and some dedicated learning methods (such as residual neural network) have been proposed one after another, more and more people now regard as an independent method.[…]
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