Since the release of the full-stack, full-scenario AI strategy at the end of 2018, Huawei has made a strong breakthrough in AI with its powerful computing advantages.

SwissCognitiveIn particular, Huawei released the world’s most powerful AI 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 AI era with an established ecosystem, which brings more worries about the AI road ahead. Such worries were even stoked by the lack of computing power.

In the seem-to-come AI winter, Huawei did not slow down its pace. Within one year, AI 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 AI and cloud products and solutions are released to “make computing power more inclusive and algorithms simpler”.

An AI Winter Is Coming?

In 1956, John McCarthy, an assistant professor at Dartmouth College, organized a workshop where the definition of artificial intelligence was formally proposed for the first time. In the next 60 years, AI 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 speech that the biggest breakthrough in machine learning was made nine years ago and no major breakthrough was made afterwards.

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Similar voices can be heard more and more often recently. Over the years, deep learning has been at the forefront of the AI revolution. Many believe that deep learning will lead us into a new era. However, the tide seems to keep receding. Questions and uncertainty are emerging about the road of AI ahead.

New Battlefield for Deep Learning

To put it simply, AI is implemented after reams of data are processed with the deep learning to form a model and this model is applied to a specific service scenario. In this regard, deep learning is an important driving force for AI.

Of course, deep learning is just one of the implementation methods of AI, and is a subset of machine learning. Deep learning itself is not independent from other learning methods as supervised and unsupervised learning 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 deep learning as an independent method.[…]

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