Xu Zhijun, Chairman in Office of Huawei: Ten Changes in AI Development

Xu Zhijun, chairman in office of Huawei, once summed up the gap between theory and reality of artificial intelligence with brilliance and calmness: on the one hand, the development of artificial intelligence industry is "brilliant" ——The number of machine learning papers published in 2017 was 20000 / more than 22 countries around the world released AI plans / more than 1100 AI startup companies were born in 2017 / AI related mergers and acquisitions reached US $24 billion in 2017 / AI related VC investment reached US $14 billion in 2017.

On the other hand, the "calmness" in the initial stage of artificial intelligence is also of concern - only 4% of enterprises have invested or deployed AI / only about 2% of retailers have invested or deployed AI / only about 5% of deployed smart cities are using AI / in 2017, only about 10% of smart phones had built-in AI / the supply-demand ratio of global AI talents was only 1%.

Xu Zhijun believes that in order to solve the huge gap between "brilliance" and "calmness" of artificial intelligence, we should carry out active changes in technology, talents and industry.

One of the changes: shorten the time of training models - according to the current technical level, training some complex models often takes days or even months, and successful innovation discovery often requires multiple iterations. This training speed seriously restricts the application innovation. We believe that the training of the future model should be completed in a few minutes or even seconds.

Change 2: abundant economic computing power - computing power is the foundation of AI, but the current computing power is very expensive and a scarce resource. If the progress of computing power is the main driving factor for the great development of AI, then the scarcity and cost of computing power are becoming the core factor restricting the all-round development of AI. Computing power should be abundant and economical, and this demand should be realized as soon as possible.

Change 3: artificial intelligence should adapt to any deployment scenario - hybrid cloud has become the main mode for enterprises to adopt cloud services. At present, AI is mainly in the cloud, with a small amount on the edge. The combination with the enterprise's business environment needs to be further deepened. AI will be everywhere in the future. It should be able to be deployed in any scenario and ensure that user privacy is respected and protected.

Change 4: more efficient and safer algorithms - algorithms are another main driving force for the development of AI, but most of the main algorithms used at present were born in the 1980s. With the wide popularization of AI, the shortcomings of these algorithms become more and more obvious. Future algorithms should be based on less data requirements, that is, data efficiency. It should also be based on lower computing power and energy consumption, that is, energy efficiency. At the same time, we should solve our own security problems and realize interpretability, which are an important technical basis for the all-round development of AI.

Change 5: higher automation level - today's artificial intelligence still needs a lot of manpower, especially in the data annotation link. Today, a new profession called "data annotator" has even been born. Some people joked that today's artificial intelligence has no "intelligence" without "artificial". Huawei believes that the automation level of AI itself should be greatly improved, such as automatic or semi-automatic in data annotation, data acquisition, feature extraction, model design and training.

Change 6: the model should be oriented to practical application - in June 2018, Benjamin, an assistant professor at Berkeley University, published a paper with a strange title "can cifar-10 classifier be generalized to cifar-10". This paper points out that the model algorithm with excellent accuracy is tested on the cifar-10 classifier, but there is a deviation on another test set created by the author, which is very close to cifar-10, and the classification and recognition accuracy decreases by 5-15 percentage points. This means that the availability of this model algorithm decreases significantly. Therefore, it can be seen that many excellent model algorithms at present are more excellent in "examination" than in "work". The future model must achieve industrial excellence, that is, meet the needs of industrial production, not just meet the "test" excellence in the test set.

Change 7: model update - the accuracy of the model is not invariable, but will change with the changes of data distribution, application environment and hardware environment. It is necessary for enterprise applications to always keep the accuracy within the expected range. However, the current model updating is non real-time and depends on manual periodic updating, so it is a semi open-loop system. The future model should be able to adapt to various changes in time, update in real time, realize the closed-loop system, and ensure that the enterprise AI application is always in the best state.

Change 8: artificial intelligence requires multi technology collaboration - every general purpose technology can be brought into full play and create huge economic value only if it is fully coordinated with other technologies. AI is no exception, but when we discuss AI, we focus more on AI itself. AI needs to fully cooperate with cloud, Internet of things, edge computing, blockchain, big data, database... And other technologies, so as to give full play to greater value.

Change 9: artificial intelligence should become a basic skill supported by a one-stop platform - today, AI is still a work that can only be completed by experts with advanced skills, mature, stable and perfect automation tools are still lacking, and obtaining an AI model is still a very complex, time-consuming and labor-consuming thing. Huawei believes that there should be a one-stop platform to provide necessary automation tools to make AI application development easier and faster. Thus, AI becomes a basic skill for all application developers and even all ICT technology practitioners.

Change 10: solve the shortage of AI talents with AI thinking - the shortage of AI talents, especially the lack of data scientists, has always been a restrictive factor for the industry. And we believe that data scientists will always be scarce. The solution should be to solve the talent shortage of AI with AI thinking. By focusing on the development of intelligent, automated, simple and easy-to-use AI platform and tool services, and providing training and education, train a large number of data science engineers, so that they can complete a large number of basic data science related work. Through the trapezoidal structure in which a large number of data science engineers, data scientists and experts in various fields cooperate with each other, we can solve the problem of AI talent scarcity.

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