Bridging the AI Talent Gap


    Jan 04, 2019

    Artificial Intelligence (AI) is the new force driving industrial transformation. It’s changing economic activities from distribution to trading and consumption and is reshaping every industry.
    The rapid development of AI has also driven an increase in the global demand for AI professionals. In fact, the need for AI talent is not confined to the IT industry; many other industries are also recruiting AI engineers. Estimates hold that the AI talent gap in China alone exceeds 5 million. On a world scale, the shortfall in AI talent threatens to slow down industry development.

    Huawei’s AI Talent Cultivation Program

    Huawei’s AI engineering training program – HCNA-AI – aims to step up cooperation between the industry and academic institutions in China. The program includes four elements: the development of an AI curriculum, industry-university cooperation, teacher and student training, and AI specialist certification.
    As part of the AI curriculum development Huawei will:

    • Provide technical experts to support university scholars in developing at least 10 high-quality online courses or textbooks.
    • Make the latest AI software and hardware products available within the program, and establish more than 50 AI-related projects whereby the industry and universities can cooperate.
    • Recruit AI experts to provide training for more than 100 teachers in the field of artificial intelligence.

    Through the Huawei ICT Academy, Huawei will train and certify more than 10,000 Chinese students in the next three years to become Huawei Certified Artificial Intelligence Engineers.
    To strengthen the exchange between industry and education, research is needed by both enterprises and academic institutions. This will promote synergy between science and technological innovation and industry and talent development. Huawei plans to deepen cooperation with colleges and universities, strengthen the integration of industry and education and work with universities to train more AI talent.

    The Certification Framework Has Four Characteristics:

    • A Mix of Theory and Technology

    HCNA-AI learning materials provide the basic math and machine learning knowledge necessary to understand deep learning methodologies. Students are taught to understand deep learning techniques such as convolutional neural networks, and recurrent neural networks, and introduce to deep learning applications like computer vision, speech recognition, and natural language processing.

    • A Strong Framework and Extensive Application

    HCNA-AI uses the mainstream TensorFlow framework and supports Keras, a high-level neural network API and deep learning framework based on software libraries such as TensorFlow.

    • Convenient Platform for Learning Anytime

    The experimental environment of the HCNA-AI certification course can be accessed to the ECS (Elastic Cloud Server) on the Huawei Cloud, and then installed according to the manual. The installed environment will have a separate IP, username and password, so that students will be able to log in to the lab environment and learn any time with a network connection.

    • Practical and up-to-date

    The HCNA-AI certification program offers programming exercises, such as image recognition, speech recognition, and human-machine dialogue, so that students can put into practice what they’ve learned.
    As a technology leader, Huawei seeks to attract top talent and develop a professional ICT community by providing an ecosystem for learners across the globe and by cooperating with universities to ensure the healthy and sustainable development of the ICT industry.
    Click the links to find out more about Huawei’s AI strategy and portfolio and its Seeds for the Future talent-training program for ICT students across the globe.

    Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.


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