What should developers do when the wave of artificial intelligence strikes? On January 16, 2018, at the just-concluded "AI Eco-Energy 2018 Forum", CSDN Vice President Meng Yan issued a AI Technology Career Upgrade Guide - "AI Technology Talent Growth Roadmap V1.0". Based on the experience of more than 10 experts, the roadmap refines the two major ways of getting started AI and the top 10 routes of advanced AI. This roadmap will be updated at any time, open and open, and hope to be a useful reference for developers to advanced AI.
1. Combining the demand structure of AI talents with the current market situation, we find that artificial intelligence is a new generation of software development, which is worthy of every technical person's dedication.
Priorities for enterprise AI applications: Profitability > "Black Technology"
The demand for AI talent market has increased dramatically: the number of technical positions has tripled in three years, and the talent gap will continue to increase.
The AI ​​talent demand structure is pyramid-shaped, research talents mainly rely on schools and academic routes, and applied talents must rely on social training and continuing education supplements.
AI engineers must lay a solid foundation for programming, software engineers to transform AI to "change brains"
2. Strong demand for AI talent: From 2014 to 2017, AI talent recruitment increased nearly 8 times, and technical jobs accounted for more than tripled.
According to the statistics of hunting, in 2017, the number of AI technical engineers was 8.8 times that of 2014, and that of data engineers was 5.9 times that of 2014, while the total number of IT jobs was only 2.65 times that of 2014. . In contrast, in 2014, AI engineers and data engineers accounted for 2.97% and 7.86% of all technical positions, respectively. This figure rose to 9.86% and 17.59% respectively in 2017; artificial intelligence related positions The total proportion of technical talent recruitment increased from 10.83% to 27.45%, which is the largest increase in technical positions.
3. The average annual salary of AI engineers far exceeds that of IT engineers: the highest annual salary of the knowledge map class is close to twice that of IT engineering technology.
According to the statistics of hunting and hiring, the average annual salary of IT engineering and technical personnel has been 179,200 in the publicly released positions since 2016, while the highest annual salary of AI domain knowledge mapping engineers is 434,200, and the average annual salary is also 340,600. Close to twice the IT engineering category. In the AI ​​segmentation field, in addition to the computer vision class, the average annual salary is 278,100, and the other direction salary is more than 300,000 yuan.
4. The AI ​​talent demand structure is pyramid-shaped, and the “Academic†route fosters the leader of the AI ​​Talent Pyramid.
GE Hinton has been working hard for more than forty years. A question of "how the brain works" has kept him focused on neural networks. Until 2006, he discovered an algorithm for effective training in feedforward neural networks. "Deep learning" started. Li Feifei's ImageNet dataset and Wu Enda's GPU high-performance computing method also followed. The algorithm, computing power and data accumulation potential for more than 30 years finally created a deep explosion of deep learning.
Ten years of trees, the students of this group of founders - Russ Salakhutdinov, Ian Goodfellow, Andrej Karparthy, Jia Yangqing, Li Mu, etc., have grown up in the explosion of deep learning and become the leader in controlling the AI ​​department of the technology giant. And the backbone.
5. The AI ​​talent demand structure is pyramid-shaped, and the “real combat†method cultivates the AI ​​technology.
The demand for talents for deep learning greatly exceeds the number of “academic†training providers, and thousands of AI technology squads need to be cultivated in a more direct way. Therefore, Wu Enda continued to launch a deep learning special course at Coursera, Jeremy Howard developed a Fast.ai course that does not require mathematics, and Udacity teamed up with Google and Didi to train the industry-deficient machine learning engineers... 4 months of fast machine learning It is possible that the growth time of AI technical talents has been greatly shortened.
6. General picture of AI technical talent growth route: entry method and advanced direction
CSDN invited more than 10 AI technical experts to write more than 60,000 words to analyze their personal experience, some of which constitute "AI Engineer Career Guide" "Programmer" topic, which has won everyone's attention and recognition.
Now, we extract the two major ways of getting started AI and the 10 major routes of advanced AI from all the content, transforming the complex content and methods into a clear chart structure, and sharing it with friends who want to get started with AI.
7. AI advanced top ten path example
8. Non-technical personnel learn AI: product managers should understand the technical principles and functions
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