Dry goods | AI engineer required, from the Angle of practice, parsing a qualified engineers have a AI

Since the beginning of this year, several major international developer conferences, whether Microsoft Build, Facebook F8 or later Google I/O, have put the banner of “AI first” on the top of the sky.

If this wave of AI is just a few slogans, a few strategies, and a few hot startups, it’s not going to be a big deal. However, what we see is that Google’s major businesses are switching to deep learning, and Microsoft, which is behind the mobile era, has built up an AI team of nearly 10,000. And the situation of domestic first-line big factories, I am afraid is similar.

This is a sign that programmers, especially would-be programmers, should pay close attention to. Looking back at the opportunities presented by the mobile Internet, it’s easy to figure out how mastering deep learning can benefit front-line jobs. However, unlike mobile development, the training cycle of AI talents has soared to more than five years due to the strict mathematical threshold and costly actual combat training. It seems that without a master’s or doctor’s degree, you must say goodbye to ai-related technical work. Is this really the case?

This article is a compilation of the AI Engineer career Guide series, which was first published in Programmer. The authors explain from a practical perspective what it takes to become a qualified engineer for various technical positions in the AI field. AI skills you will learn about what kind of tree can meet the demand of choose and employ persons, they are a line of business data science, machine learning algorithms, heterogeneous parallel computing and speech recognition, recommender systems and dialogue system and how should the skills in the areas of advanced, especially in the academic path and practical methods and how to choose.

Becoming a qualified development engineer is not an easy thing. It requires mastering a series of skills from development to debugging to optimization, and each of these skills requires sufficient effort and experience. And to become a qualified machine learning algorithm engineer (hereinafter referred to as algorithm engineer) is even more difficult, because in addition to mastering the general skills of engineers, but also need to master a not small machine learning algorithm knowledge network. This article will break down the skills required to be a qualified algorithm engineer. Let’s take a look at the skills required to be a qualified algorithm engineer.

• How to become an Excellent Recommendation System Engineer (Chen Kaijiang, CTO)

Nowadays, even though the words “big data” and “AI” bombard us with 360 degrees every day, making us easily impetuous and anxious, we have to admit that this is a good time to be a recommendation system engineer. Compared with normal code farmers, recommend system engineers do not need to throw the requirements of THE PM to the pixel level implementation, thus stacking into mountains; Unlike machine learning researchers, they don’t have to indulge in mathematical derivation to produce a beautiful, self-consistent model that can dominate the academic debate; Compared to being a data analyst, you don’t need to draw beautiful charts, make cool powerpoint presentations to the CEO, and get to the top of your life. What is the position of the recommended systems engineer? Why do you need those skills? The author will combine their own experience to answer one by one.

• How to Become a Dialogue Systems Engineer — The Way to Progress in Dialogue Systems (Wu Jinlong, Partner of IIN Interactive Technology)

Conversational systems (conversational robots) essentially allow machines to understand human language through techniques such as machine learning and artificial intelligence. It is a technology-focused training camp in the field of artificial intelligence. With the continuous development of speech recognition, NLP and other technologies, with the arrival of the Internet of everything era, the stage of dialogue robot will be bigger and bigger.

• Self-cultivation of a Data Scientist (Skill Package + Advanced Path) (Hui Lin, DuPont Commercial Data Scientist)

Before you answer that question, I want you to think about another one: Why become a data scientist? Sure, if you’re looking for $100,000 a year, that’s fine, but I hope you make it about your value. Because being a data scientist can be a hard road, but if you look at it as a way to achieve your personal value, the pursuit of goals can lead to lasting fulfillment, happiness and motivation. The position of data scientist is relatively new, so it is still developing in terms of team building and career trajectory, and has great prospects. I hope you can become a thinking, lifelong learning data scientist!

• Smart speaker wars are in full swing, so how to become a full stack speech recognition engineer? (Chen Xiaoliang, founder of Sonwise Technology)

At present, the accuracy and speed of speech recognition depends on the actual application environment. In the quiet environment, standard accent, common words, the speech recognition rate has exceeded 95%, fully reached the state of availability, which is the reason why the current speech recognition is hot. Academia discusses many speech-recognition technology trends, there are two ideas is very notable, one is the end-to-end speech recognition system, another is G.E. Hinton recently offered a capsule, capsule of Hinton theory academic dispute is bigger, also can show advantages in the field of speech recognition is worth exploring. This article is based on popular science, linking knowledge vertically and horizontally, and can be combined with the practice of simple articles, for a comprehensive understanding of speech recognition is very helpful.

• Deep learning must talk about computing power! How to become a Heterogeneous parallel computing engineer? (Liu Wenzhi, head of Sensetime’s HIGH-PERFORMANCE computing department)

With the popularity of deep learning (ARTIFICIAL intelligence), heterogeneous parallel computing has attracted more and more attention in the industry. From the beginning of deep learning must talk about GPU, to deep learning must talk about computing power. Computing power is not only related to the specific hardware, but also to the level of heterogeneous parallel computing power possessed by the people who can use the hardware. A simple analogy is: two chips with 10T and 20T computing power respectively. Someone with a heterogeneous parallel computing power of 0.8 gets a 10T chip, while someone with a heterogeneous parallel computing power of 0.4 gets a 20T chip. In fact, they may end up with similar results. People who are good at heterogeneous parallel computing can better use the power of the hardware, and the goal of this article is to tell the reader what you need to learn to become a heterogeneous parallel computing engineer.

• How to become a deep learning expert? The doctor from The Chinese Academy of Sciences who won the ali Tianchi Competition has planned a professional growth path for you. (Liu Xin, CEO of CiTECH)

Deep learning is essentially a deep artificial neural network. It is not an isolated technology, but a combination of mathematics, statistical machine learning, computer science and artificial neural network. The understanding of deep learning is inseparable from the most basic mathematical analysis (advanced mathematics), linear algebra, probability theory and convex optimization in undergraduate mathematics. The mastery of deep learning technology is inseparable from hands-on practice with programming as the core. Without a solid foundation in mathematics and computer science, deep learning is a castle in the air. Therefore, beginners who want to be successful in deep learning techniques need to understand the significance of these basic knowledge for deep learning. In addition, our professional path will also introduce the beginning of deep learning from the theoretical dimension of structure and optimization, and analyze the advanced path based on the practice of deep learning framework. This article will also share the practical experience of deep learning and the experience of acquiring cutting-edge information of deep learning.

• Actual combat path: Advanced methods of machine learning for programmers (Zhi Liang, co-founder of Lulang Software)

If we fail to smell the opportunity in machine learning when we’re in school and choose to study and work in other fields… But now they’re going to go into machine learning. How do you do it so you can be as good as these people? Or, at least, good enough? The author painful transition experience, say out for everyone reference.