Choosing Artificial Intelligence: A Risky Path

“Choose artificial intelligence, it’s a hot trend, even pigs can fly!”

Parents, hold on before pushing your children into this trend. My answer regarding whether to choose artificial intelligence as a major is a firm no!

Why? Let’s get straight to the point: the truth is that those who truly excel in the field of artificial intelligence and earn high salaries are often not undergraduates majoring in AI. Here are three fundamental truths to help your child avoid the critical four years of detours.

Truth One: AI is Not a Major, It’s an Industry

Think about it: if someone suddenly asks you, “What exactly is artificial intelligence?” Can you explain it in one sentence? It’s quite challenging because it’s inherently a complex interdisciplinary field.

In such a complicated field, it’s not ideal to study it at the undergraduate level. Four years is limited; trying to cram computer science, mathematics, electronics, and control into that time will only result in a superficial understanding of everything.

Consider this:

• In terms of programming skills, you can’t compete with those specializing in computer science or software engineering.
• In terms of mathematical foundation, you won’t match the students excelling in applied mathematics or information science.
• In terms of practical skills, you’ll fall behind those in electronic engineering or automation.

Ultimately, what do you gain? A little knowledge of everything but no real expertise.

Many parents argue, “But a certain university’s AI program is ranked first in the nation!” This is a misunderstanding. A school’s strength in AI does not equate to strong undergraduate training. The strength refers to their research capabilities at the graduate level, not the undergraduate curriculum.

Top universities are focusing on cutting-edge research: Peking University has built the first global AI for Science platform; Shanghai Jiao Tong University collaborates deeply with the Shanghai AI Laboratory; the University of Science and Technology of China has a national key lab for brain-like intelligence; Tsinghua University is tackling quantum computing. These topics are too advanced for a freshman to grasp. Undergraduates should focus on solidifying their mathematics and programming skills first.

Truth Two: Popular Majors are Competitive Battlegrounds

Choosing a major is essentially a second round of ranking within the university. The AI field is a magnet for high-achieving students. If you’re not at the top of your game, how can you expect to stand out?

To secure a good job, the chain is: good university, good mentor, good opportunities. Why would these resources come to you? It all depends on your absolute ranking in the major. Without a competitive ranking, you won’t get recommendations for graduate studies, won’t have access to top mentors, and won’t receive letters of recommendation for studying abroad.

Thus, choosing a major is like a strategic race. Unless you are confident you can be at the top, it’s wise to avoid the most competitive paths and instead find a solid foundational major that offers flexibility.

Truth Three: AI Jobs Require Advanced Degrees

Is AI a hot field? Yes, but can it elevate a fresh undergraduate? No, it cannot.

In the AI industry, landing a core R&D position right after undergraduate studies is nearly impossible. The minimum requirement is a master’s degree, preferably a PhD. AI is the field where pursuing further studies offers the best return on investment.

With the right mentor, starting salaries for master’s graduates can be around 400,000, and PhD graduates from top labs can earn over a million. For instance, Nanjing University’s LAMDA lab, led by academician Zhou Zhihua, collaborates with top companies like Huawei and Alibaba on reinforcement learning algorithms, directly impacting fields like autonomous driving and intelligent risk control.

I can share that among the nine master’s students I mentored, three chose to pursue PhDs at prestigious institutions, while the others secured job offers around 800,000. This illustrates the power of good platforms and mentors.

But here’s the catch: how can you get such a top mentor? You need to excel in your undergraduate studies, engage in research, participate in competitions, and build a portfolio. If your undergraduate curriculum is too broad and scattered, you’ll lack the energy to excel in these areas.

Thus, I always say: during undergraduate studies, it’s more important to focus on significant tasks than to spread yourself too thin.

Paths to AI Careers

Finally, here’s some reassurance for parents: if your child wants to pursue AI, there are many pathways to enter this field through graduate studies. You can major in various disciplines and still transition into AI.

Students studying computer science or software are the main players; those in biology or materials can also make significant contributions. Remember the AI for Science platform at Peking University? They clearly list codes for admissions:

• AI for Science (Computer Science): 0812J6
• AI for Science (Materials Science): 0805J6
• AI for Science (Biology): 0710J6

Biology and materials, often seen as “dead-end” majors, are being revitalized by AI. Students majoring in mechanical, electrical, or physics can excel in fields like embodied intelligence, robotics, and physical simulations, gaining unique interdisciplinary advantages.

By solidifying foundational mathematics, programming skills, and core knowledge in their major, students can then precisely pivot into AI during graduate studies. This is the true way to keep all paths leading to success.

Don’t let your child fall into the trap of being a jack of all trades but master of none during their undergraduate years. Establish a solid foundation, and they will be able to travel further and faster in the future.