"We believe that AI is the new electricity and will transform and improve nearly all areas of human lives." Andrew Ng.
Coding AI is the New Literacy
Today we take it for granted that many people know how to read and write. Someday, I hope, it will be just as common that people to know how to write code, specifically for AI. Several hundred years ago, society didn’t view language literacy as a necessary skill. A small number of people learned to read and write, and everyone else let them do the reading and writing. It took centuries for literacy to spread, and now society is far richer for it.
Words enable deep human-to-human communication. Code is the deepest form of human-to-machine communication. As machines become more central to daily life, that communication becomes ever more important.
Traditional software engineering — writing programs that explicitly tell a computer sequence of steps to execute — has been the main path to code literacy. But AI, machine learning, and data science offer a new paradigm in which computers extract knowledge from data.
This technology offers another pathway to coding — one that strikes me as even more promising. Many Sundays, I buy a slice of pizza from my neighborhood pizza parlor. The gentleman behind the counter may have little reason to learn how to build software applications (beyond personal growth and the pleasure of gaining a new skill).
But AI and data science have great value even for a pizza maker. A linear regression model might enable him to better estimate demand so he could optimize the restaurant’s staffing and supply chain.
He could better predict sales of Hawaiian pizza — my favorite! — so he could make more Hawaiian pies in advance and reduce the amount of time customers had to wait for them. Uses of AI and data science can be found in almost any situation that produces data, and I believe that a wide variety of professions will find more uses for custom AI applications and data-derived insights than for traditional software engineering.
This makes literacy in AI-oriented coding even more valuable than traditional skills. It could enable countless individuals to harness data to make their lives richer. I hope the promise of building basic AI applications, even more than that of building basic traditional applications, encourages more people to learn how to code.
If society embraces this new form of literacy as it has the ability to read and write, we will all benefit.
AI continues to create numerous exciting career opportunities, and I know that many of you aim to develop a career in the field. While taking online courses in technical topics is an important step, being an AI professional requires more than technical skills.
Lately, I’ve been thinking about how to do more to support all of you who are looking to build a career in AI.
Considering individuals at a variety of stages in their careers, what are some of the keys to success?
1. Technical skills
When learning a new skill, taking an online course or reading a textbook — in which an expert presents important concepts in an easy-to-digest format — is one of the most efficient paths forward.
2. Practical experience
After gaining a skill, it’s necessary to practice it — and learn tricks of the trade — by applying that skill to significant projects. Machine learning models that perform well in the lab can run into trouble in the real world. Practical project experience remains an important component in overcoming such problems.
Project selection
Choosing projects to work on is one of the hardest skills in AI. We can only work on so many projects at a time, and scoping ones that are both feasible and valuable — so they have a good chance of achieving meaningful success — is an important step that has to be done repeatedly in the course of a careerTeamwork
When we tackle large projects, we succeed better by working in teams than individually. The ability to collaborate with, influence, and be influenced by others is critical. This includes both interpersonal and communication skills. (I used to be a pretty bad communicator, by the way.)
5. Networking
I hate networking! As an introvert, having to go to a party to smile and shake as many hands as possible is an activity that borders on horrific. I’d much rather stay home and read a book. Nonetheless, I’m fortunate to have found many genuine friends in AI; people I would gladly go to bat for and who I count on as well. No person is an island, and having a strong professional network can help propel you forward in the moments when you need help or advice.
Job search
Of all the steps in building a career, this one tends to receive the most attention. Unfortunately, I’ve found a lot of bad advice about this on the internet. (For example, many articles seem to urge taking an adversarial attitude toward potential employers, which I don’t think is helpful). Although it may seem like finding a job is the ultimate goal, it’s just one small step in the long journey of a career.
7. Personal discipline
Few people will know if you spend your weekends learning or binge-watching TV (unless you tell them on social media!), but they will notice the difference over time. Many successful people develop good habits in eating, exercise, sleep, personal relationships, work, learning, and self-care. Such habits help them move forward while staying healthy.
Altruism
I find that individuals who aim to lift others during every step of their own journey often achieve better outcomes for themselves. How can we help others even as we build an exciting career for ourselves?
While each of these items is a complex subject worthy of an entire book, in this article I try to summarize the most important points as succinctly as possible from Andrew Ng Founder, DeepLearning.AI.