Everything you need to know about building a career in AI and ML

Having mentored 100s of girls and women on STEM careers, the most common questions I get asked these days are: “How do I get a job in AI?”, “How can I get started in learning about AI”, “I’m don’t have technical skills - can I break into the AI Industry?”

To respond to these and related questions, I am publishing a three-part AI/ML Careers Masterclass where I share everything you need to know about building a career in AI.

This is the first post in the series. Here, I talk about why everyone needs to learn about AI (Yes, not just you, but everyone around you, too).

History Repeats Itself: How the Internet changed jobs

I love to look at history and study patterns because we know that history repeats itself. The best way to think about AI and the transformational impact it’s having on the world is to look at the Internet and the “.com bubble” period and how it brought us to the world today where everything is digital.

If you think of how jobs and skills changed during this period, we can broadly categorize them into three types:

1. Some people went out and learnt how the Internet or telecommunications work and built related engineering skills such as networking protocols, server management, coding, and cybersecurity
2. Many people spent time trying to learn how the Internet is changing the world and build businesses around it — whether by creating products in those areas or offering a service. This gave rise to search, websites and blogging platforms, e-commerce, social media companies, etc.
3. Most of us today can now do basic tasks using those Internet applications such as writing/reading email, social media, surfing websites, searching for information, etc. We now have digital literacy prevalent across the workforce.

Now, let’s talk about AI/ML and how these technologies are transforming jobs: If you go back to the three categories of skills and work created as a result of the Internet disruption, we see three parallel categories of jobs being created. Let’s start with the most specialized jobs first:

1. AI Engineering Jobs This is probably the broadest category of AI jobs available today. These jobs involve building AI applications and/or models using the technologies we have today, or researching to building and improve the AI applications of tomorrow. At a general level, these jobs can require machine learning (R, Python, TensorFlow, etc.), programming, and other related engineering fields. I’ve created a post on the eight most common jobs in this category here.

But there are two other categories of jobs available that don’t require engineering skills:

2. Building AI Applications using “No Code” AI/ML tools

Ever thought of any fun ideas you could do using AI — whether it’s computer vision (training the AI to “see and recognize objects”?), chatbots (program your own dialog flows, be it text or voice conversations), or audio analysis (build AI models to detect certain sounds)? You can build your own AI applications to perform most of these common AI use cases using one of the many “no code” AI and ML tools available today. These tools allow you to experiment, design, train and deploy AI/ML models without requiring any programming or ML skills.

IBM Watson, for example, is one of the companies that demonstrated early leadership in this area. They have a plethora of tools available on their cloud platform using which you can not only build your own visual recognition, voice interactions and conversational AI applications but also analyze sentiments and personalities, build search applications, perform acoustic analysis, among others. The complete list of IBM Watson AI capabilities can be found here (listed under “AI”).

Examples of other popular no code AI/ML tools are Lobe, Deep Cognition, and “AI-as-a-Service” platform providers such as Google AI Platform, Microsoft Azure’s AI Platform, and so on.

3. The data-driven decision maker These are the skills ALL of us need to learn. The skills fall under three categories:

1. AI Fundamentals All of us need to be able to understand and answer these core questions: 1. What is AI and how does it work? (AI concepts and the understanding of algorithms used to build the AI system) 2. How is AI transforming the world? 3. What are the risks limitations and risks of using AI?
2. Data Literacy Once you understand how AI works, you will also see that the AI system is only as good as the data in it. So we all need to be more comfortable with analyzing data critically, and using it to make decisions A mathematical background can definitely help here but is not necessary. The core is to have a problem-solving mindset and critical thinking skills.
3. Soft skills Since many extensive computations will be done by AI, soft skills such as empathy and collaboration will be key skills required for the jobs of the future

Key Takeaway: For people who want to get into AI careers, but don’t have any programming skills, you can start building your AI literacy and/or use no code AI/ML tools and get started on your AI journey.

AI is real and is here today. Embrace it

AI is already a part of your daily life and is helping you accomplish more at work and is supporting your daily tasks. You can get instant answers to questions, using voice or text search. It is helping you easily search identify pictures of your loved ones, and is helping you find things personalized to your tastes, among many others. All of these applications are powered by AI.

Now, shift the lens outward and identify how AI can help your users, customers, and the world.

AI can help provide new ways to solve existing problems and reimagine things. Start understanding what AI can do today and start contributing to the future using AI.

Finally, is AI going to replace our jobs?

I often get asked about the concern whether AI is truly replacing jobs. To that, my answer is that we’ve had technology displace jobs many times in the past. If you go back to the Industrial revolution, even as machines increased productivity levels, people feared that machines will replace human labor. However, these machines also created a plethora of new jobs, from a new breed of equipment manufacturers, technicians and maintenance and optimization teams.

This time, too, AI is creating and transforming more jobs than it is displacing. The new jobs will require skills that fall into one of the three categories above. We should shift our focus from fearing AI to embracing AI, building our AI skills, and making this AI knowledge universally accessible and useful.

So go out there, and start getting excited about the potential of using AI.

Thank you for reading! In the remaining two parts in this series, I will talk about what can you do today to identify the right skills to acquire and how to start building them. If you have any questions or topics you’d like me to cover in particular, please let me know in the comments.

Also, I run a Women in AI and ML group called “AI for Her” where we share AI resources and discuss them in a weekly online meetup. If you’d like to be a part of our community, sign up our newsletter and get added to the weekly invite here. You can also join our LinkedIn Group to view AI discussions, resources and job postings.

Note: I’ve used AI and ML interchangeably in this article, but they’re not the same. Here’s a link that talks about the difference