The capacity that technology like artificial intelligence and machine learning is expanding year by year. Yet, we’re falling short of talents such as AI experts and AI specialists.
It was in the mid of the 1950s, the Gregorian calendar when it all began
A dozen scientists and mathematics from around the country summoned a meeting at Dartmouth College. Most of them settled down at a nearby Inn called Hanover. Further, they all took a stroll and walked pass the campus reaching the top floor of the math department. The room was already filled with white-shirted men discussing something called ‘a new discipline’ they do not even have a name for. People disagreed on what it was, how to name it or how to do it (Grace Solomonoff, the widow of one of the scientist later recalls).
The discussion on cybernetics to logic theories went on for weeks together creating an environment of exhilaration.
“How to build a machine that could think,” was the topic of discussion conducted in their sylvan hideaway.
Today, the entire world is willing to pour billions into the AI’s quest, whose recent advancements startled great scientists such as Andrew Ng, a Chinese-American computer scientist, and statistician. NG also co-founded and led Google Brain, a deep learning artificial intelligence research team at Google.
The AI Future is now: Deep insights on AI by Andrew Ng
Andrew Ng, the guru of artificial intelligence and one of the tops minds in machine learning is on a journey to train more AI experts prepping for the upcoming AI boom.
According to Ng, AI is non-technical and it is for everyone. He feels that the biggest fear of the AI revolution is the displacement of jobs. To which he believes training a million people to use tools and technologies will propagate faster AI adoption.
The dearth of AI talent like AI specialists, AI engineers, and AI experts are inevitable. While job displacement is a global issue, it is for us to work and come up with a solution, says Andrew. It is not the market that is running out of jobs, it is just because the current skillsets do not match the jobs of today. There is an alarming need for AI skills in the industry today.
Andrew Ng’s guide to building a career in artificial intelligence: –
1.) Acquire knowledge from research papers
One of the important points that Andrew stresses upon in all his lectures, speeches, and videos. Be it a webinar on career building or probably a deep learning class, NG never fails to advise his learners to read a maximum of two research papers on emerging technologies. He says this is by far one of the best methods of gaining in-depth knowledge in any emerging tech.
2.) Understand the emerging tech
For a professional who is looking to launch their career in the AI field, it is mandatory that the candidate first understands the basics of AI and machine learning, deep learning, neural networks, graphical models and other related technologies. Presently, most organization’s focus has shifted toward an ecosystem where techniques such as CNN, RNN, LSTM, and reinforcement learning are used. And since programming languages such as Python, R, and SQL are in-demand, candidates must have a knack for them. The core concept is to continuously stay updated.
3.) Ensure to work in research projects
Earning an internship program helps the candidate build their practical skills in these technologies. Doing so allows the candidate not only gain in-depth knowledge but also demonstrate these skills. Working on a machine learning project and building machine learning models is add-on advantage.
4.) MOOCS and Certification programs
MOOCS and online certification programs hold a high value in the current job market.
However, taking up AI certifications is an efficient way to grab the latest skills in the AI field. AI certification program covers updated skillsets, provides practical knowledge, assignments, and projects to work upon, along with examinations to assess the candidate’s capabilities in the given domain.
AI is projected to create 2.4 million jobs by 2020, and 83% of companies say that AI will become a leading technology that will create newer job roles.
Therefore, upskilling through AI certifications will eventually lead to better job opportunities.
5.) Up for some dirty work?
Getting your hands dirty by downloading the dataset, cleaning, plotting a learning curve, exploring the rights and wrongs, and predicting PCAs are some of the crucial parts that will help you build machine learning models efficiently. Ng says one should not fear to get their hands dirty.
6.) Lifelong learning
The secret of excelling at building machine learning models is not by studying research papers regularly every week. But you must delve deeper, do some research about the latest skills, online programs that can keep you relevant, and reading interesting research papers.
Eventually, you’ll keep getting better in AI. You should ensure you’re in sync with the current AI trends in the industry.
7.) Build a portfolio
You must demonstrate your projects on your portfolio. Potential employers are quite skeptical in hiring candidates that do not possess practical skills or those without hands-on experience in these technologies.
8.) Building a machine learning system
As an AI professional, you should be able to build machine learning systems. With the help of certification programs, one can easily gain skills and knowledge in building a machine learning model from the very start and excel in their AI Career.
While the future is AI, there is a bleak assurance human will be left out, provided they upskill themselves.
If there’s one thing that gives us a pause, it’s that moment when humans will know what’s there on the other side.