How to Start A Career In AI And Machine Learning
Sep 6 · 6 min read
Artificial Intelligence (AI) made headlines recently when people started reporting that Alexa was laughing unexpectedly. Those news reports led to the usual jokes about computers taking over the world, but there’s nothing funny about considering AI as a career field. Just the fact that nine out of ten Americans use AI services in one form or another every day proves that this is a viable career option.
During a Simplilearn fireside chat, Anand Narayanan, Chief Product Officer at Simplilearn, and Ronald Van Loon, a Big Data expert and Simplilearn advisory board member, discussed the future of AI and machine learning as career fields. They delved into specific types of jobs available and the training required to get them. You can watch the recording by clicking on the link below or read on to see a wrap-up of some of the major points covered.
The AI Career Landscape
AI is getting even more traction lately because of recent innovations that have made headlines, Alexa’s unexpected laughing notwithstanding. But AI has been a sound career choice for a while now because of the growing adoption of the technology across industries and the need for trained professionals to do the jobs created by this growth. Pundits predict that AI will create close to 2.3 million jobs by 2020. However, it is also forecasted that this technology will wipe out over 1.7 million jobs, resulting in about half a million new jobs worldwide. Moreover, AI offers many unique and viable career opportunities. AI is used in almost every industry, from entertainment to transportation, yet we have a massive need for qualified, skilled professionals.
AI and Machine Learning Explained
If you’re new to the field, you might be wondering, just what is Artificial Intelligence then? AI is how we make intelligent machines. It’s software that learns similar to how humans learn, mimicking human learning so it can take over some of our jobs for us and do other jobs better and faster than we humans ever could. Machine learning is a subset of AI, so sometimes when we’re describing AI, we’re describing machine learning, which is the process by which AI learns.
With machine learning, algorithms use a set of training data to enable computers to learn to do something they are not programmed to do. Machine learning provides us with technology to augment our human capabilities.
AI has widespread benefits. Both people and companies benefit from AI. Consumers use AI daily to find their destinations using navigation and ride-sharing apps, as smart home devices or personal assistants, or for streaming services. Businesses can use AI to assess risk and define the opportunity, cut costs, and boost research and innovation.
The Three Main Stages of AI
AI is rapidly evolving, which is one reason why a career in AI offers so much potential. As technology evolves, learning improves. Van Loon described the three stages of AI and machine learning development as follow:
Stage one is machine learning - Machine learning consists of intelligent systems using algorithms to learn from experience.
Stage two is machine intelligence - Which is where our current AI technology resides now. In this stage, machines learn from experience based on false algorithms. It is a more evolved form of machine learning, with improved cognitive abilities.
Stage three is machine consciousness - This is when systems can do self-learning from experience without any external data. Siri is an example of machine consciousness.
Subsets of Machine Learning
In addition to the development of machine learning that leads to new capabilities, we have subsets within the domain of machine learning, each of which offers a potential area of specialization for those interested in a career in AI.
Neural networks are integral for teaching computers to think and learn by classifying information, similar to how we as humans learn. With neural networks, the software can learn to recognize images, for example. Machines can also make predictions and decisions with a high level of accuracy based on data inputs.
Natural language processing(NLP) gives machines the ability to understand human language. As this develops, machines will learn to respond in a way a human audience can understand. In the future, this will dramatically change how we interface with all computers.
Deep learning is at the cutting-edge of intelligent automation. It focuses on machine learning tools and deploying them to solve problems by making decisions. With deep learning, data is processed through neural networks, getting closer to how we think as humans. Deep learning can be applied to images, text, and speech to draw conclusions that mimic human decision making.
Industries Currently Using AI
AI is being used in many types of applications across many different industries.
The self-driving car is probably the best-known use of AI. Predictive maintenance is another part of AI, forecasting when maintenance will be needed so it can be done proactively, leading to tremendous cost savings. AI is used in transportation, such as for train scheduling and to help Uber drivers navigate routes. Smart cities use AI to be more energy-efficient, reduce crime, and improve safety. The many applications of AI today are countless, and growing in number all the time.
Many big brands are already using AI, including IBM, Amazon, Microsoft, and Accenture. All apply machine learning on a large scale and drive innovation. In the future, more and more industries will be using AI and machine learning, driving tremendous growth in the job market. However, A data scientist pointed out that you don’t have to work for a larger company to work in AI or machine learning. All types of industries are moving towards this technology, including transportation, manufacturing, energy, farming, and finance.
How to Get Started in AI
If you’re intrigued by this career field and wondering how to get started, The learning paths for three different types of professionals; those new to the field, programmers, and those already working in data science. He also points out that various industries require different skill sets, but all working in AI should have excellent communication skills before addressing the math and computing skills needed.
For those new to the field, I suggest starting with mathematics and taking all kinds of courses in machine learning. Besides, someone wanting to move into AI should have strong computer skills as well as programming skills like C++ and an understanding of the algorithms. You should also supplement that education with general business knowledge. Most importantly, make sure any training you get is hands-on.
If you’re already a programmer and you’d like to move into AI, you can go straight into the algorithms and start coding.
For a data analyst or scientist getting more into AI, you must gain programming skills. To cross that bridge from data scientist to machine learning, you should know how to prepare data, as well as have good communication skills and business knowledge, and be proficient at model building and visualization. It takes many team members to make AI work, allowing for specializing in any number of areas. I suggest a data scientist should start by figuring out what it is you would like to do, and then focusing on that for your machine learning career.
No matter where you’re starting from, plan on continuing your education throughout your career. As Van Loon says, AI never stops learning, so you can’t stop learning either.
Specific Jobs in AI
Although we talk about AI and machine learning as broad categories, the jobs available are more accurate. They Include:
Machine Learning Researchers