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. 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.

Webinar Wrap-Up: How to Develop a Machine Learning Career

By Stuart CrequeLast updated on Apr 13, 2022154842

Table of Contents

The AI Career Landscape

What is Machine learning?

AI and Machine Learning Explained

The Three Main Stages of AI

What is a Machine Learning Engineer?

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On May 26, 2021, Ronald van Loon shared his advice on developing a machine learning career in a Simplilearn webinar. Ronald, the CEO of Intelligent World, is recognized as one of the foremost thought leaders in data science and digital transformation. He is a member of Simplilearns Advisory Board

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. 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.

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What is Machine learning?

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.

What is a Machine Learning Engineer?

To begin, Ronald defined the role of the machine learning engineer as distinct from other data-related roles like data scientist or AI architect.

First, the machine learning engineer assesses, organizes, and monitors data sets that feed machine learning systems. Because these systems learn from whatever data they are given, they need to properly select and condition that data to support the desired learning. Understanding the available data and what types of learning it can support is a foundational step.

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