What is Artificial Intelligence and Machine Learning?
From Asimov’s robots to Schwarzenegger’s Terminator, the idea of machines that can think and act like human beings has captured our imagination for over a century. Since the start of the 21st century, artificial intelligence and machine learning have made enormous advancements and will be a significant part of our future. That makes both disciplines exciting career paths for those with the right training in computer science. Because these terms are often used interchangeably, it’s helpful to have definitions for each.
Artificial intelligence (AI) is a branch of computer science focused on making computers behave in human ways, such as rational thinking, perception and action. AI already exists in many different forms, from robots that perform repetitive tasks in factories, to computers that play chess, to the digital assistant on your smartphone. AI initiatives allow machines and software programs to use pre-determined algorithms to solve problems, learn, plan, identify patterns and react accordingly.
Many of us carry advanced AI technology in our pockets. Digital assistants, like Siri and Alexa, use natural language programming (NLP) to listen to our requests and follow instructions without our needing to know any type of computer language.
We also interact with AI in less obvious ways. The car you drive was built by robots that used AI to perform precise operations. That same car may have AI that optimizes your gas mileage or hits the brakes to prevent collisions. Chances are, you’re reading this webpage because AI directed you here when you used a search engine.
Machine Learning (ML) is a branch of artificial intelligence and one of the ways AI can be achieved. Where AI initiatives make it possible for a machine to mimic human behavior based on what it has been taught, ML models help machines to teach themselves. Instead of being pre-programmed with specific algorithms, computer scientists use ML to give machines the models necessary to absorb and process vast quantities of data, then use that data to create its own algorithms and draw its own conclusions.
ML is used to create AI systems that can learn from data rather than through programming. This will allow machines to adapt to new challenges over time. For example:
- Businesses use ML to track extensive logistical networks to identify problems and increase efficiency.
- Scientists use ML to sequence genetic information and test how different drug combinations might affect human beings to create targeted cures in less time.
- Apps like Uber use ML to optimize its delivery drivers and ML will be used to help driverless vehicles navigate streets and avoid risk.
Artificial Intelligence and Machine Learning Fields
While machine learning is one of the largest subfields in artificial intelligence, it’s not the only one. The following are just a few of the other AI fields that will expand in the future.
- Deep Learning – Despite all of our advances, AI cannot match the human mind’s power or complexity. At least not yet. Deep learning focuses on solving problems by creating neural networks, machines that use our understanding of the human brain and nervous system to mimic the complexity of human thought processes.
- Robotics – The most visible form of AI may be machines designed to do work that may be too precise, repetitive or dangerous for human beings. Manufacturers worldwide have used robots on their assembly lines. But smart robots are also used to explore space, perform surgeries and clean our floors. In the near future, we’re likely to see robots on the battlefield, preparing food, delivering packages and more.
- Expert Systems – Because AI can draw on larger quantities of information and analyze it with greater precision, some AI systems are designed to mimic a specific type of expertise. Expert systems use machine learning to accurately diagnose patients and recommend treatments, play chess, schedule maintenance for military vehicles, process loan applications and manage investments by predicting stock market trends.
- Fuzzy Logic – Human beings can make decisions even if we’re presented with incomplete or uncertain information. Fuzzy logic systems draw from computer science, logic and mathematics to create AI systems capable of mimicking this behavior to make the complex decisions that the human mind does automatically.
- Natural Language Processing (NLP) – Every day, we interact with AI systems capable of reading, hearing and responding to human speech. Whether it’s Siri, Alexa, customer service chatbots or the algorithms that keep spam out of our inbox, NLP is a growing part of how we interact with technology.
Artificial Intelligence and Machine Learning Industry Trends
The US Bureau of Labor Statistics classifies Artificial Intelligence and Machine Learning under the broad category of Computer and Information Technology, and this occupation category is predicted to grow 13% over the next 10 years, 3 times as fast as the growth rate for all other occupations. Simplilearn notes that Artificial Intelligence and Machine Learning is projected to grow from $7.3B in 2020 to $30.6B in 2024, and LinkedIn named Machine Learning Engineer as one of the top 25 jobs on the rise in 2022.
Simply put, career opportunities in AI and machine learning are booming.
As for compensation, according to PayScale, the average salary for individuals who hold a Master’s in Artificial Intelligence and Machine Learning is $102,000 per year, while the average salary for individuals who hold a Bachelor's in Computer Science is $89,953 per year.
As businesses and organizations of every size seek a competitive edge, more and more are turning to artificial intelligence and machine learning. Consider the following:
- 54% of businesses have reported a boost in productivity after having implemented AI
- 62% of consumers are willing to submit data to AI to have better experiences with businesses
- 44% of organizations have reported cost savings as a result of AI implementation
Analytics Insight estimates that the AI industry, led by machine learning, is expected to grow from $18.8 billion in 2019 to $80.3 billion in 2023, with North America leading the worldwide market.
At the same time, finding employees with training in AI will be increasingly difficult. In one survey, 54 percent of respondents view AI skill shortages as the biggest challenge facing their industry.
This combination of industry growth and scarcity of professionals with AI and ML training makes it a career path offering excellent compensation and stability. Even if you’re already working in technology, studying AI and ML can improve your career prospects and help you advance your career. Beyond compensation, AI is expected to revolutionize many aspects of our world. Computer scientists with the right skills will be at the forefront of that revolution.
The career path for machine learning requires technical skills such as math, algorithms, data analysis, knowledge of programming languages and computer science, as well as non-technical skills such as communication and critical thinking. With a curriculum centered around the field’s fundamental mathematics and concepts, Drexel CCI's Master's in Artificial Intelligence and Machine Learning program provides students with the skills they need to thrive in a wide range of artificial intelligence career paths. For individuals looking for a career change to AI and Machine Learning without a related background, CCI recommends taking our AI & ML Graduate Certificate program to jumpstart your career.
What Career Can You Have with an MS in Artificial Intelligence & Machine Learning Degree?
Students who graduate from Drexel’s MS in Artificial Intelligence & Machine Learning program go on to pursue a diverse and rewarding range of careers in the AI & ML field, including:
ARTIFICIAL INTELLIGENCE SPECIALIST/DEVELOPER
Artificial intelligence specialists or developers utilize computer science and software engineering concepts to design and deploy autonomous software and systems. The average annual pay for an artificial intelligence specialist is $114,000, according to Glassdoor.
MACHINE LEARNING ENGINEER
Also known as deep learning engineers, machine learning engineers specialize in designing and programming machines and software to perform autonomous tasks to benefit a product or service. According to Indeed,the average base salary for a machine learning engineer is $151,373 in 2021.
DATA SCIENCE SPECIALIST
A data science specialist with a Master’s in Artificial Intelligence and Machine Learning designs and deploys small- to large-scale infrastructure and data science platforms to help solve data & prediction problems in a variety of industries. A data science specialist can expect to earn approximately $113,300 per year, according to Glassdoor.
MACHINE LEARNING RESEARCHER
Machine learning researchers design, analyze and test new models to help solve research problems and improve business applications and operations. According to Glassdoor, the base salary for a machine learning researcher averages at around $114,120 per year.
MACHINE LEARNING SPECIALIST
Machine learning specialists help to solve business challenges by developing and applying algorithms and devices which can learn from data and adapt to help businesses make better decisions and predictions. According to ZipRecruiter, the average annual pay for a machine learning specialist is approximately $111,295 per year.
Why Choose Drexel CCI’s Graduate Program for a Career in Artificial Intelligence & Machine Learning?
Through an interdisciplinary, hands-on curriculum with real-world datasets, state-of-the-art equipment, and cutting-edge research technology, the Drexel Master’s Degree in Artificial Intelligence and Machine Learning challenges students to respond to the growing demand for intelligent software and devices in one of today’s fastest-growing industries.
With a world-class faculty and market-responsive curriculum, Drexel CCI's MS in Artificial Intelligence and Machine Learning serves as the foundation for a successful AI ML career in a variety of artificial intelligence career paths. Our graduate programs are rooted in Drexel University's commitment to experiential learning, which provides students with hands-on, collaborative projects informed by industry and societal challenges.
AI and Machine Learning graduate students also benefit from networking events through CCI's Corporate Partners Program, as well as career counseling and job search services through Drexel's Steinbright Career Development Center.