Artificial Intelligence (AI) & Machine Learning (ML)

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.

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.

What is Artificial Intelligence (AI)?

Artificial intelligence is the science behind making computers behave like humans, and 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.

Searching, alerting and responding are all examples of tasks that AI can perform like humans. AI simply does it faster and makes decisions based on access to more information. As AI becomes increasingly intertwined with our lives, computer scientists will be needed to identify problems that can be solved with AI applications.

What is Machine Learning (ML)?

Machine learning is the science that will take artificial intelligence to a level beyond what humans can do. 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.

As AI’s role expands, ML will become increasingly necessary to create systems that are “smart” enough to manage the complexities of an increasingly connected world.

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

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

And those numbers are likely to grow. At the same time, finding employees with training in AI will be increasingly difficult. In one survey, 54% 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.

If you’d like to learn more about career paths in AI and ML, you can visit our Artificial Intelligence and Machine Learning Careers page.

What Do You Learn in Artificial Intelligence and Machine Learning?

As artificial intelligence and machine learning become more advanced and commonplace, computer scientists will need technical and scientific skills to design and advance these systems. Also, as AI and ML are used in more fields, understanding those fields will also be part of tailoring AI to the needs of the future.

Studying AI requires a foundation in computer science and/or software engineering. For those looking to specialize in machine learning, knowledge of big data, physics, statistics and applied mathematics may also be valuable. In addition:

  • Deep learning may require additional training in neuroscience and biology;
  • Robotics may require training in electrical and mechanical engineering;
  • NLP may require an understanding of linguistics and semantics;
  • Fuzzy logic may require additional knowledge of probability, non-binary logic and other types of mathematics.

More than that, as AI is used to solve increasingly complex problems, and ML is used to create the algorithms to solve those problems, students will need to understand the ethical challenges of AI to ensure that its power is not used irresponsibly.

Artificial Intelligence and Machine Learning Graduate Programs at Drexel’s College of Computing & Informatics

As more and more industries and fields of study adopt artificial intelligence and use machine learning to solve problems, having advanced training in AI and ML can increase your value as an employee and boost your career prospects. That’s why we created our Artificial Intelligence and Machine Learning Graduate Programs, to provide working professionals with the skills and experience they need to make waves in AI and ML.

Because every student’s needs are different, we offer two different options for studying AI and ML including a Master of Science and a Graduate Certificate. Choose to study in-person or online. This flexibility lets you customize your degree program so you can focus on the areas of study that have the most bearing on your career path and industry.

In the Drexel Master’s in Artificial Intelligence and Machine Leaning program, you’ll learn from faculty who are actively researching and partnering with industry in the AI field.

Ready to take your career to the next level? Visit the Master’s in Artificial Intelligence and Machine Learning program page.

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