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Computer Science, AI and Innovation for the Future

September 14, 2023

Until a couple of years ago, artificial intelligence (AI) was most often used as a plot point in science fiction thrillers. It’s now become part of the cultural zeitgeist and the foundation of our future. Fortunately, Stevens Institute of Technology has been investing our master’s in computer science with AI focus long before this recent rise to prominence.

AI is everywhere. We’ve seen it in movies as a robot janitor left to clean a deserted planet or a malevolent space computer collecting deadly, interstellar organisms. Now, it’s becoming intertwined with our daily lives in ways we’ve never imagined.

We’ve come to know virtual personal assistants, applications that create wild imagery from text prompts and more. But the usage of AI is far more powerful than entertainment purposes. It promises to make our lives and work far more efficient. With efficiency comes the bandwidth to pursue innovation and perfection.

So what is AI exactly? We’ve talked about everything from programs with specified uses to sentient robots plotting against mankind. The answer lies somewhere between those two extremes, and luckily it’s closer to the former than the latter.


From a scientific perspective, artificial intelligence is the pursuit of making a computer mimic the human brain. Natural language processing (NLP) falls into this category. NLP is the intersection of linguistics and computer science that helps computers understand language the same way our brains process it.

You’re probably more familiar with AI programs than you may realize—ChatGPT is an example you’ve certainly heard of. Still in its infancy, its potential is remarkably exciting as it continues to be refined and researched. The fact ChatGPT can already write code is a testament to what AI will truly be capable of one day.

Another aspect of AI is machine learning. The two terms are often (incorrectly) used interchangeably. The confusion makes sense as machine learning is a subset of AI. They are similar and deserve to belong in the same sentence, but they also have important differences.

Machine learning focuses on examining data, gaining insights and implementing improvements based on the results. The process is iterative, allowing data scientists to push the limits of what our technology and equipment can do. Machine learning (and deep learning) are both important components under the AI umbrella.


So what does machine learning look like in real life? It has the latitude to make discoveries with the goal of exceeding what we can hardcode ourselves. Our ability to program every possibility is feasible, but it would be incredibly time consuming.

Instead, machine learning takes in data, we rate the outputs and that guides the computer to improve its analysis. This process of teaching with self-guided improvement means we can reach desired outputs faster, with less effort and time. More impressively, the process will eventually lead to outcomes that are better than what we can hardcode ourselves.

Machine learning has a range of purposes including image recognition, identifying financial trends and aiding in medical care. It can be trained to do mundane tasks as well, like batch processing or data sorting and cleanup. With enough input and patience, it can become immensely powerful—and a key part of creating AI as a whole.


The first step in creating AI is developing the program to process the data and recognize critical patterns. This requires coding skills. Three of the most popular programming languages used for AI are C++, Python and Java.

After that initial step of coding, the program needs to be fed a large amount of data sets. Once analyzed, that’s when trends begin to emerge. This step shapes the AI’s behavior.

Following that, the AI begins to learn. With user input, coding and refinement, the process becomes iterative. The goal is for the software to recognize the patterns and, in time, perform better and faster than the people who created it.

These steps are the (simplified) process of creating AI. But accomplishing that requires a great deal of knowledge. It takes many different components working together to achieve the intended results.

We’ve already discussed one critical component—machine learning. Beyond that, development is a combination of mathematics and learning to mimic our own brains and consciousness. This complexity is why working in the field requires expertise in many different areas.


The math behind AI touches upon several disciplines. An understanding of data science is necessary to collect data, analyze it, discover insights and make the appropriate adjustments.

A background in statistics makes it easier to understand the data and how to code the program to make decisions. Linear algebra and calculus further help with data management and analysis. It also aids in optimization and refining parameters during training.

We’ve also touched on some of the principles and specialties that also play a role. Natural Language Processing allows for computers to understand, interpret and generate human language, improving human/AI interaction. Neural networks and deep learning focus on how to get computers to process information in a manner similar to our own brains.

Deep learning adds extra dimensions to machine learning by incorporating neural networks with multiple layers. By having processes that account for adding complexity allows for the field to grow and react. Another important component is parallel computing. AI has grown incredibly powerful, but it has to work within the confines of its hardware. Developments in parallel computing keep programs running efficiently so it becomes a software problem instead of a technological chokepoint..

Working together, these are the parts of AI development. The necessary understanding goes far beyond what can be offered in a boot camp or certificate course. A master’s in computer science is a natural path for those looking to find their place in this burgeoning field.


A master’s provides a holistic understanding of the many processes that come together to create AI. These topics can’t be rushed and a master’s program gives each of them the time they deserve. A mastery of mathematics and related principles is required for a successful career in AI.

The good news is Stevens offers the classes necessary to enter the field of AI. The coursework covers the full breadth of necessary knowledge to pursue that career. From a mathematical foundational standpoint you’ll dive into classes focused on topics including algorithms, systems programming and database management. To round that out, we also cover applied machine learning and human computer interaction.

We take our teaching responsibilities seriously. Stevens was named one of the Top 20 U.S. Private Schools for Best Career Placement by The Princeton Review (2022). 97% of MSCS graduates in the Class of 2021 accepted job offers within three months of graduating (*Based on data from 63% of spring 2021 full-time program graduates.)
Stevens understands that what happens after you finish school is just as important as your time in the program. When finishing your degree, you’ll have classmates, professors and alumni as part of your network. Former students have gone on to work at Amazon, Meta, IBM, Google and more. We want you to experience that same success.

One of the benefits that sets Stevens apart from other programs is the online aspect of our program. We are flexible so you don’t have to make a choice between higher education and staying at your job. Removing the requirement to be on-campus allows the best and brightest from anywhere in the country to enroll.


We are in a moment where AI is a hot topic, yet still in its infancy. It’s having a moment in the spotlight and proving it is here to stay. As time goes on it is only going to grow in sophistication. This, in turn, creates demand for those who specialize in this profession. Those who understand math, computer science and linguistic principles are the ones that thrive.

A master’s degree is the most reliable route to entering the field. With the opportunity to become proficient in complex subjects, advanced education offers a flexible path to becoming a subject matter expert. As AI grows in complexity, so will the academic demands for anyone looking to be part of this exciting, growing career. And Stevens is ready to help you get there.