Artificial intelligence has entered the mainstream, influencing everything from manufacturing to banking and even education. This cutting-edge technology is especially impactful within the rapidly evolving world of computer science, and the implications are huge for future tech professionals.
Today’s tech-savvy individuals recognize the extraordinary ability of artificial systems to collect and leverage data, which can uncover amazing opportunities and reveal solutions to some of today’s most persistent problems.
Moving forward, AI may handle many repetitive tasks once covered by human employees, but it will also uncover new and more compelling workforce opportunities.
The caveat? To leverage these options, aspiring tech professionals will need to go beyond fundamental technical skills to develop a broader range of competencies. This should include data literacy, analytical abilities, and soft skills such as communication and collaboration.
These are other essentials are covered extensively while seeking a degree in artificial intelligence, which also prepares students for the inevitable use of both humans and AI to carry out critical workplace functions. Keep reading to explore the value of this unique program and to learn how to get a degree in artificial intelligence.
Is Artificial Intelligence a Good Degree? Why Study AI
So, is artificial intelligence a good degree? The short answer is “yes.” Several ambitious students recognize the importance of familiarizing themselves with AI, but some wonder, what degree do you need to work in artificial intelligence? There is no one right approach, although a number of students gravitate toward traditional computer science degrees.
The appeal? Foundational coursework equips students with a solid technical skill set, while concentrations in artificial intelligence encourage students to apply these core skills as they tackle many real-world challenges and scenarios. Below, we’ve highlighted several other reasons to consider pursuing an AI degree online:
High Demand for AI Professionals
While some skeptics worry that AI will displace talented professionals, the reality is that the availability of compelling work will not change. Instead, the nature of that work will evolve. Data-driven professionals will be expected to work alongside AI tools, offering distinctly human competencies, such as creativity and problem-solving, while AI solutions handle the most repetitive and mundane tasks.
As artificial intelligence continues to advance, the demand for tech professionals with cutting-edge skills in data analysis, machine learning, and strategic implementation is rapidly increasing. Many major corporations, start-ups, and even the White House are actively seeking AI experts to help navigate the complexities of an AI-driven world. This trend highlights the growing opportunities for professionals who can combine technical expertise with human insight, enabling them to thrive in a job market that values adaptability and innovative thinking.
Lucrative Career Opportunities
Tech-savvy professionals with in-demand skills can command impressive compensation and enjoy robust career growth. This strong earning potential extends beyond the field of data science to include a variety of data-driven roles in emerging technology domains. Possessing specialized knowledge, skills, and capabilities related to AI and advanced analytical tools—often referred to as “AI capital”—makes professionals more attractive candidates in the job market. Graduates who develop these essential skill sets frequently receive more interview invitations and enjoy enhanced career opportunities, demonstrating how acquiring advanced technical expertise through a master’s program can significantly impact job prospects and professional success.
Diverse Career Paths
AI mastery can fuel a wide range of career opportunities, including positions involving today’s most opportunity-filled sectors and niches. These options align with distinct interests and talents, ranging from manufacturing to telecommunications and even education and hospitality. As experts from Brookings point out, novel applications exist in “finance, national security, health care, criminal justice, transportation, and smart cities.”
Opportunities for Innovation and Impact
AI experts are visionaries, intent on driving change by embracing the enhanced analytical abilities of machine learning solutions. AI is innovative in its own right and promises to fuel further breakthroughs across the many fields highlighted above. As the aforementioned research from Brookings reveals, “AI may well be a revolution in human affairs, and [could] become the single most influential human innovation in history.”
Core Concepts and Skills in an AI Degree Program
AI degree programs blend fundamental computer science skills with targeted concepts related to AI algorithms and their applications. The goal is to make AI systems easier for professionals to understand and navigate while also revealing how these can be applied to help students solve specific problems. Essential concepts include:
Programming Languages and Tools
AI systems rely on a series of algorithms, which, in turn, rely on coding for implementation. Those equipped with a strong understanding of programming languages are better positioned to create and deploy necessary algorithms.
- Core Programming Languages: Success in AI is not possible without a strong grasp of multiple programming languages. The ideal AI degree program will provide a thorough introduction to Java, C++, and Python. The more languages students master, the easier it will be to acquire additional programming languages relevant to data mining, web development, or other applications.
- AI Development Tools: A myriad of tools and frameworks help tech professionals develop and train AI models. These sometimes involve a significant learning curve, however, and are best explored under the guidance of skilled faculty members. Examples worth studying include PyTorch, TensorFlow, and Keras.
Mathematics for AI
AI’s most impactful algorithms are rooted in advanced mathematical concepts. Yes, AI can handle impressive number-crunching feats, but professionals will not be capable of fully leveraging these abilities unless they understand the mathematical theories that underscore these computations. Examples include:
- Linear Algebra: As a foundational computer science discipline, linear algebra centers around vectors and matrices, which are necessary for linear transformations. These concepts are crucial because machine learning data is so frequently represented in matrices. Linear algebra can also help expedite processing and may even influence training for deep neural networks.
- Discrete Mathematics: Used extensively in natural language processing (NLP), discrete mathematics focuses on objects that assume distinct values. This encompasses the study of sets and logical systems, along with graph and number theories. In addition to enhancing NLP, discrete mathematics can play heavily into encryption algorithms and even search strategies.
Machine Learning
As an important subset of AI, machine learning (ML) encourages computing systems to learn from data and to make predictions or even decisions without receiving explicit instructions from humans. Critical strategies include:
- Supervised Learning: Involving the pairing of training examples with output labels, supervised learning helps machine learning models make predictions based on new, previously unseen data.
- Unsupervised Learning: Meant to uncover hidden patterns or relationships within vast quantities of data, unsupervised learning relies on input features that lack corresponding labels.
- Reinforcement Learning: Emphasizing interactions with specific environments, reinforcement learning is a crucial ML paradigm that makes use of trial and error.
Deep Learning and Neural Networks
Deep learning strategies such as deep neural networks allow AI systems to learn from data. These involve hierarchical representations, which help AI experts uncover meaningful patterns. Ideally, the AI professionals of tomorrow will feel comfortable working with these solutions:
- Neural Network Architectures. Feedforward networks are especially essential and involve the unilateral flow of information through hidden layers and without feedback loops. Meanwhile, convolutional neural networks rely on specialized layers for mastering hierarchical representations.
- Training Deep Networks. Many advanced training techniques allow AI professionals to help neural networks recognize key patterns. It is especially important to master backpropagation, which involves iterative adjustments based on output errors. Gradient descent is also iterative but focuses on reducing errors between predictions and outcomes.
Natural Language Processing (NLP)
Natural language processing is inherently complex given the ambiguity of human language, but a strong overview should equip professionals to make the most of this exciting technology. To that end, AI degree programs should cover the following:
- Fundamentals of NLP. Foundational concepts such as tokenization allow text to be broken down into simple units known as tokens. Meanwhile, stemming removes suffixes to return complex words to their root forms. Similarly, lemmatization emphasizes the dictionary forms of words, typically while removing inflections.
- Advanced NLP. Advanced tasks involving Named Entity Recognition (NER) classify proper nouns, relying on categories such as people or locations. This can be valuable when extracting information from sizable datasets. Advanced NLP also encompasses sentiment analysis (sometimes referred to as opinion mining), which aims to determine opinions or feelings conveyed in text. This can be valuable for analyzing reviews, social media comments, and other forms of customer feedback.
Robotics and Autonomous Systems
Robotic systems increasingly incorporate AI functionality, which enhances efficiency and accuracy in autonomous solutions. AI professionals are expected to understand these integrations so that they can leverage autonomous, AI-driven solutions in real-world settings. Key concepts include:
- Kinematics: Revealing how robots use their joints and grippers, kinematics must be understood to optimize control and to enhance motion planning.
- Dynamics: Another critical branch of mechanics, dynamics reveal how various forces impact robotic motions and may draw heavily on Newton’s laws of motion.
- Control Systems: Capable of manipulating robotic system operations, control systems involve commands that determine how robots behave.
Computer Vision
Allowing machines to interpret visual information, computer vision leverages machine learning to automatically reveal patterns based on visual data. This involves the manipulation of digital images through image processing, in which techniques such as sharpening or noise reduction prepare images for high-level analysis. From there, convolutional neural networks and other ML strategies can aid in image detection.
Ethics in AI
In addition to understanding the many techniques and concepts highlighted above, tomorrow’s AI professionals must recognize the ethical implications of AI. As emerging strategies are discussed, AI degree programs must also delve into issues related to bias, and the potentially unfair outcomes that could occur if existing biases are amplified via AI. Furthermore, concerns related to privacy or potential data breaches must be discussed in detail.
Practical Experience and Project Work
In addition to exploring the theoretical concepts that underscore today’s most exciting tech opportunities, online AI degree programs should also promote real-world applications. These are often achieved through capstone projects, which allow students to synthesize a range of impactful concepts while solving realistic and relevant problems.
Also important are internships and field experiences, which take concepts from AI degree programs online into the real world. Internships can also prove powerful from a networking perspective, allowing promising AI professionals to build strong relationships with other tech-driven individuals. These experiences also look wonderful on a resume and can help distinguish the most promising professionals.
What Can You Do with an Artificial Intelligence Degree?
So, what can you do with an artificial intelligence degree, exactly? AI now plays a prominent role in nearly every industry imaginable. Moving forward, competence with AI systems will give professionals at all levels, and in all sectors, a definitive edge when seeking top jobs. However, with many positions, AI mastery is not merely appreciated — it’s essential. Upon earning a degree in AI, these options should be well within reach:
- AI Research Scientist: These innovative professionals are committed to pushing the boundaries in AI and ML, often by developing new models and algorithms.
- Machine Learning Engineer. Meant to bridge the gap between software engineering and data science, ML engineers develop and deploy ML models as they relate to specific business challenges or goals.
- Data Scientist. Working with sizable data sets, these professionals rely on advanced statistical techniques to help them extract actionable insights that can be applied to solve complex business problems.
- Robotics Engineer. Tasked with developing autonomous systems and integrating AI, robotics engineers draw heavily on principles from computer science and mechanical engineering to develop powerful robotic systems.
- NLP Engineer. Helping computer systems understand and interpret human language, NLP engineers use high-level concepts from machine learning and linguistics to process and analyze text-based data.
Start Your Online AI Degree at Baker College
Are you excited about emerging technologies and ready to uncover solutions to today’s most significant challenges? With an AI-centric skill set and an AI degree online, you can drive exciting outcomes across numerous fields.Take the next step and pursue a degree in AI with Baker College. Reach out to learn more about our Artificial Intelligence concentration and its role within our Bachelor of Science in Computer Science program.