What is AI?
Table of Contents
By: Jacob Given, Edited by: Joey Morris
Published: April 21, 2025
As the AI field continues to grow, so does the demand for AI professionals. Individuals working in AI typically have a background in data or computer science. Continue reading to learn about artificial intelligence, its applications, and common pathways into the field.
Artificial intelligence explained
What is AI? Artificial intelligence (AI) refers to the development of computer systems that use complex algorithms and data science to perform tasks that typically require human intelligence. AI systems can understand and respond to human language, identify trends in large data sets, and make decisions in complex and unpredictable situations, enabling them to glean insights and adapt based on historical data.
Individuals and organizations can leverage different forms of AI for various use cases. NASA, for example, utilizes methods like natural language processing, neural networks, and deep learning to analyze large data sets and make predictions.
How does AI work?
At the most general level, AI makes decisions and predictions based on data patterns.
Using neural network architecture that mimics the human brain, AI models undergo a process called "training," which adjusts the model's internal parameters to improve its predictive accuracy.
During training, a model attempts to solve problems or make predictions based on patterns in a data set. It then receives feedback on which predictions or answers were accurate.
Based on this feedback, the model adjusts its probabilistic weights to become more accurate. Typically, the more data an AI has been trained on, the more accurate and effective its outputs.
A good AI model adjusts only to relevant patterns in its training data, enabling it to adapt successfully to new situations.
Components of AI
Machine learning
Machine learning utilizes algorithms for the predictive analysis of large data sets. Algorithms are comprised of a structured set of steps that take one or more inputs and determine an output.
Technologies like social media leverage algorithm-based machine learning to analyze user behavior and deliver targeted advertisements for their partners. Other applications of machine learning include fraud detection, spam filtering, and facial recognition.
Neural networks
Neural networks mimic the architecture of the human brain. With neural networks, algorithms can incorporate layers of interconnected, artificial neurons to process information.
Neural networks undergo a process called "training." During training, the model makes predictions based on inputs and receives corrective feedback. Based on the feedback, the model adjusts the probabilistic weights of the neural network to improve its predictive power.
Natural language processing
Natural language processing refers to the ability of AI models to interpret and act on ordinary language rather than structured input like code. Large language models (LLMs), for example, utilize natural language processing.
LLMs are comprised of neural networks trained on vast data sets. They interpret natural language inputs and, through probabilistic processes, produce the output most likely to satisfy the user. Because large language models use probability to determine output, they may make mistakes known as "hallucinations."
Deep learning
Neural networks can involve as few as one layer of neurons, but the predictive power of a neural network increases with more layers of neurons.
Deep learning is a type of machine learning in which multiple layers of neural networks can learn autonomously from unstructured data. Deep learning powers technology like virtual assistants, self-driving cars, and AI-assisted X-ray assessment.
What are some common applications of AI?
- Medical imaging: Advanced models may be able to make diagnoses faster, cheaper, and more accurate than manual methods.
- Fraud detection: AI can detect anomalous transactions, flagging suspicious activity and protecting consumers from crimes like identity theft.
- Inventory optimization: Developers can leverage AI to forecast demand and suggest replenishment based on factors like seasonality, market trends, and historical data.
- Chatbot support: Using LLMs, companies can provide chatbots to help customers learn information relevant to their specific needs.
- Content recommendations: Algorithms on streaming services and social media platforms analyze user behavior to provide more satisfying recommendations.
- Facial recognition: Developers can leverage machine learning to identify individuals based on facial features.
What steps can you take to learn AI?
Aspiring AI professionals have several paths into the field, including traditional degrees, courses, certificates, and executive education programs in AI. Monali Mirel Chuatico is a data engineer at Mission Lane who teaches data analytics tools and supports individuals on their professional development journeys. When asked about the essential skills for a career in AI, Chuatico emphasized the importance of foundational expertise, stating that "data analysis, data engineering, or data science skills, along with at least a bachelor's degree in computer science," are critical. She also noted, "There are some specializations within computer science, such as AI and machine learning. Some may also want to pursue a master's in data science or machine learning."
Depending on your time, budget, and professional goals, you may choose to complete a shorter program, like a single course or certificate. If you're in the market for a bachelor's degree, consider computer science. If you've already completed an undergraduate program, you could pursue a master's in computer science.
Chuatico recommends that students focus on math skills while studying. Common areas of mathematics that are used in the field of AI include statistics, probability, calculus, and linear algebra. Additionally, consider learning programming skills in languages like Python.
AI is a rapidly evolving field, so it's crucial to stay on top of new trends and technical breakthroughs. Consider subscribing to professional newsletters and attending conferences and workshops so you don't miss out on the latest developments.
What are the ethical concerns of AI?
Numerous ethical concerns surround the development and deployment of AI, including:
- Copyright issues: AI companies use vast amounts of data to train their models, typically without transparency or permission.
- Surveillance and privacy: Technology like facial recognition in public spaces presents serious concerns about surveillance and privacy.
- Intellectual impact: Outsourcing intellectual labor to AI can degrade an individual's critical thinking skills over time.
- Economic impact: Advancements in AI pose a real risk of replacing certain roles and positions, which may increase strain on the global economy.
- Environmental impact: AI requires a massive amount of energy and water, risking significant environmental harm.
While there are valid concerns and anxieties surrounding AI, Chuatico encourages a different perspective. She points out that "we should think of AI more as a tool to help automate certain tasks and make predictions and decisions." Importantly, she reminds us that humans are still essential in the process, as we need to train AI models and provide the data necessary for them to function effectively.