AI vs. Algorithm: What’s the Difference?

Artificial intelligence refers to a subset of computer science that focuses on enabling machines to make decisions and “think,” while algorithms are the “instructions” that make AI possible. AI technology uses algorithms to interact with its training materials and understand what outcome it should work toward. The algorithms are a set of instructions that AI uses and also have countless applications in people’s lives. 

Although you might use them together, the two pieces of technology are different. That being said, artificial intelligence and algorithms are both at the center of some of today’s exciting advances in computing, like autonomous vehicles, virtual assistants, large language models like ChatGPT, and more. Learn more about AI and algorithms, including how they work together to power technology like natural language processing and predictive modeling. 

AI vs. algorithm

An algorithm is a set of simple or complex instructions designed to perform automatically, while AI relies on multiple algorithms to function. Essentially, an algorithm is a component of the artificial intelligence structure. Explore a more detailed breakdown of these two technological concepts.

What is artificial intelligence?

Artificial intelligence (AI), is the technology that allows computers and robots to use machine learning and neural networks to make decisions without human instruction. AI often mimics how humans think by making decisions based on past experience and the information available, which can include sensors for computer “sight,” “hearing,” and other senses. 

AI programs can range from very simple to very complex. In some forms of artificial intelligence, computer scientists use algorithms and data sets to train the computer and instill in it a set of instructions and a base of knowledge. For businesses and professional use, AI offers the capability to understand a significant quantity of data and find patterns in a much shorter time than it would take a human to analyze the same data. 

Applications of AI

Artificial intelligence has practical and fun uses in many different industries and applications. AI helps power technology like: 

• Natural language processing (NLP): NLP allows users and computers to interact using language that feels more like human speech. 

• Computer vision: Computer vision uses sensors and cameras to allow a computer to “see” what objects are in its environment. 

• Predictive modeling: AI can parse a vast amount of data to look for patterns and predict how specific outcomes might play out. 

• Language translation: Artificial intelligence can quickly translate vast amounts of text from one natural language to another. 

In turn, these technologies empower a wide variety of use cases for artificial intelligence in your day-to-day lives and in business, health care, banking, manufacturing, and other industries. Explore a few examples of AI capabilities: 

• Manufacturing: In an industrial factory, artificial intelligence can monitor equipment and alert technicians when machines need maintenance before a problem stops production. AI can also help managers monitor patterns regarding HVAC energy consumption, which can help determine ideal temperature levels and how to save energy.

• Health care: Health care professionals can use artificial intelligence to understand patient records better and get insight into their health concerns faster. Professionals in this industry can also use AI to automate transcripts of remote conferences or in-person exams for more accurate records. 

• Fraud prevention: AI can analyze spending patterns and flag potentially fraudulent transactions. Biometric capabilities also make securing accounts easier and verify that users can access their accounts. 

• Customer service: Companies can use artificial intelligence to offer enhanced or personalized customer service through chatbots or virtual assistants who provide support around the clock whenever customers need it. 

Advantages and challenges of AI

Artificial intelligence offers essential benefits, such as reducing the margin of human error in tasks and calculations. AI is not prone to the same range of errors as humans. 

An AI chatbot can work around the clock without the constraints of an eight-hour work shift, providing continuous support that a human customer service representative or data analyst might not be able to offer. Additionally, AI excels at handling repetitive or tedious tasks with unwavering consistency and without experiencing boredom. Considering these advantages as a whole, you can see that AI may offer a cost reduction that could be an attractive benefit in and of itself. 

At the same time, AI represents challenges as well. The qualities that separate AI from humans can also pose a few drawbacks. Artificial intelligence lacks the depth of creativity and artistic understanding unique to your intelligence as a human. Additionally, although it may save costs down the line, implementing AI tech can be expensive. AI can also introduce ethical issues, such as data privacy concerns, bias present in data sets integrated into AI predictions, or copyright issues for using works created by a human as training materials for the AI.

Why are algorithms important in AI?

Think of algorithms as the building blocks that help create AI applications. AI algorithms factor in all the training data that developers and programs use to help computers and connected devices learn, complete tasks, and even sometimes grow independently of human intervention.

Read more: What Are AI Algorithms?

What is an algorithm?

Algorithms are fundamental to all aspects of computing, providing clear sets of operations for various tasks, from data sorting to complex calculations. Although algorithms are an essential component of artificial intelligence, an algorithm can be a very simple set of instructions, such as a recipe, or a very complex set of instructions for a neural network. 

When it comes to artificial intelligence, algorithms can be much more complex. AI algorithms work with training materials, the vast data sets representing the information the AI “knows.” The algorithm instructs the AI on how to interact with the training material and the desired outcome. 

Examples of algorithms

Whether you know it or not, your brain executes algorithms all day. When you drive your car up to an intersection with a traffic light, you receive a visual input in the form of a red, yellow, or green light. You determine the correct action to take (stopping, preparing to stop, or continuing through) based on what input you receive. You use this algorithm to make safe decisions when driving across intersections. Following a recipe, following the steps you take to get ready to leave your home every day, following instructions on the back of a bottle of shampoo.

Applications of algorithms

For artificial intelligence, the three main types of algorithms are reinforcement learning, supervised learning, and unsupervised learning, which enable processes and applications such as: 

• Data analysis: Artificial intelligence algorithms can process extensive amounts of data to look for patterns, make predictions, and analyze data to help you make better decisions. 

• Search engines: Search engines use algorithms to rank the websites that provide the best answers to user queries. 

• Voice-powered digital assistants: Using natural language processing, voice-powered digital assistants like Alexa or Siri can respond with search algorithms to a voice prompt. 

Advantages and challenges of algorithms

The most significant advantage of algorithms is that they already represent the core of society’s current systems. That allows you to examine their benefit and how they improve lives directly. Additionally, as algorithms continue to advance and interpret vast amounts of data, the knowledge gleaned will lead to scientific discoveries.

AI algorithms offer a broad range of functions, with various types existing, each with unique applications. For example, supervised learning algorithms rely on labeled data for training, learning, and continuing to grow. In contrast, unsupervised AI algorithms use unlabeled data to gain insights about relationships between data points. 

However, algorithms are not without risks and challenges. They suffer from the same ethical concerns as artificial intelligence, such as building unconscious biases into algorithms that inadvertently perpetuate injustice. By prioritizing human needs and ethical considerations, we can guide algorithm development towards a more just and inclusive future.

Getting started in AI and algorithms on Coursera

Although AI and algorithms operate in the same sphere, they have many distinctions. Still, you can use AI and algorithms for applications in various industries. To learn more, you can explore foundational courses that delve into the principles of AI and algorithms and convey the skills necessary to navigate and innovate in this dynamic field.

Consider AI Foundations for Everyone Specialization offered by IBM on Coursera, where you can uncover the core concepts of AI and discover if a career in artificial intelligence is right for you. If you’d prefer to dive deeper into algorithms, Algorithms, Part I from Princeton University could be an excellent match.