Artificial intelligence uses in IT

Cars that drive and park themselves, home temperature and lighting systems that can be adjusted by voice command and cameras that can identify faces– this sort of technology that peppers our everyday lives was once relegated to sci-fi novels and films. These features and many more that you interact with on a daily basis are powered by artificial intelligence, commonly referred to in brief as AI. Predating the futuristic show “The Jetsons,” the term “artificial intelligence” was coined at Dartmouth College in 1956 by Marvin Minsky and John McCarthy, who are known as the founders of the field. They defined artificial intelligence as the ability of machines to perform certain tasks, which need the intelligence showcased by humans and animals.¹

Today, the prevalence of artificial intelligence in IT is practically boundless. From machine learning to natural language processing and deep learning, AI touches all of our lives in obvious and unobvious ways. AI is an exciting field of study to break into and because of its wide application, there are numerous career opportunities to use AI in IT professionally.

Machine learning is a segment of artificial intelligence in which algorithms are set up to use historical data as input to predict new output values. With machine learning, software applications are programmed to become more accurate over time at predicting outcomes. There are several different subcategories of machine learning like supervised vs unsupervised learning, reinforcement learning and deep learning,² which you can learn about in a graduate-level machine learning course.

Examples of machine learning:³

• Image identification and sorting: Google and Apple both have features in their photo storage applications to identify and group photos of particular people from reading visual information. The review website Yelp also uses machine learning to process the millions of photos uploaded by users to group them into appropriate categories including menus, food, and interior photos.
• Consumer insights and recommendations: Consumer-facing companies from all sorts of industries use machine learning in their apps and websites to help make recommendations for products you might like based on browsing and purchasing history (think Netflix and Spotify, as well as goods retailers). Some email marketing platforms are also able to suggest or automate follow-up emails when a customer has left a website without purchasing an item. Duolingo even uses machine learning to collect data on your responses to gauge your comprehension and schedule or suggest refresher courses.

Another common use of artificial intelligence in IT is natural language processing. This branch of computing programs computers to understand text or voice data and respond or perform a function in return. Natural language processing is built from the Python programming language which includes an open source collection of resources known as the Natural Language Toolkit or NLTK. The NLTK includes text processing libraries for classification, tokenization, tagging, parsing, semantic reasoning and more.⁴

Examples of natural language processing⁴:

• Chatbots and virtual assistants: Siri, Cortana, Alexa and Watson are the robot representatives of some of the world’s top tech brands and are all built on artificial intelligence’s capability of natural language processing. They use speech recognition to take in commands and natural language generation to be able to respond appropriately. Chatbots, which you most likely encounter on retail websites or when first contacting the help desk of a website, are able to process responses and recognize patterns to respond with helpful information.
• Social media analysis: It’s not always a team of social media consultants who are examining your tweets about certain brands or events. Bots can be programmed to sweep social media posts and analyze the types of language used in them to gauge public perception to different campaigns, designs or events in popular culture. Similarly, some companies are beginning to use these types of algorithms to scan inboxes and prevent spam from getting through based on combinations of key grammatical errors, threatening or urgent language and financial requests.
• Machine translation: Google Translate is one of the most widely used natural language processing devices in use today. While there are still advancements to be made with this technology in regards to recognizing the difference between tones, metaphors, and so on from one language to the next, it’s still incredibly useful.

The artificial intelligence market is expected to grow to more than $300 billion by 2026⁵ and 76% of enterprises report prioritizing AI and machine learning over other IT initiatives in 2021.⁶ As applications of AI in IT continue to expand it’s a safe bet that you will encounter it as you pursue a professional career based on computing even if you’re not working directly with AI technology. For example, the number of data scientists jumped 76% between 2020 and 2021, resulting in 29% of enterprises interviewed having over 100 data scientists working on initiatives today.⁶ These individuals will see their work come into play as they collaborate with programmers to understand and input data into algorithms.

Machine learning engineer
Average salary $130,632⁷

Software developer
Average salary $110,140⁸

Computer programmer
Average salary $93,000⁹

Data scientist
Average salary $100,910¹⁰

Information security analyst
Average salary $102,600⁹

The online Master of Science in Computer and Information Science from Marquette University is designed to suit your career goals. Learn from industry experts and take courses in the fundamentals of AI, data mining, internet of things, data warehousing, data analytics and more to prepare for your specialized artificial intelligence in IT career. Even if you’re just starting off in computing and IT you can develop the necessary skills to take on a dynamic and rewarding career with our career-changer pathway option.