How does AI work? Basic things you need to know

Artificial intelligence (AI) enables machines to learn from data and recognize patterns in the data to perform tasks more efficiently and effectively. It powers a wide range of products and services, including Netflix’s algorithms that recommend TV shows and movies based on your preferences and Waymo’s self-driving cars.

But what’s going on behind the scenes? How does AI actually work? Read this article to learn more about the basics of artificial intelligence.

Artificial Intelligence Definition: What is AI?

Artificial intelligence is the theory and field of programming computer systems to learn from data sets and identify patterns within data sets. These advanced algorithms and models perform human tasks such as speech and image recognition and decision-making. AI relies on machine learning and neural networks, as well as more complex concepts such as deep learning and natural language processing.

AI is a complex technology with hundreds, if not thousands, of possibilities for creating solutions for businesses in a variety of industries. This enables machine learning algorithms to make our lives easier and better by automating tasks, powering virtual assistants, generating transcripts of Zoom calls, and more. Generative AI allows you to create prompts that request content from processors like ChatGPT and Google Gemini.

How does AI work?

Creating AI requires defining a problem, determining an outcome, organizing a dataset, choosing the right technology, and testing a solution. If the intended solution does not work, you can continue experimenting to achieve the desired result.

Below, we explain how AI works in five steps: input, processing, results, adjustment, and evaluation.

input

Data is first collected from various sources in the form of text, audio, video, etc. They are divided into categories such as those that can be read by algorithms and those that cannot. Next, create protocols and standards by which the data will be processed and used for specific outcomes.

process

Once the data is collected and entered, the next step is to let the AI ​​decide what to do with the data. The AI ​​sorts and deciphers the data using the patterns it is programmed to learn until it recognizes similar patterns in the data filtered into the system.

result

After a processing step, AI can use these complex patterns to predict the outcome of customer behavior or market trends. In this step, the AI ​​is programmed to determine whether certain data is “pass” or “fail.” That is, it determines whether it matches a previous pattern. It determines the results that can be used to make decisions.

adjustment

When a dataset is deemed a “failure,” the AI ​​learns from its mistakes and repeats the process again under different conditions. You may need to adjust the algorithm’s rules to fit the dataset in question, or you may need to make slight changes to the algorithm. This step allows you to go back to the results step and better match the conditions in your current dataset.

evaluation

The final step for the AI ​​to complete the assigned task is evaluation. Here, AI technology integrates insights from datasets and makes predictions based on results and adjustments. The feedback generated from the adjustments can be incorporated into the algorithm before proceeding.

4 main types of AI

Learning with AI falls into the categories of “narrow intelligence,” “artificial general intelligence,” and “superintelligence,” each of which represents evolving AI capabilities, many of which are still unknown. In fact, artificial general intelligence is not yet here.

The four main types of AI today are:

• Theory of mind: Although this type of AI does not yet exist, it has the potential to understand how other entities think and feel, allowing the AI ​​to act differently towards the entities around it.

• Self-awareness: Although self-aware AI does not yet exist, it goes beyond theory of mind to understand that the self exists as an entity, recognize its own state of being, and predict the emotions of others.

Fields that make up AI: Overview of AI

It can be confusing to distinguish between AI and machine learning, and the various subfields within artificial intelligence. Here we briefly discuss some of these areas.

• Machine learning: Machine learning is a subset of AI that incorporates computer science, mathematics, and coding. Machine learning focuses on developing algorithms that allow machines to learn from data and predict trends without human assistance.

• Deep learning: Deep learning is a field of AI that mimics the human brain by learning how to structure and process information to make decisions. This subset of machine learning can learn from unstructured data without supervision, rather than being programmed to perform a specific task.

• Computer vision: Computer vision is an interdisciplinary field focused on how computers can gain understanding from images and videos. With AI, computer vision can automate activities that the human visual system typically performs.

Learn more about using artificial intelligence on Coursera.

Anyone can learn AI, and it’s beneficial to learn AI, whether or not you’re directly involved in its development. To see how AI works behind the scenes, consider signing up for DeepLearning.AI’s AI ForEveryone and learn the basics in 10 hours.

If you’re interested in learning how to leverage AI to achieve efficiency and innovation, consider enrolling in the Microsoft Copilot: Your Everyday AI Companion Specialization. Harness the power of generative AI and Copilot across Microsoft’s productivity suite, including Word, Excel, Teams, and PowerPoint.