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What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, and adapting to new information. 

AI applications range from virtual assistants and image recognition to complex tasks such as autonomous vehicles and medical diagnosis. They contribute to sustainable design by optimizing resource use and reducing waste.

In this video, AI Product Designer Ioana Teleanu talks about how AI is changing the world.

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    Some voices, like Harari, are raising fair  and important concerns about the dangers of AI   and the uncontrolled way in which the industry is evolving at such a rapid pace. However, even AI reluctants talk about the unquestionable benefits that AI can bring to our lives; some of which being: *improving healthcare* – AI can support better diagnosis, identify health problems earlier, with more accuracy.

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    AI can predict the spread of disease, identify sick patients before they infect others. And it plays a significant role in drug  discovery and pharmaceutical advancements. *Better education* – with the help of AI, everyone can now have their personal learning assistant, one that can adapt learning to match each student's goals, strengths, weaknesses, background, and so on. *Reduce impact on the environment* – AI can help us tackle environmental challenges by

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    optimizing resource usage, monitoring climate change, and predicting environmental disasters. For example, AI can optimize energy usage in buildings or traffic flow in cities to reduce carbon emissions. *Smarter agriculture* – AI is being used to increase crop yields and optimize farm operations through precision agriculture. This involves using AI and other technologies to monitor crop health, predict weather patterns, and make farming more sustainable. *Safer transportation* – AI plays a significant role in the development of autonomous vehicles

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    which have the potential to make transportation safer and more efficient. AI can also *optimize logistics and supply chain operations*. And on an *individual level*, which also obviously adds up and expands on a societal level. AI is making us more productive. It can augment our natural capabilities and overall makes us better smarter workers – and thinkers.

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    While doing all of the positives I've  mentioned, it also creates new businesses, new roles and new type of innovation. Sure, it's also replacing a lot of roles, and that's a fair, legitimate worry in the AI public discourse space. But all industrial and technological revolutions have had similar fears in common. And in the end, the world simply reshifted and rearranged in a new order of opportunities.

AI systems use algorithms and computational models to analyze vast datasets, identify patterns, and make decisions. Machine learning, a subset of AI, enables systems to improve performance over time by learning from experience without explicit programming, which can support a circular economy by enhancing resource efficiency and recycling processes. Deep learning, a specialized subset of machine learning, centers around deep neural networks with multiple layers, which mimics the human brain's complexity. These networks autonomously extract intricate patterns from extensive datasets, enabling advanced capabilities like image recognition and natural language processing, and can be applied in incremental modular design to iteratively improve products and systems.

An illustrated infographic that demonstrates how machine learning and deep learning fits in with artificial intelligence.

© Interaction Design Foundation, CC BY-SA 4.0

Table of contents
  1. What is Artificial Intelligence (AI)?
  2. The AI Landscape: Different Types of Artificial Intelligence
    1. Narrow AI
    2. General AI
  3. The AI Revolution: Generative AI
    1. AI-Generated Art
  4. What Programs Use AI?
  5. Artificial Intelligence in Design
  6. Will AI Replace Designers?
  7. The Future of Artificial Intelligence
  8. Learn More about Artificial Intelligence
  9. Further Reading

The AI Landscape: Different Types of Artificial Intelligence

Artificial Intelligence encompasses a spectrum of capabilities, from specialized task-oriented systems to intelligence that mirrors human cognitive functions. At the core of this distinction lies the difference between Narrow AI, also known as Weak AI, and General AI, also known as Strong AI or Artificial General Intelligence (AGI).

Narrow AI

Narrow AI refers to systems tailored for specific, well-defined tasks within a limited scope. Examples of narrow AI models are common in our daily lives, from voice recognition tools like Siri or Alexa to recommendation algorithms powering platforms like Netflix and Spotify. Chatbots assisting with customer service on websites and specialized image recognition software in facial recognition or medical imaging analysis are also instances of narrow AI. Its defining characteristic is its lack of capacity to generalize knowledge beyond its designated domain.

General AI

On the other end of the spectrum is General AI, an advanced form capable of comprehending, learning, and applying knowledge across various tasks—mimicking the breadth of human intelligence. Unlike narrow AI, AGI can reason, problem-solve, adapt, and exhibit self-awareness. The ultimate goal of AGI is to perform any intellectual task that humans can, seamlessly transfer knowledge between domains, and autonomously improve over time.

While narrow AI excels in specific functions, AGI is the pinnacle of AI development. Currently, however, most AI systems are narrow, designed for specialized tasks and lacking the broad adaptability of AGI. Achieving AGI remains the significant and ambitious objective of AI research and development.

An illustration that represents Narrow AI also known as Weak AI vs General AI or Strong AI. The image includes text descriptions, examples and an illustration of a brain.

© Interaction Design Foundation, CC BY-SA 4.0

The AI Revolution: Generative AI

On the spectrum of AI, generative AI is positioned between narrow and general AI. It’s a category of artificial intelligence that focuses on creating new content, data, or artifacts rather than performing specific predefined tasks, embodying the principles of humanity-centered design to ensure ethical and societal benefits. It involves machines that can produce outputs, such as images, text, or other forms of content, that weren't explicitly programmed into them. Generative AI often employs deep learning and neural networks to learn patterns from large datasets to generate novel outputs. Outputs are created in response to AI prompts. Effective prompts, or prompt engineering, are an essential part of human-ai interaction.

ChatGPT, a generative language model by OpenAI, was released in 2022. Within five days, over a million people had signed up for it. Unlike traditional programs with fixed responses, ChatGPT can dynamically generate answers based on the patterns it learned from vast amounts of text data. This ability makes it versatile—you can ask it questions, request information, or even use it for creative writing. This type of AI is valuable for various tasks, from aiding in research to helping with creative projects.

DALL-E, another application from open AI, generates images. Similar to ChatGPT, it creates a unique output from text inputs or prompts. For instance, you can ask DALL-E to generate an image of a "giant rubber duck" or a "surreal cityscape with floating buildings," and it will produce an original image matching that description. This kind of AI is part of the broader category of generative models designed to create new content. DALL-E showcases how AI can be used for artistic and creative endeavors, offering users a new way to generate visual content.

AI-Generated Art

AI-generated art refers to artworks that are created with the assistance or direct involvement of artificial intelligence. In this process, artists or an individual collaborate with AI systems, which can include machine learning models and generative algorithms. These AI tools analyze vast datasets and learn patterns to generate new artistic outputs. AI-generated art spans various forms, including visual arts, music, literature, and more. The unique aspect of AI-generated art lies in the fusion of human creativity with the computational capabilities of AI, challenging traditional ideas of the arts and opening up new possibilities for artistic expression.

Unsupervised from Refik Anadol's Machine Hallucinations project, is a fascinating example of AI-generated art. It exemplifies the intersection of technology and creativity. Unsupervised, a product of deep learning algorithms processing vast datasets from the Museum of Modern Art (MoMA), generates abstract images guided by intricate patterns and associations within the museum's collection. This artwork is a testament to the capabilities of generative AI—its potential to create unique and unexpected outputs beyond explicit programming.

Learn more about AI-generated art, its challenges and opportunities in this video. 

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    What comes to your mind when you think  about AI? Is it the vision of a new evil tech conquering the world or a friendly robot that makes our lives convenient? What about an artist? If you've used tools like Midjourney or DALL-E, then you've already encountered one form of generative AI. Generative AI is a class of artificial intelligence algorithms and models designed to generate new content, such as images, text, music and more.

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    Generative AI operates by learning patterns and structures from large datasets: the training data. Then, when we prompt it, it tries to deliver exactly what we ask for. The output is similar to the training data, but not exactly the same. This generative AI has raised eyebrows and stirred a controversial discussion that cuts across industries: art, technology and copyright laws. On one hand, you can make the argument that technology makes art more democratic.

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    Using AI, anyone with a vision can create art without having the traditional skills to execute it. On the other hand, artists whose work is used to train the AI, often without their permission, can argue that it's a copyright infringement, particularly if the output of the AI system competes with the training dataset. The generative AI we're most familiar with  creates images based on what we humans ask for, using the training set that we provide.

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    But what if we can create art that looks nothing like its training data, in other words, truly original art? Not only compositions based on human-imagined prompts. What would a machine dream about after seeing the entire public collection of the Museum of Modern Art (MoMA)? AI artist Refik Anadol explored this question. In collaboration with data scientists, he tested various AI models and their studio-trained AI

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    to create original art. The team at Refik Anadol Studio trained the  AI using the publicly available data of the MoMA collection in its 200-year history, from Van Gogh and Pablo Picasso, to even images of Pac Man. They used 380,000 extremely high-resolution images taken from more than 180,000 art pieces. It's astonishing that it looks nothing like its inputs. If you've never heard of it, you have to check it out.

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    It's overwhelming, mind-blowing. The result is a large 24x 24-foot or 7.5 x 7.5-meter installation, which is called 'Unsupervised'. 'Unsupervised' is a constantly changing animation of abstract visuals based on the machine's interpretation of MoMA's collection. MoMA writes that: "It reimagines the history of modern art and dreams about what might have been

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    and what might be to come." When we experienced this piece of art, we had no doubt that it was indeed a completely new style of art. We have not seen anything that comes even close to that artistic expression. It's original in a way that you can compare to how Picasso and Van Gogh created completely new art styles while being inspired by the past. What's more, the training data that the artwork relies upon responds to its environment.

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    The AI uses real-time data about the weather and live feed of the visitors in the museum to adapt the visualization, giving the illusion of a living being. And it does feel as if it's alive. If you ever see one of Refik Anadol's art pieces, you will have a hard time leaving. 'Unsupervised' is a captivating and aesthetic  installation that has a profound emotional effect on you when you see and hear it. However, we must remember that this installation is unsupervised only from an *output* point of view.

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    To attain this level of autonomy, it has undergone extensive training by a whole team of data scientists and artists for several years. AI still relies on human processing to sort data for it to learn from. Oscar Wilde, the 19th-century playwright, famously said: "Life imitates art far more than art  imitates life." Humans have the incredible capacity to imagine and to come up with visions that do not exist anywhere on the planet.

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    Popular culture has often inspired people to bring fictional concepts to life. Imagination is our superpower. What if we could train technology to imagine the world? Most experts think it's unlikely, at least for now. No one lives alone in a dark room all  their lives and creates something out of the blue. We are all inspired by things we see hear, smell, touch and taste. We add our spin to it to produce art. The same will be true for AI.

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    It will always need inspiration, which will likely always come from humans.

Transparency becomes a crucial concern as the origin of information and the decision-making process of these AI systems can be obscured. The potential for bias, privacy implications, and the need for explainability in AI-generated content underscore the intricate landscape that artists and technologists navigate. 

In this video, UX design pioneer Don Norman, talks about how we can collaborate with AI—AGI is not a reality just yet so the AI apps we use need human input. 

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    Don't forget the word A: Artificial it isn't intelligent is pattern matching and it's doing things, but it has actually no deep understanding at all. None at all. But it could do wonderful things. So I've tried using it. well, you know, I'm not so good at sketching. Maybe I can ask you to draw the pictures that illustrate my concepts. And it works reasonably well, except I can't just say, Oh, here's a picture I want

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    and I get a nice, wonderful result. I have to say, here's a picture I want and then look at it and say, That's not what I want. And then I have to figure out a way of describing what I want and then go back and forth. We don't do the technical drawings anymore, but whether we do it with the aid of a computer program it can do the stress analysis and tell us whether it's strong enough, whether it's going to work, that that simply makes your work more powerful.

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    Let it do that stuff. So it's a collaboration because we think of the idea and then we have to judge what it produces to say, whether that's at all what we thought of. And sometimes and this is a great thing about collaboration, sometimes it will produce something that's so weird and strange, and we sit and look at it and say, Oh, wow, I would never have thought of that. Maybe that's a really important direction to go. And then you can spend a few more days shaping it with it.

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    So we're going to have to learn to think and design in a very different way. But that's not true of every advanced technology over the ages. Every time a new technology comes in, it changes the way we behave. And most of the time in a positive ways. But it takes a while for us to get used to it. And that's what I think will happen with AI.

UX design pioneer Don Norman warns that these programs are not truly intelligent yet. They don't have wants, needs or a sense of self as humans do. Instead, they make decisions based on patterns in data too large for humans to process.

AI follows a complex set of logical rules called algorithms. Multiple algorithms connect in a way that mimics the human brain, called a neural network. This network can learn and improve its process over time. We call this "machine learning."

Artificial intelligence has already improved technologies like voice recognition and language translation. Even still, AI has shown even more potential and some surprising new applications.

For example, AI can create art and literature in the style of human authors and artists. Yet, they don't express emotions or create their own artistic style without human help.

This emerging technology has a variety of exciting and frightening uses. AI programs make it easy to pretend to be someone else or pass off AI content as your own. On top of that, the ethics of sentient AI will be a hot topic in various fields as the technology advances.

What Programs Use AI?

Text Generators

  • ChatGPT: This program can write new text by comparing itself to similar works on the subject.

  • Bard: A chatbot by Google used to create a more intelligent and conversational search algorithm.

Image Generators

  • Midjourney: An image generator that uses subject and style prompts to create new works of art.

  • Dalle-2: Similar to Midjourney but specializes in realistic images.

Video and Speech

  • Gen-1 Video editor: A video editor that shifts a video into a different style. For example, making a live-action video into an animation.

  • DeepFaceLab: One of many programs that make "deep fakes." Deep-fakes are videos that change faces and voices to impersonate other people. The most famous example is Jordan Peele’s Obama deep fake video from 2018.

  • Dragon Speech recognition: This program learns speech patterns to turn speech into text. It was the basis of most modern speech recognition software.

UX Design

  • Galileo AI: Entire user interfaces can be generated based on text prompts.

  • Genius: The AI design companion for Figma that fleshes out a full layout from a few design elements.


Artificial Intelligence in Design

Interaction designers use AI technologies in a variety of ways. Artificial intelligence improves search algorithms for web searches, streaming services and other platforms. They can analyze terabytes of data to find patterns a human brain couldn't.

There is no doubt that AI will change how users interact with products and services. AI voice assistants and chatbots are examples of interfaces that adapt to user inputs in real-time. UX designers design the voice and the functions of voice assistants to appeal to users. Even though chatbots are text, they still need to make sense in the product's context of use. Like any interface, designers want to make a user experience that users trust and enjoy using.

“There’s a very simple formula, perceived trustworthiness plus perceived expertise will lead to perceived credibility.
Since AI is in service to human beings, I can't imagine a case where UX isn’t relevant…If you blow the UX design, it doesn't matter how good the AI is.”
-Dan Rosenberg, UX Professor at San Jose University.

The goal of artificial intelligence today is to be credible. They should be reliable tools and assistants for humans performing specific tasks. This credibility comes partly from a well-designed user experience and intuitive user interface.

Will AI Replace Designers?

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    Instead of sidelining human designers AI might become a valuable partner in the design process AI will not replaces. A person using AI will. You've probably seen this already. And I just want everybody in to think of themselves as the person who will use AI to win the AI race and and thrive in the AI age My personal take on this is that it's far from being able

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    to replace designers because of a couple of reasons: The first one being that I can't yet figure out what problems to solve. So essentially, AI can't understand that there is this person that has their own particular background, history, experiences, personality quirks, irrational behavior, so their entire psychological landscape and this person has these needs and these needs manifest themselves in a certain environment in a context.

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    And so AI can't yet make those connections. And it doesn't have systems thinking capabilities in the way that us humans have, plus empathy and other things You can already integrate AI into your design workflow to stay ahead of the curve, enhance your capabilities and futureproof your career.

The potential for AI to replace human workers is possible. But, it is more likely to be used to assist humans in making decisions. For example, AI could assist in usability tests or find patterns in user feedback or other user research tasks. AI has the potential to transform the essential tasks of a UX researcher.

“When it comes to [user] research, it is such a strategic discipline I can't think that we will ever automate it. If we are talking about general usability testing, that is going to be something where AI is going to play a big role. AI does something extremely well and that’s pattern recognition.”
-Greg Nudelman, Head of Design at LogicMonitor and Author on UX for AI

The Future of Artificial Intelligence

Many experts see the potential for AI to change human-computer interaction but also have doubts. AI systems can improve data analysis, assist translation, and help creatives bring ideas to life.

Yet, all this brings up deep ethical questions. Creatives of all types are forced to compete with AI, which can plagiarize their work in minutes. The question of who owns that AI content is also unclear.

Some communities have banned AI art entirely, even as the ability to tell them apart from human work diminishes. Even if AI does not fully replace humans, what will our economy or workplace look like if AI replaces daily tasks or even jobs?

In the future, “Strong AI” would learn, think, and generally function on the same level as humans. As fully sentient beings, there are moral questions of ownership and legal definitions of autonomy to grapple with.

Despite these challenges, tech companies are investing heavily in AI to explore the possibilities.

Learn More about Artificial Intelligence

Discover how to design for AI and how you can incorporate AI tools into your design process in our course, AI for Designers.

For more on the role of AI and other technologies on design, take our course: Design for a Better World with Don Norman.

Norman, Donald A. Design for a Better World: Meaningful, Sustainable, Humanity Centered. Cambridge, MA, MA: The MIT Press, 2023.

Read more articles and essays by Don Norman on JND.org.

Watch our Master Class: AI-Powered UX Design: How to Elevate Your UX Career with Ioana Teleanu.

Watch our Master Class: How To Design for and With Artificial Intelligence with Dan Rosenberg.

Watch our Master Class: How To Design Experiences for AI with Greg Nudelman.

Further Reading

To learn more about the differences between AI, machine learning, deep learning and neural networks, read AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?.

Read more about AI and its various applications in the Investopedia article, Artificial Intelligence: What It Is and How It Is Used.

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Literature on Artificial Intelligence (AI)

Here's the entire UX literature on Artificial Intelligence (AI) by the Interaction Design Foundation, collated in one place:

Learn more about Artificial Intelligence (AI)

Take a deep dive into Artificial Intelligence (AI) with our course AI for Designers .

In an era where technology is rapidly reshaping the way we interact with the world, understanding the intricacies of AI is not just a skill, but a necessity for designers. The AI for Designers course delves into the heart of this game-changing field, empowering you to navigate the complexities of designing in the age of AI. Why is this knowledge vital? AI is not just a tool; it's a paradigm shift, revolutionizing the design landscape. As a designer, make sure that you not only keep pace with the ever-evolving tech landscape but also lead the way in creating user experiences that are intuitive, intelligent, and ethical.

AI for Designers is taught by Ioana Teleanu, a seasoned AI Product Designer and Design Educator who has established a community of over 250,000 UX enthusiasts through her social channel UX Goodies. She imparts her extensive expertise to this course from her experience at renowned companies like UiPath and ING Bank, and now works on pioneering AI projects at Miro.

In this course, you’ll explore how to work with AI in harmony and incorporate it into your design process to elevate your career to new heights. Welcome to a course that doesn’t just teach design; it shapes the future of design innovation.

In lesson 1, you’ll explore AI's significance, understand key terms like Machine Learning, Deep Learning, and Generative AI, discover AI's impact on design, and master the art of creating effective text prompts for design.

In lesson 2, you’ll learn how to enhance your design workflow using AI tools for UX research, including market analysis, persona interviews, and data processing. You’ll dive into problem-solving with AI, mastering problem definition and production ideation.

In lesson 3, you’ll discover how to incorporate AI tools for prototyping, wireframing, visual design, and UX writing into your design process. You’ll learn how AI can assist to evaluate your designs and automate tasks, and ensure your product is launch-ready.

In lesson 4, you’ll explore the designer's role in AI-driven solutions, how to address challenges, analyze concerns, and deliver ethical solutions for real-world design applications.

Throughout the course, you'll receive practical tips for real-life projects. In the Build Your Portfolio exercises, you’ll practice how to integrate AI tools into your workflow and design for AI products, enabling you to create a compelling portfolio case study to attract potential employers or collaborators.

All open-source articles on Artificial Intelligence (AI)

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