In the past year, generative artificial intelligence has captured the world’s imagination. This powerful type of artificial intelligence (AI) can create new content based on patterns it learns from existing data. That data varies but can include photos, songs, writing, and other content. Generative AI can make fantastical images, write poetry or code, and even produce a rap track that sounds like the real thing.
Soon, generative AI will be as central to our lives as the smart phone. Yet to many, generative AI is a mystery. Let’s look at what generative AI is, what it’s not, and how it might change our lives at work and at home.
Understanding generative AI.
Defining generative AI.
The key to understanding why generative AI is a big deal lies in its name. It is artificial intelligence that can generate new content that didn’t exist before.
Generative AI doesn’t just analyze existing data — it creates new content. Imagine you ask a generative AI-powered chatbot, such as ChatGPT, to suggest a slogan for your new coffee company. In a couple of seconds, the chatbot reviews decades of coffee slogans and brews up (excuse the pun) a new slogan, “Perk Up Your Day, One Sip at a Time.” Not a bad start to your slogan research.
The power of generative AI goes way beyond wordplay. It can analyze millions of lines of DNA data and build new proteins from scratch. Doctors are also using generative AI to improve cancer treatment, accurately outlining tumor targets for radiation. Artists are using generative AI applications like Adobe Firefly to express themselves and create commercial work.
Prompt: highly detailed little bird on a cobble street with palm trees
AI vs. generative AI.
Artificial intelligence is exactly what it sounds like — machines mimicking human intelligence to perform tasks. Common examples are voice assistants like Siri and Alexa, and the customer service chatbots that pop up when you ask Amazon about a missing package.
While artificial intelligence that is not generative is still useful, generative AI is transformative. We’ve only just begun to experience how and where it will help us achieve results that would have either taken much longer or not been possible at all.
Why generative intelligence is so intelligent.
In the past, computer applications couldn’t perform a task unless humans first provided explicit instructions on how to accomplish the task. Those instructions are called “programming.” Although sophisticated programming can yield impressive results, a traditional computer application can’t do something humans didn’t include in its programming.
Generative AI systems are more flexible because they rely on machine learning, which doesn’t require explicit programming. Instead, humans give computers access to large amounts of data. The machines train themselves to recognize patterns in that data and, most importantly, to draw conclusions from what they’ve learned. (That’s where the learning part of “machine learning” comes in.) The size and quality of the dataset is important. AI is only as good as the data on which it’s trained.
Answering the question “How does generative AI work?” is complex, and gaining a deep understanding of it requires effort. The beauty of generative AI, however, is that you don’t need to understand everything about it to benefit from it. You can simply find an app, such as Firefly, type in what you want to see — “three labradoodle puppies run on the grass” — and presto, you’re now a generative AI user. No programming degree required.
Prompt: three labradoodle puppies run on the grass
Applications of generative AI.
Corporations and generative AI.
Individuals and generative AI.
Individuals already use generative AI to answer general questions and conduct research. (Note that the answers and research require human fact-checking — more on these and other drawbacks in the “Limitations and challenges of generative AI” section below.)
Making art with generative AI is also popular with individuals. You can quickly test concepts, build mood boards, and dream up extraordinary scenes from everyday language. There can be problems here, too, because many AI art generators are trained on copyrighted imagery.
However, there can also be concerns around copyright. To help address these concerns, Adobe trained Firefly on licensed images in Adobe Stock along with openly licensed content and public domain content where the copyright has expired. Since Firefly is designed to be used commercially, it can open doors to many other areas, such as commercial art, design, gaming, virtual environments, and more.
Prompt: interior Design, a perspective of of a living room and a kitchen with an island, large windows with natural light, Light colors, vegetation, modern furniture, skylight, modern minimalistic design
Benefits and advantages of generative AI.
Generative AI can understand large amounts of intricate data much more quickly than humans can. This fact is behind two key potential benefits of generative AI:
- Greater productivity
- Improved efficiency
Imagine you work at a company that stores its proprietary information in written reports, spreadsheets, relational databases, and even graphical charts. You can harness generative AI to analyze all of those sources, make connections between them, and answer your questions. The AI can even share recommendations based on its synthesis.
Boosts in productivity and efficiency are likely across many industries. If you’re a marketing manager at a small business, generative AI can help you quickly resize an online ad to match the specs of the many places it will appear. Then you can work with generative AI to report on the asset performance, spotting trends and opportunities you can roll into the next wave of marketing.
There’s a third key potential benefit of generative AI: - Enhanced creativity
People, of course, are capable of great creativity. But even the best of us can be stuck in a rut. For example, Graphic designers can use AI as a brainstorming partner. It can produce novel ideas that send you in different directions, like a kaleidoscope that makes a familiar view fresh. In these cases, AI is not so much a virtual expert as it is your creative copilot.
Limitations and challenges of generative AI.
The AI isn’t always right.
As we talked about in the section “Applications of Generative AI,” generative AI tools like ChatGPT are not always factually accurate. There may come a time when finetuned datasets and algorithms reduce the risk, but in the meantime, we humans must be skeptical consumers of what we read. Validate the information by comparing it to a trusted source.
Bias can be anywhere.
Fact-checking is relatively easy. Blocking societal biases, such as those around gender or race, from generative AI results is more difficult. Yet that too is necessary. To prevent societal biases from appearing in generative AI results, the people responsible for the AI must identify and mitigate bias from design to development to deployment, and be committed to ongoing oversight.
As users, we can also help root out bias. Say you enter the text prompt “scientist in a lab coat holding a test tube” into an AI art generator. Do the results only show one type of person, no matter how many times you click the “generate” button? You could send a message to the makers of the generator about the blind spot, and then refine your text prompt to produce more diverse results.
Prompt: scientist in a lab coat holding a test tube
Generative AI can use a lot of energy.
Companies developing generative AI tools should also be aware of the energy currently necessary to train and maintain these tools. The industry is waking up to the need to reduce its carbon footprint, but there’s still a long way to go.
Intellectual property rights are an issue.
Professional creators are rightfully concerned about copyright infringement. These concerns are currently being addressed by the courts. Adobe is one example of a company working to assist creators. In addition to developing Firefly’s generative AI responsibly, Adobe is also helping create industry standards through the Content Authenticity Initiative (CAI) and working toward a universal “Do Not Train” tag that lets creators control whether allow AI models can train on their work.
Integrating generative AI into your workflow.
Generative AI is powerful. You have power, too. As an individual, informing yourself is top priority. Check privacy policies before using a generative AI tool. If you don’t like a policy, avoid that tool. Think twice before uploading personal information to any generative AI tool once you’re past the sign-up stage. If you want something to remain confidential, keep it out of the tool. If you intend to use results commercially, make sure the tool is set up to avoid copyright infringement. Businesses adopting generative AI should always review generated results for accuracy, bias, and copyright infringement.
Along with keeping tabs on developing regulation, it’s the best way to protect the company against reputational and legal risks. People should drive concepting and strategy. Remember, the AI is your sidekick. You’re the boss.
Embrace the future of design with Adobe Firefly’s generative AI.
Generative AI is already changing our lives. As a virtual expert, generative AI may improve efficiency and productivity in many industries. As a brainstorming partner, generative AI can enhance our creativity.
The technology is evolving so quickly that tomorrow’s generative AI may look very different than today’s. If we explore the tools with curiosity and caution, we can enjoy their benefits — and avoid any pitfalls.
Prompt: a Japanese tea garden