How to use this guide
This primer addresses the common questions that people have about generative artificial intelligence (AI) systems. It contains information about generative AI capabilities, six key issue areas where generative AI is having impact, and how generative AI may be used in the future. Although this primer is designed primarily for journalists, we hope it will be useful for a wide range of audiences who want to learn more about generative AI.
For a list of resources for where to find technical experts for stories, please see Finding Experts in AI. For information about AI more generally, please see AI 101. Visit Emerging Tech Primers to see all primers.
What is generative AI?
Generative AI is a subset of artificial intelligence technologies that are used to create new content, such as images or text, based on patterns in large amounts of existing content. Generative AI differs from classification AI—like email spam filtering or tumor detection used in medical settings—because generative AI systems are designed to make content, not to make decisions.
Generative AI is just one of several technologies that people call “Artificial Intelligence.”
What generative AI Capabilities exist today?
While ChatGPT captured the public’s attention by allowing people to generate uncanny and seemingly confident responses to a vast array of written prompts, there is a diverse set of generative AI applications that have been made available to businesses and consumers within the last two years.
How might generative AI be used?
Although generative AI tools are still in the early stages of development, they are already being used to produce content at surprisingly high speeds, low costs, and with relative ease for end users. This newfound accessibility has labor and operational implications for software engineering, media production, education, the commercial art market, and more. No one knows exactly what will emerge from the explosion of generative AI tools hitting the market, but early experiments point toward the potential for larger scale disruption to business, security, and society at large:
Traditionally, the cost of making individually personalized content, such as ads with your face in them or movie trailers narrated in the sound of a loved one’s voice, was prohibitively high. People may now easily use generative AI to realize this level of extreme personalization, either for their own fulfillment or to manipulate others.
The Rise of “No-Code” Application Development
Historically, in order to develop websites or computer applications, you needed to know programming languages. Now, it is becoming possible to use conversational language to prompt an AI tool to produce computer code for you (even if today’s systems are still imperfect). These tools may lower costs by expanding the number of people who are able to create and contribute to software development and make a wide range of products and services more accessible. However, they could also negatively impact how much people are paid for these skills and change the nature of work to make it more tedious and less collaborative.
Better Augmented Reality
Real-time rendering of believable digital environments is computationally intensive and expensive. These graphical requirements have been a pernicious issue for augmented or virtual reality applications because time delays in rendering can create a jarring and unnatural user experience. Generative AI systems could be used to approximate (if not perfectly replicate) complex physical phenomena, like lighting and shadows, making these virtual scenes feel more immersive.
There are still many unknowns and opportunities for discovery. We have only scratched the surface on possible uses of these tools.
Key Issues in Generative AI
There are a number of promising applications of generative AI systems, a subset of artificial intelligence technologies that are used to create new content based on patterns in large amounts of existing content. These uses are not without their risks, however. The following sections highlight a number of the most pressing issues associated with generative AI, with links to a number of illustrative articles exploring perspectives on each of these issues.
How will generated content affect the trustworthiness of media?
Media created to mislead is not a new problem, but generative AI makes it much easier to create mis- and disinformation at scale and to create convincing human-like AI interactions that could be used to exploit users with scams or security attacks. As generated content improves, it will be harder to detect inauthentic content in the wild, and it will be easier for smaller actors to manufacture large-scale disinformation campaigns.
Expectations & Claims
What are the limitations of generative AI systems?
People selling AI products and services benefit from systems being perceived to be more reliable and capable than they are, from the anthropomorphization of “smart assistants” to the typing animations of text-generators like ChatGPT. Peeking behind the curtain reveals that the AI tools on the market are specialized in scope, not general-purpose “intelligences,” and still have crucial vulnerabilities and flaws. Although it might be tempting to ascribe greater power to these systems, there are still many questions about whether they are appropriately effective for the widespread adoption we are already seeing, let alone that we are on the verge of “Artificial General Intelligence” that surpasses humans in a broad range of capabilities. Systems like ChatGPT and Bard were designed to produce confident sounding text, not factual statements.
Who owns what?
Many datasets used to build today’s generative AI have been compiled by scraping, or extracting information, from the web. For example, to make a generative AI tool that can output digital images, developers scraped millions of existing images hosted on large art platforms, like Flickr and DeviantArt. Content collected online in this manner is often used without the consent or knowledge of the original creator. Even if the original content is not reproduced by the system (although in some cases it can be), this process leads to thorny questions around attribution, intellectual property, monetization of generative AI tools, and economic harms to creative industries.
Future of work
How will generative algorithms impact peoples’ livelihoods?
Generative tools can be used as assistants, augmenting human creativity, but they can also be used to automate certain types of work, from writing copy to creating spot art for articles to coding. There are many open questions about what tasks will be most easily automated and whether that automation will result in a reduction in total jobs, a profound change in how certain work is valued, or a restructuring of labor as new jobs are created. For example, a software developer that once created website templates could either (1) lose their job because someone else can use a tool to do themselves what they would have hired the developer to do, (2) get a reduction in salary as they face more competition in the market or, (3) no longer code as much manually, but instead be in charge of generating outputs using the AI.
|Expanded creativity||Generative AI could make it much easier for people to create websites and apps without needing to know how to code|
|Lost opportunities||Commercial artists fear being replaced by a cheaper alternative|
|Writing aid or alternative||ChatGPT will exacerbate existing inequalities in schools if we don’t act|
|Augmenting work||How professionals can use ChatGPT today|
|Invisible labor||Automating some tasks just creates a different kind of work|
How do generative systems perpetuate societal harms?
Unless the data ingested into AI models is carefully curated—which datasets scraped from the web rarely are—tools built using that dataset will reflect the biases of the unfiltered internet. Even with careful dataset curation, however, AI tools need to be fine-tuned by human content moderators to mitigate systemic biases. In some cases, creators or deployers of a system will manually override the AI system to limit output of harmful material, but these sorts of interventions are necessarily brittle and imperfect.
How will generative AI impact future AI development?
Future datasets scraped from the web will be impacted as everyday people, content farms, and disinformation campaigns saturate the internet with generated content. New AI models that are trained using these datasets may perpetuate existing biases documented in large language models like GPT-3 or image generation models like Stable Diffusion. Detecting generated content to exclude it from datasets is an active field of research but is by no means a solved problem. This feedback loop of using content produced by machines to train machines to produce more content could reduce the quality and performance of future AI systems.
What comes next?
While these are the early days, many experts agree that generative AI systems will have far-reaching implications for society. Unlike blockchain and other emerging technologies that have caught the tech industry’s eye, generative AI tools have sparked the public’s interest and imagination with creatives and business leaders alike identifying ready applications. Most immediately, here are some things that could come next:
- Tech companies vying to both define and control new markets carved out by generative AI tools, deploying work-in-progress tools into an unregulated space
- The establishment of legal frameworks and precedent to better define both intellectual property rights and consumer protection with regards to generative AI and the AI space more broadly
- The immediate disruption of some existing labor markets while new areas of work are still being defined
This work was produced by Eleanor Tursman and B Cavello, and was made possible thanks to generous support from Siegel Family Endowment, the Patrick J. McGovern Foundation, and the John S. and James L. Knight Foundation.