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Intro to Generative A.I.

Abstract circuit-board inspired artwork
June 20, 2023

Aspen Digital

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, seven key issue areas where generative AI is having impact, and how generative AI may be used in the future. Although this primer was originally designed primarily for journalists, we have found that it is a valuable resource for anyone communicating about generative AI.

For a list of resources on where to find technical experts, 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.

Chart showing how generative AI fits into the larger umbrella of tech that people call "artificial intelligence"

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. Increasingly, AI developers are creating multimodal tools, tools which incorporate multiple sources of input data (such as images, audio, or text) at once.

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:

Hyper-personalized Content

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.

Information ecosystems

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.

DeepfakesHumans may find deepfake faces more trustworthy than real ones
Confident nonsense“While the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good,” burdening human moderators
Democratizing malwareChatGPT makes it possible for anyone to produce malware and phishing emails
Impact on elections
Generative AI is already being used to influence voters and reduce trust in the electoral system

Expectations & Claims

What are the limitations of generative AI systems?

AI hypeAn AI model “passing” an exam designed for humans is not indicative of intelligence
Confident but inaccurateCNET used ChatGPT to generate articles that ended up riddled with errors
False equivalencyEquating GPT’s text prediction capabilities with consciousness is misleading
Unintuitive failuresCertain strange prompts (like usernames) elicit nonsense from ChatGPT

Intellectual Property

Who owns what?

Sensitive dataScraping isn’t flawless—personal health data was found in a popular image dataset
IP protections at workGoogle won’t release its text-to-music generator because there is a chance it will reproduce copyrighted music it was trained on
Fights for attributionA class action lawsuit is filed against Microsoft for lacking attribution for code used to train Github Copilot
Copyright ramificationsAI-generated works should not be permitted copyright protection

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 creativityGenerative AI could make it much easier for people to create websites and apps without needing to know how to code
Impact on creative industries The use of generative AI in Hollywood was a key sticking point in labor negotiations
Augmenting workHow professionals can use ChatGPT today
Invisible laborAutomating some tasks just creates a different kind of work

Environmental impacts

How does generative AI contribute to climate change and consume natural resources?

Carbon footprintGenerative AI use eats up resources needed to mitigate the climate crisis
Energy usage versus other technologies ChatGPT uses five to nine times as much electricity as a Google search
Freshwater usageGenerative AI systems contribute to stress on local water infrastructure in drought-prone areas
Reducing climate impactsSome argue that companies can make AI greener by fine-tuning existing models instead of training new ones

Discriminatory effects

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.

Biased assumptionsThe “magic avatars” created using Lensa AI sexualize women and whiten people of color regardless of their users’ wishes
Mitigating harmsGPT-3 will make biased statements against certain groups, but it may be possible to mitigate this with extra training focused on fairness
Content moderationIn protecting the world from biased and discriminatory outputs in these systems, content moderators suffer the consequences

Feedback loops

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, B Cavello, and Tom Latkowski, 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.

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