Generative Artificial Intelligence 101: What Is Generative AI, and What Are Its Benefits?
What Is Generative AI, and What Are Its Benefits?
To understand the impact of generative artificial intelligence technology (Generative AI), it is helpful to understand first how this technology differs from artificial intelligence that has been in routine use by many industries.
Artificial intelligence encompasses any theory, computer system, or software that is developed to perform tasks that traditionally required human intelligence. Generative artificial intelligence technology is a subset of this technology.[1]
Starting with the basics, Generative AI is an algorithm that can be used to create new content, including audio, code, images, text, simulations, and videos. These algorithms are referred to as models or programs.
When using a Generative AI model, a user enters a prompt, or a description of the new content the user is requesting. This prompt can take the form of a question, a description of an image, the plot of a book or movie, and so on. The more detail the user includes in their prompt, the more specific the output, or resulting new content, will be.
To create output, Generative AI relies upon past inputs, data that has been uploaded to the program, which essentially creates a database of information. These inputs effectively “teach” or “train” the program. This input informs the program’s output. While input can be data uploaded specifically to train the model, Generative AI programs also “learn” from user prompts, adjusting in response to variations in user requests.
By comparison, traditional artificial intelligence (Traditional AI) works within a defined set of “rules” implemented at the time of the program’s creation. Traditional AI’s purpose is usually to analyze and understand the input data by observing and classifying patterns in the data. Traditional AI programs have the capacity to learn from the input information, but the program’s output is limited to analysis or predictions based upon the input data. A Traditional AI program cannot create anything new or not already within its input database.[2] Examples of Traditional AI include computer chess programs, voice activated assistants (i.e. Siri or Alexa), spam filters, fraud detection programs, recommendation engines streaming platforms, or Google’s search algorithm. Various industries, entities, and even individuals have not only become accustomed to Traditional AI, but also many are now dependent upon the services and products that Traditional AI programs produce.
Generative AI is a new iteration of artificial intelligence, not confined to the boundaries of pre-established rules. It can create new content that is based upon patterns the program learns from its database of input information. The output is more than a regurgitation—it is a wholly original response specifically tailored to the user’s prompt. Generative AI output can be text, images, audio, or even a compilation of media formats.[3]
This technology has been touted as revolutionary because of its ability to produce high level content, closer in quality to human produced content than any algorithm ever before. In the past, artificial intelligence technology was able to compile and analyze data, but humans were still necessary to expand upon Traditional AI’s findings. Generative AI removes the need for human involvement in the development of new content.
For this reason, Generative AI is undeniably appealing to entities of all kinds, as it can streamline tasks that previous required substantial manpower, time, and resources. Generative AI can create systems that increase efficiency and reduce associated costs, freeing up resources that can be dedicated elsewhere. Generative AI’s potential to streamline processes in the workplace is unprecedented and comes from an increased ability to understand natural language, which, historically, was a skill that only humans could provide.[4]
Attorneys at Stafford Rosenbaum LLP have a long history of working with a wide range of clients to address their intellectual property and technological needs, which benefits large corporations and small nonprofits, municipalities and utilities, and numerous independent creatives, including authors, filmmakers, playwrights, musicians, and visual artists. Our knowledge and understanding of the legal risks and pitfalls associated with the use of Generative AI is yet another way that our legal team continues to guide our clients through everchanging technological advancements. For these reasons, we are enthusiastic to share what we know in Stafford Rosenbaum’s new Generative Artificial Intelligence 101 blog series!
Up next in the Generative AI 101 series we ask, is Generative AI racist? Stay tuned for a discussion on Generative AI’s bad habits: creepy distorted images, sexist and racist content, and outright hallucinations.
[1] “The Benefits and Limitations of Generative AI: Harvard Experts Answer Your Questions,” Harvard Online (2023), https://www.harvardonline.harvard.edu/blog/benefits-limitations-generative-ai (last visited Jul 9, 2024).
[2] Bernard Marr, “The difference between Generative AI and traditional AI: An easy explanation for anyone,” Forbes (2023), https://www.forbes.com/sites/bernardmarr/2023/07/24/the-difference-between-generative-ai-and-traditional-ai-an-easy-explanation-for-anyone/ (last visited Jul 9, 2024).
[3] Valenzano, Jason. “Unveiling AI’s Secrets: The Interplay of Generative and Non-Generative Techniques,” Medium (2024), https://medium.com/@jason.s.valenzano/unveiling-ais-secrets-the-interplay-of-generative-and-non-generative-techniques-1800f98cbbd2 (last visited Jul 9, 2024).
[4] Chui, Michael; Hazan, Eric; Roberts, Roger; Singla, Alex; Smaje, Kate; Sukharevsky, Alex; Yee, Lareina; and Rodney Zemmel, “The economic potential of generative AI: The next productivity frontier” McKinsey Digital (2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
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