GEORGETOWN LAW TECHNOLOGY REVIEW

GENERATIVE AI
Sadie O’Connor*

TABLE OF CONTENTSINTRODUCTION ................................................................................... 394
I.

WHAT IS GENERATIVE AI? .......................................................... 395
A.
B.
C.
D.
E.

II.
A.
B.

GAI versus Traditional AI ................................................. 395
How Does It Work? ......................................................... 396
Architectural Models ........................................................ 397
Open Source versus Closed Source AI ................................ 399
Major Players .................................................................. 399
1. ChatGPT ............................................................................... 400
2. DALL-E ................................................................................. 400
LIMITATIONS AND OVERALL POTENTIAL ..................................... 401
Limitations............................................................................ 401
Overall Potential ................................................................... 403

CONCLUSION ....................................................................................... 404

INTRODUCTION
Generative artificial intelligence (“GAI”) is AI 1 that is capableof creating various forms of new content (audio, code, images, text,
simulations, and videos).2 With AI becoming increasingly present intoday’s world and rapidly growing in capability, its advantages,
disadvantages, and potential uses have become a popular topic ofdiscussion.3 The main differentiating factor between GAI and*Georgetown University Law Center, J.D. Candidate 2024; Universityof South Carolina, B.A. in Criminology and Criminal Justice & B.A. in
Experimental Psychology, 2020.
1
Artificial intelligence (AI) is a broad term used to describe systems
that allow machines to perform human cognitive functions or tasks. See
Christopher Manning, Ahmad Rushdi & Nigam Shah, ABCs of AI, STAN.
MED. MAG. (Nov. 10, 2023), https://stanmed.stanford.edu/brief-glossaryartificial-intelligence-ai/ [https://perma.cc/JXE9-SMY8].
2What is Generative AI?, MCKINSEY & CO.,https://www.mckinsey.com/featured-insights/mckinsey-explainers/whatis-generative-ai [https://perma.cc/9M7K-UAM8].
3
Generative AI to Become a $1.3 Trillion Market by 2032, Research
Finds,BLOOMBERG(June1,2023),

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Traditional AI is GAI’s ability to create new content.4The landscapeof GAI is still evolving. Popular GAI algorithms such as ChatGPT
and DALL-E are easily accessible online5, allowing them to increase
their data set and learn from the prompts uploaded by their millions
of users.6 Theoretically, this learning process should allow GAI
algorithms to improve and become “smarter” for future use. This
Technology Explainer provides a high-level overview of how GAI
works, a summary of its capabilities, a quick look at different typesof GAI, and a discussion of its limitations and overall potential.
I.

WHAT IS GENERATIVE AI?

A. GAI versus Traditional AI
The main distinction between GAI and more traditional forms ofAI, also referred to as “narrow” or “weak” AI, 7 is GAI’s ability tocreate something new, specifically new content.8 GAI is able tocreate unique samples of a given type of content by drawing fromtheir training data set.9 Traditional AI algorithms normally operateby carrying out a specific function or completing a task using a data
set that contains information on how that function or task has
previously been done.10 In other words, traditional AI is able tofollow a set of rules, make predictions, or utilize instructions tocomplete a task; but it is not creating anything new in doing so.

https://www.bloomberg.com/company/press/generative-ai-to-become-a-13-trillion-market-by-2032-research-finds/[https://perma.cc/ENK4HNX3].
4
Bernard Marr, The Difference Between Generative AI and Traditional
AI: An Easy Explanation for Anyone, FORBES (July 24, 2023, 1:41 AM),https://www.forbes.com/sites/bernardmarr/2023/07/24/the-differencebetween-generative-ai-and-traditional-ai-an-easy-explanation-foranyone/?sh=57757e97508a [https://perma.cc/WF29-A3MJ].
5
What Is Generative AI?, supra note 2.
6
Jake Siegel, The Art of the Prompt: How to Get the Best Out ofGenerative AI, MICROSOFT,
https://news.microsoft.com/source/features/ai/the-art-of-the-prompt-howto-get-the-best-out-of-generative-ai/ [https://perma.cc/E6WP-JYBB] (Oct.
6, 2023, 10:58 PM).
7
Marr, supra note 4.
8
Id.; Toloka Team, History of Generative AI, TOLOKA (Aug. 22, 2023),
https://toloka.ai/blog/history-of-generative-ai/ [https://perma.cc/7UV3BKFL].
9
Toloka Team, supra note 8.
10
Marr, supra note 4.

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Conversely, GAI algorithms such as DALL-E are able to create acompletely new image by following the user’s instructions. 11
B. How Does It Work?
GAI algorithms utilize machine learning (“ML”) systems, also
referred to as ML models, which use the structures found in sets of
inputted training data sets as the building blocks to create newthings.12 Such algorithms are “trained” to learn from massive
quantities of data, compiled within training data sets, and are capableof learning from their own outputs based on that data set. 13 The data
sets used to train GAI usually consist of materials such as texts (likebooks, articles, or websites in the case of large language models(“LLM”)), images, and audio, depending on the function of the AI
algorithm. 14 The GAI system processes these data sets and uses ML
algorithms and statistical models to create similar, but not identical,
outputs, resulting in “original” content.15 However, most GAI
platforms require that a user give prompts to the algorithm in order
to direct the creation of new content.16 GAI algorithms are thus able
to adapt to many prompted tasks without having to be re-trained. 17
Using this model, GAI models are able to generate text (including
conversational text), images, videos, audio (speech and music), andcode. 18
One of the most prominent types of GAI models is the large
language model (“LLM”); LLMs are capable of creating natural
language responses in response to a prompt, keyword, or question. 19
11What Is Generative AI?, supra note 2.
Generative AI, ACCENTURE (2023), https://www.accenture.com/usen/insights/generative-ai [https://perma.cc/G49Y-QBJU]; Generative
Artificial Intelligence, CORNELL CTR. FOR TEACHING INNOVATION (2023),
https://teaching.cornell.edu/generative-artificial-intelligence[https://perma.cc/PUG6-CTPU].
13
Id.
14
Generative AI, supra note 12; Generative Artificial Intelligence,supra note 12; Mike Priest, What Are Large Language Models and How
Do They Work?, BOOST.AI (Oct. 17, 2023), https://boost.ai/blog/llmslarge-language-models/ [https://perma.cc/N8MW-MTPX].
15
U.S. GOV’T ACCOUNTABILITY OFFICE, GAO-23-106782, SCIENCE &TECH SPOTLIGHT : GENERATIVE AI (2023).
16
Id.
17
Generative AI, supra note 12.
18
Bijal Mehta & Ben Winters, The Many Forms of Generative AI,
ELEC. PRIV. INFO. CTR. (Sept. 7, 2023), https://epic.org/the-many-formsof-generative-ai/ [https://perma.cc/VN2N-QKJ9].
19
Generative Artificial Intelligence, supra note 12; Generative AI,
supra note 12.
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Due to the flexibility of LLMs, they can be configured to complete awide range of tasks.20 LLMs were originally used to translate text todifferent languages without losing context. 21 To accomplish its
natural language output, LLMs use writing patterns found in a
written language data set to identify trends and utilize them to imitate
a human writing style. 22 Sometimes LLMs operate so successfully
they create written language that is indistinguishable from humanauthored text.23
C. Architectural Models
Another major ML technique is the use of transformer
architecture, which is a “neural network that learns context and thus
meaning by tracking relationships in sequential data.”24 Thesemodels apply attention techniques to detect ways in which “dataelements in a series influence and depend on each other.”25 In otherwords, transformer models use attention, which prompts thealgorithm’s consideration of the relationship between words
regardless of their sequence or proximity in a sentence, to analyzeeach word in a sentence simultaneously, instead of sequentially andindividually. 26 Transformer models are capable of being trained on alarge data set 27 to “make accurate predictions that drive their wider
use, generating more data that can be used to create even bettermodels.”28
Transformer architecture largely contributed to the creation ofself-supervised learning by allowing a computer see the same
20
What are Large Language Models (LLM)?, AMAZON WEB SERVS.,
https://aws.amazon.com/what-is/large-language-model/[https://perma.cc/56SB-BK2N].
21
Thomas Davenport & Nitin Mittal, How Generative AI is Changing
Creative Work, HARV. BUS. REV. (Nov. 14, 2022),
https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work[https://perma.cc/8Z8M-2TVB].
22
U.S. GOV’T ACCOUNTABILITY OFFICE, supra note 15.
23
Marr, supra note 4.
24
Rick Merritt, What Is A Transformer Model?, NVIDIA (Mar. 25,2022), https://blogs.nvidia.com/blog/what-is-a-transformer-model/[https://perma.cc/NN2R-3EXL].
25
Id.
26
Rob Toews, Transformers Revolutionized AI. What Will Replace
Them?, FORBES (Sept. 3, 2023, 6:00 PM),https://www.forbes.com/sites/robtoews/2023/09/03/transformersrevolutionized-ai-what-will-replace-them/ [https://perma.cc/Q4EFB3W8].
27
Id.; Merritt, supra note 24.
28
Merritt, supra note 24.

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Generative AI[Vol. 8patterns as humans. 29 It can carry out many purposes such astranslating text and speech in real time as well as detecting trends
and anomalies.30 Thus, transformer architecture is commonly used insearch engines such as Google and Bing. 31 But transformers havealso been used as the model for AI platforms such as ChatGPT,
Midjourney, and Google’s Bidirectional Encoder Representations
from Transformers (BERT).32 As an example of transformer
architecture in action, BERT technology supports a question andanswer system in which each word of a sentence is processed inrelation to other words in the same sentence, allowing the system tobetter account for the intent behind the question when forming ananswer; a marked difference from Google’s traditional searchengine, which analyzes each word individually in the order thesentence was written. 33 Transformer architecture models are versatileand can be applied to any application using sequential text, image, orvideo data sets and prompts.34
Diffusion generative models operate similarly to other GAI
algorithms in that they are used to generate data output, such as animage similar to that which was used to “train” the algorithm. 35
Diffusion models work by terminating training data sets using anoising process 36 and subsequently learning to recover the deleted
data sets.37 Noising allows the diffusion model to “generate data by
simply passing randomly sampled noise through the learned
denoising process.”38 In other words, diffusion models “[consist] ofa forward process (or diffusion process), in which a datum (generally
an image) is progressively noised, and a reverse process (or reverse
diffusion process), in which noise is transformed back into a sample
29

Id.
Id.; Toews, supra note 26.
31
Merritt, supra note 24; Toews supra note 26.
32
What is Generative AI?, supra note 2; Pandu Nayak, Understanding
Searches Better Than Ever Before, GOOGLE: THE KEYWORD (Oct. 25,2019), https://blog.google/products/search/search-languageunderstanding-bert/ [https://perma.cc/Z973-ZSCA]; Toews, supra note 26.
33
Nayak, supra note 32.
34
Merritt, supra note 24.
35
Ryan O’Connor, Introduction to Diffusion Models for Machine
Learning, ASSEMBLYAI (May 12, 2022),
https://www.assemblyai.com/blog/diffusion-models-for-machine-learningintroduction/#references [https://perma.cc/7JX5-M3K3].
36
The noising process gradually introduces Gaussian noise to destroy
the training data, leaving the model to learn to recover that data by
reversing the process; eventually the model is able to use this process togenerate new data using new noise inputs. Id.
37
Id.
38
Id.
30

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from the target distribution.”39 By going backward and reversing thenoising process, the diffusion model can generate new data output.40
D. Open Source versus Closed Source AI
Open-source AI software is available for public use,
modification, and distribution, meaning anyone can contribute to thecode’s development.41 Conversely, closed source AI software isowned by a company or individual and is not publicly available, withusers needing to purchase or being granted a license to use it. 42 There
are many open sources of GAI, but developers usually keep an evenmore advanced, closed source version to which they retain exclusive
intellectual property rights.43
E. Major Players
Large technology corporations are either creating their own
versions of GAI or heavily investing in other companies that are
creating GAI algorithms. For example, Amazon invested billions ofdollars in Anthropic AI44 and partnered with Stability AI,45 Google
created BARD AI,46 Microsoft invested billions in OpenAI, 47 Adobe

39

Id.
Id.
41
Open Source AI vs Closed Source AI, UNDERSTANDING A.I. (Sept. 5,2023), https://www.understandingai.net/open-source-ai-vs-closed-sourceai/ [https://perma.cc/HDE9-RM9L]; Theodore Chen, Open vs. ClosedSource Generative AI, DELOITTE (Nov. 21, 2023),
https://www2.deloitte.com/uk/en/blog/ai-institute/2023/open-vs-closedsource-generative-ai.html [https://perma.cc/V3WE-7PNX].
42Open Source AI vs Closed Source AI, supra note 41.
43
Id.
44
Expanding Access to Safer AI with Amazon, ANTHROPIC (Sept. 25,2023), https://www.anthropic.com/news/anthropic-amazon[https://perma.cc/UFY8-CD5J].
45
About, STABILITY.AI (2023), https://stability.ai/about[https://perma.cc/5RJN-WUE7].
46
BARD, https://gemini.google.com/ [https://perma.cc/9PL3-9K9N].
47
Microsoft and OpenAI Extend Partnership, MICROSOFT (Jan. 23,2023),
https://blogs.microsoft.com/blog/2023/01/23/microsoftandopenaiextendpa
rtnership/ [https://perma.cc/ZBZ4-YK3U].
40

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Generative AI[Vol. 8is integrating Adobe Sensei into its existing products, 48 and Meta is
working on their own GAI platform.49
1. ChatGPT
ChatGPT is one version of a Generative Pretrained Transformer(GPT) developed by OpenAI. 50 ChatGPT is a free chatbot capable ofgenerating answers to most questions using a conversational tone. 51
Because of its dialogue format, ChatGPT can also answer follow up
questions, admit its mistakes, challenge incorrect premises, andreject inappropriate requests.52
2. DALL-E
OpenAI has also developed DALL-E, a tool that makes AIgenerated art in response to written prompts. 53 It creates from a data
set consisting of a wide range of text – image pairs that contain bothan image and a natural language text description of what is
depicted .54 In addition to creating new images, DALL-E is also able
to combine and transform existing images to better fit a written
prompt.55 Although DALL-E is one of the most widely used GAI
image systems, there are other available GAI image generators
available such as Midjourney. 56 GAI text-to-image platforms such as
DALL-E will undoubtedly be utilized for creative purposes, and
OpenAI plans on analyzing “how models like DALL-E relate tosocietal issues like economic impact on certain work processes andprofessions, the potential for bias in the model outputs, and thelonger term ethical challenges implied by this technology.”57

48

ADOBE, https://business.adobe.com/products/sensei/adobesensei.html [https://perma.cc/9YB2-WJM2].
49
Building Generative AI Features Responsibly, META (Sept. 27,2023), https://about.fb.com/news/2023/09/building-generative-ai-featuresresponsibly/ [https://perma.cc/QT3S-ZWNH] .
50
What is Generative AI?, supra note 2.
51
Id.; Introducing ChatGPT, OPENAI (Nov. 30, 2022),
https://openai.com/blog/chatgpt [https://perma.cc/6Y7Q-WFV2].
52
Introducing ChatGPT, supra note 51.
53
What is Generative AI?, supra note 2.
54
DALL-E: Creating Images from Text, OPENAI (Jan. 5, 2021),
https://openai.com/research/dall-e [https://perma.cc/EM2D-43X8][hereinafter DALL-E].
55
Id.
56
Amber Jackson, Top 10: Generative AI Tools, AI MAG. (Nov. 8,2023), https://aimagazine.com/top10/top-10-generative-ai-tools[https://perma.cc/YCQ2-4B6T].
57
DALL-E , supra note 54.

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LIMITATIONS AND OVERALL POTENTIAL
A. Limitations

As with any new technology, GAI is not without limitations.
Since its development, several issues have been illuminatedregarding inaccurate information, lack of human judgment,
intellectual property rights, privacy, and national security. AI
algorithms can’t guarantee accuracy, and sometimes GAI can“hallucinate” or produce false information. 58 An AI “hallucination”
is an erroneous response that seems credible. 59 Since GAI algorithms
are capable of “hallucinating” false information, users must be
cautious of its limitations. For example, GAI algorithms have
hallucinated case law that does not exist when asked to write a legal
brief.60 Deepfakes, images or videos that are manipulated to seemrealistic when they are entirely manufactured, are another example
of false information that can be produced by GAI,61 althoughwatermarks can be used to identify AI-generated images andvideos.62 As a result of this limitation, oversight of GAI will likely
be necessary.63
GAI mechanisms lack actual human judgment, limiting its
capability to make ethical choices.64 Not only must humans submit
and tailor prompts to get the response they seek, but human
involvement is also needed to monitor AI-generated content.65 This
human involvement is needed to safeguard against biased,insufficient, or inappropriate content that is produced by thealgorithm. 66 Since users may not know about GAI’s limitations, there
is an additional concern that GAI’s human-like tone will lead people
Generative AI, supra note 12; U.S. GOV’T ACCOUNTABILITY OFFICE,
supra note 15.
59
U.S. GOV’T ACCOUNTABILITY OFFICE, supra note 15.
60
Sara Merken, New York Lawyers Sanctioned for Using Fake
ChatGPT Cases in Legal Brief, REUTERS (June 26, 2023, 4:28 AM),https://www.reuters.com/legal/new-york-lawyers-sanctioned-using-fakechatgpt-cases-legal-brief-2023-06-22/.
61
Davenport & Mittal, supra note 21.
62
Id.
63
U.S. GOV’T ACCOUNTABILITY OFFICE, supra note 15.
64
Id.; see e.g., Adam Zewe, Study: AI Models Fail to Reproduce
Human Judgements About Rule Violations, MASS. INST. TECH. (May 10,2023), https://news.mit.edu/2023/study-ai-models-harsher-judgements0510 [https://perma.cc/7WP7-BEUD] (finding their AI models suggested
harsher punishments for rule violations as compared to humansuggestions).
65
Davenport & Mittal, supra note 21; Generative AI, supra note 12.
66
Generative AI, supra note 12.
58

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Generative AI[Vol. 8to place too much trust in the information an algorithm generates. 67
Further, if GAI continues to be viewed as a substitute for humancreation, people working in certain industries could face the risk oflosing their jobs to GAI. 68
Another limitation of GAI relates to questions of the legal
ownership of machine-generated content and data, as well as therights afforded owners of content used in training data sets. 69 Thereis currently a debate over whether AI-generated content can be
copyrighted, given that the author is not human.70 At the time of thiswriting, one U.S. federal court has already ruled that AI-generated
artwork is unable to gain copyright protection. 71 Further, some areconcerned that GAI algorithms are simply appropriating art since
they rely on their training data sets, which rely on pre-existing
images often created by an assortment of human authors, to generate
their unique content.72 Therefore, GAI algorithms also present
intellectual property concerns with regards to the data that is used tocreate the data set necessary to train the algorithms in the first place.
GAI algorithms also present personal privacy concerns as wellas national security risks.73 A human-authored prompt entered into apublic GAI algorithm is not kept confidential, and there appears to
be a lack of transparency in when and how GAI algorithms store theinformation used in prompts. 74 It remains to be seen how privacy
protections such as Section 230 of the Communications Decency Act
will apply to GAI platforms and their creators.75 Further, national
security risks sparked by GAI algorithms are amplified when usedon an international scale.76 For example, GAI may write harmful

67

CAPGEMINI RSCH. INST., WHY CONSUMERS LOVE GENERATIVE AI(June 2023), https://prod.ucwe.capgemini.com/wpcontent/uploads/2023/06/GENERATIVE-AI_Final_WEB_060723.pdf[https://perma.cc/9QWQ-FZLV].
68
What is Generative AI?, supra note 2.
69
Generative AI, supra note 12.
70
Simon Hembt, The Debate on AI-Created Art and CopyrightProtection, BIRD & BIRD (Sept. 1, 2023),
https://www.twobirds.com/en/insights/2023/global/the-debate-on-aicreated-art-and-copyright-protection [https://perma.cc/JN9C-PT84].
71
Id.
72
Kyle Chayka, Is A.I. Art Stealing From Artists?, NEW YORKER (Feb.
10, 2023), https://www.newyorker.com/culture/infinite-scroll/is-ai-artstealing-from-artists [https://perma.cc/8DH7-LHEH].
73
U.S. GOV’T ACCOUNTABILITY OFFICE, supra note 15.
74
Generative AI, supra note 12; U.S. GOV’T ACCOUNTABILITY OFFICE,
supra note 15.
75
Id.
76
Id.

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code to perform an effective cyberattack that would be harder to trace
than human-generated code. 77
Finally, open-source GAI algorithms are subject to “model
misuse.” Model misuse can result in inappropriate content,
deepfakes, spam, and disinformation.78 This is often achieved bymodifying the open-source code to remove filters and otherprotections against these types of outputs. 79 Therefore, closed source
GAI mechanisms are usually better protected against model
misuse. 80
B. Overall Potential
Because GAI software is still new and constantly improving, its
pros are better categorized as potential rather than existing
advantages. GAI has the potential to encourage more creative work
as well as accelerate the accumulation of economic value within abusiness through its generative function.81 It can “produce a wide
variety of credible writing in seconds, then respond to criticism tomake the writing more fit for purpose” and generate “instantaneous,largely correct code.”82 This means GAI algorithms can be tools ofgreat utility for many, as they can improve the efficiency of certain
tasks, so long as its corresponding limitations are addressed. 83
Further, companies can customize GAI algorithms using their own
data sets and utilize them for customer service and product designs;
increasing performance and driving competitive advantage. 84 As a77

Id.
Kate Koidan, Beyond ChatGPT: Open-Source Projects in Generative
AI, TOPBOTS (May 24, 2023), https://www.topbots.com/open-sourceprojects-generative-ai/ [https://perma.cc/HY9U-N725].
79
Id.
80
BSR FAQ: Generative AI, BUS. FOR SOC. RESP. (Oct. 2023),
https://www.bsr.org/reports/Generative_AI_FAQ.pdf[https://perma.cc/U6FW-ETA6].
81
Work, Workforce, Workers, Reinvented in the Age of Generative AI,
ACCENTURE (Jan. 16, 2024),
https://www.accenture.com/content/dam/accenture/final/accenturecom/document-2/Accenture-Work-Can-Become-Era-Generative-AI.pdf[https://perma.cc/KK96-J4JL].
82
What is Generative AI?, supra note 2.
83
U.S. GOV’T ACCOUNTABILITY OFFICE, supra note 15; The Economic
Potential of Generative AI: The Next Productivity Frontier, MCKINSEY &CO. (June 14, 2023), https://www.mckinsey.com/capabilities/mckinseydigital/our-insights/the-economic-potential-of-generative-ai-the-nextproductivity-frontier [https://perma.cc/8ZW2-XM5Q].
84
Generative AI, supra note 12.
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404[Vol. 8result of its efficiency, GAI mechanisms can boost productivity as atool in conjunction with human work. 85
CONCLUSIONGAI algorithms are capable of creating various forms of newcontent through its utilization of recognizable patterns over large
data sets. GAI is capable of creating original text, images, code,
videos, and audio. An increasing number of individuals andinstitutions are seeking to learn more about how to utilize GAI andreap the benefits of its advantages. However, GAI has displayedsome limitations. As GAI mechanisms continue to be developed andimproved, there is potential for some of their limitations to beaddressed. Technology companies can adapt GAI to mesh with their
existing platforms and choose whether to share their version as open
source or keep it more private as closed source. The existence of GAI
showcases just one example of the increasing capabilities of AI
algorithms generally. GAI presents promising technologicaladvances in the AI space, but it remains to be seen how many of its
disadvantages can be solved as GAI algorithms continue to improve.

85

Id.