Generative AI

Generative AI

by James G. Barr

Docid: 00018051

Publication Date: 2302

Publication Type: TUTORIAL


One of the latest and most controversial developments in the field of
artificial intelligence (AI), generative AI refers to programs
that can generate novel and unique content, literally creating new digital
images, video, audio, text, and code. Generative AI has experienced
increased scrutiny owing to its ability to disrupt or even displace the
human element in creative arts. Educators, for example, worry that high
school students will leverage ChatGPT or other generative AI programs to
write or substantially contribute to their term papers. Professional
writers are concerned that next-gen GAI programs will rob them of their
livelihood – a fear also shared by many white collar workers.

Report Contents:

Executive Summary

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One of the latest and most controversial developments in the field of
artificial intelligence (AI), generative AI refers to programs
that can generate novel and unique content, literally creating new digital
images, video, audio, text, and code.

Faulkner Reports
Artificial Intelligence Tutorial
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Deepfake and AI-Generated
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Enterprise Uses for
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Generation Tutorial

In the words of a leading generative AI program, ChatGPT (the January 9,
2023 version), produced by OpenAI:

“Generative AI is a type of artificial
intelligence that is used to generate new and unique data, such as text,
images, and audio. This is done by training a model on a data set of
existing data, and then using that model to generate new, similar data.
Generative AI models can be used for a variety of tasks, such as image
synthesis, language translation, and text generation.”

Figure 1 offers an example of image synthesis in the form of a generative
AI (or GAI) portrait.

Figure 1. Example of a Generative AI Portrait

Figure 1. Example of a Generative AI Portrait

Source: Wikimedia Commons

Generative AI in general – and ChatGPT in particular – have experienced
increased scrutiny owing to their ability to disrupt or even displace the
human element in creative arts. Educators, for example, worry that high
school students will leverage ChatGPT or other generative AI programs to
write or substantially contribute to their term papers. Professional
writers are concerned that next-gen GAI programs will rob them of their

Compounding the problem is the issue of intellectual property. If a GAI
program responds to a few well-worded and incisive questions by producing
a masterwork – the elusive “great American novel” – who has rights of
authorship? The person who posed the questions? The company that produced
the program? Neither? Both? It’s a legal minefield which, ironically,
might have to be negotiated by a future GAI legal aide.

While generative AI may be troublesome for those in the creative
community, it offers great opportunities for enterprise adopters. As
ChatGPT itself reveals: “Generative AI is used today in a wide range of
applications, including:

Generating images and videos
– Generative models can be trained on large data sets of images and
videos, and then used to generate new, similar images and videos. This can
be used for tasks such as video game character generation, digital art,
and creating realistic special effects in movies.

Language generation
Generative models can be trained on large data sets of text and used to
generate new text. This can be used for tasks such as machine translation,
text summarization, and content creation.

Audio generation
Generative models can be trained on large data sets of audio and used to
generate new, similar audio. This can be used for tasks such as music
composition, speech synthesis, and sound effects generation.

Generative design
Generative models can be used to create new designs for products,
buildings, and other structures. This can be used for tasks such as
architectural design, industrial design, and fashion design.

Anomaly detection
Generative models can be trained on normal patterns of data and used to
identify abnormal patterns. This can be used for tasks such as fraud
detection, fault detection, and medical imaging.

Recommender systems
Generative models can be trained on data about users’ preferences and used
to generate personalized recommendations for content, products, etc.”

A Way to Go

Despite its potential, generative AI is, at present, a proverbial work in
progress. Analyst Matteo Wong observes that while “[the] ability of AI to
answer any prompt with human-sounding language can suggest that the
machine has some sort of intent, even sentience,” today’s results are more
representative of the efforts of a precocious child than a seasoned

“Although ChatGPT can generate fluent and sometimes elegant prose,
easily passing the Turing-test benchmark that has haunted the field of AI
for more than 70 years, it can also seem incredibly dumb, even dangerous.
It gets math wrong, fails to give the most basic cooking instructions, and
displays shocking biases.”1

The Market

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According to Precedence Research, the global generative AI market,
estimated at $10.79 billion in 2022, is projected to reach around $118.06
billion by 2032, expanding at an impressive compound annual growth rate
(CAGR) of 27.02 percent during the 2023-2032 forecast period.2

Automating White Collar Work

While still in its technological infancy, cost-conscious enterprise
planners see the potential for automating not just “blue-collar” jobs,
but, surprisingly, more expensive and intellectually-challenging
“white-collar” work. As analyst Derek Thompson reminds us, “[in] 2013,
researchers at Oxford published an analysis of the jobs most likely to be
threatened by automation and artificial intelligence. At the top of the
list were occupations such as telemarketing, hand sewing, and brokerage
clerking. These and other at-risk jobs involved doing repetitive and
unimaginative work, which seemed to make them easy pickings for AI. In
contrast, the jobs deemed most resilient to disruption included many
artistic professions, such as illustrating and writing. The Oxford report
encapsulated the conventional wisdom of the time – and, perhaps, of all
time. Advanced technology ought to endanger simple or routine-based work
before it encroaches on professions that require the fullest expression of
our creative potential. Machinists and menial laborers, watch out. Authors
and architects, you’re safe.

“This year [and last], we’ve seen a flurry of AI products that seem to do
precisely what the Oxford researchers considered nearly impossible: mimic
creativity. Language-learning models such as GPT-3 now answer questions and
write articles with astonishingly human-like precision and flair.
Image-generators such as DALL-E 2 transform text prompts into gorgeous – or,
if you’d prefer, hideously tacky – images. [Last] summer, a digital art
piece created using the text-to-image program Midjourney won first place in
the Colorado State Fair; artists were furious.”3

Market Drivers

In addition to reducing the need for human talent, the market for
generative AI systems is being bolstered by:

  • Early adoption in the healthcare, IT, robotics, banking, and finance
  • R&D investment from major market players like Apple and Microsoft.
  • The use of generative AI in building metaverse worlds.
  • The trend of creating digital artworks using text-based descriptions.4

Market Inhibitors

Like all evolving technologies, generative AI is experiencing “growing
pains,” with issues related to:

  • Disinformation, as GAI is not always adept at
    interpreting open source information, as available on the Internet, for
  • Security, as GAI can be employed to produce
    “deepfakes,” images and videos which seem realistic but are actually
    false and deceptive.
  • Privacy, as GAI is injected into healthcare and other
    personal-privacy-sensitive applications.
  • Copyright, as GAI creates intellectual property with
    no legally-established owner or controlling authority.
  • Support, as few enterprise staff are trained in GAI
    systems and techniques.5

Use Cases

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As itemized by McKinsey analysts, the enterprise uses for generative AI
literally “abound”. Among the first batch are:

  • Sales & Marketing – Crafting personalized
    messages for individual customers, and creating conversational
    assistants (or chatbots) aligned to specific businesses and product
  • Operations – Developing effective and efficient
  • Information Technology/Engineering – Writing,
    reviewing, and, often overlooked, documenting program code or
    instruction sets.
  • Risk & Legal – Answering complex questions, often
    by mining mountains of documentation; also, drafting and reviewing
    annual reports and other official materials.
  • Research & Development – In medicine, for
    example, accelerating drug discovery and development through better
    understanding of diseases and chemical structures.
  • Human Resources – Fashioning incisive interview
    questions to aid in candidate assessment.
  • Interpersonal Communication – Optimizing employee
    e-mail and text exchanges to improve understanding and avoid
    counterproductive language or tone.6

In reporting on the generative AI market, Precedence Research cites
applications including:

  • Audio Synthesis – Generative AI can transform a
    computer-generated voice into something sounding authentically human.
  • Healthcare – When combined with 3D printing, CRISPR
    (DNA sequencing), and other technologies, GAI can help create prosthetic
    limbs, organic molecules, and other medical material.
  • Identity Protection – “In October 2022, GAI avatars
    were deployed in news reports regarding the prejudice towards LGBTQ
    people in Russia to obfuscate the identities of interviewees.”7

Promise or Peril

Like many technologies that preceded it – video surveillance, biometric
identification, self-driving vehicles – generative AI (much like AI in
general) is viewed as a hopeful development or something to be feared.

In discussing the impact and influence of ChatGPT, analyst Reid Hoffman
fairly summarizes the opposing camps, using popular science fiction series
as metaphors.

  • Pro-GAI – “For some, ChatGPT promises to
    revolutionize the way we search for information, draft articles, write
    software code, and create business plans. When they use ChatGPT, they
    see Star Trek: a future in which opportunities for personal
    fulfillment are as large as the universe itself.”
  • Anti-GAI – “Others see only massive job displacement
    and a profound loss of agency, as we hand off creative processes that
    were once the domain of humans to machines. When they use ChatGPT, they
    see Black Mirror: a future in which technological innovation
    primarily exists to annoy, humiliate, terrify, and, most of all,
    dehumanize humanity.”

Evidence of GAI resistance has already surfaced, as New York City’s
public school system has banned ChatGPT from the classroom, and a number
of online art communities have blocked the uploading of images created by
AI image generators such as DALL-E, Midjourney, and Stable Diffusion.

While Hoffman considers himself pro-GAI, or  “firmly in the Star
Trek camp,” many enterprise officials, even those anxious to exploit the
full capabilities and efficiencies made possible by generative AI, might
be inclined to deploy GAI slowly and judiciously, focusing on applications
that improve productivity without necessarily slashing headcount.8

The Future

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Figure 2. A Generated Image of a “A Writer Laboring Over
Her Work”

Figure 2. A Generated Image of a A Writer Laboring Over Her Work

Source: Stable Diffusion

Predicting the future of technology is always difficult. In the case of
generative AI, however, we can actually ask the technology, at least one
of its earliest incarnations. According to ChatGPT:

“The future of generative AI is likely to see
continued advancements in the capabilities of generative models and the
diversity of their applications. Some potential areas of development

    • “Improved model architectures and training methods, resulting in
      more realistic and high-quality generated data.
    • “Wider adoption of generative AI in various industries, such as
      entertainment, art, and design.
    • “Greater use of generative models in combination with other AI
      techniques, such as reinforcement learning and transfer learning, to
      solve more complex tasks.
    • “Development of new types of generative models, such as models that
      can generate multiple types of data (e.g. text and images) or models
      that can generate data in multiple languages.

“However, it is also important to consider the
ethical implications of generative AI, such as issues of bias and
accountability. As the technology develops, it will be important to
consider how to ensure that these systems are used responsibly and for the
benefit of society.”

While acknowledging the “ethical implications,” the technology is, not
surprisingly, bullish on itself.

Some humans, however, are less sanguine about the prospect of co-existing
with generative AI. Analyst Annie Lowrey predicts that:

“In the next five years, it is likely that AI
will begin to reduce employment for college-educated workers. As the
technology continues to advance, it will be able to perform tasks that
were previously thought to require a high level of education and skill.
This could lead to a displacement of workers in certain industries, as
companies look to cut costs by automating processes. While it is difficult
to predict the exact extent of this trend, it is clear that AI will have a
significant impact on the job market for college-educated workers.”9

Preparing for Generative AI

From a practical perspective, McKinsey recommends that enterprise
executives assemble a cross-functional team to examine the likely effects
of generative AI on their industry and their business, starting with a few
basic questions:

“Where might the technology aid or disrupt our
industry and/or our business’s value chain?

“What are our policies and posture? For
example, are we watchfully waiting to see how the technology evolves,
investing in pilots, or looking to build a new business? Should the
posture vary across areas of the business?

“Given the limitations of the models, what are
our criteria for selecting use cases to target?

“How do we pursue building an effective
ecosystem of partners, communities, and platforms?

“What legal and community standards should
these models adhere to so we can maintain trust with our stakeholders?”10

Importantly, since generative AI is self-generating in terms of its
development, the time available to answer these questions, and to
formulate a GAI strategy may be short, increasing the urgency for prompt
analysis, if not actual action.

For individuals in the creative community, whether writers, artists, or
performers, it might be wise to concentrate on hard-to-machine-replicate
capabilities, weaving, for example, humor, satire, or improvisation into
their work.

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About the Author

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James G. Barr is a leading business continuity analyst
and business writer with more than 40 years’ IT experience. A member of
“Who’s Who in Finance and Industry,” Mr. Barr has designed, developed, and
deployed business continuity plans for a number of Fortune 500 firms. He
is the author of several books, including How to Succeed in Business
BY Really Trying
, a member of Faulkner’s Advisory Panel, and a
senior editor for Faulkner’s Security Management Practices.
Mr. Barr can be reached via e-mail at

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