Creativity in the Age of AI: Generative AI Issues in Art Copyright & Open Source
Summary
TLDRThe panel discussion delves into the intersection of open source, copyright, and generative AI, highlighting the challenges and opportunities these technologies pose. Scott Draves, an AI artist and engineer, shares his experiences with open source generative art and its impact on the art world. Pam Samuelson, a law professor, addresses the legal complexities surrounding copyright infringement and the use of copyrighted works as training data for AI. The conversation explores the potential of generative AI to transform personal expression, education, and creativity, while also considering the need for trust, accountability, and the role of open source in democratizing AI technology.
Takeaways
- 🎨 The panel discussion focused on the intersection of Open Source, copyright, and generative AI, highlighting the complexities and ongoing legal debates surrounding these issues.
- 🤖 Scott Draves, an AI artist and engineer, discussed the history of generative AI and its evolution from his personal experiences, emphasizing the importance of open source in enabling artistic and technological innovation.
- 🌐 Draves mentioned his early work, including the 'flame' algorithm, which became a foundational piece in generative art and was an example of the collaborative spirit of the open source community.
- 🚀 The development of AI technology has seen a significant leap in recent years, with implications for personalized education, new forms of personal expression, and interactive media.
- 🧠 Draves argued for the necessity of open source models in AI, stating that having transparent and accessible code, data, and models fosters trust, independent research, and equitable distribution of AI benefits.
- 📚 The talk by Pam Samuelson, a law professor, addressed the copyright challenges posed by generative AI, noting that there are active lawsuits and global conversations about the governance of AI.
- ⚖️ Samuelson discussed the five exclusive rights granted by copyright and the limitations and exceptions to these rights, such as fair use, which is a defense often raised in cases involving AI and copyright.
- 🔍 The issue of whether AI-generated outputs infringe on derivative works was highlighted, with ongoing lawsuits challenging the boundaries of copyright law in the context of AI.
- 🎵 The discussion touched on the potential legal ramifications of AI-generated music covers, noting the complexities of copyright infringement versus creative expression and the right of publicity.
- 🌐 The panelists acknowledged that the legal landscape for AI and copyright is still developing, with technology often outpacing the law and requiring adaptation and new interpretations of existing legal frameworks.
- 📖 The Warhol vs. Goldsmith case was mentioned as an example of the ongoing legal discourse around fair use and transformative works, with implications for the use of AI in creating new works based on existing content.
Q & A
What is the main focus of the panel discussion in the transcript?
-The main focus of the panel discussion is on Open Source and copyright issues related to generative AI, particularly in the context of art and its legal implications.
Who is moderating the panel and what are their background and roles?
-The panel is moderated by Professor Russ Altman, who is the Kenneth Fong professor and professor of bioengineering, genetics, medicine, and biomedical data science. He is also an associate director of Stanford Hai.
What is the significance of having a bioengineer faculty moderate the panel on generative AI and copyright?
-The significance lies in the interdisciplinary nature of the topic, as generative AI intersects with both technology and legal domains, and a bioengineer brings a unique perspective that can enhance the understanding of these complex issues.
What is the background of Scott Draves, the first speaker introduced in the transcript?
-Scott Draves is an AI artist and engineer known for his work in open-source and generative art since the 80s and 90s. He is recognized for creating the 'flame' algorithm, which became a significant visual style and influenced a whole genre of art.
What is the Electric Sheep project mentioned by Scott Draves?
-Electric Sheep is an infinite, evolving AI screensaver created by Scott Draves that uses feedback from its audience to learn and generate animations, representing an early example of collective intelligence in AI.
What are the four key tenets of open source that Scott Draves discusses in relation to AI models?
-The four key tenets are: the code for training the models should be open, the inference engine code should be open, the training data should be open, and the model weights should be open.
What is the main concern regarding the use of copyrighted works as training data for AI models?
-The main concern is whether ingesting copies of copyrighted works as training data constitutes an infringement of copyrights in those works, as most content on the open web is protected by copyright law.
What is the significance of the fair use defense in the context of AI-generated outputs?
-The fair use defense is significant because it allows for the non-infringing use of copyrighted works under certain conditions, such as for educational purposes, criticism, commentary, news reporting, and research. It is often invoked when discussing the ingestion of copyrighted works as training data for AI models.
What is the role of the United States Copyright Office in addressing generative AI issues?
-The United States Copyright Office is holding listening sessions to gather stakeholders' interests and concerns about generative AI and copyright. It will publish a report with recommendations, potentially by the end of the year, to guide its approach to these issues.
How does the concept of 'forking' and fine-tuning of AI models contribute to the diversity of truth and beauty in AI-generated content?
-Forking and fine-tuning allow individuals or groups to take a foundation model, add more data, and change its character, which can lead to different notions of truth and beauty. This enables a variety of perspectives and cultural expressions in AI-generated content, rather than a single, uniform output from a single company's model.
What is the potential impact of generative AI on personal expression and creativity?
-Generative AI can lead to new forms of personal expression, such as easy-to-use Photoshop alternatives that respond to user commands, interactive fiction, and personalized content creation. It has the potential to democratize creativity and enable individuals to express themselves in novel ways.
Outlines
🎨 Introduction to Generative AI and Open Source
The speaker begins by expressing gratitude for being part of the panel and introduces Scott Draves, an AI artist and engineer, and Pam Samuelson, a law professor and co-director of the Center for Law and Technology. The discussion focuses on generative AI, its history, and its implications on art and copyright, particularly in the context of open source. Scott shares his journey in creating open source art since the 80s and 90s, highlighting the transformative impact of his work on the art world and the concept of collaborative creation.
💡 The Evolution of Generative AI and its Impact
The speaker delves into the evolution of generative AI, from his early attempts at creating algorithms that generate images to the current state of the technology. He discusses the potential applications of AI, such as personalized education and assistance, and emphasizes the importance of open source in democratizing AI. The speaker argues that open source enables customization, trust, and widespread adoption of AI, while also addressing the challenges of trust and accountability in proprietary models.
📚 Copyright Challenges in the Age of Generative AI
The speaker shifts focus to the legal and policy challenges surrounding generative AI, particularly copyright issues. He outlines the current lawsuits against generative AI, the concerns of policymakers, and the ongoing discussions about the role of copyright in the digital age. The speaker explains the basics of copyright law and its limitations, and discusses the fair use doctrine as a potential defense for using copyrighted works as training data for AI.
🤖 The Role of Generative AI in Creative Reuse
The speaker explores the role of generative AI in enabling creative reuse of copyrighted works. He argues that while some view AI-generated outputs as infringing derivative works, others see it as a tool that promotes the progress of science and culture. The speaker also touches on the complexities of defining 'original expression' in the context of AI and the potential for AI to create new forms of creative expression that do not directly copy existing works.
🔍 Navigating the Legal Landscape of Generative AI
The speaker discusses the ongoing legal disputes surrounding generative AI, particularly in the music industry. He highlights the challenges of regulating AI-generated music covers and the potential implications of right of publicity and false representation laws. The speaker emphasizes the need for a nuanced approach to copyright and intellectual property in the context of AI, recognizing the balance between protecting creators' rights and fostering innovation.
🌐 The Future of Generative AI and Copyright
The speaker concludes by speculating on the future of generative AI and copyright. He addresses the potential impact of AI on the viability of fair use claims and the need for a balanced approach to legal frameworks. The speaker suggests that while existing laws may need to evolve to accommodate AI, the focus should be on fostering innovation and creativity while respecting the rights of creators.
Mindmap
Keywords
💡Generative AI
💡Open Source
💡Copyright
💡Fair Use
💡Creative Commons
💡Data Licensing
💡Right of Publicity
💡Foundation Models
💡Trust and Accountability
💡Liberal Licensing
Highlights
Discussion on Open Source and copyright in relation to generative AI.
Introduction of panel moderator, Professor Russ Altman, and his diverse academic and professional roles.
Scott Draves, AI artist and engineer, shares his journey in open source and generative art since the 80s and 90s.
The significance of 'flame number 149' as a pivotal piece in the history of open source artwork.
Generative AI's role in enabling artists and fostering meta interactivity through programming and internet collaboration.
The Electric Sheep project as an example of collective intelligence created 24 years ago and its ongoing relevance.
Scott Draves' perspective on the rapid advancements in AI's understanding of human language and image generation.
The potential implications of generative AI, including personalized education and new forms of personal expression.
The importance of open source in AI development, including transparency, trust, and the ability to fine-tune models.
Pam Samuelson discussing copyright challenges posed by generative AI and the ongoing global conversation.
Three active lawsuits challenging generative AI on copyright and related grounds, and the U.S. Copyright Office's efforts to address these issues.
The critical questions around ingesting copyrighted works as training data, AI-generated outputs as derivative works, and removal or alteration of copyright management information.
The role of fair use in the context of using copyrighted works for training data, and its legal precedents.
The distinction between AI's interest in facts versus original expression in a work, and how this impacts copyright considerations.
The potential market competition between AI-generated images and those created by human artists.
The impact of generative AI on the rights of publicity and the potential legal issues arising from the use of samples of voices or likenesses.
The Warhol versus Goldsmith case and its limited impact on the broader discussion of AI and copyright.
Assessment of the viability of fair use claims in the context of AI, given the increasing commercial value of data.
The importance of forking and fine-tuning in open source models to allow for diverse interpretations of truth and beauty.
Transcripts
that was great and we're going to
continue on in this next panel talking
more about Open Source and copyright so
I want to first introduce our panel
moderator uh Professor Russ Altman Russ
is the Kenneth Fong professor and
professor of bioengineering of genetics
of Medicine of biomedical data science
and by courtesy of computer science he's
also one of the associate directors of
Stanford Hai and uh when I met Russ I
felt my long-lost brother so thank you
Russ
thank you thank you James uh this
session is called generative AI issues
in art copyright and open source so
what's more natural than having a
bioengineer faculty moderate it
so I'm really happy to uh very uh
briefly introduce Scott Draves who will
speak first he's an AI artist and
engineer and Pam Samuelson will speak
after Scott she's a professor of law at
Berkeley law school and co-director of
Center for Law of the Center for Law and
technology so um with those brief
introductions I'll just throw it to
Scott
thank you uh and thank you thanks for uh
all your attention this morning it's uh
it's an honor to be here so a lot of
people have a take on on generative Ai
and uh before I give you yet another one
um just uh where do you know where does
my where does mine come from and where
do I come from and uh I was trying to
think of you know what I can best
contribute here uh today and I thought
one thing is just you know where where
is where does generative AI come from
what's the the history here
and so this image here's an example uh
you know I've been doing this open
source and generative uh art since the
80s and 90s and uh this is uh this image
is uh flame number 149 uh from 1994
where it got a an award at the pre-arse
Electronica and is really what made me
realize I you know I was an artist
before uh that was
and um
this uh was perhaps the you know the
first open source artwork and it's was
really very much it wasn't just an image
it was it was code that allowed other
artists to create their own images
and this artwork was very much about
enabling other artists and um the sort
of meta interactivity of uh you know
programming exchanging code over the
internet and uh you know building things
together
and so this flame algorithm uh as as it
became known created a whole genre and
visual style and eventually I couldn't
even go into a bookstore or like a look
at a magazine rack without without
seeing some version of it and of course
so
uh
there we go here we go
uh so uh here's an album cover uh that
used it and of course these were all
images uh I didn't get any sort of
credit credit for this my name isn't
isn't here it was made by another artist
just using the algorithm
and um you know really what I learned
from this was uh you know I put this
code out there with a plan
um
but what I learned was that uh sort of
giving up control uh the real power
isn't sort of getting what you want
getting other artists to use it or to
contribute to give me code back to uh
but really getting things that I didn't
know I wanted or or I didn't want and
enabling other people to do things uh
that were really unexpected
and so just uh by in 1999
uh the Electric Sheep was the I created
this infinite evolving AI screensaver
you know which used feedback from its
audience and so it learned from
everybody who was watching it and it
tried to satisfy that that human desire
and it generated animations
excuse me and um this was a collective
intelligence created uh 20 24 years ago
and actually still running and so this
this image
sorry uh here we go
and so this image is actually a still
frame from a Super Bowl commercial that
IBM and uh H R Block Rand so
it definitely went places that that I
didn't expect and and didn't plan
so but um but for most of my career I
was you know I was a regular Tech guy I
got the PHD in computer science with
with James
uh and you know worked on regular
technology in unrelated to uh but I had
a you know an interlude in my career for
for five years I worked you know
full-time as an artist
uh just doing open source stuff
and I'll show one more uh much lesser
known
example here
of my sort of super talking about
history this is sort of like a super
early attempt at something that is now
like stable diffusion uh so this is a
generative algorithm that takes a an
image as input and sort of uh and
generates stuff that looks uh based on
based you know is modeled after the
inputs and so
as you can see the the here's an attempt
at doing hands it's it still has the
same problems after all these years
oops sorry
there we go
uh yeah this was this was 1993 though so
and so all the these were uh I'll
predate you know uh the adoption of
neural networks we we have today
so
um
but that's
um
so
that's sort of where I'm coming from
somebody who's you know been doing this
uh the open source stuff and the the
generative AI stuff
and uh you know learned about it but
it's all just Prelude to what's been
happening these days and so and you know
and it's Child's Play compared to you
know what the algorithms uh today today
are doing so over the last three years
I've seen a you know an incredible leap
in the computer's ability to understand
you know human language and generate
images
and so what what are the implications of
this
um you know
um you know I would say you know there
we could have like a a private tutor for
every child you know a personal
assistant in everyone's pocket uh you
know new forms of personal expression
you know Photoshop that's easy to use
and just does what you tell it
um
uh interactive fiction you know who
knows uh these are these are just really
like the obvious ideas some of them from
fiction itself but uh you know like
let's say uh George uh melies you know
we're really just at the beginning of
this you know we're still pointing a
camera at a stage and we don't know
what's really going to take off and
where this technology uh is gonna gonna
take us and and what it's unintended
consequences are
um
so
and these you know these these models
these you know language models and image
models contain uh Notions of Truth and
Beauty but you know but who defines uh
truth and uh who defines
Beauty
and you know how can you trust
uh that the the model that you're using
you know serves your interests versus
say the the company that created it
so
um
you know the the models that have been
sort of making headlines are proprietary
products uh from from secretive
organizations and you know there's
issues with trust and accountability
so uh there but there's the good news is
there's an alternative when and that's
open source so and there's four four
parts to that
we need the code for doing for training
the models to be open uh we know we need
the code that runs the models the
inference engine to be open
we need the training data to be open and
uh the model waits to be open
and you know if we have
those four things then you know what
what are the implications
you know if if you can see the data that
goes in the models that allows
independent you know research and you
know third-party audits uh and it can
really help with the trust and Alignment
because you know you can find uh you
know uh what's uh how you can really see
how it works
and the you know liberal licensing
because you know open source makes stuff
free to use and that's going to really
help spread the benefits
of of AI to to everyone
and if the licensing is liberal
which means like allowing commercial use
then
that can motivate investment I mean you
can get like a virtual virtuous a
virtuous uh cycle of of improvement
um
so
and it's really important though that
one of the things that open source
enables is this forking and fine-tuning
of of the models and so you can take a
foundation model and add a little bit
more data and you know change change its
character and change its notion of Truth
and and change it
and that allows you know uh each person
or for every country or every culture or
every identity to create their own
models so instead of having just one
model from one company you can have each
person or each organization or each
group of people you know creating their
own model and you know we're really
lucky the beauty of these pre-trained
models the foundation models is that
making them is extremely expensive but
the fine tuning is actually really easy
and cheap you can just do that in a few
days days
and so you know this
so instead of having you know one notion
of Truth you know we can have you know
freedom of choice and you know bias I
think is really inherent
in the model so we need to just be able
to
um take you know do the one we want and
ultimately every person can have you
know their their own model
so I think that
open source will help a lot with a bunch
of the these sort of hard problems
but there's really one that remains
which is what happens if uh these
powerful tools are used by people for
say nefarious purposes
um
and you know the harm can result and
there's really I think
ultimately
uh I'm optimistic about this and if you
know if you look at the history of
Technology
um I think the ultimately I believe in
in human nature and that the uh there's
more good people than bad people and the
benefits
outweigh the problems and we will
identify the problems and address them
uh you know the best we can as as the
cookie crumbles
so
and uh you know my experience is that
you know open source is part of you know
that solution of making these
uh making this technology work best for
everyone
and so that's uh
the end of my introduction thank you
[Applause]
so good morning
I'm very happy to be here thank you for
the invitation
um uh copyright challenges to generative
AI it's the focus of of my talk today
we could have a really long conversation
about the governance challenges
generally the generative AI is posing
there's a global conversation about this
particular topic obviously
the general public has
embraced uh the chat jpt and many of
these systems that you read about in the
in the paper and a lot of technologists
are enthusiastic about it too but policy
makers are concerned about everything
from uh privacy and cyber security
defamation disinformation just to name a
few so we're just going to talk about
copyright today and a thing to know as
we begin this is that some authors
artists and programmers are very
positive about this uh development and
about copyright as not being an obstacle
and others are very negative there are
three lawsuits right now
challenging generative AI AI on
copyright and related grounds the United
States copyright office is holding some
listening sessions about stakeholders
interests in what how the copyright
office should be thinking about
the main issues that it is addressing
and it will publish a report probably
sometime by the end of the year and the
copyright office actually has a a
website that's all about generative Ai
and so lots and lots of materials there
if you're interested in it I'm going to
only have time to talk about three
questions and I never have to do it
really fast so the question about
whether ingesting copies of copyrighted
works as training data is that an
infringement of copyrights in those
Works obviously pretty much everything
that's out there on the open web is
actually protected by copyright law
unless it was authored by the US
government and that means that there's a
lot of copyrighted stuff out there
um even if people aren't uh exploiting
it in the way that uh that many of the
copyright Industries do there's the The
Works are still protected
um and then uh a second question uh is
whether uh AI generated outputs are um
infringing derivative uh works of
ingested uh content uh two of the
lawsuits against uh stability
um raise this issue as well as the first
one uh and then a third question uh has
to do with removal or alteration of uh
what copyright law calls copyright
management information things about sort
of what is what is the name of the work
uh who's the author and on what terms is
available and the stability cases
raise this issue as does a class action
lawsuit against GitHub and open AI over
the Codex large language model and
co-pilot the the programming assistant
tool that Microsoft is hosting in uh in
the cloud
the briefest thing I can say about
copyright is that um it it attaches
automatically by operation of law from
the first fixation of a work in a
tangible form and the author is the one
who gets those rights and these are the
five major
exclusive rights that copyright grants
to them the right to last for
practically forever and copyright only
protects the original expression of an
author not the ideas not the facts not
the methods there are lots and lots of
unprotected stuff in copyrighted works
and copyrights exclusive rights are
limited by fair use and various other
doctrines and there's a special
copyright like law that makes
intentional removal or alteration of
copyright management information
um illegal if you know that it will
facilitate uh copyright infringement uh
Fair uses are non-infringing and
um so it's a defense to a charge of
infringement and fair use is usually the
defense that's raised when we're talking
about the ingesting of uh copyright
Works uh in uh in as training data and
there are various factors I'm not going
to be able to go into this right now but
I'm certainly happy to talk at the Break
um about these things so is ingesting
um works as training data copyright
infringement or not there are at least
several cases and I'm listing two of
them here uh where the court basically
threw out copyright claims on fair use
grounds so that field put a bunch of his
work up on on the internet uh Google of
course crawls the web and copied it to
index the contents and to let people
find Fields work and Fields that Ah
that's a copyright infringement and the
court said no it's fair use because
Google is using the works not to exploit
the content but to just let people know
that the works uh exist and are out
there and in the author's Guild versus
Google case and Appellate Court rule the
Google's digitization of tens of
millions of in copyright books from
research Library collections was fair
use when it was done for the purpose of
indexing uh the contents and serving up
Snippets in response to search queries
so stability is going to be relying on
these in similar cases to support its
fair use defenses
but there are a lot of people out there
who really don't like the ingestion
without permission and they can't really
easily opt out they weren't paid for uh
the value of their contributions uh and
some part of the concern is that the
images that are being generated through
stable diffusion and others of these
images
generators is that they will compete in
the market with the images that artists
actually are doing and it's in some
sense you're competing against yourself
in some sense at least that's the way
some of the artists think about it and
this cartoon actually is kind of
illustrates how some people think about
generative AI
um
now they're a countervailing
considerations
generally speaking the people who
develop these large language models are
not really interested in the original
expression in a work they're really
interested in essentially understanding
the facts and they kind of think of uh
of of of documents and the like as raw
material for computational uses that are
actually not exploiting the expression
and so that's sort of a way of kind of
looking at it and also generative AI
enables a lot of creative reuses of
things and so what copyright law is
supposed to be doing is promoting the
progress of science or knowledge and
culture and so the people who favor
um
generative AI are thinking that this is
actually a good thing that that
generative AI advances the purposes of
copyright uh outputs as a derivative
Works
um so
authors have exclusive rights to prepare
derivative works but the course have
basically said something can't infringe
the derivative work right if you haven't
just extracted some expression from an
existing work and then put it into a
second work so it's not enough just to
be based upon an existing work you
actually have to have
essentially taken expression and reused
it on the class action lawsuit against
stability and claims that every output
of stable diffusion is an infringing
derivative work and I think that's
actually hard to say under existing
precedence the that that's a sound
result but in general
the text and images that are generated
in response to user props are not going
to be substantially similar in their
expression to the works that were in the
training data and if that's true through
then that's unlikely to infringe the
derivative work right
and
you know there are some examples that
people have shown where you can
essentially like Mickey Mouse and
Superman you do a prompt with them
you're going to get an image that's
going to look like Mickey Mouse or
Superman
of the cases against stability
um Getty is says Hey stability you
ingested 12 million photographs and
captions from our online database that's
actually copyright infringement also
you know there are some images they
claim that are infringing derivative
infringing of the derivative work right
and also there's a copyright management
information claim in this particular
case and here's an example I don't know
whether you can see it but uh
this is getting images and this is the
mangled uh stuff uh and so that's a
basis for claiming uh that there is uh
that that's uh that uh stable diffusion
uh is at least violating the copyright
management information uh law uh and so
with that uh you know these lawsuits are
in very early stages
um it's going to be many years actually
probably until we know what the results
are from the courts but it's actually
good to remember this isn't the first
disruptive technology that's ever been
uh out there um so I like to remember
that piano rolls were a disruptive
technology of the day because the
composers basically said hey that's my
music but it didn't look like a
copyright infringement to the courts and
so it went up to the Supreme Court said
no it's not a copy because it's part of
a machine so copyright law has had to
evolve to kind of recognize that that
you know technology has to be responded
to and sometimes you amend the law in
order to protect copyright owners and
sometimes you know you just let the
technology happen so you know the
recording industry hated
um MP3 players but guess what
um they're like they're legal so anyway
I'll just leave that with the uh for a
discussion and thanks very much for your
time
so I think we have microphones for
questions so if you want to raise your
hand I'll start things off thank you
both for for those really stimulating
comments
um you you referred to a bunch of
lawsuits uh that are ongoing and I and I
was wondering is that how this is going
to play out is it going to be the courts
that figure this out versus kind of a
prospective either regulatory approach
or self-governance by the companies that
are involved in I might be very
interested in both of your thoughts
about those alternatives to just having
the courts make these decisions and
using those precedents well the
copyright office as I said has a clear
intent to put a marker down in terms of
the sort of the major questions that I
was just raising and so they'll have to
they'll have a say about do they have a
good history of moving uh no
um but
um you know they have motivation they
can't do anything by themselves right
they can make recommendations to
Congress to pass legislation
and we all know how functional Congress
is right now so um you know part of what
happens is that when you have a
dysfunctional
Congress and you know the you know the
Biden Administration whatever they might
think about this
um they can't do anything either okay so
lots of things end up in court because
there was a big dispute and there's no
other entity that really can deal with
this I should actually mention that um
one of the things that is important is
that fair use is a limitation on
copyright in the United States there are
a number of other countries that
actually have fair use defenses in their
copyright laws too but most of the other
developed countries have special
exceptions for text and data mining and
so at least for the purpose of ingestion
of copyright works for generative AI the
text and data mining exceptions would
actually kick in and at least for
scientific and non-profit uses
that cannot be overridden by contract
and right now there's a question about
whether people can opt out I remember
the discussion on the first session
about opting out and it's not clear you
can opt out
so Scott on the same issue you had the
four key tenets of Open Source Code open
inference code open data open and I
wonder if you can comment on the data
open I just wanted to hear a little bit
more about how open and how this
interacts with your thoughts about
copyright and
sure the when I say the data open I mean
that the you know the all the the list
of images or texts all the all the
documents that are fed into the training
algorithm is a published list that
anyone can can inspect it's and there's
actually a proposal in the European
Union that um as part of the AI act that
they're considering uh is to have a
requirement that generative AI systems
have to disclose uh what copyright Works
um they ingested so that's kind of
consistent with your kind of preference
for open data in that way yeah so and
then once if if you have a list you can
look and see are are you on it um and
you know copyright lies certainly too
complicated for me to understand I have
no idea uh you know I what what's going
to happen and um uh but I I do believe
that there is you know some some list of
uh you know there's tons of uh public
domain imagery out there there's tons of
information out there there there's all
kinds of Licensing available and I
believe there is a list of data there is
a data set uh that would be uh sort of a
useful and and legal uh and you know we
should we should pursue it you know
together so like let's let's look at the
list and uh
come up with a consensus it's actually
also important to know especially for
any of you out there who are trying to
develop some of these models the
Creative Commons official position
is that using
a Creative Commons works as training
data is actually not a violation of the
Creative Commons licenses now this is in
contrast to the the GitHub lawsuit in
which the four programmers have sued
GitHub open Ai and Microsoft
for breaching open source licenses
because the generative
system that was built on top of a model
that was ingested the open source code
is actually there's claiming it's a
breach of license because there's no
attribution right and so that's actually
an issue that where they at least taking
that position now again
who knows what the courts will do with
that I did want to take some questions
from the audience do we have
I saw this hand first
thank you very much for your
thought-provoking comments uh one of the
points you made were about uh truth and
Beauty right
crude
coming from a scientific background you
kind of think about it as objective
evidence something which is backed by
objective evidence but beauty is more
subjective
it can be interpreted by the Iowa to
Beholder right so can you elaborate on
that in in terms of how can we make sure
that
we don't get into the world of
alternative facts
but maintain one notion of truth if it
makes sense
uh you know there's certainly scientific
uh truth is maybe uh the the easier part
like at least we have like you know the
scientific method that we have a way
that we can agree a process to figure
out what that is obviously it's executed
by people and imperfect
um but there's you know there's there's
all kinds of truths uh you know there's
historical truths uh and there's you
know personal truths and I just really
don't see us uh coming up with a single
definition for that and so that's why we
need you know the ability for people to
fine-tune their own versions of the
models and
ultimately the you know
Define their Define their own truths and
yeah those could be if you disagree with
them you you might uh call it
misinformation or alternative facts I
think uh
you know uh but I you know I I just
don't see uh any way of uh you know uh
uh regulating a single answer there and
I think it's better to uh you know allow
um you know diversity of those terms A
diversity of Truth and Beauty
um it's so that's that's uh that's
that's uh how I that's my position
we have one two and then if we're really
lucky three
um thank you so much for uh your
comments I have a question that sort of
um touches on both aspects of uh
generative AI from the artist's
perspective as well as when you look at
copyright infringement and so I don't
know if everyone is familiar with uh the
new trend of Music covers by generated
AI machines
um but more so coming from the consumer
um listening to a piece of music and
wondering what it would sound like if a
different artist sang it
um and I know Universal Music put out a
statement saying they would pursue legal
action against those types of music
covers
um but when another human makes a cover
of a musical piece it's not illegal
right there they have the right to
creatively Express themselves
um on a piece of music and so I'm I'm
wondering from the artist's perspective
as well as from a legal perspective how
can we always even possible to regulate
this intersection of generative AI meets
consumers that are paying to have access
to certain types of music as well as
copyright and intellectual property from
the artist's perspective
degree because we do have a panel in the
afternoon that addresses that too so
thank you so much for the great question
well I think that to the extent that
some software tool
enables people to you know do their
karaoke as a as a kind of
generate the music that you want to hear
that's going to be something that
Universal probably won't know about and
won't care about
it's kind of like fan fiction kind of
stuff which mostly has been either
considered fair use or been tolerated by
people but you try to go you try to go
public with that you try to
commercialize it and Universal I
guarantee Universal will be there and
one thing that actually is going to be I
think more significant
in the generative AI space then it has
been in other contexts and that is right
of publicity so there is this law it's
really state law which basically says
that people have a right to control the
use of their names and likenesses and
some for kind of commercial purposes so
if somebody takes a picture of you and
then uses that as an advertisement for
some other product that's actually a
violation of right of publicity and that
the the Drake song that went viral is
something where I think there was a
right of publicity issue in that case I
mean I didn't turn into litigation
because it just got taken down really
fast because I can tell you that
Universal makes its voice heard really
loud with anybody that's hosting stuff
like that so it got taken down pretty
fast but it went viral really really
fast too and it's certainly not going to
be the only time when a sample of
someone's voice is used to
then make a music that sounds like the
that's them when it's not so they're
also laws against false representation
and so there are kind of some existing
laws other than copyright that um that
may come may have some bearing on that
we are now entering the lightning round
all right really quickly I I really
appreciate um your your response got to
the question about scientific truth and
I think it's really important to push
back on this privileging of scientific
truth because it's you know I think from
this audience it is just as ambiguous as
the definitions of beauty I think I
wanted to ask you quickly if you have
you've been really unique in giving away
a huge amount of your your work and even
without asking for credit or attribution
so I'm curious if you have any regrets
or advice on that and uh and really
quick to Pam if we have time but uh is
it has this recent Warhol versus
Goldsmith's decision changed anything in
in regard to AI
um I I have uh I don't have any regrets
I mean I've lived a Charmed existence
I've been incredibly lucky
um and it's it's hard to untangle you
know like what what would have happened
um but so I think it's it's it's I think
it's working great I would recommend it
and um
yeah I I don't know uh
let's go let's go to the next one thank
you just
I'm sorry
yeah go so short version
um the United States Supreme Court just
decided that that a commercial license
of an image that
Andy Warhol created in 1984
so the commercial license in 2016
was
not uh a fair use of that image they
didn't reach the question about sort of
how far and how whether the works when
they were created were infringing or not
they just basically said that's the that
Goldsmith had abandoned those claims and
so really really narrow issue as a
commercial license
of that particular image Court didn't
say it was uh it was necessarily
infringing but said that it wasn't the
transformative enough use of the image
so you know there's some dicta in that
opinion that a lot of people who hate
fair use are going to just love but for
them for the most part really narrow
narrow ruling
we do have some questions from the from
the audience remotely and but many of
the issues have at least been touched
remote uh and glancing blows
um I wanted to just ask you Pam just uh
to speculate or sort of what your
assessment is of the um viability of
fair use claim given that the fourth
factor is uh maybe increasingly like uh
in question uh
since the AI companies now have created
this booming market for data and the
value of that data now is sort of widely
recognized and a lot of companies who
have access to a lot of data are
creating licensing agreements for the
use of that data in training AI does
that change your you know guess I guess
about how courts will fall on the fair
use question well I think
I think that uh Getty Images lawsuit
against stability
um that's a that's a that's a more kind
of
um important consideration because
um getting images says I don't I I'm
completely happy for you to use uh my
images and the captions uh as uh
training data but I have a licensing
program for that and so it's not fair
use the class action lawsuit I think is
quite a different thing because the the
three artists that claim to be the class
plaintiffs
are trying to represent all of the
artists of all the visual images in the
world that
um that were ingested you can't get a
license there so it seems to me that
that the fair use claim is going to be
stronger where it's infeasible to
um to do it I don't know that the
existence of look every time somebody
brings a lawsuit they're basically
saying I
lice I have licenses okay or I'm willing
to license this particular thing so the
fact that you want to license the fact
that you have a licensing program
doesn't actually necessarily mean that
it's not fair use I am not going to
predict here but certainly the Getty
Images case has a a stronger response to
the fair use claim than the the Anderson
class action case
well I want to thank the panelists and
thank the uh for the questions
[Applause]
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