Editorial: What Have - Recherche en IA spécialisée au MIT

Editorial: What Have
Large-Language Models and
Generative Al Got to Do With
Artiﬁcial Life?

Alan Dorin
Monash University
Computational and Collective

Intelligence Group

Department of Data Science and AI
Faculty of Information Technology
alan.dorin@monash.edu

Susan Stepney
Université d'York
Department of Computer Science
susan.stepney@york.ac.uk

Accessible generative artiﬁcial intelligence (AI) tools like large-language models (LLMs) (par exemple., Chat-
GPT,1 Minerva2) are raising a ﬂurry of questions about the potential and implications of generative
algorithms and the ethical use of AI-generated text in a variety of contexts, including open science
(Bugbee & Ramachandran, 2023), student assessment (Heidt, 2023), and medicine (Harrer, 2023).
De la même manière, among the graphic and visual arts communities, the use of generative image synthesis algo-
rithms (par exemple., DALL-E,3 Midjourney,4 Stable Diffusion5) that take text prompts as input and produce
works in the style of a particular human artist, or no artist who ever lived, are causing consterna-
tion and posing challenging questions (Murphy, 2022; Plunkett, 2022). The use of generative AI to
create deep fakes has also been in the spotlight (Ruiter, 2021), as has its role in answering scientiﬁc
research questions directly (Castelvecchi, 2023).

To our minds, the questions these technologies are raising do not seem to be of a fundamentally
different character to questions asked about AI for many years. They largely concern (un) what is
possible, (b) what is right, et (c) the implications of the technology’s use. Par exemple,

1. Can AI generate documentary “evidence” that is indistinguishable from reality? Can AI
generate artifacts that are competitive with (or superior to) those made by a human?

2. How is the concept of “truth” confused or undermined by the output of these

technologies? Is it ethical to load examples of a human’s art to generate a model for
replicating their style?

3. Who owns the intellectual property rights of AI-generated artifacts? Who is accountable if
an AI-generated artifact causes harm? Will I lose my job to an AI; does AI make creative
writers or artists redundant?

The sense of urgency and public interest in these questions has increased as generative models
become practical and ubiquitous tools with levels of skill or expertise that appear to be of above
average human competency.

1 https://openai.com/blog/chatgpt.
2 https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html.
3 https://openai.com/product/dall-e-2.
4 https://www.midjourney.com.
5 https://stablediffusionweb.com.

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UN. Dorin and S. Stepney

Editorial

Of general relevance to academic publishers, including Artiﬁcial Life, is that LLMs can compose
text structured in the form of research paper sections or even whole articles. And these texts are
souvent (currently) hard to distinguish from human-written text, even by software trained to detect
text generated by language models (LMs). When questioned (on February 20, 2023) “What should
a journal editor do about LMs?” You.com’s chatbot suggested,

A journal editor should ensure that all submitted manuscripts adhere to the journal’s style
guide, and that the language used is appropriate for the journal’s audience. They should
also ensure that any language models (LMs) used to assist with the writing process are
properly cited and that any generated text is labeled as such. En plus, the journal
editor should ensure that the LMs are used in accordance with the journal’s policies and
ethical considerations.

That’s a generic response that begins tangentially to the questioner’s intended focus. As any
human reader can immediately see, the chatbot is not aware of the social context of the question,
nor has it had the opportunity to gauge the questioner’s level of expertise. But the text is “good
enough”—it does address relevant issues, and it’s an answer that probably took you a few seconds
to read, interpret, and critique. It may take you more time to read and analyze the text and this
paragraph than it took for us and the chatbot to generate it. Was this therefore a waste of your
temps, or ours? Is the chatbot wasting your time? Are we? Some journals and publishers have drafted
formal policies that require the use of LLMs for writing submissions to be explicitly acknowledged
(par exemple., at Springer-Nature; “Tools Such as ChatGPT,» 2023). In practice, their use (or misuse) may
be very difﬁcult to detect.

Artiﬁcial Life, and its publisher MIT Press generally, is also adopting the policy that any use of
generative AI, for any part of a submitted work, including but not limited to text, images, sound,
data, mathematics, logic, reasoning, programming code, or algorithms, must be prominently, explic-
itly, and unambiguously labeled and its source formally cited (par exemple., via a name, manufacturer, URL,
version number, or access date).

Journals and publishers have also moved to prevent LLMs from being listed as authors on ar-
ticles. Par exemple, Springer-Nature’s policy was online earlier this year, et, although it has now
seemingly been removed from its original location, variants of it have been incorporated into the
authorship policies of some journals:

Large Language Models (LLMs), such as ChatGPT, do not currently satisfy our
authorship criteria. Notably an attribution of authorship carries with it accountability for
the work, which cannot be effectively applied to LLMs. Use of an LLM should be
properly documented in the Methods section (and if a Methods section is not available, dans
a suitable alternative part) of the manuscript. (Nature, 2023)

In a policy that remains online at time of writing, the journal Science states,

Text generated from AI, machine learning, or similar algorithmic tools cannot be used in
papers published in Science journals, nor can the accompanying ﬁgures, images, ou
graphics be the products of such tools, without explicit permission from the editors. Dans
addition, an AI program cannot be an author of a Science journal paper. A violation of this
policy constitutes scientiﬁc misconduct. (Science, 2023)

Artiﬁcial Life and MIT Press are taking an approach in alignment with those of the editorial boards
(and publishing house legal teams) of such journals: that authorship is associated with responsibility
and accountability for an article. Cependant, for Artiﬁcial Life, the issue doesn’t stop there.

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UN. Dorin and S. Stepney

Editorial

The implications of generative AI are relevant to a broad spectrum of society. But an interest in
generative computational processes is arguably at the center of Artiﬁcial Life research. How might
LLMs be speciﬁcally relevant to Artiﬁcial Life, as opposed to the subdiscipline (yes, that’s ironic) de
AI? Here are a few ideas.

The production of novelty can be explored through the use of an LM that continually takes as
its input text composed by humans, other LMs, and its own output. Such a system is relevant to our
ﬁeld’s interests in feedback loops, open-endedness, and the emergence of complexity. Is this system
engaged in language acquisition through “social” interactions?

LMs might be used to explore questions related to the emergence of meaning in language. Can
meaning be generated by an LM, or is it speciﬁc to living things? Can LMs evolve to be better
interpreters and writers? How does the text LMs generate change the way humans produce and
use language?

If any work responding to these questions was presented in the form of a formal research exper-
iment documented in an article, then this would naturally fall within the scope of human-authored
recherche. Cependant, an LLM-generated poem, song, or essay can be of value to researchers in Arti-
ﬁcial Life exploring these topics (even if it isn’t very good; Cave, 2023). The coauthorship of such
a work by an LM and a human as a way of communicating ideas about Artiﬁcial Life would be
interesting to consider. Dans ce cas, the text contributed by the LM would need to be quoted as an
“example” within the text of a submission made by a human author who determined that it was
worthy of submission. Even though the text itself is a direct, self-referential, et, we would hope,
revealing exploration of an LM system’s quirks, capabilities, or limitations, the determination of its
relevance must, for the time being at least, be made by and attributed to a human.

There is a precedent for such work, much of which has been explored under the banners of
cybernetic, generative, and Artiﬁcial Life art (par exemple., see many historical examples in Benthall, 1972;
Ohlenschläger, 2012; Reichardt, 1968; Whitelaw, 2004). In such contexts, the art is published. Comme-
sociated commentary and/or explanations may come later, and these might not be authored by the
same system or person who made the original work.

An interview, debate, discussion, or duet between a human and an AI, or between several com-
puter programs, can also challenge our ideas about living systems and their exchange of information,
the use of language, or the production of improvised movement and sound. The inclusion of ex-
tracts from discussions with computer chatbots dates back at least to the advent of ELIZA in the
1960s: “Men are all alike—IN WHAT WAY—They’re always bugging us about something or other”
(Weizenbaum, 1966, p. 36). Likewise collaborative improvisations performed by robots, algorithms,
and humans have an established place in music (Bown, 2011; Eldridge, 2005). As far as we know,
the journal hasn’t published such works previously. But we could.

For something along these lines to be published today, as with any contribution, it would of
course need to provide novel perspective or insight. Cependant, the main point here is that in these
scenarios, even though we might intuitively feel that the generative AI system warrants the status
of contributor at the level of coauthor, we have to insist on a human author of the submission.
They would have ultimate responsibility for the work produced by the generative algorithm so
que, par exemple, if the LLM’s poetry inﬂuenced thought in a positive and productive way, or if it
incited violence, we would have somebody accountable to thank or blame. If the article’s publication
required the payment of an open access fee, we would also have somebody from whom to extract
the payment!

We haven’t yet received any submissions made by generative AI (as far as we know). But this
issue contains novel work by human authors. En fait, we have recently published a spate of varied
special issues reporting on the research presented at human gatherings, some in person, some online.
These have covered a wide range of exciting activity in the Artiﬁcial Life community: Issue 28:2 a
extended versions of selected papers from the 2019 Artiﬁcial Life conference; 28:3 is a collection
of articles on embodied intelligence; 28:4 is the Artiﬁcial Life 2021 conference special issue; 29:1
explores agent-based models of human behavior. We extend our thanks to all the guest editors
for their hard work in handling the selection and review of articles for their issues. New ideas for

Artiﬁcial Life Volume 29, Nombre 2

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UN. Dorin and S. Stepney

Editorial

special issues in the subdomains of Artiﬁcial Life are always welcome. If you have an idea, please
contact us.

After that run of special

issue of contributed
research articles that cover a wide range of Artiﬁcial Life topics—including distributed con-
trol, emergence, dynamical systems, self-organization, game theory, artiﬁcial chemistry, et
biocomputing—addressed through theory, models, simulations, and physical experiments.

issues, we welcome you back to a general

We start with a letter from Bull and Liu, on “A Generalised Dropout Mechanism for Dis-
tributed Systems.” They use a modiﬁed NK model to sharpen the criteria for determining when
local control is more beneﬁcial than global control. Suivant, we have an article from Gershenson,
on “Emergence in Artiﬁcial Life.” He uses the difference in information present at different levels
of a system as the basis for a new deﬁnition of emergence, one of the fundamental components
of ALife.

The article from Howison et al., “On the Stability and Behavioral Diversity of Single and Col-
lective Bernoulli Balls,” describes a platform for investigating how dynamical systems may be used
as the basis for designing a variety of agent behaviors. This platform, both in simulation and as a
physical system, comprises a collection of “Bernoulli balls” in an airﬂow, interacting with each other
and with the ﬂow. The aim is to develop a dynamical system with a diverse set of possible behaviors.
Ichinose et al. present “How Lévy Flights Triggered by the Presence of Defectors Affect Evolu-
tion of Cooperation in Spatial Games.” Lévy ﬂights model a kind of random motion with both small
and big displacements. Here Lévy ﬂights are combined with game theory concepts in an agent-based
model. The authors investigate how the presence of defectors changes the optimal behaviors.

Suivant, Scott and Pitt investigate “Interdependent Self-Organizing Mechanisms for Cooperative
Survival.” Complex survival games, where cooperation is needed to survive intermittent catastro-
phes, need complex strategies. Here the authors look at social self-organization, lequel, as any com-
plex domain, has aspects that can make the situation better in some cases and, in other cases, worse.
They conclude that such systems need to be able to reﬂect on their own operation through some
kind of self-model.

Sienkiewicz and J˛edruch tell us about “DigiHive: Artiﬁcial Chemistry Environment for Model-
ing of Self-Organization Phenomena.” This two-dimensional continuous space simulation environ-
ment supports experiments with the goal of facilitating open-ended simulations. It steers more
toward natural physical and biological systems (par exemple., it includes energy conservation), plutôt que
toward the more abstract operation of some other artiﬁcial chemistries. The authors describe the
rationale and operation of the system and use it to investigate aspects of self-organization and
self-replication in cellular-like systems.

Enfin, Svahn and Prokopenko examine “An Ansatz for Computational Undecidability in RNA
Automata.” An ansatz is an “educated guess” about the form of the solution to a problem that can
be used to provide a stepping-stone to ﬁnding the solution. Here the approach uses the known
computational power of a set of automaton models as the form of solution and shows how RNA
behaviors map to these models to demonstrate the computational power of this biological form of
computing.

Les références
Benthall, J.. (1972). Science and technology in art today. Thames and Hudson.

Bown, Ô. (2011). Experiments in modular design for the creative composition of live algorithms. Computer

Music Journal, 35(3), 73–85. https://doi.org/10.1162/COMJ_a_00070

Bugbee, K., & Ramachandran, R.. (2023). The ethics of large language models: Who controls the future of open science?

https://impactunofﬁcial.medium.com/the-ethics-of-large-language-models-who-controls-the-future-of
-open-science-43cca235401d

Castelvecchi, D. (2023). How will AI change mathematics? Rise of chatbots highlights discussion. Nature, 615,

15–16. https://doi.org/10.1038/d41586-023-00487-2, PubMed: 36808415

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Cave, N. (2023, Janvier). I asked Chat GPT to write a song in the style of Nick Cave and this is what it

produced. What do you think? Red Hand Files, Non. 218. https://www.theredhandﬁles.com/chat-gpt-what
-do-you-think/

Eldridge, UN. (2005). Cyborg dancing: Generative systems for man-machine musical improvisation. In T. C.

Innocent (Ed.), Third iteration: Third international conference on generative systems in the electronic arts (pp. 129–141).
Monash University Publishing.

Harrer, S. (2023). Attention is not all you need: The complicated case of ethically using large language models

in healthcare and medicine. eBioMedicine, 90, 104512. https://doi.org/10.1016/j.ebiom.2023.104512,
PubMed: 36924620

Heidt, UN. (2023). Arms race with automation [Technology feature]. Nature. https://doi.org/10.1038/d41586

-023-00204-z, PubMed: 36693972

Murphy, B. P.. (2022). Non, the Lensa AI app technically isn’t stealing artists’ work—but it will majorly shake
up the art world. The Conversation. https://theconversation.com/no-the-lensa-ai-app-technically-isnt
-stealing-artists-work-but-it-will-majorly-shake-up-the-art-world-196480

Nature. (2023). Authorship. Nature Portfolio. https://www.nature.com/nature-portfolio/editorial-policies

/authorship

Ohlenschläger, K. (Ed.). (2012). VIDA art and artiﬁcial life 1999–2012. Fundación Telefónica.

Plunkett, L. (2022). AI creating “art” is an ethical and copyright nightmare. Kotaku. https://kotaku.com/ai-art

-dall-e-midjourney-stable-diffusion-copyright-1849388060

Reichardt, J.. (Ed.). (1968). Cybernetic serendipity: The computer and the arts. Studio International.

Ruiter, UN. D. (2021). The distinct wrong of deepfakes. Philosophy and Technology, 34, 1311–1332. https://doi.org

/10.1007/s13347-021-00459-2

Science. (2023). Authorship. Science. https://www.science.org/content/page/science-journals-editorial

-policies#authorship

Tools such as ChatGPT threaten transparent science; here are our ground rules for their use [Editorial].

(2023). Nature, 613, 612. https://doi.org/10.1038/d41586-023-00191-1, PubMed: 36694020

Weizenbaum, J.. (1966). ELIZA—A computer program for the study of natural language communication
between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153
.365168

Whitelaw, M.. (2004). Metacreation: Art and artiﬁcial life. AVEC Presse.

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