Category: Artificial Intelligence (AI)

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Human innovation and the creative agency of the world in the age of generative AI

My newly published paper on the relationship between creativity, innovation & novelty, generative AI/LLMs, future potentials, and the creative agency of the world:
Peschl (2024). Human innovation and the creative agency of the world in the age of generative AI. Possibility Studies & Society, https://doi.org/10.1177/27538699241238049 (online first, open access)

Abstract
With the advent of Large Language Models, such as ChatGPT, and, more generally, generative AI/cognitive technologies, global knowledge production faces a critical systemic challenge. It results from continuously feeding back non- or poorly-creative copies of itself into the global knowledge base; in the worst case, this could not only lead to a stagnation of creative, reliable, and valid knowledge generation, but also have an impact on our material (and subsequently our social) world and how it will be shaped by these rather uninspired automatized knowledge dynamics.
More than ever, there appears to be an imperative to bring the creative human agent back into the loop. Arguments from the perspectives of 4E- and Material Engagement Theory approaches to cognition, human-technology relations as well as possibility studies will be used to show that being embodied, sense-making, and enacting the world by proactively and materially interacting with it are key ingredients for any kind of knowledge and meaning production. It will be shown that taking seriously the creative agency of the world, an engaged epistemology, as well as making use of future potentials/possibilities complemented and augmented by cognitive technologies are all essential for re-introducing profound novelty and creativity.

Keywords: AI, cognitive technologies, creative agency of the world, creativity, 4E cognition, embodied cognition, engaged epistemology, future potential, innovation, large language models, possibility, radical novelty, World

Download here: https://doi.org/10.1177/27538699241238049 (online first, open access)

#AI #cognitivetechnologies #creativeagencyoftheworld #creativity #4Ecognition #embodiedcognition #engagedepistemology #futurepotential #innovation #LLM #radicalnovelty

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On the “creativity”​ of AI — Preliminary critical remarks

Praise for the creative capabilities of recent developments in AI technologies is ubiquitous in the media and relevant blogs.

Although really astonishing (as an example, have a look here [and the examples in the article]), creating novel ideas by using AI seems to have intrinsic limitations.

To us humans, these results sometimes seem really “creative”, “novel”, or surprising. However this is mainly due to the limitations in our own imagination, which is simply not capable of processing such huge amounts of data.

In essence, AI’s creativity is the result of hyper-complex processes of learning and adaptation that is based on an almost endless ocean of data/”knowledge” (without meaning). This has several implications concerning the underlying premises of such an AI-driven understanding of creativity and bringing forth novelty:

  1. These systems are based almost exclusively on already existing knowledge. Hence, their learning algorithms apply a strategy of learning from the past.
  2. This leads to a form of creativity that is grounded in the idea of (re-)combining already existing concepts/things. This is an accepted and valid strategy well known from creativity and innovation research. However, we have to keep in mind, that the results will remain in the realm of the predictable, or, from a Kuhnian perspective, within the paradigm of what already exists.
  3. It is a purely “brain/mind-based” form of creativity that does not take into account the world and its potentials (e.g., affordances) as a possible source of novelty (e.g., by interacting and engaging with it).

If we are interested in really “ground breaking”, radical, or disruptive innovations, these strategies will not suffice. As we show in our research, we will have to follow a strategy of Emergent Innovation, “Learning from the future and future potentials as they emerge” as well as acquire futures skills and a perspective on innovation that is grounded in an enactive understanding of cognition.

Will AI be able to sense the future by learning from it, its affordances and potentials, and from interacting with and enacting its environment?

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Course on the Ethics and Governance of Artificial Intelligence — MIT Media Lab

This course will pursue a cross-disciplinary investigation of the development and deployment of the opaque complex adaptive systems that are increasingly in public and private use.  We will explore the proliferation of algorithmic decision-making, autonomous systems, and machine learning and explanation; the search for balance between regulation and innovation; and the effects of AI on the dissemination of information, along with questions related to individual rights, discrimination, and architectures of c

Source: The Ethics and Governance of Artificial Intelligence — MIT Media Lab