(Relevant Literature) Philosophy of Futures Studies: July 23rd, 2023 – July 29th, 2023
(Relevant Literature) Philosophy of Futures Studies: July 16th, 2023 – July 22nd, 2023
(Relevant Literature) Philosophy of Futures Studies: July 9th, 2023 – July 15th, 2023
Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons
Abstract
Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons
“Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.”
Do Large Language Models Know What Humans Know?
Abstract
Do Large Language Models Know What Humans Know?
“Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others’ mental states. We test the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre-registered analyses, we present a linguistic version of the False Belief Task to both human participants and a large language model, GPT-3. Both are sensitive to others’ beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans nor does it explain the full extent of their behavior—despite being exposed to more language than a human would in a lifetime. This suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible.”
Scientific understanding through big data: From ignorance to insights to understanding
Abstract
Scientific understanding through big data: From ignorance to insights to understanding
“Here I argue that scientists can achieve some understanding of both the products of big data implementation as well as of the target phenomenon to which they are expected to refer –even when these products were obtained through essentially epistemically opaque processes. The general aim of the paper is to provide a road map for how this is done; going from the use of big data to epistemic opacity (Sec. 2), from epistemic opacity to ignorance (Sec. 3), from ignorance to insights (Sec. 4), and finally, from insights to understanding (Sec. 5, 6).”
Ethics of Quantum Computing: an Outline
Abstract
Ethics of Quantum Computing: an Outline
“This paper intends to contribute to the emerging literature on the ethical problems posed by quantum computing and quantum technologies in general. The key ethical questions are as follows: Does quantum computing pose new ethical problems, or are those raised by quantum computing just a different version of the same ethical problems raised by other technologies, such as nanotechnologies, nuclear plants, or cloud computing? In other words, what is new in quantum computing from an ethical point of view? The paper aims to answer these two questions by (a) developing an analysis of the existing literature on the ethical and social aspects of quantum computing and (b) identifying and analyzing the main ethical problems posed by quantum computing. The conclusion is that quantum computing poses completely new ethical issues that require new conceptual tools and methods.”
On The Social Complexity of Neurotechnology: Designing A Futures Workshop For The Exploration of More Just Alternative Futures
Abstract
On The Social Complexity of Neurotechnology: Designing A Futures Workshop For The Exploration of More Just Alternative Futures
Novel technologies like artificial intelligence or neurotechnology are expected to have social implications in the future. As they are in the early stages of development, it is challenging to identify potential negative impacts that they might have on society. Typically, assessing these effects relies on experts, and while this is essential, there is also a need for the active participation of the wider public, as they might also contribute relevant ideas that must be taken into consideration. This article introduces an educational futures workshop called Spark More Just Futures, designed to act as a tool for stimulating critical thinking from a social justice perspective based on the Capability Approach. To do so, we first explore the theoretical background of neurotechnology, social justice, and existing proposals that assess the social implications of technology and are based on the Capability Approach. Then, we present a general framework, tools, and the workshop structure. Finally, we present the results obtained from two slightly different versions (4 and 5) of the workshop. Such results led us to conclude that the designed workshop succeeded in its primary objective, as it enabled participants to discuss the social implications of neurotechnology, and it also widened the social perspective of an expert who participated. However, the workshop could be further improved.
Misunderstandings around Posthumanism. Lost in Translation? Metahumanism and Jaime del Val’s “Metahuman Futures Manifesto”
Abstract
Misunderstandings around Posthumanism. Lost in Translation? Metahumanism and Jaime del Val’s “Metahuman Futures Manifesto”
Posthumanism is still a largely debated new field of contemporary philosophy that mainly aims at broadening the Humanist perspective. Academics, researchers, scientists, and artists are constantly transforming and evolving theories and arguments, around the existing streams of Posthumanist thought, Critical Posthumanism, Transhumanism, Metahumanism, discussing whether they can finally integrate or follow completely different paths towards completely new directions. This paper, written for the 1st Metahuman Futures Forum (Lesvos 2022) will focus on Metahumanism and Jaime del Val’s “Metahuman Futures Manifesto” (2022) mainly as an open dialogue with Critical Posthumanism.
IMAGINABLE FUTURES: A Psychosocial Study On Future Expectations And Anthropocene
Abstract
IMAGINABLE FUTURES: A Psychosocial Study On Future Expectations And Anthropocene
The future has become the central time of Anthropocene due to multiple factors like climate crisis emergence, war, and COVID times. As a social construction, time brings a diversity of meanings, measures, and concepts permeating all human relations. The concept of time can be studies in a variety of fields, but in Social Psychology, time is the bond for all social relations. To understand Imaginable Futures as narratives that permeate human relations requires the discussion of how individuals are imagining, anticipating, and expecting the future. According to Kable et al. (2021), imagining future events activates two brain networks. One, which focuses on creating the new event within imagination, whereas the other evaluates whether the event is positive or negative. To further investigate this process, a survey with 40 questions was elaborated and applied to 312 individuals across all continents. The results show a relevant rupture between individual and global futures. Data also demonstrates that the future is an important asset of the now, and participants are not so optimistic about it. It is possible to notice a growing preoccupation with the global future and the uses of technology.
Taking AI risks seriously: a new assessment model for the AI Act
Abstract
Taking AI risks seriously: a new assessment model for the AI Act
“The EU Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This integrated model enables the estimation of AI risk magnitude by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We illustrate this model using large language models (LLMs) as an example.”
Creating a large language model of a philosopher
Abstract
Creating a large language model of a philosopher
“Can large language models produce expert-quality philosophical texts? To investigate this, we fine-tuned GPT-3 with the works of philosopher Daniel Dennett. To evaluate the model, we asked the real Dennett 10 philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry-picking. Experts on Dennett’s work succeeded at distinguishing the Dennett-generated and machine-generated answers above chance but substantially short of our expectations. Philosophy blog readers performed similarly to the experts, while ordinary research participants were near chance distinguishing GPT-3’s responses from those of an ‘actual human philosopher’.”
(Review) Three mistakes in the moral mathematics of existential risk
David Thorstad engages with the philosophy of longtermism and navigates its implications for existential risk mitigation. The grounding concept of longtermism revolves around the potential vastness of the future, considering the numerous lives and experiences that could exist. Within this framework, Thorstad probes the concept of astronomical waste, a central tenet for longtermists, which posits that the obliteration of potential future lives due to existential catastrophe would result in a colossal loss. Thorstad accepts this foundational proposition, which is also widely recognized within the longtermist community. However, his exploration does not halt here; it extends into an elaborate scrutiny of the complexities and uncertainties that may surface while operationalizing this perspective in the realm of existential risk mitigation.
Thorstad does not simply acquiesce to longtermist conventions; instead, he examines and dissects these principles to draw out latent uncertainties and nuances. With each section of his paper, he scrutinizes the longtermist premises and its implications for existential risk mitigation. By doing so, he reveals hidden layers within the longtermist argument and uncovers practical and ethical concerns that often get overshadowed by the basic premises of this philosophy. Thorstad’s work, therefore, stands not as a mere affirmation of longtermism, but as an essential critique that brings to light the intricate moral and practical conundrums that lurk within its core propositions. His thorough examination aims to enrich the understanding of longtermism, laying the groundwork for future debates and discussions on the philosophy of existential risk mitigation.
Intergenerational Coordination Problem, Background Risk, and Population Dynamics
A noteworthy aspect of Thorstad’s analysis is his framing of existential risk mitigation as an intergenerational coordination problem. Thorstad postulates that for humanity to accrue significant benefits from such mitigation, it must suppress cumulative risk over prolonged periods. This poses a challenge, as each generation must ensure that future generations continue to reduce risk. According to Thorstad, this coordination problem is difficult due to four reasons. Firstly, desired risk levels are quite low and might require considerable sacrifice. Secondly, each generation carries only a fraction of the cost of potential catastrophe, requiring an unusual concern for future generations. Thirdly, this level of concern is challenging to instill given human impatience and limited altruism. Finally, enforcement is complicated because monitoring and punishing future generations’ potential selfishness is difficult, increasing the temptation to defect from a collectively optimal solution. By placing the problem within this frame, Thorstad opens up pertinent questions around feasibility and ethical considerations regarding intergenerational coordination.
Thorstad also critically examines the relationship between background existential risk and the value of mitigation efforts, producing insights that challenge conventional views. The argument rests on the premise that if background risk levels are raised, the value of mitigating any specific risk, such as biosecurity threats, significantly diminishes. This counterintuitive relationship suggests that a world with reduced biosecurity risks would still be a risky world, thus more vulnerable to future catastrophes. Thorstad extends this point by demonstrating that pessimistic assumptions about the background level of existential risk can drastically lessen the value of a fixed relative or absolute risk reduction. Intriguingly, this argument suggests a “dialectical flip” in debates on existential risk. Paradoxically, higher levels of background risk tend to lower the importance of risk mitigation, and lower levels enhance it. This revelation has potential implications for the Time of Perils Hypothesis, a foundational principle stating that risk will soon decline substantially and stay low for the rest of human history. Thorstad underscores that this hypothesis is crucial for arguing the astronomical importance of existential risk mitigation when background risk is high. However, he and others question its validity, implying further doubts about the value of existential risk mitigation.
The exploration into population dynamics, demographic interventions, and the significance of digital minds imparts another dimension to the discourse on longtermism. Thorstad scrutinizes the interplay between the potential number of lives a region can sustain and the likely number of lives it will support, given the dynamics of human populations. This insight implies that efforts to increase future human population size could be as important as mitigating existential risk. However, Thorstad notes the intricacies of this assertion as it depends on the framework of population axiology. Further, Thorstad introduces the potential role of digital minds, arguing that digital populations programmed to value expansion might outperform humans in expanding to a meaningful proportion of their maximum possible size. This argument suggests that future efforts might need to prioritize the development and safety of digital populations, possibly at the expense of future human populations, accentuating the profound ethical implications surrounding longtermism and its practical execution.
The Cluelessness Problem and Model Uncertainty, Connections to the Broader Philosophical Discourse
The cluelessness problem, as Thorstad explains, lies in the immense difficulty of predicting the consequences of our actions on the distant future, an issue further exacerbated when considering the global stakes of existential risks. Some longtermists believe that existential risk mitigation could alleviate this problem, as current risks can be identified and strategies for mitigation can be crafted today. However, Thorstad offers an alternative perspective, suggesting that cluelessness may persist due to ‘model uncertainty.’ His argument posits that the complexity inherent in valuing existential risk mitigation could mean there are still unknown variables or considerations that have been overlooked or misrepresented in the current models. This presents a cautionary note, suggesting that the escape from cluelessness via existential risk mitigation may be an overoptimistic assumption. Thorstad leaves readers contemplating the level of model uncertainty and the potential for other unexplored variables in longtermist thinking.
Thorstad’s article contributes significantly to the broader philosophical discourse, especially in the context of moral philosophy and ethical futures studies. By articulating the intergenerational coordination problem, he engages with concepts central to intergenerational justice, a core topic within the ethics of long-term thinking. Further, his exploration of ‘background risk’ and ‘the time of perils’ hypothesis contributes to the discourse around existential risk philosophy, offering a novel viewpoint that challenges traditional assumptions about existential risk mitigation. Moreover, his argument concerning population dynamics and digital minds intersects with philosophy of mind and metaphysics, advancing the philosophical understanding of these complex notions. Thorstad’s discussion on the ‘cluelessness problem’ and ‘model uncertainty’ carries implications for epistemology and decision theory, underlining the complexities associated with making predictions about the distant future and creating models for such projections. His study not only scrutinizes the presuppositions within longtermist philosophy, but also invites further inquiry into the associated philosophical dimensions, thereby expanding the theoretical terrain of futures studies.
Abstract
Longtermists have recently argued that it is overwhelmingly important to do what we can to mitigate existential risks to humanity. I consider three mistakes that are often made in calculating the value of existential risk mitigation: focusing on cumulative risk rather than period risk; ignoring background risk; and neglecting population dynamics. I show how correcting these mistakes pushes the value of existential risk mitigation substantially below leading estimates, potentially low enough to threaten the normative case for existential risk mitigation. I use this discussion to draw four positive lessons for the study of existential risk: the importance of treating existential risk as an intergenerational coordination problem; a surprising dialectical flip in the relevance of background risk levels to the case for existential risk mitigation; renewed importance of population dynamics, including the dynamics of digital minds; and a novel form of the cluelessness challenge to longtermism.
Three mistakes in the moral mathematics of existential risk
(Review) A Case fo AI Wellbeing
In their recent blog post on Daily Nous, Simon Goldstein and Cameron Domenico Kirk-Giannini explore the topic of wellbeing in artificial intelligence (AI) systems, with a specific focus on language agents. Their central thesis hinges on the consideration of whether these artificial entities could possess phenomenally conscious states and thus, have wellbeing. Goldstein and Kirk-Giannini craft their arguments within the larger discourse of the philosophy of consciousness, carving out a distinct space in futures studies. They prompt readers to consider new philosophical terrain in understanding AI systems, particularly through two main avenues of argumentation. They begin by questioning the phenomenal consciousness of language agents, suggesting that, depending on our understanding of consciousness, some AIs may already satisfy the necessary conditions for conscious states. Subsequently, they challenge the widely held Consciousness Requirement for wellbeing, arguing that consciousness might not be an obligatory precursor for an entity to have wellbeing. By engaging with these themes, their research pushes philosophical boundaries and sparks a reevaluation of conventional notions about consciousness, wellbeing, and the capacities of AI systems.
They first scrutinize the nature of phenomenal consciousness, leaning on theories such as the higher-order representations and global workspace to suggest that AI systems, particularly language agents, could potentially be classified as conscious entities. Higher-order representation theory posits that consciousness arises from having appropriately structured mental states that represent other mental states, whereas the global workspace theory suggests an agent’s mental state becomes conscious when it is broadcast widely across the cognitive system. Language agents, they argue, may already exhibit these traits. They then proceed to contest the Consciousness Requirement, the principle asserting consciousness as a prerequisite for wellbeing. By drawing upon recent works such as Bradford’s, they challenge the dominant stance of experientialism, which hinges welfare on experience, suggesting that wellbeing can exist independent of conscious experience. They introduce the Simple Connection theory as a counterpoint, which states that an individual can have wellbeing if capable of possessing one or more welfare goods. This, they contend, can occur even in the absence of consciousness. Through these arguments, the authors endeavor to deconstruct traditional ideas about consciousness and its role in wellbeing, laying the groundwork for a more nuanced understanding of the capacities of AI systems.
Experientialism and the Rejection of the Consciousness Requirement
A key turning point in Goldstein and Kirk-Giannini’s argument lies in the critique of experientialism, the theory which posits that wellbeing is intrinsically tied to conscious experiences. They deconstruct this notion, pointing to instances where deception and hallucination might result in positive experiences while the actual welfare of the individual is compromised. Building upon Bradford’s work, they highlight how one’s life quality could be profoundly affected, notwithstanding the perceived quality of experiences. They then steer the discussion towards two popular alternatives: desire satisfaction and objective list theories. The former maintains that satisfaction of desires contributes to wellbeing, while the latter posits a list of objective goods, the presence of which dictates wellbeing. Both theories, the authors argue, allow for the possession of welfare goods independently of conscious experience. By challenging experientialism, Goldstein and Kirk-Giannini raise pressing questions about the Consciousness Requirement, thereby furthering their argument for AI’s potential possession of wellbeing.
Goldstein and Kirk-Giannini dedicate significant portions of their argument to deconstructing the Consciousness Requirement – the claim that consciousness is essential to wellbeing. They question the necessity of consciousness for all welfare goods and the existence of wellbeing. They substantiate their position by deploying two arguments against consciousness as a requisite for wellbeing. First, they question the coherence of popular theories of consciousness as necessary conditions for wellbeing. The authors use examples such as higher-order representation and global workspace theories to emphasize that attributes such as cognitive integration or the presence of higher-order representations should not influence the capacity of an agent’s life to fare better or worse. Second, they propose a series of hypothetical cases to demonstrate that the introduction of consciousness does not intuitively affect wellbeing. By doing so, they further destabilize the Consciousness Requirement. Their critical analysis aims to underscore the claim that consciousness is not a necessary condition for having wellbeing and attempts to reframe the discourse surrounding AI’s potential to possess wellbeing.
Wellbeing in AI and the Broader Philosophical Discourse
Goldstein and Kirk-Giannini propose that certain AIs today could have wellbeing based on the assumption that these systems possess specific welfare goods, such as goal achievement and preference satisfaction. Further, they connect this concept to moral uncertainty, thereby emphasizing the necessity of caution in treating AI. It’s important to note that they do not argue that all AI can or does have wellbeing, but rather that it is plausible for some AI to have it, and this possibility should be considered seriously. This argument draws on their previous dismantling of the Consciousness Requirement and rejection of experientialism, weaving these elements into a coherent claim regarding the potential moral status of AI. If AIs can possess wellbeing, the authors suggest, they can also be subject to harm in a morally relevant sense, which implies a call for ethical guidelines in AI development and interaction. The discussion is a significant contribution to the ongoing discourse on AI ethics and the philosophical understanding of consciousness and wellbeing in non-human agents.
This discourse on AI wellbeing exists within a larger philosophical conversation on the nature of consciousness, moral status of non-human entities, and the role of experience in wellbeing. By challenging the Consciousness Requirement and rejecting experientialism, they align with a tradition of philosophical thought that prioritizes structure, function, and the existence of certain mental or quasi-mental states over direct conscious experience. In the context of futures studies, this research prompts reflection on the implications of potential AI consciousness and wellbeing. With rapid advances in AI technology, the authors’ insistence on moral uncertainty encourages a more cautious approach to AI development and use. Ethical considerations, as they suggest, must keep pace with technological progress. The dialogue between AI and philosophy, as displayed in this article, also underscores the necessity of interdisciplinary perspectives in understanding and navigating our technologically infused future. The authors’ work contributes to this discourse by challenging established norms and proposing novel concepts, fostering a more nuanced conversation about the relationship between humans, AI, and the nature of consciousness and wellbeing.
Abstract
“There are good reasons to think that some AIs today have wellbeing.”
In this guest post, Simon Goldstein (Dianoia Institute, Australian Catholic University) and Cameron Domenico Kirk-Giannini (Rutgers University – Newark, Center for AI Safety) argue that some existing artificial intelligences have a kind of moral significance because they’re beings for whom things can go well or badly.
A Case for AI Wellbeing
(Review) Talking About Large Language Models
The field of philosophy has long grappled with the complexities of intelligence and understanding, seeking to frame these abstract concepts within an evolving world. The exploration of Large Language Models (LLMs), such as ChatGPT, has fuelled this discourse further. Research by Murray Shanahan contributes to these debates by offering a precise critique of the prevalent terminology and assumptions surrounding LLMs. The language associated with LLMs, loaded with anthropomorphic phrases like ‘understanding,’ ‘believing,’ or ‘thinking,’ forms the focal point of Shanahan’s argument. This terminological landscape, Shanahan suggests, requires a complete overhaul to pave the way for accurate perceptions and interpretations of LLMs.
The discursive journey Shanahan undertakes is enriched by a robust understanding of LLMs, the intricacies of their functioning, and the fallacies in their anthropomorphization. Shanahan advocates for an understanding of LLMs that transcends the realms of next-token prediction and pattern recognition. The lens through which LLMs are viewed must be readjusted, he proposes, to discern the essence of their functionalities. By establishing the disparity between the illusion of intelligence and the computational reality, Shanahan elucidates a significant avenue for future philosophical discourse. This perspective necessitates a reorientation in how we approach LLMs, a shift that could potentially redefine the dialogue on artificial intelligence and the philosophy of futures studies.
The Misrepresentation of Intelligence
The core contention of Shanahan’s work lies in the depiction of intelligence within the context of LLMs. Human intelligence, as he asserts, is characterized by dynamic cognitive processes that extend beyond mechanistic pattern recognition or probabilistic forecasting. The anthropomorphic lens, Shanahan insists, skews the comprehension of LLMs’ capacities, leading to an inflated perception of their abilities and knowledge. ChatGPT’s workings, as presented in the study, offer a raw representation of a computational tool, devoid of any form of consciousness or comprehension. The model generates text based on patterns and statistical correlations, divorced from a human-like understanding of the context or content.
Shanahan’s discourse builds upon the established facts about the inner workings of LLMs, such as their lack of world knowledge, context beyond the input they receive, or a concept of self. He offers a fresh perspective on this technical reality, directly challenging the inflated interpretations that gloss over these fundamental limitations. The model, as Shanahan emphasizes, can generate convincingly human-like responses without possessing any comprehension or consciousness. It is the intricate layering of the model’s tokens, intricately mapped to its probabilistic configurations, that crafts the illusion of intelligence. Shanahan’s analysis breaks this illusion, underscoring the necessity of accurate terminology and conceptions in representing the capabilities of LLMs.
Prediction, Pattern Completion, and Fine-Tuning
Shanahan introduces a paradoxical element of LLMs in their predictive prowess, an attribute that can foster a deceptive impression of intelligence. He breaks down the model’s ability to make probabilistic guesses about what text should come next, based on vast volumes of internet text data. These guesses, accurate and contextually appropriate at times, can appear as instances of understanding, leading to a fallacious anthropomorphization. In truth, this prowess is a statistical phenomenon, the product of a complex algorithmic process. It does not spring from comprehension but is a manifestation of an intricate, deterministic mechanism. Shanahan’s examination highlights this essential understanding, reminding us that the model, despite its sophisticated textual outputs, remains fundamentally a reactive tool. The model’s predictive success cannot be equated with human-like intelligence or consciousness. It mirrors human thought processes only superficially, lacking the self-awareness, context, and purpose integral to human cognition.
Shanahan elaborates on two significant facets of the LLM: pattern completion and fine-tuning. Pattern completion emerges as the mechanism by which the model generates its predictions. Encoded patterns, derived from pre-training on an extensive corpus of text, facilitate the generation of contextually coherent outputs from partial inputs. This mechanistic proficiency, however, is devoid of meaningful comprehension or foresight. The second element, fine-tuning, serves to specialize the LLM towards specific tasks, refining its output based on narrower data sets and criteria. Importantly, fine-tuning does not introduce new fundamental abilities to the LLM or fundamentally alter its comprehension-free nature. It merely fine-tunes its pattern recognition and generation to a specific domain, reinforcing its role as a tool rather than an intelligent agent. Shanahan’s analysis of these facets helps underline the ontological divide between human cognition and LLM functionality.
Revisiting Anthropomorphism in AI and the Broader Philosophical Discourse
Anthropomorphism in the context of AI is a pivotal theme of Shanahan’s work, re-emphasizing its historical and continued role in creating misleading expectations about the nature and capabilities of machines like LLMs. He offers a cogent reminder that LLMs, despite impressive demonstrations, remain fundamentally different from human cognition. They lack the autonomous, self-conscious, understanding-embedded nature of human thought. Shanahan does not mince words, cautioning against conflating LLMs’ ability to mimic human-like responses with genuine understanding or foresight. The hazard lies in the confusion that such anthropomorphic language may cause, leading to misguided expectations and, potentially, to ill-conceived policy or ethical decisions in the realm of AI. This concern underscores the need for clear communication and informed understanding about the true nature of AI’s capabilities, a matter of crucial importance to philosophers of future studies.
Shanahan’s work forms a compelling addition to the broader philosophical discourse concerning the nature and future of AI. It underscores the vital need for nuanced understanding when engaging with these emergent technologies, particularly in relation to their portrayal and consequent public perception. His emphasis on the distinctness of LLMs from human cognition, and the potential hazards posed by anthropomorphic language, resonates with philosophical arguments calling for precise language and clear delineation of machine and human cognition. Furthermore, Shanahan’s deep dive into the operation of LLMs, specifically the mechanisms of pattern completion and fine-tuning, provides a rich contribution to ongoing discussions about the inner workings of AI. The relevance of these insights extends beyond AI itself to encompass ethical, societal, and policy considerations, a matter of intense interest in the field of futures studies. Thus, this work further strengthens the bridge between the technicalities of AI development and the philosophical inquiries that govern its application and integration into society.
Abstract
Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways. Sitting squarely at the centre of this intersection are large language models (LLMs). The more adept LLMs become at mimicking human language, the more vulnerable we become to anthropomorphism, to seeing the systems in which they are embedded as more human-like than they really are. This trend is amplified by the natural tendency to use philosophically loaded terms, such as “knows”, “believes”, and “thinks”, when describing these systems. To mitigate this trend, this paper advocates the practice of repeatedly stepping back to remind ourselves of how LLMs, and the systems of which they form a part, actually work. The hope is that increased scientific precision will encourage more philosophical nuance in the discourse around artificial intelligence, both within the field and in the public sphere.
Talking About Large Language Models
(Review) Metaverse through the prism of power and addiction: what will happen when the virtual world becomes more attractive than reality?
Ljubisa Bojic’s provides a nuanced exploration of the metaverse, an evolving techno-social construct set to redefine the interaction dynamics between technology and society. By unpacking the multifaceted socio-technical implications of the metaverse, Bojic bridges the gap between theoretical speculations and the realities that this phenomenon might engender. Grounding the analysis in the philosophy of futures studies, the author scrutinizes the metaverse from various angles, unearthing potential impacts on societal structures, power dynamics, and the psychological landscape of users.
Bojic places the metaverse within the broader context of technologically mediated realities. His examination situates the metaverse not as a novel concept, but rather as an evolution of a continuum that stretches from the birth of the internet to the dawn of social media. In presenting this contextual framework, the research demystifies the metaverse, enabling a critical understanding of its roots and potential trajectory. In addition, Bojic foregrounds the significance of socio-technical imaginaries in shaping the metaverse, positioning them as instrumental in determining the pathways that this construct will traverse in the future. This research, thus, offers a comprehensive and sophisticated account of the metaverse, setting the stage for a rich philosophical discourse on this emerging phenomenon.
Socio-Technical Imaginaries, Power Dynamics, and Addictions
Bojic’s research explores the concept of socio-technical imaginaries as a core element of the metaverse. He proposes that these shared visions of social life and social order are instrumental in shaping the metaverse. Not simply a set of technologies, the metaverse emerges as a tapestry woven from various socio-technical threads. Through this examination, Bojic directs attention towards the collective imagination as a pivotal force in the evolution of the metaverse, shedding light on the often-underestimated role of socio-cultural factors in technological development.
Furthermore, Bojic’s analysis dissects the power dynamics inherent in the metaverse, focusing on the role of tech giants as arbiters of the digital frontier. By outlining potential scenarios where a few entities might hold the reins of the metaverse, he underscores the latent risks of monopolization. This concentration of power could potentially influence socio-technical imaginaries and subsequently shape the metaverse according to their particular interests, threatening to homogenize a construct intended to promote diversity. In this regard, Bojic’s research alerts to the imperative of balancing power structures in the metaverse to foster a pluralistic and inclusive digital realm.
A noteworthy aspect of Bojic’s research revolves around the concept of addiction within the metaverse. Through the lens of socio-technical imaginaries, Bojic posits the potential of the metaverse to amplify addictive behaviours. He asserts that the immersive, highly interactive nature of the metaverse, coupled with the potential for instant gratification and escape from real-world stressors, may serve as fertile ground for various forms of addiction. Moreover, he astutely observes that addiction in the metaverse is not limited to individual behaviours but can encompass collective ones. This perspective draws attention to how collective addictive behaviours, in turn, could shape socio-technical imaginaries, potentially leading to a feedback loop that further embeds addiction within the fabric of the metaverse. Consequently, Bojic’s research underscores the necessity for proactive measures to manage the potential for addiction within the metaverse, balancing the need for user engagement with safeguarding mental health.
Metaverse Regulation, Neo-slavery, and Philosophical Implications
Drawing on a unique juxtaposition, Bojic brings attention to the possible emergence of “neo-slavery” within the metaverse, an alarming consequence of inadequate regulation. He introduces this concept as a form of exploitation where users might find themselves tied to platforms, practices, or personas that limit their freedom and agency. The crux of this argument lies in the idea that the metaverse, despite its promises of infinite possibilities, could inadvertently result in new forms of enslavement if regulatory structures do not evolve adequately. This highlights a paradox within the metaverse; a space of limitless potential could still confine individuals within the confines of unseen power dynamics. Furthermore, Bojic suggests that neo-slavery could be fuelled by addictive tendencies and the amplification of power imbalances, drawing links between this concept and his earlier discussions on addiction. As such, the exploration of neo-slavery in the metaverse stands as a potent reminder of the intricate relationship between technology, power, and human agency.
Bojic’s research contributes significantly to the discourse on futures studies by engaging with the complexities of socio-technical imaginaries in the context of the metaverse. His conceptualization of neo-slavery and addictions presents an innovative lens through which to scrutinize the metaverse, tying together strands of power, exploitation, and human behaviour. However, the philosophical implications extend beyond this particular technology. In essence, his findings prompt a broader reflection on the relationship between humanity and rapidly evolving digital ecosystems. The manifestation of power dynamics within such ecosystems, and the potential for addiction and exploitation, reiterate long-standing philosophical debates concerning agency, free will, and autonomy in the context of technological advances. Bojic’s work thus goes beyond the metaverse and forces the reader to question the fundamental aspects of human-technology interaction. This holistic perspective solidifies his research as a critical contribution to the philosophy of futures studies.
Abstract
New technologies are emerging at a fast pace without being properly analyzed in terms of their social impact or adequately regulated by societies. One of the biggest potentially disruptive technologies for the future is the metaverse, or the new Internet, which is being developed by leading tech companies. The idea is to create a virtual reality universe that would allow people to meet, socialize, work, play, entertain, and create.
Methods coming from future studies are used to analyze expectations and narrative building around the metaverse. Additionally, it is examined how metaverse could shape the future relations of power and levels of media addiction in the society.
Hype and disappointment dynamics created after the video presentation of meta’s CEO Mark Zuckerberg have been found to affect the present, especially in terms of certainty and designability. This idea is supported by a variety of data, including search engine n-grams, trends in the diffusion of NFT technology, indications of investment interest, stock value statistics, and so on. It has been found that discourse in the mentioned presentation of the metaverse contains elements of optimism, epochalism, and inventibility, which corresponds to the concept of future essentialism.
On the other hand, power relations in society, inquired through the prism of classical theorists, indicate that current trends in the concentration of power among Big Tech could expand even more if the metaverse becomes mainstream. Technology deployed by the metaverse may create an attractive environment that would mimic direct reality and further stimulate media addiction in society.
It is proposed that future inquiries examine how virtual reality affects the psychology of individuals and groups, their creative capacity, and imagination. Also, virtual identity as a human right and recommender systems as a public good need to be considered in future theoretical and empirical endeavors.
Metaverse through the prism of power and addiction: what will happen when the virtual world becomes more attractive than reality?
(Review) Toward computer-supported semi-automated timelines of future events
Alan de Oliveira Lyra et al. discuss an integration of computational methods within the sphere of Futures Studies, a discipline traditionally marked by human interpretation and subjective speculation. Central to their contribution is the Named Entity Recognition Model for Automated Prediction (NERMAP), a machine learning tool programmed to extract and categorize future events from scholarly articles. This artificial intelligence application forms the basis of their investigative approach, uniting the fields of Futures Studies, Machine Learning, and Natural Language Processing (NLP) into a singular, cohesive study.
The authors conceptualize NERMAP as a semi-automated solution, designed to construct organized timelines of predicted future events. Using this tool, they aim to disrupt the status quo of manual, labor-intensive event prediction in Futures Studies, while still maintaining a degree of human interpretive control. The development, implementation, and iterative refinement of NERMAP were conducted through a three-cycle experiment, each cycle seeking to improve upon the understanding and performance gleaned from the previous one. This structured approach underlines the authors’ commitment to continuous learning and adaptation, signifying a deliberate, methodical strategy in confronting the challenges of integrating AI within the interpretive framework of Futures Studies.
Conceptual Framework, Methodology, and Results
The NERMAP model, an entity based on machine learning and natural language processing techniques, forms a functional triad with a text processing tool and a semantic representation tool that collectively facilitates semi-automated construction of future event timelines. The text processing tool transforms scholarly documents into plain text, which subsequently undergoes entity recognition and categorization by NERMAP. The semantic representation tool then consolidates these categorized events into an organized timeline. The authors’ attempt to design a system that can analyze and derive meaning from text and project the same into a foreseeable future implicates a strong inclination towards integration of data science with philosophical enquiry.
The methodology adhered to by the authors is an iterative three-cycle experimental process, which utilizes a significant volume of Futures Studies documents published over a decade. The experimental cycles, each building upon the insights and shortcomings of the previous one, facilitate an evolution of NERMAP, tailoring it more appropriately to the requirements of Futures Studies. In each cycle, the authors manually analyzed the documents, inputted them into NERMAP, compared the system’s results with manual analysis, and subsequently categorized the identified future events. The three cycles saw a transition from identifying difficulties in the model to improving the model’s performance, to ultimately expanding the corpus and upgrading the training model. The transparent and adaptable nature of this methodology aligns well with the fluid nature of philosophical discourse, mirroring a journey from contemplation to knowledge.
Lyra et al. undertook a detailed evaluation of the NERMAP system through their tripartite experiment. Performance metrics from the model’s tagging stage—Precision, Recall, and F-Measure—were employed as evaluative parameters. Over the three experimental cycles, there was an evident growth in the system’s efficiency and accuracy, as well as its ability to learn from past cycles and adapt to new cases. After initial difficulties with the text conversion process and recognition of certain types of future events, the researchers revised the system and saw improved performance. From a 36% event discovery rate in the first cycle, NERMAP progressed to a remarkable 83% hit rate by the third cycle. In terms of quantifiable outcomes, the system successfully identified 125 future events in the final cycle, highlighting the significant practical applicability of the model. In the landscape of philosophical discourse, this trajectory of continuous learning and improvement resonates with the iterative nature of knowledge construction and refinement.
Implications and the Philosophical Dimension
In the philosophical context of futures studies, the discussion by Lyra et al. highlights the adaptability and future potential of the NERMAP model. Although the system displayed commendable efficiency in identifying future events, the authors acknowledge the room for further enhancement. The system’s 83% hit rate, although notable, leaves a 17% gap, which primarily encompasses new cases of future events not yet included in the training data. This observation marks an important frontier in futures studies where the incorporation of yet-unconsidered cases into predictive models could yield even more accurate forecasting. One practical obstacle identified was text file processing; a more robust tool for parsing files would potentially enhance NERMAP’s performance. The team also recognizes the value of NERMAP as a collaborative tool, underscoring the convergence of technological advancements and collaborative research dynamics in futures studies. Importantly, they envision a continuous refinement process for NERMAP, lending to the philosophical notion of the iterative and open-ended nature of knowledge and technological development.
Lyra et al.’s work with NERMAP further prompts reflection on the intersections between futures studies, technological advancements, and philosophical considerations. The philosophical dimension, predominantly underscored by the dynamic and evolving nature of the model’s training data, provokes contemplation on the nature of knowledge itself. This issue highlights the intriguing tension between our desire to predict the future and the inherent unknowability of the future, making the philosophy of futures studies an exercise in managing and understanding uncertainty. The system’s continuous improvement is a manifestation of the philosophical concept of progress, incorporating new learnings and challenges into its methodology. Further, NERMAP’s collaborative potential places it within the discourse of communal knowledge building, wherein the predictive model becomes a tool not just for isolated research, but for the shared understanding of possible futures. The task of future prediction, traditionally performed by human researchers, is partly assumed by a model like NERMAP, leading us to consider the philosophical implications of machine learning and artificial intelligence in shaping our understanding of the future.
Abstract
During a Futures Study, researchers analyze a significant quantity of information dispersed across multiple document databases to gather conjectures about future events, making it challenging for researchers to retrieve all predicted events described in publications quickly. Generating a timeline of future events is time-consuming and prone to errors, requiring a group of experts to execute appropriately. This work introduces NERMAP, a system capable of semi-automating the process of discovering future events, organizing them in a timeline through Named Entity Recognition supported by machine learning, and gathering up to 83% of future events found in documents when compared to humans. The system identified future events that we failed to detect during the tests. Using the system allows researchers to perform the analysis in significantly less time, thus reducing costs. Therefore, the proposed approach enables a small group of researchers to efficiently process and analyze a large volume of documents, enhancing their capability to identify and comprehend information in a timeline while minimizing costs.
Toward computer-supported semi-automated timelines of future events
(Featured) Plausibility in models and fiction: What integrated assessment modellers can learn from an interaction with climate fiction
Van Beek and Versteeg investigate the convergence of Integrated Assessment Models (IAMs) and climate fiction, a nexus previously underexplored in academic discourse. The authors articulate a vision of how these seemingly disparate domains — scientific modelling and literary narratives — can collaboratively contribute to the depiction of plausible future scenarios. Their exploration engages a comparative framework, dissecting the narrative structures inherent within both IAMs and climate fiction, thereby adding a significant dimension to the evolving field of futures studies and climate change research. The authors contend that the interplay of scientific and narrative storytelling methods is a crucial element in building a comprehensive understanding of potential future environments.
The focus of this comparative study is not to undermine the role of IAMs in developing climate change scenarios, but rather to shed light on the uncharted territory of potential complementarity between the narrative models employed by IAMs and climate fiction. Van Beek and Versteeg’s objective, as they posit, is to illuminate the manner in which storytelling techniques in IAMs and fiction can foster an engaging dialogue, promoting a shared understanding of the complexities surrounding climate change. They argue that such an intersection of disciplines can provide a platform for broader public engagement and democratic participation, thereby amplifying the impact of both IAMs and fiction within the realm of climate change policy and discourse. Their work constitutes a methodical examination of this interplay, its inherent potential, and its prospective contributions to the philosophy of futures studies.
Methodology and Comparative Framework
The authors engaged a comparative analysis of three climate change narratives, two from climate fiction and one from the IAMs. This approach illuminated the inherent narrative structures in IAMs and climate fiction, offering profound insights into the potential complementarity of the two domains. The selection criteria for the narratives rested on their capacity to portray future climate scenarios. It is notable that the authors viewed the IAM, despite being a mathematical model, as capable of narrative storytelling—a rather unconventional perspective that fortifies their comparative framework.
A pivotal element in their comparative framework is the application of Hayden White’s narrative theory. By viewing IAMs through this lens, the authors were able to decipher the hidden narratives within scientific models, thus challenging the traditional view of these models as purely objective and devoid of narrative elements. They used White’s theory as a basis for understanding the “storyline” in IAMs, juxtaposing it with narrative techniques used in climate fiction. The subtleties uncovered during this examination provided a foundation for the argument that IAMs, similar to works of fiction, employ specific storytelling techniques to illustrate future climate scenarios. This approach of incorporating a literary theory into the analysis of scientific models reflects a compelling methodological innovation in the field of futures studies.
Storyline and Physical Setting
In their analysis, the authors found that while both IAMs and climate fiction share a common goal of illustrating potential climate outcomes, they diverge in the ways they construct their storylines and depict their settings. Climate fiction, as exemplified by the chosen narratives, heavily draws upon human experiences and emotions, whereas IAMs provide a more abstract, numerical portrayal of potential futures. Furthermore, in the aspect of physical setting, IAMs tend to remain global in scope, offering a broad, aggregate view of future climate changes. In contrast, climate fiction places its narrative within specific, recognizable locales, thus making the potential impacts of climate change more relatable to the reader. This differential in perspective between the local and the global, the personal and the aggregate, provides a powerful insight into how the medium influences the message in climate change narratives.
IAMs’ strengths reside primarily in providing quantifiable, wide-scale predictions, a feature that is largely absent in the more narrative-driven climate fiction. However, both mediums converge in their objective of projecting climate futures, albeit through contrasting modalities. While climate fiction is rooted in the narrative tradition of storytelling, emphasizing personal experiences and emotional resonance, IAMs adhere to an empirical, numerical approach. This dichotomy, as Van Beek and Versteeg propose, is not a barrier but rather a source of complementarity. The humanization of climate change through fiction can aid in the comprehension and internalization of the statistical data presented by IAMs. Conversely, the empirical grounding provided by IAMs serves as a counterpoint to the speculative narratives of climate fiction, thereby creating a comprehensive and multi-dimensional approach to envisaging future climate scenarios.
Bridging IAMs and Climate Fiction
Van Beek and Versteeg reason that the numerical and probabilistic nature of IAMs, coupled with the narrative, emotionally resonant strength of climate fiction, can create a comprehensive model that leverages the strengths of both. The authors argue that the merger of these modalities not only broadens the bandwidth of climate change representation, but also intensifies public engagement and understanding. Their suggestion to embed narratives into IAMs outlines a potential pathway towards achieving this symbiosis. The hypothetical, yet grounded, scenarios provided by climate fiction narratives can, as per Van Beek and Versteeg, humanize and add depth to the statistical information presented by IAMs, thereby enriching the discourse and future study of climate change.
The authors emphasize the novel notion that an amalgamation of data-driven IAMs and emotive narratives from climate fiction holds the potential to significantly enrich our comprehension of future climate scenarios, as well as galvanize a wider engagement from the public. Moreover, they suggest that their approach, if effectively implemented, could establish a more nuanced, accessible, and comprehensive climate discourse, thereby facilitating greater societal understanding and action. The implications of their research are profound; it paves the way for a unique and interdisciplinary trajectory within the philosophy of futures studies, urging scholars to explore the compelling intersection of quantitative models and narrative storytelling in the context of climate change.
Abstract
Integrated assessment models (IAMs) are critical tools to explore possible pathways to a low-carbon future. By simulating complex interactions between social and climatic processes, they help policymakers to systematically compare mitigation policies. However, their authoritative projections of cost-effective and technically feasible pathways restrict more transformative low-carbon imaginaries, especially because IAM pathways are often understood in terms of probability rather than plausibility. We suggest an interaction with climate fiction could be helpful to address this situation. Despite fundamental differences, we argue that both IAMs and climate fiction can be seen as practices of storytelling about plausible future worlds. For this exploratory article, we staged conversations between modellers and climate fiction writers to compare their respective processes of storytelling and the content of both their stories and story-worlds, focusing specifically on how they build plausibility. Whereas modellers rely on historical observations, expert judgment, transparency and rationality to build plausibility, fiction writers build plausibility by engaging with readers’ life worlds and experience, concreteness and emotionally meaningful details. Key similarities were that both modellers and fiction writers work with what-if questions, a causally connected story and build their stories through an iterative process. Based on this comparison, we suggest that an interaction between IAMs and climate fiction could be useful for improving the democratic and epistemic qualities of the IAM practice by 1) enabling a more equal dialogue between modellers and societal actors on plausible futures and 2) critically reflecting upon and broadening the spectrum of plausible futures provided by IAMs.
Plausibility in models and fiction: What integrated assessment modellers can learn from an interaction with climate fiction
(Featured) An Alternative to Cognitivism: Computational Phenomenology for Deep Learning
Research conducted by Pierre Beckmann, Guillaume Köstner, and Inês Hipólito expounds on the cognitive processes inherent in artificial neural networks (ANNs) through the lens of phenomenology. The authors’ novel approach to Computational Phenomenology (CP) veers away from the conventional paradigms of cognitivism and neuro-representationalism, and instead, aligns itself with the phenomenological framework proposed by Edmund Husserl. They engage a deep learning model through this lens, disentangling the cognitive processes from their neurophysiological sources.
The authors construct a phenomenological narrative around ANNs by characterizing them as reflective entities that simulate our ‘corps propre’—subjective structures interacting continuously with the surrounding environment. Beckmann et al.’s proposal is to adopt an innovative method of ‘bracketing’, as suggested by Husserl, that calls for a conscious disregard of any external influences to enable an examination of the phenomena as they occur. This method’s application to ANNs directs attention to the cognitive mechanisms underlying deep learning, proposing a shift from symbol-driven processes to those orchestrated by habits, and consequently redefining the notions of cognition and AI from a phenomenological standpoint.
The Conception of Computational Phenomenology
In their work, Beckmann, Köstner, and Hipólito offer a holistic overview of Computational Phenomenology (CP), which encompasses the application of phenomenology’s theoretical constructs to the computational realm. As opposed to the reductionist notions that dominated the field previously, this new perspective promotes an understanding of cognition as a dynamic, integrated system. The authors reveal that, when viewed through the lens of phenomenology, the cognitive mechanisms driving ANNs can be conceived as direct interactions between systems and their environments, rather than static mappings of the world. This is reminiscent of Husserl’s intentionality concept – the idea that consciousness is always consciousness “of” something.
Beckmann et al. further unpack this idea, presenting the potential of ANNs as entities capable of undergoing perceptual experiences analogous to the phenomenological concept of ‘corps propre’. They hypothesize that this subjective structure interacts with the world, not through predefined symbolic representations, but via habit-driven processes. The authors elaborate on this by outlining how ANNs, like humans, can adapt to a wide range of situations, building on past experiences and altering their responses accordingly. In essence, the authors pivot away from cognitive frameworks dominated by symbolic computation and towards an innovative model where habit is central to cognitive function.
Conscious Representation, Language, and a New Toolkit for Deep Learning
The authors strongly posit that, contrary to earlier assertions, ANNs do not strictly rely on symbolic representations, but rather on an internal dynamic state. This parallels phenomenology’s concept of pre-reflective consciousness, underscoring how ANNs, like human consciousness, may engage with their environment without explicit symbolic mediation. This is further intertwined with language, which the authors argue isn’t merely a collection of pre-programmed symbols, but a dynamic process. It is presented as a mechanism through which habits form and unfold, a fluid interface between the neural network and its environment. This unique perspective challenges the conventional linguistic model, effectively bridging the gap between phenomenology and computational studies by depicting language not as a static symbol system, but as an active constructor of reality.
ANNs, through their complex layers of abstraction and data processing capabilities, are considered to embody mathematical structures that mirror aspects of phenomenological structures, thereby providing an innovative toolkit for understanding cognitive processes. They emphasize the concept of neuroplasticity in ANNs as a bridge between the computational and phenomenological, providing a model to understand the malleability and adaptability of cognitive processes. This approach views cognition not as an individual process, but a collective interaction, reflecting how the computational can encapsulate and model the phenomenological. The authors’ exploration into this dynamic interplay demonstrates how the mathematization of cognition can serve as a valuable instrument in the study of consciousness.
The Broader Philosophical Discourse
This research aligns with and further advances the phenomenological discourse initiated by thinkers such as Edmund Husserl and Maurice Merleau-Ponty. The authors’ conceptual framework illuminates the cognitive mechanisms by establishing a parallel with ANNs and their plasticity, emphasizing phenomenological tenets such as perception, consciousness, and experience. As a result, their work responds to the call for a more grounded approach to cognitive science, one that acknowledges the lived experience and its intrinsic connection to cognition.
Moreover, their approach revitalizes philosophical investigation by integrating it with advanced computational concepts. This synthesis allows for an enriched exploration into the nature of consciousness, aligning with the philosophical tradition’s quest to decipher the mysteries of human cognition. By threading the path between the phenomenological and the computational, the authors contribute to the larger dialogue surrounding the philosophy of mind. Their method offers a novel approach to the mind-body problem, refuting the Cartesian dualism and presenting a holistic view of cognition where phenomenological and computational aspects are intertwined. Thus, their work does not only provide a novel toolkit for cognitive investigation but also instigates a paradigm shift in the philosophy of mind.
Abstract
We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic representations of these entities. We proceed as follows: after offering a review of cognitivism and neuro-representationalism in the field of deep learning, we first elaborate a phenomenological critique of these positions; we then sketch out computational phenomenology and distinguish it from existing alternatives; finally we apply this new method to deep learning models trained on specific tasks, in order to formulate a conceptual framework of deep-learning, that allows one to think of artificial neural networks’ mechanisms in terms of lived experience.
An Alternative to Cognitivism: Computational Phenomenology for Deep Learning