This paper is based on Ludwig Wittgenstein's (late) teaching on language and meaning, and its aim is to show how we can avoid the anthropomorphization of artificial intelligence or interpreting the work (the question of giving meaning) of AI as similar to or the same as the work of a human being. The way of determining the meaning of certain linguistic units performed by an AI and a human differs because the languages they operate with have a different set of (...) rules or criteria that are indicators of what a certain linguistic entity means. The core of AI in terms of meaning is its logical base, according to which it operates/calculates/manipulates the given information. On the other hand, a human being finds his/her criteria in the language activity performed in the language communities. (shrink)

This paper argues against John Searle in defense of the potential for computers to understand language (“Strong AI”) by showing that semantic meaning is itself a second-order system of rules that connects symbols and syntax with extralinguistic facts. Searle’s Chinese Room Argument is contested on theoretical and practical grounds by identifying two problems in the thought experiment, and evidence about “machine learning” is used to demonstrate that computers are already capable of learning to form true observation sentences in the same (...) way humans do. Finally, sarcasm is used as an example to extend the argument to more complex uses of language. (shrink)

We argue that the later Wittgenstein’s philosophy of language and mathematics, substantially focused on rule-following, is relevant to understand and improve on the Artificial Intelligence (AI) alignment problem: his discussions on the categories that influence alignment between humans can inform about the categories that should be controlled to improve on the alignment problem when creating large data sets to be used by supervised and unsupervised learning algorithms, as well as when introducing hard coded guardrails for AI models. We cast these (...) considerations in a model of human–human and human–machine alignment and sketch basic alignment strategies based on these categories and further reflections on rule-following like the notion of meaning as use. To sustain the validity of these considerations, we also show that successful techniques employed by AI safety researchers to better align new AI systems with our human goals are congruent with the stipulations that we derive from the later Wittgenstein’s philosophy. However, their application may benefit from the added specificities and stipulations of our framework: it extends on the current efforts and provides further, specific AI alignment techniques. Thus, we argue that the categories of the model and the core alignment strategies presented in this work can inform further AI alignment techniques. (shrink)

Classical natural language processing endeavored to understand the language of native speakers. When this proved to lie beyond the horizon, a scaled-down version settled for text analysis and processing but retained the old name and acronym. But text ≠ language. Any combination of signs and symbols qualifies as text. Language presupposes meaning, which is what connects it to real life. Failing to distinguish between the two results in confusing humanoids (machines thinking like humans) with machinoids (humans thinking like machines). As (...) scientific English (SciEng) became the lingua franca of science, it has acquired all the traits of a machine language: reduced vocabulary, where fewer and fewer words have taken on more and more meanings; prescribed use of pronouns; depersonalized rigid syntactic forms and rules of composition. Compliance with SciEng standards can be automatically verified, which means that Sci Eng can be automatically imitated, what is referred to as AI writing (ChatGPT). The article discusses an attempt to automatically correct deviations from the rules by what is touted as AI editing. (shrink)

Most, if not all, philosophers agree that computers cannot learn what words refers to from raw text alone. While many attacked Searle’s Chinese Room thought experiment, no one seemed to question this most basic assumption. For how can computers learn something that is not in the data? Emily Bender and Alexander Koller ( 2020 ) recently presented a related thought experiment—the so-called Octopus thought experiment, which replaces the rule-based interlocutor of Searle’s thought experiment with a neural language model. The Octopus (...) thought experiment was awarded a best paper prize and was widely debated in the AI community. Again, however, even its fiercest opponents accepted the premise that what a word refers to cannot be induced in the absence of direct supervision. I will argue that what a word refers to _is_ probably learnable from raw text alone. Here’s why: higher-order concept co-occurrence statistics are stable across languages and across modalities, because language use (universally) reflects the world we live in (which is relatively stable). Such statistics are sufficient to establish what words refer to. My conjecture is supported by a literature survey, a thought experiment, and an actual experiment. (shrink)

The article discusses the process of “conceptual borrowing”, according to which, when a new discipline emerges, it develops its technical vocabulary also by appropriating terms from other neighbouring disciplines. The phenomenon is likened to Carl Schmitt’s observation that modern political concepts have theological roots. The authors argue that, through extensive conceptual borrowing, AI has ended up describing computers anthropomorphically, as computational brains with psychological properties, while brain and cognitive sciences have ended up describing brains and minds computationally and informationally, as (...) biological computers. The crosswiring between the technical languages of these disciplines is not merely metaphorical but can lead to confusion, and damaging assumptions and consequences. The article ends on an optimistic note about the self-adjusting nature of technical meanings in language and the ability to leave misleading conceptual baggage behind when confronted with advancement in understanding and factual knowledge. (shrink)

Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity — for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. -/- According to the theory, advances in AI — specifically of the machine learning type that’s able to take on new information and rewrite its code accordingly — will eventually catch up with the wetware of the biological brain. (...) In this interpretation of events, every AI advance from Jeopardy-winning IBM machines to the massive AI language model GPT-3 is taking humanity one step closer to an existential threat. We’re literally building our soon-to-be-sentient successors. -/- Except that it will never happen. At least, according to the authors of the new book Why Machines Will Never Rule the World: Artificial Intelligence without Fear. (shrink)

Wittgenstein’s ideas are a common ground for developers of Natural Language Processing (NLP) systems and linguists working on Language Acquisition and Mastery (LAM) models (Mills 1993; Lowney, Levy, Meroney and Gayler 2020; Skelac and Jandrić 2020). In recent years, we have witnessed a fast development of NLP systems capable of performing tasks as never before. NLP and LAM have been implemented based on deep learning neural networks, which learn concepts representation from rough data, but are nonetheless very effective in tasks (...) such as question answering, textual entailment, and translation (Devlin et al. 2019; Kitaev, Cao and Klein 2019; Wang et al. 2019). In this paper, I will debate some Wittgensteinian concepts that impact the architectures of many NLP deep-learning systems. I will focus, in particular, on the attempt to build a specific kind of architecture to model a private language. The discussion, I think, helps extract philosophical assumptions leading the research and development of AI systems capable of language modelling. In this paper, I will address some of the main features of NLP systems2 used for word embedding and one proposal to manipulate through a neural network a form of private language (Lawney et al. 2020). In §2, I will reconstruct the complex path of the private language argument (PLA). In §3, I will discuss connectivist language models and introduce notions about NPL systems' architecture. An overview of this kind of model is helpful to introduce the work of Lowney, Levy, Meroney and Gayler (2020). They submit that their model can respond to the issues raised by Wittgenstein in the notorious Private Language Argument (PLA). This argument unexpectedly turned out to be relevant not only for the philosophy of language but also for NLP and LAM modellers. I will describe the language game concept in NLP, how it is embedded, and its role in inductive systems development. This central concept in Wittgenstein’s work is relevant to describe the role of context in understanding the meanings of words. In §4, I present the Wittgensteinian main concepts in play in the connectivist paradigm. I argue that the connectionist theoretical framework can better catch the dependency of word meaning on context. There seems to be a correlation between Wittgenstein's invitation to look, an invitation to dismiss the aim of theorizing about languages, and the absence of theory-ladenness in deep learning technologies involved in NLP. In §5, I criticize Lowney and colleagues’ claim, whose model does not successfully capture Wittgenstein’s Beetle in the Box case. Moreover, I argue that even if we can distinguish a strong and a weak definition of a private language, Wittgenstein’s argument also holds for deep-learning models, and his worries are still a good guide for NLP developers. (shrink)

Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for (...) self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of social inequalities through machine learning-based systems. (shrink)

What is ‘certainty’ in our everyday life and work? AI changing what it means to ‘be certain’. For example, when we engage with others and are doing our work, we make judgments in which we ‘trust our instinct’. Are we still able to do so if we engage with the ‘certainty’ of the machine? How is ‘being certain’ in ‘dialogue’ affected by our conceptions of what it means to be human in interaction with the ‘machine’, and consequent distinctions between body (...) and mind? Descartes first likened the human body to a machine (albeit a machine created by God) and endowed reason as being a transcendent universal instrument. La Mettrie proposed that language and culture separates humans from ‘animals’. However, in Diderot’s writing, ‘knowledge’ is seen as embodied, coming from our senses and language, and where gesture and rhythm evolve in practice. For D’Alembert, human ‘instinct’ is a skill, a learnt practice, and Rousseau believes that human certainty is something that cannot be measured. These questionings of the dichotomies of body and mind and human and animal, that relate practice and knowledge, also question the place and meaning of ‘rules’ of engaged practice. For Wittgenstein, one learns to make judgments through personal experience, ‘rules in practice’, whereby language is necessarily culturally situated and embodied, and calculation and Judgement are part of each other. (shrink)

This article engages the anthropological assumptions underlying the apprehensions and promises associated with language in artificial intelligence (AI). First, we present the contours of two rivalling paradigms for assessing artificial language generation: a holistic-enactivist theory of language and an informational theory of language. We then introduce two language generation models – one presently in use and one more speculative: Firstly, the transformer architecture as used in current large language models, such as the GPT-series, and secondly, a model for 'autonomous machine (...) intelligence' recently proposed by Yann LeCun, which involves not only language but a sensory-motor interaction with the world. We then assess the language capacity of these models from the perspectives of the two rivalling language paradigms. Taking a holistic-enactivist stance, we then argue that there is currently no reason to assume a human-comparable language capacity in LLMs and, further, that LeCun's proposed model does not represent a significant step toward artificially generating human language because it still lacks essential features that underlie the linguistic capacity of humans. Finally, we suggest that proponents of these rivalling interpretations of LLMs should enter into a constructive dialogue and that this dialogue should continuously involve further empirical, conceptual, and theoretical research. (shrink)

In this paper, I reflect on the puzzle that machine learning presents to social theory to develop an account of its distinct impact on social reality. I start by presenting how machine learning has presented a challenge to social theory as a research subject comprising both familiar and alien characteristics (1.). Taking this as an occasion for theoretical inquiry, I then propose a conceptual framework to investigate how algorithmic models of social phenomena relate to social reality and what their stochastic (...) mode of operation entails in terms of their sociality (2.). Analyzed through a theoretical lens that relies on central tenets of sociological systems theory, I find that machine learning implies a distinct epistemic transformation, based on how algorithmic modeling techniques process meaning as represented in data embedded in vector space. Building on this characterization, I introduce my conceptualization of stochastic technology as distinct from mechanistic technologies that rely on causal fixation (3.). Based on this understanding, I suggest that real-world applications of machine learning are often characterized by a constitutive tension between the stochastic properties of their outputs and the ways in which they are put to use in practice. Focussing on the large language models LaMDA and ChatGPT, I examine the epistemological implications of LLMs to account for the confusion of correlation and causality as the root of this tension. Next, I illustrate my theoretical conception by way of discussing an essay on image models by German media artist Hito Steyerl (4.). Following a critical reflection on Steyerl's characterization of Stable Diffusion as a “white box ”, I finally propose to conceive ofmachine learning-based technologies as stochastic contingency machines that transform social indeterminacy into contingent observations of social phenomena (5.) In this perspective, machine learning constitutes an epistemic technology that operates on meaning as extractable from data by means of algorithmic data modeling techniques to produce stochastic accounts of social reality. (shrink)

Creativity has a special role in enabling humans to develop beyond the fulfilment of simple primary functions. This factor is significant for Artificial Intelligence (AI) developers who take replication to be the primary goal, since moves toward creating autonomous artificial-beings beg questions about their potential for creativity. Using Wittgenstein’s remarks on rule-following and language-games, I argue that although some AI programs appear creative, to call these programmed acts creative in our terms is to misunderstand the use of this word in (...) language. I conclude that replication is not the best way forward for AI development in matters of creativity. (shrink)

Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related (...) to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number of (...) subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

LANGUAGE NOTE | Document text in English; abstract also in Chinese. 腦機介面 (BCIs) 是大腦和電腦無需人工交互即可直接交流的一系列技術。隨著人工智能 (AI) 時代的到來，我們需要更多地關注腦機介面和人工智能的融合所帶來的倫理問題。那麼，與機器一起思考會帶來什麼樣的倫理問題？在本文中，圍繞這一主題，我們將重點關注以下問題：自主性、完整性、身分認同、隱私，以及 作為一種增強的方式，該技術在兒科領域的應用會帶來怎樣的風險和潛在收益。我們的結論是，雖然該技術存在多種令人擔憂的問題，同時也有可能帶來好處，但仍存在很大的不確定性。如果生命倫理學家想在這一領域有所建樹 ，他們就應該做好準備來迎接我們對醫學和醫療保健領域中一些我們視為核心價值的理解的重大轉變。 Brain-Computer Interfaces – BCIs – are a set of technologies with which brains and computers can communicate directly, without the need for manual interaction. As we are witnessing the dawn of an era in which Artificial Intelligence (AI) quite possibly will come to dominate the technological innovation landscape, we are compelled to ask questions about the ethical issues which the convergence of BCIs and AI (...) is poised to bring about. What are the ethics of thinking with machines? In this paper, we explore this question, focusing on some of the main arenas of ethical debate and contention, ranging from autonomy and integrity to identity and privacy, and discuss the risks and potential benefits of the technology in the domains of paediatric populations, and as a means of human enhancement. We conclude that, while there are multiple concerns as well as possibilities for the technology to do good, there are great uncertainties at play. If bioethicists want to stay relevant in this field, they ought to prepare themselves for seismic shift in how we conceptualise much of what we take to be core values in medicine and healthcare. (shrink)

This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u’s meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes’ view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in (...) the first section, I explain Descartes’ view about language and mind. To show that Turing bites the bullet with his imitation game, in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and Abelsons’ Script Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartes’ denial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms. (shrink)

Although the AI paradigm is useful for building knowledge-based systems for the applied natural sciences, there are dangers when it is extended into the domains of business, law and other social systems. It is misleading to treat knowledge as a commodity that can be separated from the context in which it is regularly used. Especially when it relates to social behaviour, knowledge should be treated as socially constructed, interpreted and maintained through its practical use in context. The meanings of terms (...) in a knowledge-base are assumed to be references to an objective reality whereas they are instruments for expressing values and exercising power. Expert systems that are not perspicuous to the expert community will lose their meanings and cease to contain genuine knowledge, as they will be divorced from the social processes essential for the maintenance of both meaning and knowledge. Perspicuity is usually sacrificed when knowledge is represented in a formalism, with the result that the original problem is compounded with a second problem of penetrating the representation language. Formalisms that make business and legal problems easier to understand are one essential research goal, not only in the quest for intelligent machines to replace intelligent human beings, but also in the wiser quest for computers to support collaborative work and other forms of social problem solving. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Technology and Our Relationship with GodAnselm Ramelow O.P.God's Original Plan and the FallTechnology may appear to be a very secular thing, but to assume that technology can be understood without God would be a mistake. Technology is deeply involved in our relationship with God. This involvement is, moreover, profoundly ambivalent.1To begin with the positive side of this ambivalence: the growing awareness of the dangers of technology should not lead (...) Christians to think that technology is necessarily a bad thing. It is, in fact, not even merely a "necessary evil." Rather, we can find in the use of technology an unfolding of our God-given rational nature. If we believe that God "made us in his image and likeness," then this quite directly implies two things: (1) God is a maker (he made us), and (2) since he made us in his image and likeness, we are makers as well. The making of technology thus reflects our dignity as made in the image and likeness of God.2 Accordingly, the Church has been more positive towards the development of technology than one might expect, and some of our technologies (e.g., agricultural, architectural, and time-keeping technologies) have their roots in medieval monasteries.3 [End Page 159]We should therefore expect that Adam and Eve, had they not fallen, nevertheless would have become makers of technology in some form or other. They would have exhibited inventiveness and tool use, though perhaps not a tool use focused on warding off evil (which did not exist in Eden), but concerned with promoting positive forms life, such as tools of art and communication. Art and communication technologies are, like all tools, means to an end; but these in particular contain their ends in themselves. In Aristotelian terms, their making (poiesis) concerns a praxis (such as "making conversation"). That is why we sometimes forget to list such technologies among our typical examples of technologies. We forget, for example, that, among the technologies of communication, language as a physical tool (sounds or written marks) is an obvious example. And it is a prelapsarian feature: the book of Genesis has Adam naming things before the Fall. In doing so, Adam echoes God's own creative "technique" of speaking or calling things into being.4 Even in its oral form, language is a matter of human making and a technology of communication. In paradise, communication would not have been merely instrumental, not merely a means, but an intrinsic good, embodying knowledge and intersubjective communion.Other forms of prelapsarian technology, however, are a matter of speculation. And whether or not one agrees with Jacques Ellul's thesis that there was no such prelapsarian technology,5 the ambivalence of technology is clear from the very beginning as well. This is at least what we see in the book of Genesis. For, as a matter of biblical record, it was Cain and his descendants who founded cities and developed technology (e.g., Tubal-Cain as the "forger of all instruments of bronze and iron" in Gen 4:22). And the history [End Page 160] of these urban civilizations does not display the best part of human behavior. But if, as we have said, technology is not necessarily a bad thing, then this must be a corruption of technology. What might this corruption consist in?I want to suggest that it consists precisely in the corruption of our relationship with God, from which technology can never abstract and in which it is, for better or for worse, embedded. Unsurprisingly, therefore, as technology progresses, this relationship becomes more and more explicit, and it does so in a paradoxical way: initially, it is an attempt to put humanity in charge and control, to replace our need for reliance on God by allowing us to play God ourselves. But in our current situation, particularly with the emergence of artificial intelligence (AI) and machine learning, the roles appear reversed: rather than putting humanity in charge, technology in turn is increasingly in control—to the point of becoming itself a god or idol that rules human life. As a result, we only end up having replaced one God with another... (shrink)

In our book, Why Machines Will Never Rule the World, Jobst Landgrebe and I argue that we can engineer machines that can emulate the behaviours only of simple systems, which means: only of those systems whose behaviour we can predict mathematically. The human brain is an example of a complex system, and thus its behaviour cannot be emulated by a machine. We use this argument to debunk the claims of those who believe that large language models are poised to achieve (...) a level of intelligence that will equal or even surpass that of the human brain. The essay was published under the title "Why Machines Will Never Rule the World". (shrink)

In natural language understanding, a crucial goal is correctly interpreting open-textured phrases. In practice, disagreements over the meanings of open-textured phrases are often resolved through the generation and evaluation of interpretive arguments, arguments designed to support or attack a specific interpretation of an expression within a document. In this paper, we discuss some of our work towards the goal of automatically generating and evaluating interpretive arguments. We have curated a set of rules from the code of ethics of various professional (...) organizations and a set of associated scenarios that are ambiguous with respect to some open-textured phrase within the rule. We collected and evaluated arguments from both human annotators and state-of-the-art generative language models in order to determine the relative quality and persuasiveness of both sets of arguments. Finally, we performed a Turing test-inspired study in order to assess whether human annotators can tell the difference between human arguments and machine-generated arguments. The results show that machine-generated arguments, when prompted a certain way, can be consistently rated as more convincing than human-generated arguments, and to the untrained eye, the machine-generated arguments can convincingly sound human-like. (shrink)

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in (...) reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored. (shrink)

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding (...) the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them. (shrink)

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set (...) of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely “following a moral code”. In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society. (shrink)

“This species of device is so radically new that many of its uses will become clear only after it’s been put into operation.” It’s what he said to me. ‘Cause he understood. He knew the real challenge was not building the thing but asking it the right questions in a language intelligible to the machine. And he was the only one who spoke that language.”(Benjamín Labatut, The MANIAC)“Let the whole outside world consist of a long paper tape.”(John von Neumann, 1948) (...) Introduction The Old Dream of... (shrink)

In a recent paper, Paul Coates defends a sophisticated dispositional account which allegedly resolves the sceptical paradox developed by Kripke in his monograph on Wittgenstein's treatment of following a rule (Kripke, 1982). Coates' account appeals to a notion of 'homeostasis', unpacked as a subject's second-order disposition to maintain a consistent pattern of extended first-order dispositions regarding her linguistic behavior. This kind of account, Coates contends, provides a naturalistic model for the normativity of intentional properties and thus resolves Kripke's sceptical paradox. (...) In this paper I argue that Coates' second-order dispositional account cannot solve the sceptic's problems regarding meaning and normativity. My main contention is that in order for second-order dispositions to be able to effectively regulate the coordinated responses constitutive of first-order dispositions, those first order dispositions must be independently identifiable. Yet that's precisely what Kripke's sceptical argument calls into question. I shall also argue, in a more positive fashion, that Coates' own appeal to practical breakdowns may suggest a different —and more effective— response to the sceptic's concern. (shrink)

Fundamental problems of the uses of formal techniques and of natural and instrumental practices have been raised again and again these past two decades, in many quarters and from varying viewpoints. We have brought a number of quite basic studies of these issues together in this volume, not linked con ceptually nor by any rigorously defined problematic, but rather simply some of the most interesting and even provocative of recent research accomplish ments. Most of these papers are derived from the (...) Boston Colloquium for the Philosophy of Science during 1973-80, the two exceptions being those of Karel Berka (on scales of measurement) and A. A. Zinov'ev (on a non-tradi tional theory of quantifiers). Just how intriguing these results (or conjectures?) seem to us may be seen from some brief quotations: (1) Judson Webb: ".... the abstract machine concept has many of the appropriate kinds of properties for modelling living, reproducing, rule following, self-reflecting, accident-prone, and lucky creatures... the a priori logical results relevant to the abstract machine concept, above all Godel's, could not conceivably have turned out any better for the mechanist. " (2) M. L. Dalla Chiara: "... modal interpretation (of quantum logic) shows clearly that it possesses a logical meaning which is quite independent of quantum mechanics. " (3) Isaac Levi: (as against Peirce and Popper) "... infallibilism is con sistent with corrigibilism, and a view which respects avoidance of error is an important desideratum for science. (shrink)

The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by these algorithms, and how their real world meaning is translated into machine language and vice versa, how their output is understood by the end user. This “semantic” black box adds to the “mathematical” black box present in many AI systems in which the underlying (...) “reasoning” process is often opaque. In this way, whereas it is often claimed that the use of AI in medical applications will deliver “objective” information, the true relevance or meaning to the end‐user is frequently obscured. This is highly problematic as AI devices are used not only for diagnostic and decision support by healthcare professionals, but also can be used to deliver information to patients, for example to create visual aids for use in shared decision‐making. This paper provides an examination of the range and extent of this problem and its implications, on the basis of cases from the field of intensive care nephrology. We explore how the problematic terminology used in human communication about the detection, diagnosis, treatment, and prognosis of concepts of intensive care nephrology becomes a much more complicated affair when deployed in the form of algorithmic automation, with implications extending throughout clinical care, affecting norms and practices long considered fundamental to good clinical care. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing by Naomi BaronLuke MunnBaron, Naomi. Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing. Stanford University Press, 2023. 344pp.Who Wrote This? is Naomi Baron’s latest book exploring the emergence of AI language models and their potential implications for writing. A linguist, educator, and emeritus professor at American University, Baron should be (...) well placed to tease out the nuances of language, showcase the creativity of human literary canon across history, and deliver a set of findings and recommendations informed by deep experience. Indeed, since the release of ChatGPT in November 2022, there is a hunger for this kind of expertise and guidance both inside and outside academia, as the deluge of debates, panels, and forums in the past year have attested. This is the perfect storm, one that should be harnessed by a linguist with two decades of work on digital writing and reading into a powerful book. Instead, the reader must wade through a morass of truisms, historical anecdotes, and wiki-like summaries of research studies, interspersed with surface-level ponderings.The problems begin almost immediately with the structure of the book itself. While the book is ostensibly divided into chapters, each chapter is atomized into dozens of subsections, some no more than a paragraph or two in length. In this whirlwind tour, the reader is swiftly confronted with a jumble of quotes from journalists, jokes about language, historical events, a “laypersons AI roadmap,” and recent news stories, flitting from one to the next with barely a pause. The “AI Applications” section takes this same piecemeal approach, laying out the possible uses of artificial intelligence and then painstakingly stepping through each one. Each subsection features a few paragraphs that regurgitates material you might find, more cohesively and in-depth, in a Wikipedia article. The material on the history of artificial intelligence is handled simplistically and uncritically, skipping across a handful of heroic figures like Yann LeCun, Yoshua Bengio, and Fei-Fei Li.While anecdotes and quotes abound, an argument is much harder to locate. On the one hand, Baron seems to uncritically echo the AI hype that has dominated news headlines in the past year. “We’ve come a long way,” she opines, “we have vacuum cleaning robots, drones, and self-driving [End Page 156] cars” (59). The reader learns that AI can generate poetry, correct grammar, play games, and beat masters (AlphaGo), and even identify cancer (machine learning for mammograms). These use-cases, and the ambitious claims that accompany them, are never really challenged in any sustained theoretical or methodological way. On the other hand, Baron seems to be highly conservative when it comes to the proper domain of “the human” and of writing in particular. “AI isn’t up for self-expression or thinking” (14) she says in passing, dismissing the recent revolutionary improvements in large language models that she’s just hyped up.The possibility of machinic intelligence and creativity, in fact, is a fascinating if messy one. Turing was not so interested in demonstrably “proving” that computers could be intelligent, but instead (and very much in line with his everyday navigation of British sexual norms) in developing something that could “pass” as human. Intelligence was not an acid-test, but a set of gestures that could be performed or pantomimed, emulated until they were “good enough” to deceive. N. Katherine Hayles has continued this trajectory in her work on “non-conscious cognition,” removing the barriers to what qualifies as intelligence and subsequently extending cognition down to unicellular organisms and plants. Within my own work on language models alongside colleagues (Munn et al. 2023; Magee, Munn, and Arora 2023), we highlight how models like GPT 3.5 achieve an approximation of natural speech. What is important is not the authenticity or actual intelligence of this feat, but that this “good enough” ability means that users treat language models as an interlocutor, a listener, a subject. This projection, even if misplaced, opens up this interaction and requires a conceptualization that is at least as attuned to memory, affect, and subjectivity as software interfaces and linear... (shrink)

Paul Ricoeur is widely regarded as one of the most distinguished philosophers of our time. In The Rule of Metaphor he seeks 'to show how language can extend itself to its very limits, forever discovering new resonances within itself'. Recognizing the fundamental power of language in constructing the world we perceive, it is a fruitful and insightful study of how language affects how we understand the world, and is also an indispensable work for all those seeking to retrieve some kind (...) of meaning in uncertain times. (shrink)

A wide-ranging history of the intellectual developments that produced the modern idea of the algorithm. Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians long before the computer age. How do you draw the line between computational rules and the complexities of making systems comprehensible to people? Here Jeffrey M. Binder offers a compelling tour of four visions (...) of universal computation that addressed this issue in very different ways: G. W. Leibniz's calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole's nineteenth-century logic system; and the early programming language ALGOL, whose name is short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. To what extent can meaning be controlled by individuals, like the values of a and b in algebra, and to what extent is meaning inevitably social? By attending to this long-neglected question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day. Machine learning, in its increasing dependence on words, now places this boundary in jeopardy, making its stakes all the more urgent to understand. The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction. (shrink)

Given (1) Wittgensteins externalist analysis of the distinction between following a rule and behaving in accordance with a rule, (2) prima facie connections between rule-following and psychological capacities, and (3) pragmatic issues about training, it follows that most, even all, future artificially intelligent computers and robots will not use language, possess concepts, or reason. This argument suggests that AIs traditional aim of building machines with minds, exemplified in current work on cognitive robotics, is in need of substantial revision.

Turing was probably the first person to advocate the pursuit of robotics as a route to Artificial Intelligence and Wittgenstein the first to argue that, without the appropriate history, no machine could be intelligent. Wittgenstein anticipated much recent theorizing about the mind, including aspects of connectionist theo- ries of mind and the situated cognition approach in AI. Turing and Wittgenstein had a wary respect for each other and there is significant overlap in their work, in both the philosophy of mathematics (...) and the philosophy of AI. Both took (what would now be called) an externalist stance with respect to machine intelligence. But whereas Turing was concerned only with behaviour, Wittgenstein emphasized in addition history and environment. I show that Wittgenstein's externalist analysis of psychological capacities entails that most, even all, future "artificially intelligent" computers and robots will not use language, possess concepts, or reason. The ar- gument tells, not against AI, but only against AI's traditional and romantic goal of building an artificial "res cogitans" - as first embraced by Turing and now ex- emplified in the work of Brooks and others on cognitive robotics. This argument supports the stance of the growing number of AI researchers whose aim is to pro- duce, not thinking and understanding machines, but high-performance "advanced information processing systems.". (shrink)

Academic integrity is a set of ethical ideals and values that guide the behavior of individuals in academic and educational settings. It encompasses honesty, trustworthiness, fairness, and a commitment to upholding the highest standards of ethical conduct in the quest for knowledge, learning, and research. Academic integrity is essential in maintaining the trustworthiness, reputation, and effectiveness of educational institutions and scholarly communities. Whereas, AI, or Artificial Intelligence, is a broad field of computer science that focuses on creating frameworks, software, or (...) machines that can perform tasks that would typically require human intelligence. These tasks include problem-solving, learning from experience, understanding natural language, recognizing patterns, and making choices. AI systems aim to mimic or replicate human cognitive functions, and they can range from simple rule-based systems to highly complex, autodidactic neural networks. AI can significantly impact academic integrity and research in both positive and potentially challenging ways. (shrink)

Kurt Gödel’s Incompleteness theorem is well known in Mathematics/Logic/Philosophy circles. Gödel was able to find a way for any given P (UTM), (read as, “P of UTM” for “Program of Universal Truth Machine”), actually to write down a complicated polynomial that has a solution iff (=if and only if), G is true, where G stands for a Gödel-sentence. So, if G’s truth is a necessary condition for the truth of a given polynomial, then P (UTM) has to answer first that (...) G is true in order to secure the truth of the said polynomial. But, interestingly, P (UTM) could never answer that G was true. This necessarily implies that there is at least one truth a P (UTM), however large it may be, cannot speak out. Daya Krishna and Karl Potter’s controversy regarding the construal of India’s Philosophies dates back to the time of Potter’s publication of “Presuppositions of India’s Philosophies” (1963, Englewood Cliffs Prentice-Hall Inc.) In attacking many of India’s philosophies, Daya Krishna appears to have unwittingly touched a crucial point: how can there be the knowledge of a ‘non-cognitive’ mokṣa? [‘mokṣa’ is the final state of existence of an individual away from Social Contract]—See this author’s Indian Social Contract and its Dissolution (2008) mokṣa does not permit the knowledge of one’s own self in the ordinary way with threefold distinction, i.e., subject–knowledge-object or knower–knowledge–known. But what is important is to demonstrate whether such ‘knowledge’ of non-cognitive mokṣa state can be logically shown, in a language, to be possible to attain, and that there is no contradiction involved in such demonstration, because, no one can possibly express the ‘experience-itself’ in language. Hence, if such ‘knowledge’ can be shown to be logically not impossible in language, then, not only Daya Krishna’s arguments against ‘non-cognitive mokṣa’ get refuted but also it would show the possibility of achieving ‘completeness’ in its truest sense, as opposed to Gödel’s ‘Incompleteness’. In such circumstances, man would himself become a Universal Truth Machine. This is because the final state of mokṣa is construed as the state of complete knowledge in Advaita. This possibility of ‘completeness’ is set in this paper in the backdrop of Śrī Śaṅkarācārya’s Advaitic (Non-dualistic) claim involved in the mahāvākyas (extra-ordinary propositions). (Mahāvākyas that Śaṅkara refers to are basically taken from different Upaniṣads. For example, “Aham Brahmāsmi” is from Bṛhadāraṇyaka Upanisad, and “Tattvamasi” is from Chāndogya Upaniṣad. Śrī Śaṅkarācārya has written extensively. His main works include his Commentary on Brahma-Sūtras, on major Upaniṣads, and on ŚrīmadBhagavadGītā, called Bhāṣyas of them, respectively. Almost all these works are available in English translation published by Advaita Ashrama, 5 Dehi Entally Road, Calcutta, 700014.) On the other hand, the ‘Incompleteness’ of Gödel is due to the intervening G-sentence, which has an adverse self-referential element. Gödel’s incompleteness theorem in its mathematical form with an elaborate introduction by R.W. Braithwaite can be found in Meltzer (Kurt Gödel: on formally undecidable propositions of principia mathematica and related systems. Oliver & Boyd, Edinburgh, 1962). The present author believes first that semantic content cannot be substituted by any amount of arithmoquining, (Arithmoquining or arithmatization means, as Braithwaite says,—“Gödel’s novel metamathematical method is that of attaching numbers to the signs, to the series of signs (formulae) and to the series of series of signs (“proof-schemata”) which occur in his formal system…Gödel invented what might be called co-ordinate metamathematics…”) Meltzer (1962 p. 7). In Antone (2006) it is said “The problem is that he (Gödel) tries to replace an abstract version of the number (which can exist) with the concept of a real number version of that abstract notion. We can state the abstraction of what the number needs to be, [the arithmoquining of a number cannot be a proof-pair and an arithmoquine] but that is a concept that cannot be turned into a specific number, because by definition no such number can exist.”.), especially so where first-hand personal experience is called for. Therefore, what ultimately rules is the semanticity as in a first-hand experience. Similar points are voiced, albeit implicitly, in Antone (Who understands Gödel’s incompleteness theorem, 2006). (“…it is so important to understand that Gödel’s theorem only is true with respect to formal systems—which is the exact opposite of the analogous UTM (Antone (2006) webpage 2. And galatomic says in the same discussion chain that “saying” that it ((is)) only true for formal systems is more significant… We only know the world through “formal” categories of understanding… If the world as it is in itself has no incompleteness problem, which I am sure is true, it does not mean much, because that is not the world of time and space that we experience. So it is more significant that formal systems are incomplete than the inexperiencable ‘World in Itself’ has no such problem.—galatomic”) Antone (2006) webpage 2. Nevertheless galatomic certainly, but unwittingly succeeds in highlighting the possibility of experiencing the ‘completeness’ Second, even if any formal system including the system of Advaita of Śaṅkara is to be subsumed or interpreted under Gödel’s theorem, or Tarski’s semantic unprovability theses, the ultimate appeal would lie to the point of human involvement in realizing completeness since any formal system is ‘Incomplete’ always by its very nature as ‘objectual’, and fails to comprehend the ‘subject’ within its fold. (shrink)

'Practices without foundations' is, in genesis and in effect, a discussion of the following quotation , which serves therefore as an epigraph to it: ;Nelson Goodman's discussion of the 'new riddle of induction' ... deserves comparison with Wittgenstein's work. Indeed ... the basic strategy of Goodman's treatment of the 'new riddle' is strikingly close to Wittgenstein's sceptical arguments .... Although our paradigm of Wittgenstein's problem was formulated for a mathematical problem it ... is completely general and can be applied to (...) any rule or word. In particular, if it were formulated for the language of color impressions, as Wittgenstein himself suggests, Goodman's 'grue', or something similar, would play the role of 'quus'. But the problem would not be Goodman's about induction--"Why not predict that grass, which has been grue in the past will be grue in the future?"--but Wittgenstein's about meaning: "Who is to say that in the past I didn't mean grue by 'green', so that now I should call the sky, not the grass, 'green'?" vskip9pt ;This fascinating remark of Kripke's suggests a project, that of charting out both the interpretive and philosophic points of connection and disconnection between Wittgenstein, Goodman and Kripke. That project is this dissertation. ;My work severely tests Kripke's remark. For I argue that Kripke not only misreads Wittgenstein, but also mistakes the extremity of the 'scepticism' of his version of Wittgenstein for the priority of the philosophical puzzle that Goodman has bequeathed to us. ;To reach this conclusion, I show that Kripke's Wittgenstein's 'meaning-scepticism' is unanswerable if statable, but actually unstatable; and that, under certain interpretations, the same is true of putative scepticisms derivable from Goodman's text. The upshot is that the sceptical reading of most twentieth century philosophy must be displaced in favour of 'post-scepticism' and 'philosophical ethnography'. The latter concepts, sketched toward the end of the dissertation, are perhaps its telos. (shrink)

The article is devoted to the applicability of Wittgenstein’s following the rule in the context of his philosophy of mathematics to real mathematical practice. It is noted that in «Philosophical Investigations» and «Remarks on the Foundations of Mathematics» Wittgenstein resorted to the analysis of rather elementary mathematical concepts, accompanied also by the inherent ambiguity and ambiguity of his presentation. In particular, against this background, his radical conventionalism, the substitution of logical necessity with the «form of life» of the community, as (...) well as the inadequacy of the representation of arithmetic rules by a language game are criticized. It is shown that the reconstruction of the Wittgenstein concept of understanding based on the Fregian division of meaning and referent goes beyond the conceptual framework of Wittgenstein language games. (shrink)

This paper analyzes responsible human–machine interaction concerning artificial neural networks (ANNs) and large language models (LLMs) by considering the extension of human agency and autonomy by means of artificial intelligence (AI). Thereby, the paper draws on the sociological concept of “interobjectivity,” first introduced by Bruno Latour, and applies it to technologically situated and interconnected agency. Drawing on Don Ihde’s phenomenology of human-technology relations, this interobjective account of AI allows to understand human–machine interaction as embedded in the social lifeworld. Finally, the (...) paper develops a connectionist account of responsible AI, thereby focusing on patterns such as social goals and actions. (shrink)

Transcript of an Interview on the podcast: Irreverend: Faith and Current Affairs.

The article offers a discursive account of what generality in machine learning research means and how it is constructed in the development of general artificial intelligence from the perspectives of cultural and media studies. I discuss several technical papers that outline novel architectures in machine learning and how they conceive of the “world”. The agency to learn and the learning curriculum are modulated through worlding (in the sense of setting up and unfolding of the world for artificial agents) in machine (...) learning engineering. In recent computer science articles, large models trained on Internet-scale datasets are framed as general world simulators—despite their partiality, historicity, finite nature, and cultural specificity. I introduce the notion of “model worlds” to refer to composable interactive environments designed for the purpose of machine learning that partake in legitimising that claim. I discuss how large models are grounded through interaction in model worlds, arguing that model worlds mediate between the sheer scale of language models and their hypothetical capacity to generalise to new tasks and domains, rehashing the empiricist logic of “big data”. Further, I show that the emerging capacity of artificial agents to generalise redraws the epistemic boundary between artificial agents and their learning environments. Consequently, superficial statistics of language models and abstract action are made meaningful in distilled model worlds, giving rise to synthetic agency. (shrink)

The dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution for the challenges faced in language learning and various other voice-based advents. In the English TTS system, the accent labeling of unregistered words is another very important link besides the phonetic conversion. Since the importance of the primary stress is much (...) greater than that of the secondary stress, and the primary stress is easier to call than the secondary stress, the labeling of the primary stress is separated from the secondary stress. In this work, the labeling of primary accents uses a labeling algorithm that combines morphological rules and machine learning; the labeling of secondary accents is done entirely through machine learning algorithms. After 10 rounds of cross-validation, the average tagging accuracy rate of primary stress was 94%, the average tagging accuracy rate of secondary stress was 94%, and the total tagging accuracy rate was 83.6%. This perceptual study separates the labeling of primary and secondary accents providing the promising outcomes. (shrink)

The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA).,The decision results derived by the AMA are acquired via fuzzy logic (...) interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM).,Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge.,This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles.,To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties.,This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics. (shrink)

As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. This article discusses the struggle for truth in AI systems and the general responses to date. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. It (...) conceptualizes this performance as an operationalization of truth, where distinct, often-conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct’s successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening “reality” are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of “truth” do we as listeners desire? (shrink)

While most theoreticians of meaning in the first half of the twentieth century subscribed to a representational theory (viewing meanings as entities stood for by the expressions), the second half of the century was marked by the rise of various versions of use-theories of meaning. The roots of this ‘pragmatist turn’ are detectable in the writings of the later Wittgenstein, the Oxford speech act theorists (Austin, Grice) and the American neopragmatists (Quine, Sellars). Though it is now rather popular (and sometimes (...) even fashionable) to invoke the use-theory of meaning, it is by far not so popular to inquire what such a theory really is. In this paper we try to give at least a part of the answer, whereby we find out that the usual conception of such a theory is unsatisfactory. We propose that for an improvement we must, together with Wittgenstein and Sellars, conceive language as a (tool of a) rule-based activity, which enables us to replace the concept of disposition, usually constituting the backbone of the use-theory, by the concept of propriety. The resulting normative version of the use-theory then becomes the investigation of the rules which expressions acquire vis-`a-vis the rules of the relevant language games – especially of the rules of inference. (shrink)