Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Therefore, HCI had to develop or respond to the challenges presented by AI, which is perhaps one reason for the emergence of HCAI. Besides HCI, it may be proposed that HCAI also stems from the approach of ‘human-centred design’. Human-centred design is an ‘approach to interactive systems development that aims to make systems usable and useful by focusing on the users, their needs and requirements, and by applying human factors/ergonomics, and usability knowledge and techniques’ (ISO 9241–210:2019(E)). It aims to incorporate human values earlier in the design process of technologies and to emphasise the importance of the social sciences (Hanington 2010). The HLEG defines it similarly, with the integration of human values being central to AI development and use: ‘The human-centric approach to AI strives to ensure that human values are central to the way in which AI systems are developed, deployed, used and monitored (High-Group on AI 2019, p. 37). While human-centred design has been extensively discussed by many scholars (see Hanington 2010; Norman 1988/2013, Norman and Draper 1986), human-centred AI has been much less discussed, with little to no critical evaluation of it being done. There is an influential lineage of thought that refers to human–machine dynamics, where HCAI is the next step in this process. While this paper is not necessarily a critical examination of these other positions (human-centred design and HCI, for example), the criticisms directed against HCAI in the following sections may also be relevant to many of these other approaches. The following sections will concentrate on HCAI and issues within this approach when analysed through the lens of Foucault’s work, with a specific focus on his critique of the human in of Things. 3. 1 What is human? Human–AI hybridisation is desirable and unproblematic (Claim 1)HCAI researchers advocate a human–machine hybrid intelligence or a human–machine system that is better than either one alone, with the end goal of a human–machine symbiotic world (Xu et al. 2022) (such a view stems from a long lineage of thinking about the hybridisation of human–machine intelligence, most notably (Engelbart 1962; Licklider 1969). [SEP] Justify:

Output: The text contrasts the claim by discussing the concept of human-centred AI (HCAI) and human-centred design, suggesting that while these approaches aim to incorporate human values and needs into AI systems, they do not imply that AI can perfectly understand and respond to all human emotions. The focus on human values and usability indicates a recognition of the limitations of AI rather than an assertion of its abilities.


Input: Task: pass the claim using the given text. Claim: Sociological research helps us understand social issues and policies. [SEP] Text: Figure 3: Schematic diagram of the types of regions and flow scenarios that can potentially be elucidated via a combination of seismic anisotropy observations, mineral physics constraints, and geodynamic simulations. (a) Mantle flow field (arrows: flow directions, length proportional to flow velocity; background colors indicate temperature) for a geodynamic simulation that includes sinking cold material (blue), reminiscent of a slab, a hot (red) structure similar to a large-low velocity province (LLVP; left), and rising plumes (close to LLVP edge). (b-e) Zoom-ins representing different flow scenarios. Ultra-low velocity zone (ULVZ) material is shown in yellow, subducted mid-ocean ridge basalts in brown, aligned crystals in green and LLVP material in red. (b) Representation of flow near LLVP edge. ULVZ materials and partial melts potentially cause shape-preferred orientation, while crystals are aligned via crystallographic-preferred orientation; either scenario causes seismic anisotropy. (c) Flow near a developing thermal upwelling. (d) A region of horizontal flow, with a potential thin and hot layer of chemically distinct material at the base of the mantle. (e) A region of slab-downwelling. Note that in contrast to panel (a), in which reddish colors show regions with relative higher temperature, the reddish colors in panels (b)-(e) represent chemically distinct LLVP materials. Credit: Wolf et al. [2024], Figure 16 (modified) — (jonathan. wolf@yale. edu; 0000-0002-5440-3791), ’s Note: It is the policy of AGU Publications to invite the authors of 10. 1029/2024EO245021. Published on 1 July 2024. This 2024. The authors. CC BY-NC-ND 3. 0Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited. [SEP] Justify:

Output: The text does not address the claim regarding sociological research and its role in understanding social issues and policies. Instead, it focuses on a schematic diagram related to geodynamic simulations and seismic anisotropy observations, which are unrelated to sociological research or social policies.


Input: Task: corroborate the claim using the given text. Claim: Certain biohacking methods, such as cold exposure, are based on scientific studies showing potential health benefits. [SEP] Text: We previously generated a mouse strain carrying a LacZ reporter inserted between exons 3 and 5 of the Nrg4 genomic locus (Nrg4Lacz; Figure 2A) (26, 29). LacZ gene expression is indicative of the transcriptional activity from the endogenous Nrg4 promoter, providing an independent tool to track Nrg4 expression in mouse tissues. We subjected a cohort of heterozygous Nrg4Lacz mice to housing at different temperatures and performed whole-mount LacZ staining in adipose tissue and the liver, followed by fixation and sectioning. Like endogenous Nrg4, the highest β-galactosidase (β-gal) activity was observed in brown fat from mice housed at ambient RT (Figure 2, B and C). LacZ staining appeared relatively weak in iWAT from mice housed at TN and ambient RTs. In contrast, we observed strong β-gal activity in iWAT from cold-acclimated mice, consistent with the robust induction of endogenous Nrg4 expression under this condition. Compared with BAT and iWAT, LacZ staining in eWAT was relatively weak and was slightly induced by CA. These results demonstrate that adipose tissue Nrg4 expression in adipose tissue is highly responsive to environmental temperature and correlated with cold-induced beige adipogenesis in iWAT. Figure 2Cold-inducible expression of Nrg4 in LacZ reporter mice. (A) A diagram depicting Nrg4-KO alleles containing a LacZ insertion into the endogenous Nrg4 locus. (B) Whole-mount β-gal staining of the liver, eand iWAT from Nrg4-LacZ reporter mice housed at 10°C (Cold), 25°C (RT), or 30°C (TN). (C) Tissue sections from B. Scale bar: 100 μm. NRG4 is required for beige adipocyte induction and preservation of metabolic health. Activation of the beige fat program is associated with metabolic health in mice and humans (31, 32). Recent single-cell RNA-Seq studies indicate that Nrg4 is abundantly expressed in beige adipocytes (33). Importantly, NRG4 serves as a molecular marker of beige adipocyte precursor cells in human adipose tissue (34). [SEP] Justify:

Output: The text corroborates the claim by describing scientific studies that highlight the effects of cold exposure on the expression of the Nrg4 gene in adipose tissue, which is related to metabolic health. It specifically states that cold-acclimated mice showed strong β-galactosidase activity in adipose tissue, which is consistent with the induction of beige adipogenesis associated with metabolic health benefits. This aligns with the notion that certain biohacking methods like cold exposure may have potential health benefits supported by scientific evidence.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: It is a ‘symbiosis of human and artificial intelligence’ (Horvatić and Lipic 2021, p. 2). This symbiosis is a way to ‘improve’ human abilities: ‘With artificial intelligence, we have the chance to create a change for improving the abilities of the mind, similar to what engines and motors have done for the body’ (Schmidt 2020, p. 4). HCAI researchers claim that we should ‘augment humans and human intelligence’ (He et al. 2021, p. 11). Human intelligence can be extended through the use of AI (Mhlanga 2022). Essentially, we should combine AI and ‘natural’ intelligence (which HCAI anthropocentrically limits only to human intelligence) ‘to empower, amplify, and augment human performance, rather than replace people’ (Holzinger et al. 2022, p. 1). AI should not replace ‘natural [human] intelligence’, but instead, it should be used to augment it, ‘like “power steering for the brain”’ (Holzinger et al. 2022, p. 2). There is pressure on individuals to hybridise themselves because ‘[n]ot using AI will create major disadvantages, as others will be faster, see things you do not see, and will be able to predict events long before you encounter them’ (Schmidt 2020, p. 4). Essentially, AI should ‘amplify, augment, empower, and enhance human performance’ (Shneiderman 2022, p. 4). The hybridisation discussed in HCAI concerns intellectual and physical human capacities (Schmidt 2020, p. 4) However, this raises several questions about what is, and will be, constituted as ‘human’ and whether this will change due to this hybridisation. Will these hybrid human AIs be more or less human than humans who do not adopt and integrate AI in such a way? If we use ‘human’ to centre AI development, how will those who do not hybridise themselves be viewed and valued? I believe Foucault’s work can offer some insights on this matter. Foucault stated that within the broad history of our species, the ‘human’ is a relatively ‘recent invention’. [SEP] Justify:

Output: The text contrasts the claim by suggesting that while AI can augment and improve human capabilities, it does not imply that AI assistants are capable of understanding and perfectly responding to all human emotions. The emphasis on the hybridization of human and artificial intelligence indicates a reliance on human input and complexity, suggesting limitations in AI's understanding of emotions. Phrases such as 'will these hybrid human AIs be more or less human than humans who do not adopt and integrate AI' suggest an examination of the limitations and distinctions between human and AI intelligence, thus questioning the extent to which AI can truly comprehend human emotions.


Input: Task: pass the claim using the given text. Claim: Languages evolve over time and can influence thought processes. [SEP] Text: Printable page How do intensive gaming experiences evolve over time in clinical and non-clinical contexts? based on reviews by A recommendation of: STAGE 2 : - version 2 Read report on server Now published in -CN Submission: posted 15 May 2023Recommendation: posted 14 August 2023, validated 14 August 2023 Cite this recommendation as:Chambers, C. (2023) How do intensive gaming experiences evolve over time in clinical and non-clinical contexts? . in Registered Reports, 100466. 10. 24072/pci. rr. 100466 This is a stage 2 based on: for : A ReportVeli-:// the last 5 years the inclusion of “gaming disorder” in the ICD-11 been controversial (et al, 2018), mirroring wider public debate about the effects of gaming on mental health. One of the major gaps in understanding the validity of gaming disorder as an identifiable mental illness is the absence of qualitative studies comparing the lived experience of gamers who seek treatment with esports players who do not report health problems. Here, Karhulahti et al. (2023) tackle this question in the second of two Stage 2 associated with their previous programmatic Stage 1 submission. Using interpretative phenomenological analysis, the authors undertook in-depth interviews over a 1-year period with treatment-seeking participants (N=5) and esports-playing participants (N=4) who did not experience gaming-related health problems. The authors sought to answer the folllwing primary question: How do the experiences and meanings of playing videogames—shaped by the individuals’ diverse sociocultural contexts—evolve in those with related health problems (as defined by treatment-seeking) and those who play esports games several hours per day while self-reporting no related health problems? Both groups exhibited intense relationships with gaming that were cyclical over time across various dimensions, with fluctuations occurring in response to changes in health, occupation, and social networks. The observed variation over time was substantial, with individuals attaching and detaching from games involving hundreds or thousands of hours. The authors report treatment-seeking being followed by a search of new gaming and life meanings, while intensive gaming without related problems continued as an integrated part of the self, with resilience adapting and evolving in the face of unexpected life events. [SEP] Justify:

Output: The text discusses the evolution of gaming experiences in clinical and non-clinical contexts but does not address the claim regarding the evolution of languages or their influence on thought processes. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Certain biohacking methods, such as cold exposure, are based on scientific studies showing potential health benefits. [SEP] Text: Gene expression analysis indicated that mRNA levels of thermogenesis genes, such as Ucp1, Cox7a1, Dio2, and Cidea, were significantly elevated in iWAT from mice treated with hNRG4-Fc (Figure 7C). Accordingly, UCP1 protein levels in iWAT were strongly induced by hNRG4-Fc (Figure 7D). In contrast, thermogenic gene expression in BAT remained comparable between 2 treatment groups, indicating that hNRG4-Fc exerts depot-specific effects on the thermogenic gene program. Figure 7Recombinant NRG4-Fc promotes cold-induced beiging and improves metabolic health in mice. (A) Rectal temperature and body and tissue weight of cold-acclimated mice treated with 3 i. p. injections of Fc (n = 5) or NRG4-Fc (n = 6, 1. 5 mg/kg). (B) H&E staining of iWAT sections. Scale bar: 100 μm. (C) qPCR analysis of thermogenic genes in iWAT and BAT. (D) Immunoblots of total iWAT lysates. (E) Blood glucose (left) and plasma insulin (right) concentrations from HFD-fed mice before and after biweekly treatments of Fc or NRG4-Fc (1. 5 mg/kg) for 9 weeks. (F) Insulin tolerance test. Data represent mean ± SEM. *P < 0. 05, **P < 0. 01, ***P < 0. 001, ****P < 0. 0001. A, C, and E indicate (F) AUC using 2-tailed unpaired Student’s t-test and (F) ITT using 2-way ANOVA. We further explored whether chronic treatments with hNRG4-Fc improve insulin sensitivity in HFD-fed obese mice. A cohort of WT mice were subjected to HFD feeding for 2 months. Blood glucose and plasma insulin levels were comparable prior to the initiation of treatments (Figure 7E). We performed i. p. injection of Fc or hNRG4-Fc (1. 5 mg/kg) twice per week for a total of 9 weeks. [SEP] Justify:

Output: The text corroborates the claim by providing scientific analysis regarding cold exposure's effects, specifically mentioning that 'mRNA levels of thermogenesis genes, such as Ucp1, Cox7a1, Dio2, and Cidea, were significantly elevated in iWAT from mice treated with hNRG4-Fc.' This indicates an underlying biological mechanism related to cold-induced thermogenesis, which supports the claim about biohacking methods emphasizing health benefits through cold exposure.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Foucault’s understanding of the human may offer insights into the belief in HCAI of a human–AI hybridisation. This hybridisation of humans and AI will affect what we define and classify as human, which may also impact what we value as being human. If we evaluate human well-being as the goal of HCAI, human–AI hybridisation confuses what is or remains ‘human’ when this hybridisation increases. As well as being practically problematic, this human–AI hybridisation raises serious ethical questions about degrees of humanness. It may reduce the value of those who cannot or do not want to hybridise with AI if we use the human–AI hybrid as our paradigm of the human and the goal of AI development (criticisms of human-technology hybridisation have frequently been discussed in cyborg and transhumanist literature, cf. Clark 2001; Haraway 2000; Hayles 2000). The non-human, or lesser-than-human, may either be the new AI-human hybrids or those who refuse to or cannot hybridise. An issue with AI-human hybridisation and redefinition of the human is that the human is not simply a descriptor but also an inherently normative inflection (Lightbody 2010). The answer to ‘What is human? ’ has allowed many interpretations to emerge over the previous centuries, which has at times resulted in the exclusion and disenfranchisement of certain groups (Lightbody 2010). For Foucault, when we define human, we must also describe what is non-human or other, that which ‘is inherently different, dangerous, and, perhaps, evil’ (Lightbody 2010, p. 21). Therefore, defining the human may lead to suffering and abuse of certain people and groups defined as inhuman or other than human (Lightbody 2010). Using a definition of human and non-human has been a tool to create tension between people, and oppressed groups and even as a way to excuse genocide: ‘During the Holocaust, Nazis referred to Jews as rats. Hutus involved in the Rwanda genocide called Tutsis cockroaches. Slave owners throughout history considered slaves subhuman animals’ (Talk of the Nation 2011). While these are extreme examples of how distinguishing between the human and non-human can be problematic, their relevance for the distinction required in HCAI is still essential. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and ethical implications surrounding the hybridisation between humans and AI. It suggests that as AI develops and hybridises with humans, there are significant uncertainties around defining what is 'human' and 'non-human.' This indicates a limitation in AI's ability to understand human emotions perfectly, as it raises more questions about what it means to be human and the potential for devaluation of those who do not hybridise. Furthermore, the references to historical examples of othering highlight the challenges and potential harms in a simplistic view of AI's emotional understanding.


Input: Task: pass the claim using the given text. Claim: Languages evolve over time and can influence thought processes. [SEP] Text: Taking into account their findings, the authors propose life thinning and resilience integration processes to help describe and explain how some individuals end up seeking treatment for their gaming, while for others gaming supports them and becomes integrated into their identity. Following one round of in-depth review and revision, the recommender judged that the manuscript met the Stage 2 criteria and awarded a positive recommendation. URL to the preregistered Stage 1 protocol: Level of bias control achieved: Level 4. At least some of the data/evidence that was used to answer the research question existed prior to in-principle acceptance(IPA) but the authors certify that they did not access any part of that data/evidence prior to IPA. List of eligible PCI RR-friendly journals: Collabra: Psychology F10001. van -van, Przybylski AK (2018) A weak scientific basis for gaming disorder: Let us err on the side of caution. Journal of Behavioral Addictions, 7, 1–9. 2. Karhulahti V-(2023). : A [Stage 2 Registered Report], acceptance of Version 2 by in Registered Reports. Conflict of interest:The recommender in charge of the evaluation of the 2. As a longitudinal study, this is the second Stage 2 report I have reviewed. The first Stage 2 report was the first set of data (DOI: 10. 1525/collabra. 38819) and this current report includes data from one-year follow up. I have two potential conflicts of interest that I want to declare. First, I have recently published with Karhulahti (10. 12688/openreseurope. 15532. 1). Indeed, the topic of the publication - registered reports of qualitative research - was sparked from my role reviewing the Stage 1 and and first Stage 2 reports for this longitudinal study (DOI: 10. 1525/collabra. 38819). Second, I was a co-editor of a special section in the of (DOI: 10. 1111/bjso. 12628) in which Karhulahti published a paper (DOI: 10. 1111/bjso. 12573). recall correctly, Karhulahti and I discussed the risks of collaboration to the Stage 2 review and only worked on the 'primer' paper once the first Stage 2 paper had been completed and reviewed. [SEP] Justify:

Output: The text is irrelevant to the claim since it does not address or provide any information about the evolution of languages or their influence on thought processes. Instead, it focuses on topics related to gaming disorder treatment and research evaluations, which do not relate to language or cognitive influence.


Input: Task: pass the claim using the given text. Claim: Certain biohacking methods, such as cold exposure, are based on scientific studies showing potential health benefits. [SEP] Text: Escalona-García LA, Pedroza-Islas R, Natividad R, Rodríguez-Huezo ME, Carrillo-Navas H, Pérez-Alonso C. Oxidation kinetics and thermodynamic analysis of chia oil microencapsulated in a whey protein concentrate-polysaccharide matrix. FEBS Letters. 2016;175:93-103. DOI: 10. 1016/j. jfoodeng. 2015. 12. 00973. Tontul I, Topuz A. Influence of emulsion composition and ultrasonication time on flaxseed oil powder properties. FEBS Letters. 2014;26:454-460. DOI: 10. 1016/j. powtec. 2014. 05. 00274. . Maltodextrin: A consummate carrier for spray-drying of xylooligosaccharides. FEBS Letters. 2018;18:963-996. DOI: 10. 1016/j. foodres. 2018. 01. 00475. . Water sorption and glass transition temperature of spray dried açai (Euterpe oleracea Mart. ) juice. FEBS Letters. 2008;94:215-221. DOI: 10. 1016/j. jfoodeng. 2009. 03. 00976. Bicudo MOP, Jó Microencapsulation of juçara (Euterpe edulis M. ) pulp by spray drying using different carriers and drying temperatures. FEBS Letters. 2015;33:153-161. DOI: 10. 1080/07373937. 2014. 93787277. . Effects of spray drying conditions on the physicochemical and antioxidant properties of the Gac (Momordica cochinchinensis) fruit aril powder. FEBS Letters. 2010;98:385-392. DOI: 10. 1016/j. jfoodeng. 2010. 01. 01678. Fazaeli M, Emam-. Effect of spray drying conditions and feed composition on the physical properties of black mulberry juice powder. FEBS Letters. 2012;90:667-675. DOI: 10. 1016/j. fbp. 2012. 04. 00679. . Effect of inlet temperature on physicochemical properties of spray-dried jamun fruit juice powder. [SEP] Justify:

Output: The text is irrelevant to the claim as it lists a series of studies and their DOIs related to various scientific topics but does not mention biohacking methods, cold exposure, or their potential health benefits. Thus, it does not corroborate or contrast with the claim.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: If we are to adopt the HCAI belief that the hybridity between humans and AI will blur the lines of what is and is not human, this opens the possibility of creating these kinds of tensions and divisions among people and groups. In the extreme examples given above, the other is seen as less than human, where the human has a reduced, or devoid, moral obligation towards them. Definitions of the human and the non-human have been used throughout history to justify violence, oppression, and hatred by one group over another (Smith 2011, 2020, 2021). Essentially, if we endorse HCAI’s AI-human hybridity proposal becomes true, there is a distinct possibility that this hybridity will blur the boundaries of what is (or is not) considered human. Not only is this problematic from a practical standpoint (the human becomes blurred in the AI-human hybrid), but it is also questionable from an ethical standpoint (it may lead to the oppression and abuse of those considered less than human, as a result of the new distinctions created by AI-human hybridisation). 3. 2 The human is not at the centre of the AI universe (Claim 2)The previous section demonstrated that using humans as the goal of AI development is problematic if we acknowledge the HCAI goal of human–AI hybridisation. What is or is not human becomes challenging to identify due to increasing levels of AI hybridisation. As a result, this may also have normative implications about who is valued or valued more due to this changing definition of the human. Apart from these concerns, HCAI proponents make another problematic claim: humans are currently not at the centre of AI development. For example, one of the leading proponents of claims that we must implement a ‘Revolution’ to place humans at the centre of AI development (Shneiderman 2020). Shneiderman states that most people viewed Earth as the centre of the solar system before the Polish scientist demonstrated that it is the sun. Footnote 12 Shneiderman uses the and applies this as an analogy to AI. He claims that algorithms are currently the centre of AI development. However, we should place humans at the centre of the AI universe, in a similar way that Copernicus put the sun (and not Earth) as the centre of the universe (Shneiderman 2020). [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and tensions involved in human-AI hybridity and the blurring of boundaries between human and non-human. It suggests that this hybridity creates ethical concerns and challenges the current understanding of what constitutes human emotion and moral obligation. Therefore, it implies that AI assistants may not be able to understand or respond to human emotions as fully or ethically as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Languages evolve over time and can influence thought processes. [SEP] Text: I am tempted to share that the presence of conflicts of interest at this point of this longitudinal registered report is, for me, a happy coincidence. Nevertheless, it is perhaps less coincidence than a confluence of forces that sees qualitative researchers and open science taking an interest in each other and the new technologies of registered reports and open review, where reviewer and reviewed get to learn something about each others interests and perspectives at the early stage of a project. I share this information so that you, dear reader can judge my review. Below, I have structured my review according to the criteria for assessing a at Stage 2 from PCI RR (accessed 2023-05-27; [PCI (peercommunityin. org)]( 2A. Whether the data are able to test the authors’ proposed hypotheses (or answer the proposed research question) by passing the approved outcome-neutral criteria, such as absence of floor and ceiling effects or success of positive controls or other quality checks. ** This criterion addresses whether the data quality is sufficient to be able to test the stated hypotheses, according to the pre-specified conditions in 1E. Since not all protocols are able to pre-specify outcome-neutral tests, this assessment is not relevant to all forms of RRs. Where it is relevant and pre-specified, it is possible that the failure of a crucial outcome-neutral test could, in severe circumstances, lead to the rejection of a Stage 2 manuscript. **The paper reports that the data has been submitted to an archive but no citation is provided, so it is presumably not available at the time of this Stage 2 report was submitted. 2 baseline report has a data availability statement clarifying that the data is published in a Archive and commenting on access requests. As far as I can tell, the Stage 2 baseline report does not provide bibliographic information (i. e. the citation) on this data archive. I appreciate that the review of the submission of the data to the Finish archive is a separate process to this and it may well be a lengthy process. Nevertheless, the bibliographic information is important in making sure the data is accessible. Indeed, providing this bibliographic information in this Stage 2 registered report of the longitudinal data helps make the data accessible to its readers. Consequently, can I request the authors find a way to delay the final publication of this paper until the full bibliographic information of the data is available? # 2B. [SEP] Justify:

Output: The text provided is irrelevant to the claim regarding the evolution of languages and their influence on thought processes. It discusses the structure of a review process and data accessibility issues but does not address language or its effects on cognition.


Input: Task: pass the claim using the given text. Claim: Biohacking methods can fully reverse aging and extend lifespan indefinitely. [SEP] Text: I was born and raised in Cape Town, and now reside in Orange City, Iowa where I lecture Anatomy and Physiology to nursing students as my job and research for fun. Protein expression profiling work began in 2013 as part of and then ground to a halt when I took up my teaching post. I was also doing research on NT-020 and other lifespan extending compounds and am eager to get back to this research. Alessia MontesanoLove for "brain" started during my in Naples at "Univeristà degli Studi di II", in particular thanks to anatomy, neurology and neurosurgery classes, these lighted the fire for research. After becoming DVM, I start PhD course "for Biomedical and Veterinary Research". It gives me the chance to combine my major interests with the study of laboratory models, used during the research, in particular focused on the comparative neuroanatomy. My goal is find out which are the mechanisms that influence and change the brain structure and function during the aging process. Well, I would like give my contribute to help people with diseases correlated with aging. Also, I would like to find the way to resolve the ethical issue, that involve using animals in researching, applying the 3Rs rules and helping the ethics committees to find the best combination. Lab Notes6 PostedEvidence for alpha-synucleinopathy in N. furzeri GonarezhouFebruary 24, 20161277Some resultsMay 28, 20150076Status of fundingSeptember 4, 20141264Press and Media14. Additional InformationI am not mentioning the particular dietary supplements. After my resveratrol research was misused to sell resveratrol supplements I have decided not to mention them without having definitive results that they extend lifespan or the slow/prevent the progression of brain diseases. These are the fish:stain that I work with lives on average, 9 to 12 weeks. Western blots (shown below) are used to identify and measure protein levels using antibodies. The proteins are extracted from tissue and then separated by size through a gel (that acts as a filter that slows down larger proteins). The proteins are then transferred onto nitrocellulose paper (this paper prevents the proteins from dissolving into solution). The paper, called the blot, is then incubated with antibodies that identify specific proteins. [SEP] Justify:

Output: The text is largely focused on the author's academic background, research interests, and anecdotes regarding their work on lifespan-extending compounds. It does not provide any specific evidence or claims that corroborate or contradict the assertion that biohacking methods can fully reverse aging and extend lifespan indefinitely. Instead, it includes a personal narrative about research endeavors related to aging mechanisms without making definitive claims on reversing aging or extending life indefinitely.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Using the analogy of the as a way to place humans at the centre of the universe appears somewhat counterintuitive because it is usually understood as a metaphor to demonstrate that ‘[n]o longer was humanity at the centre of the universe, about which all else revolved, but rather humanity was but one small part of a much larger system in constant movement’ (Hornsby 2023). The metaphor of the has often been used to point out the flaws of anthropocentrism and the misguided placement of ourselves at the centre of the universe (Freud 2021; Kuhn 1957; Sagan & Druyan 2011). Footnote 13 However, Shneiderman uses the Copernican Revolution, which is often used as a metaphor for greater human humility and understanding of our limited place on the planet, to place humans at the centre of the (AI) universe (Shneiderman 2020). Regardless of the striking contradictions with using the as a metaphor for AI development, Shneiderman’s position—that humans are not at the centre of AI development—is a core theme throughout HCAI literature. This type of human-centric attitude is what Foucault was often critical of. Foucault pointed out that examples such as the Copernicus revolution should work as moments to realise our limited place in the universe and allow us to emancipate ourselves from the binds of our self-importance: ‘We are inclined to believe that man has emancipated himself from himself since his discovery that he is not at the centre of creation, nor in the middle of space, nor even, perhaps, the summit and culmination of life’ (Foucault 1994, p. 348). Unfortunately, it appears that we have not emancipated ourselves from ourselves but rather built artificial constructs that think, act, and speak like us while also claiming that these entities are (in fact) at the centre of the universe and not us. Furthermore, instead of self-reflecting on this possibility, the HCAI approach states that we need to (re)claim our place at the centre of the AI universe before it is too late. Those defending this view state that the field of AI is too stringently focused on developments, advances, and technological ingenuity, and those working in the field often become distracted from the needs of humans (this criticism against the lack of including the human perspective from computer development stems from HCI. [SEP] Justify:

Output: The text contrasts the claim by discussing the perspective that humans should not view themselves as the center of the AI universe. It critiques the notion that AI constructs can fully understand and replicate human emotions, indicating that while AI may think, act, and speak like humans, it does not place them at the core of AI development. The mention of human-centric attitudes in the HCAI literature suggests a recognition of the limitations of AI in comprehending human experience, demonstrating a fundamental disagreement with the claim that AI can perfectly respond to all human emotions.


Input: Task: pass the claim using the given text. Claim: Languages evolve over time and can influence thought processes. [SEP] Text: Whether the ** introduction, rationale and stated hypotheses (where applicable) are the same as the approved Stage 1 submission. ** **This criterion assesses whether the authors have remained consistent in their framing of the study at Stage 2. Aside from changes in tense (e. g. future tense to past tense), correction of typographic and grammatical errors, and correction of clear factual errors, the introduction, rationale and hypotheses of the Stage 2 submission must remain identical to those in the approved Stage 1 manuscript. To make any changes clear, authors are required to submit a tracked changes version of the manuscript at Stage 2. **This paper mostly has the same rationale as the Stage 1 report but I would recommend elaborating on the differences. First, I would recommend you elaborate that this is a 'new' Stage 2 report rather than an update to the original Stage 1 report. What I've learnt from my limited experience with registered reports is that the Stage 1 and Stage 2 reports are the same 'report' but that new unknown information is added at Stage 2. That is, the Stage 1 report comprises 1) the methodology and 2) the rationale for this before before we learned about the findings. In the Stage 2 report, the researchers put the methodology into practice and therefore add 1) what we found when we tried to do what we planned to do and 2) what we have learnt from these findings. The recommendation of PCI RR for the inclusion of a tracked changes version highlights that the researchers should be showing, and the reviewers should be mindful of, any changes between Stage 1 and 2. Indeed, I can look back at my comment on the revised Stage 1 submission and I am reminded that I mistakenly thought the 'final 10. 1525/collabra. 38819]( As such, we are in new territory, figuring out how to do Stage 2 reports when we are used to persuading journals to publish our work after study completion and we have little ability to change what we did. I do think this issue crops up throughout the paper as it's difficult a time to see if new detail is a posthoc addition. Consequently, can you succinctly explain why this Stage 2 report is as it is, perhaps ensuring you provide enough clarity that someone unfamiliar with, and indeed those familiar with, the format? [SEP] Justify:

Output: The text is largely focused on the process and evaluation of registered reports in academic research, specifically the consistency between Stage 1 and Stage 2 submissions. It does not discuss the evolution of languages or their influence on thought processes in any capacity. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Biohacking methods can fully reverse aging and extend lifespan indefinitely. [SEP] Text: elegans studies in this matter, since DPI inhibits oxidative damage in the worms, it was also shown to reduce accumulation of the age pigment, lipofuscin in C. elegans, indicative of increased longevity [47]. However in the latter study, C. elegans fed on E. feacalis, a pathogenic bacterium that induces the worm killing, exhibited a reduced lifespan upon its treatment with DPI probably due to the fact that DPI interfered with the worm oxidative anti-bacterial defense mechanisms increasing its sensitivity to the pathogenic bacteria [47]. In contrast with these results, but consistent with our data, recent results showed that DPI can function as a senomorphic drug, reducing the number of beta-gal positive senescent cells in vitro. More importantly, DPI treatment of old mice decreased the number of senescent cells, reduced the expression of inflammatory mediators and ameliorated signs of aging, such as liver fibrosis and immune cell infiltration, and improved physical performance [48]. Overall, these data support the idea that DPI can extend lifespan and reverse aging in mammalians as well as C. elegans. All of these data are coherent with the well-established longevity-promoting effects of anti-oxidants, decreasing the production of oxygen free radicals and other reactive oxygen species (ROS) and abrogating their deleterious effect on cell survival [49, 50]. Interestingly, the protective effect we observed for DPI against aging was obtained at very low concentrations thus with little if any toxicity produced, an added value to its lifespan-increasing function. Subsequently, we evaluated combination treatments using more than one antibiotic but at lower concentrations, in the aim of increasing efficacy. Indeed, our double antibiotic combination (doxycycline-azithromycin, DOXY-AZI) included concentrations of 1 μM (0. 5 μg/ml) of doxycycline and 1 μM (0. 75 μg/ml) of azithromycin. The maximum concentration of azithromycin in the plasma of a patient receiving a normal daily dose of 500 mg or a multiple dose regimen of azithromycin is 0. 4 μg/ml. As a consequence, the dose of azithromycin we used for these longevity experiments on worms is within the range of an achievable dose in an adult patient. [SEP] Justify:

Output: The text corroborates the claim by presenting evidence from studies indicating that DPI treatment has potential longevity-promoting effects in organisms, including C. elegans and old mice. Specifically, it mentions that DPI can reduce senescence markers, decrease inflammatory responses, and ameliorate aging signs, suggesting its efficacy in extending lifespan and potentially reversing aging processes in mammalian models. Additionally, the results support the broader claims of biohacking interventions aimed at increasing lifespan.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: See Bødker and Iversen (2002); Bødker (2006); Göransdotter and Redström; Höök and Löwgren 2021). HCAI proponents state that AI development ‘needs to focus much more on humans than it currently does’ (Bingley et al. 2023, p. 6). This perspective assumes that the human is not (currently) the centre of AI development and that technology or algorithms are at the centre of a ‘technical, machine-centred approach, with little focus on the human’ (Mhlanga 2022, p. 10). Footnote 14 When analysing this view through the lens of Foucault’s work, this HCAI position would appear misguided. This is because Foucault stated that the human has fundamentally been the centre of our scientific analysis in the modern episteme—throughout which the many human sciences are illustrative of this (Foucault 1994). The study of the human in the modern episteme is more important than any other object of concern in earlier epistemes, such as Truth, God, and Logos (Downing 2008, p. 45). The human of the modern episteme is ‘a being such that knowledge will be attained in him of what renders all knowledge possible’ (Foucault 1994, p. 318). What differentiates the modern episteme’s analysis of the human is that it proposes that the study of the human differs from the analysis of other objects of scientific analysis. Fundamentally, in the modern episteme, the human receives special attention in the hierarchy of observable things (Maniglier 2013). Despite this, HCAI researchers claim that AI development does not place the human at the centre. Footnote 15 If this view of HCAI is correct, then AI is being developed with some other goal in mind or the interests of something other than humans. If the human is not at the centre, then who or what is at the centre (for example, human-centred designers earlier claimed what is at the centre is the process or function of the technology’s design, rather than the human, cf. Hanington 2010)? [SEP] Justify:

Output: The text contrasts the claim by discussing the ongoing debate about the role of humans in AI development, specifically stating that HCAI proponents argue that AI development currently does not focus sufficiently on humans. It suggests that AI might not be centrally aimed at understanding or responding to human emotions, thereby challenging the assertion that AI assistants can perfectly understand and respond to all emotions.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 10. 7554/eLife. 28075. 014 Author response The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. Reaction time (RT) is one of the most widely used metrics in studies of primate behavior, based on the idea that it is an external reflection of internal neural processing time. This idea has been very influential, and had extensive impact on how neural data are interpreted. The present study challenges this view, by making a compelling case that RT is significantly affected by previous experience, and hence is not necessarily a reliable indicator of the time required for computation on the current trial. The study is simple and well designed. The results are appropriately analyzed and have broad implications for the interpretation of the widely used RT measure. The few concerns of the reviewers can be addressed with editing of the text for clarity. Reviewer #1: This study makes a case that reaction time (RT) is a free parameter that can be affected by past experience, rather than a simple consequence of the time required for computation in the current trial. The results have broad implications for the interpretation of the widely used RT measure. In general, the study appears to be well designed, and the results appropriately analyzed. My main reservation is that some of the effects sizes are quite small, making the results less convincing. Also, the presentation could be improved in some places to make this relatively simple study easier to digest by the reader. Although the effect sizes in our study are quite small, as Reviewer #2 notes they are on the order of the effects on processing time (~20-100ms) typically reported in other studies seeking to measure how RT varies given differing delay periods, increasing numbers of stimuli or responses, changes in task difficulty, with learning, and so forth. Hence, although these effects comprise only a fraction of the total RT, they have an important bearing on the interpretability of other studies that attempt to draw conclusions about processing time based on changes in RT. In Figure 1, the increase in RT was not significant, and hence should not be described as an increase in subsection “Experiment 1: RTs were biased to be longer or shorter according to performance in a previous task”, and other places in the text. We have revised the wording as appropriate. [SEP] Justify:

Output: The text does not address the claim regarding dialects and their reflection of social and geographic backgrounds. It primarily discusses a scientific study about reaction time (RT) in primate behavior and implications for interpreting neural data. As such, it lacks any mention or relevance to the topic of dialects, rendering it irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: The majority of biohackers successfully modify their genetic code using homemade CRISPR kits. [SEP] Text: Third, the authors should discuss how their method can be applied to biologic studies and what are the novel strengths and limitations to their approach. Reviewer #1: […] The paper does push the envelope further by showing additional technical improvements, which are substantive, above and beyond other reports. However, little biological insight is provided and it would be helpful to show novel biology to make this report of wide interest to the field. Nonetheless, as a Tools and Resources submission, this condition is relaxed a bit as long as the technique provides a rigorous proof of principle. We thank the reviewer for the supportive words. In the revised manuscript, we now provide an example of how the enrichment technology may be used. We now show that knockout of the cohesin complex member STAG2 significantly decreases erythroid colony formation in human CD34+ HSPCs (new Figure 1D). Since the sgRNA used to manipulate STAG2 has moderate efficiency (compared to other genes presented in our manuscript), the magnitude of the observed erythroid differentiation deficit in knockout cells could only be observed because our presented methodology allowed for the purification of modified cells. This further emphasizes the potential of our presented technology. Knockdown strategies using “INDEL-based”Cas9 approaches without the ability to purify modified cells would most likely miss such an important phenotype. 1) Analysis of clones derived from single cell methylcellulose assays harboring monoallelic integration showed gene specific differences in the modification of non-integrated alleles. What do the authors think was the cause of these gene specific differences? And how could these be influenced? We believe that the differences observed in the modification of the non-integrated allele in clones with mono-allelic integration could be due to differences in the accessibility of the chromatin, which was recently examined in Chen et al. , Nature Comm. 2017, Apr 7;8:14958. This could influence the efficiencies of the individual sgRNAs and dynamics of INDELs vs. HR. 2) For the biallelic targeted integration in HPSCs, why was the rate of double positivity different in cells that have received RNP+AAV targeting e. g. Runx1 (14%) vs. CCR5 (1. 2%)? Is this due to a lower transduction efficiency for the CCR5? Or other mechanisms interfering with HR? [SEP] Justify:

Output: The text is irrelevant to the claim as it does not provide any information regarding biohackers, the use of homemade CRISPR kits, or the success rates of genetic modifications by individuals outside of regulated environments. Instead, the text focuses on technical aspects and analysis of CRISPR methodologies within a scientific review context. Therefore, it does not corroborate or contrast the claim.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Footnote 16Instead of proposing that some other species or entity is the primary focus of AI development, perhaps HCAI researchers are claiming that the values that guide machine-centred approaches are more likely to cause harm than those found within HCAI. Or that the values of HCAI are somehow better than those of the machine-centred approach (this is a similar claim made in HCI, interactive design, and user-centred design, from which HCAI appears to take influence; cf. Göransdotter, M and Redström, Höök and Löwgren 2021). Therefore, we should prioritise human-centred values over machine-centred values. If HCAI researchers endorse this, then they would have to demonstrate that the values implicit within machine-centred approaches do not care about humans either at all or enough. Humans are not at the centre of current AI development, and thus, we need human-centred AI to replace it (also reflected in earlier human-centred design approaches, cf. Göransdotter and Redström; Hanington 2010). Proposing that current values in AI are not human-centred at all is a bold claim and would require showing that ‘machine-centred’ values completely disregard humans. One way to do this would be to outline machine-centred values and demonstrate how they are against humanity. As there does not appear to be anyone that calls themselves ‘machine centred’ (a term made up by HCAI advocates),Footnote 17 we need to intuit the values this position may support (e. g. , technological ingenuity and scientific innovation). While one may claim that these values sometimes lead to harm towards humans, this does not necessarily make them against humanity (similarly, values endorsed in HCAI may also cause harm and benefit, depending on how they are interpreted and implemented). Assuming that HCAI advocates do not want to go down this route, this paper will evaluate the less incredulous proposition that current values in AI development are not human-centred enough. To do this, HCAI proponents would have to demonstrate how certain values are more human-centred and other values are less human-centred. [SEP] Justify:

Output: The text contrasts the claim by discussing how current AI development is not centered around human values, indicating that AI does not adequately prioritize understanding human emotions. It argues that to advocate for human-centered AI, there needs to be evidence that current machine-centered approaches neglect human needs and values. This implies that AI assistants, as they currently exist, may not fully comprehend or perfectly respond to human emotions.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: Figure 4A: differences in trajectory are small, and inconsistent between subjects. We agree that there are inconsistencies between different movement trajectories, but this may be due to idiosyncrasies with particular curved-movement shapes themselves that makes them more or less difficult to execute, for which we did not have a strong a priori hypothesis. However, this figure and our findings summarize a repeated-measures analysis across participants per trajectory; thus, any significant point (i. e. , yellow dot) indicates a consistent difference in the manner in which that trajectory was executed across all participants. Since these effects appear fairly small, we also performed the procrustes distance analysis to allow us to better quantify how different these trajectories were from each other in comparison to the path cue. We have now clarified this point. Subsection “Experience-dependent RT biases were observed for curved reaches around barriers”: Success in hitting the target while avoiding barriers should be reported. If the success rate is already close to 100% for uncued, then a ceiling effect could be what is preventing detection of an effect on performance despite the altered reaction time. We have added these scores. The success rate is fairly high, but is not close enough to 100% to suggest that there is a ceiling effect preventing detection of a performance difference on cued trials that would reflect the observed differences in RT. If anything, there is a bigger difference in success between groups than across conditions within group, again suggesting a ceiling effect is unlikely. In any event, our kinematic analysis is a far more sensitive measure of changes in performance than task success. While we can see a clear change in how movements were executed depending on condition, there was no effect of order (group) or interaction between group and condition. This more strongly supports the argument that modulations of RT do not impact performance. Reviewer #2: This is a pretty straightforward study, whose conclusions might almost be considered too simple. However, given that the RT is one of the most heavily studied aspects of human behavior, it is essential to understand the factors that influence it. Historically, the RT has been heavily used because it is one of the few external measures of internal processing time. Without neural recordings, one cannot view internal processes such as movement planning preparation. However, it is generally believed assumed that one can at least use the RT to measure how long those processes take. [SEP] Justify:

Output: The text does not address the claim about dialects reflecting social and geographic backgrounds. It discusses various analyses related to movement trajectories and reaction times without making any reference to language, dialects, or their correlation with social or geographic backgrounds.


Input: Task: pass the claim using the given text. Claim: By following certain biohacking protocols, you can eliminate the need for sleep entirely. [SEP] Text: — (@ProfSnarky) February 11, 2018 14. (@ReadTheSyllabus): If you’re a professor who is utterly frustrated with inattentive, indifferent, and oblivious students, then you’re bound to relate to College Professor. With “cynical observations of colleagues and students,” is guaranteed to get a few laughs out of you with ludicrous accounts of college students that ask questions first and read the syllabus never! Good parking should have been the clue @jiminmilktease pic. twitter. com/2WKrxibr8j— (@ReadTheSyllabus) February 9, 2018 15. (@anonymousprofs): If you are certain that you simply don’t need to follow another college professor, will make you reconsider. With their edgy retorts to disgruntled college students, this group of witty professors will keep you asking for more. And who knows? You might even pick up a thing or two to use in your own classroom! Best teaching eval comment this year year: "This prof thinks she is some kind of expert. " Correct you are! Perhaps it's the PhD I earned. — (@anonymousprofs) May 30, 2016 16. RedPen/BlackPen (@redpenblackpen): RedPen/BlackPen translates funny ideas into unique, artistic comics, mainly focusing on academic research. It does a fantastic job of presenting some of the roadblocks researchers have to face while conducting research or writing their manuscripts. But what charmed me the most is the simple, honest appeal of the doodles that make up each comic. Navigating this landscape on a daily basis. #ManuscriptEarth #Writing #LOTR pic. twitter. com/7xwaYjuymc— RedPen/BlackPen (@redpenblackpen) February 21, 2018 17. (@LousyLibrarian): With a dash of irony and just the right helping of sarcasm, brings you the woes and struggles of being an underappreciated librarian in the age of the Internet. As the library swarmed with unattended kids, my colleague complained, "People just think of us as glorified babysitters. " thought: you know, that's not really fair. No one thinks we're glorified. — (@LousyLibrarian) February 19, 2018 18. [SEP] Justify:

Output: The text is irrelevant to the claim. It discusses various social media interactions and observations about professors and librarians without addressing the idea of biohacking or sleep elimination. Therefore, it does not corroborate or contrast with the claim.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Therefore, ethnocentrism may also lead to a type of cultural imperialism, where one group (the one whose values are ‘superior’) imposes their values on another because their values are more human (many have written on cultural imperialism, cf. Appiah 2017; Freire 2018; Said 1977, 2012; Tomlinson 2001). Therefore, the claim that AI is not being developed with the human at the centre is misleading and ethically problematic. There is no clear evidence to demonstrate that machine-centred approaches do not care about humans at all or enough. While HCAI proponents disagree with the values that machine-centred approaches prioritise, this does not necessarily mean they are less human than those HCAI endorses. 3. 3 We should place humans at the centre of AI development (Claim 3)As was shown in Sect. 2. 1. , HCAI’s definition of the human is unclear, particularly, when applying their AI-human hybridisation model. Furthermore, HCAI researchers’ claim that humans are not already the centre of our concern is also problematic, both from a practical perspective and an ethical perspective. HCAI proponents also make the normative claim that we should place humans at the centre of AI development. The human is both subject and object of the knowledge and decisions generated by AI, and this is justification for why humans should be placed at the centre: ‘humans should be placed at the centre of the discussion as humans are ultimately both the actors and the subjects of the decisions made via algorithmic means’ (Lepri et al. 2017, p. 6). However, Sect. 2. 1 demonstrated that if we adopt HCAI’s belief that the humans will be hybridised with AI, then the definition of human becomes blurred. Not only could this lead to practical difficulty in identifying what is human, but it is also ethically problematic. For example, people who choose not to hybridise (or those who do) may be viewed as less than human. Therefore, placing humans at the centre of our concerns in AI is difficult to normatively defend based on the account that the human is the main subject and object of algorithmic decision-making alone. However, there may be other reasons for justifying HCAI’s placement of the human as the centre. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and ethical issues surrounding AI development, particularly in relation to human emotions and hybridization with AI. It highlights that the definition of 'human' becomes unclear when AI is involved, and the claim that AI can fully understand human emotions is problematic. It emphasizes that while some advocate for placing humans at the center of AI, the ability of AI to genuinely comprehend and respond to human emotions remains ambiguous and ethically challenging.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: In the simplest version of this idea, movement planning preparation are key events whose culmination results in movement onset. The simplest version would be activity rising to a threshold. Under this conception, the time from stimulus to movement onset by definition reflects the time for movement preparation. A more nuanced (and probably more accurate) view is that preparation is independent from movement triggering, but that triggering normally waits for preparation to finish. In this view, the RT is typically still assumed to mostly reflect the time for preparatory processing to complete, because it is assumed that motivated subjects (humans or animals) pull the trigger almost immediately upon completion of preparation. The idea that the RT is a behavioral window on processing time has been very influential, and has extensively impacted how neural data are interpreted. For example, the ability to relate pre-movement neural activity to RT has been important in understanding the role of that activity, and in making the case that it is preparation-related. The present study makes a strong case that the RT reflects enough different factors that it shouldn't necessarily be considered a reliable indicator of computational processing time. The changes in RT due to these 'non-processing-time' factors are not large, but they are as large as the processing time that is usually inferred from changes in RT with delay period (20–100ms depending on task difficulty). I was persuaded by the experiments that the RT is indeed often driven by factors other than processing time. This might seem kind of obvious, but it isn't considered that often (or if it is, it tends to be ignored as an inconvenient fact). The present work is compelling, and relates to other recent work from the same lab that argued that the time of movement onset can be chosen independently of the time for preparation to complete. The present work demonstrates that this is perhaps the normal state of affairs, with the RT reflecting a variety of history-based effects. This is in some ways inconvenient and annoying: it means that the RT should be used with caution as a metric of computation time. On the other hand, the present work opens up new avenues to studying the factors that determine when the 'movement trigger' is pulled, and how that process is or isn't influence by the progress of movement preparation. Both these contributions (the caution, and the new avenue) are important. The manuscript is generally well written, but could be tightened and clarified in places. I found the 'Changes in motivation' section of the Discussion a bit rambling and confusing. [SEP] Justify:

Output: The text does not address the claim directly or indirectly. It focuses on movement planning and reaction times in a behavioral context, lacking any discussion about dialects or their connection to social and geographic backgrounds. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4920598/v1This work is licensed under a CC BY 4. 0 LicenseStatus:Under ReviewVersion 1posted 5You are reading this latest preprint versionAbstractObject classification is crucial for autonomous vehicle navigation, enabling robust perception of the surrounding environment. This paper proposes an innovative method to enhance object classification accuracy for autonomous vehicles by fusing depth estimates from monocular cameras with conventional color image features. We demonstrate that estimating depth using a deep neural network and integrating this information with RGB features consistently improves classification performance, particularly for autonomous vehicle applications. Our approach outperforms baseline methods, achieving a classification accuracy of 94. 46% on the KITTI dataset, an improvement from 93. 5%. This work highlights the potential of low-cost monocular cameras for advanced 3D perception, crucial for developing safer and more reliable autonomous vehicles. Our depth-aware RGBD object classification not only improves perception capabilities but also presents an alternative to expensive lidar-based systems. Fusionobject detectionperceptionlidarmonocular cameraFiguresFigure 1Figure 21. IntroductionObject detection and classification has a wide range of applications in the field of [1–5]. Perception of the 3D environment has turned out to be vital for self-driving cars. It can tremendously help in self-driving by estimating how far an object is from the vehicles, the pose of the vehicle and even behavioral analysis of static and dynamic objects in addition to robust object detection and classification. To understand the 3D environment more accurately, selecting hi-resolution sensors for estimating depth turns out to be very important for the vehicle industries. The sensors need to be cheap, portable, dense resolution and cover the entire field of view of the mobile platform. Recently, lidars have emerged as an accurate depth sensing device for the outside environment. However, lidars are expensive, bulky, and have low resolution that gives sparse data compared to standard color cameras. Also, the issue of calibration and fusion are additional overheads if multiple sensors are to be used for robust object detection. The motivation for this work comes from the idea of using a monocular color camera alone for depth estimation with deep learning and using fused color image features for improved object classification/recognition. Specifically, the proposed algorithm consistently enhances the object classification performance on Avs by estimating depth and fusion with color image information. 2. [SEP] Justify:

Output: The text corroborates the claim by discussing how object classification is essential for autonomous vehicle navigation and detailing the use of monocular cameras in conjunction with deep learning for depth estimation. It highlights the importance of accurate perception of the environment for self-driving cars and states that the proposed algorithm enhances object classification performance, thus supporting the claim that AI is utilized for processing data from sensors and cameras in autonomous vehicles to ensure safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: For example, there is a claim that we should place human at the centre to escape the Western-dominated, colonising approach found within AI. Some HCAI advocates claim that AI is Western-dominated and overlooks other cultures (Mhlanga 2022; Murphy and Largacha-Martínez 2022): AI is portrayed, as an idealized rendition of rationality, other modes of knowledge are overwhelmed. Consistent with the Western penchant for searching for ultimate foundations—Ideas, God, natural laws, etc. —algorithms have been given a seignorial status (Murphy and Largacha-Martínez 2022, p. 1). attitude claims AI is a form of objective knowledge, while humans are emotional and flawed. worldview assumes that AI can arrive at more objective decisions than those made by humans (Lepri et al. 2017). AI can perform tasks quicker than humans, process larger quantities of data, and it does ‘not get tired, hungry, or bored and they are not susceptible to corruption or conflicts of interests’ (Lepri et al. 2017, p. 1). The Western-dominated approach to AI claims that algorithms do not contain the flaws of human reasoning and thus, human knowledge should be made subservient to the rational advice of AI decision-making (Murphy & Largacha-Martínez 2022). In response to this colonising attitude, some HCAI advocates claim that we need to place the humans at the centre of AI decision-making to ‘decolonise AI’ (Murphy and Largacha-Martínez 2022). However, the colonising attitude that these authors are trying to get away from is (actually) the exact colonising attitude that also underpins HCAI—namely, a rationalist understanding of humankind, which is grounded in Western ideology about the human’s place in the world. Murphy and Largacha-Martínez 2022 are attempting to get away from a biased, Western approach to AI, by implementing an approach that is inherently underpinned by this Western worldview (HCAI). HCAI, at its core, is a Western rationalist ideology that views AI as something like stone, iron, and bronze, a raw material that will allow humans to develop and be used for human purposes (Schmidt 2020). [SEP] Justify:

Output: The text contrasts the claim by discussing how AI is often portrayed as objective and devoid of human flaws, suggesting that it cannot understand human emotions, which are flawed and complex. The text highlights the idea that AI systems are based on rationality and objective knowledge, which fundamentally differs from the emotional and imperfect nature of human decision-making.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: springen Skip to search - Accesskey = s. Petras/in Search… Posted in. Referate by Ben on 3. August 2006 Petras, V. : in Search: Mapping between of Discourse and Documentary Languages. Dissertation at of the University of California, Berkeley (2006) darf und will nicht verschweigen, dass die Autorin einst studentische Hilfskraft bei ihm war und schon in dieser Zeit mit M. Bank eine durchaus bemerkenswerte Korrektur[1] der bekannten Arbeit von Giles und Lawrence (in Science, April 1998) publiziert hat. hat eigentlich keinen Thesaurus sondern eher eine Klassifikation. [2] Die „Deskriptoren“ sind in ihrer Begrifflichkeit zu breit für einen typischen Thesaurus, so dass im Durchschnitt auch nur 7 „Deskriptoren“ vergeben werden (S. 130 bzw. 230). [3] Bei 450. 000 Records/J ist das nicht viel. Daran erkennt man, dass die Indexierungsbreite und die Indexierungsspezifität in der Weise miteinander korrespondieren, dass mit wachsender begrifflicher Breite der Deskriptoren bzw. der Klassen die Indexierungsbreite zwangsläufig abnehmen muss. [4] Je größer die Zahl der Dokumente ist, um so höher muss auch die Indexierungsbreite sein. Darin liegt ja der eigentliche Grund für das Entstehen der Dokumentation im letzten Jahrhundert. Wenn beispielsweise 427. 340 Records durch 8. 447 verschiedene erschlossen werden (S. 129), erbringt jeder dieser Deskriptoren durchschnittlich 50 Treffer. Bei 7 Deskriptoren/Record sind es Ø 350 Hits. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not discuss or provide any insights on dialects, their characteristics, or their relation to social and geographic backgrounds. Instead, it appears to focus on indexing and documentation processes related to different records and descriptors.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: Related StudyThere are many researchers that propose object detection and classification by processing RGB-D imagery. Most of the depth images are obtained from active depth sensors like Kinect, or Intel Senz3D cameras [6, 7]. These sensors can produce dense, reliable depth-maps which can be fused with RGB cameras relatively easily. However, Kinect and Intel-can work only indoor scenarios, and it completely fails in outdoor environments due to sunlight interference. Fusion of lidars with cameras can also be a good alternative for detection problems that enables high accuracy range measurements, but Lidar measurement is sparse and even if it is possible to get a reliable dense depth map by super resolution [8, 9]. Bad weather conditions such as rain, fog, snow can significantly decrease effectiveness of lidar based algorithms [10, 11]. Lidars are currently expensive devices that increase the development of AVs and there is no large-scale dataset with Lidar scans that causes limitations on deep learning methods and AV applications. Then, this research study is aimed to use monocular cameras for depth map estimation, and to explore whether the depth estimates are reliable enough for improved RGB-D object classification. There is plenty of literature in this field; but Eigen et al. [12] changed the landscape of this research by incorporating deep learning to estimate depth. Much new research has now used their own deep learning paradigm to estimate depth [13, 14]. Most of the previous research relies on aggregate statistics, and it is hard to decide which of them would perform best based on the statistics. Therefore, in this research, the proposed algorithm presents a state-of-the-art technique to estimate depth to check whether it can improve RGB-D object classification. 3. MethodSince estimating depth is vital for scene understanding and improved 3D perception, the proposed algorithm presents a method for estimating depth and robust object classification with monocular camera. A single camera has high resolution, portable and much cheaper than depth cameras. Also, several state-of-the-art methods have already found impressive object classification performance based on color imagery. The effort for this research is to estimate the depth map from a monocular camera to leverage information with color images to improve object classification performance. The idea is that if monocular cameras can infer depth alone, the cost of purchasing depth sensors can come down significantly. However, the proposed algorithm works based on depth inferred from a monocular camera with combined color features. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of cameras, specifically monocular cameras, for depth map estimation and object classification, which is essential for scene understanding and navigation in autonomous vehicles. It mentions the aim of the research is to improve RGB-D object classification through processing data from cameras, aligning with the claim that AI processes data from sensors in autonomous vehicle technology.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: HCAI proponents claim that the current challenges that we face in the world are simply obstacles that a rational human can overcome with the help of AI, as we have done throughout history (et al. 2023, p. 391). However, it is only a certain type of human: one that welcomes a future of ubiquitous AI, or at least, one who does not resist it. Other cultures, nationalities, and religions have no choice but to accept, adopt, and integrate AI within every aspect of their lives and opting out is not an option. HCAI proponents are essentially deterministic about AI (as are most who are writing about AI for that matter), claiming that AI is here to stay and going back to a pre-AI world is not an option: ‘Opting out is like putting your head in the sand’ (Schmidt 2020, p. 4). 3. 4 AI is the next step in the continuous path of human progress (Claim 4)In HCAI, progress is continuous, and AI, when used correctly, will help us advance and achieve our full human potential. AI is simply another tool that we should capitalise on for human progress. This type of attitude, particularly the view of ‘progress’, is something that Foucault was critical of in his work. does not say that progress is not possible, he states that the idea of it being a processual and progressive thing, whereby, each age or epoch develops towards something, towards an advancement of humankind, is a simplification of our past and a negation of the tragic periods we have witnessed throughout history (Foucault 1972). This interpretation of progress claims that humanity is constantly progressing (for example, with and through AI), albeit with a few hiccups along the way (May 2014, p. 15). Modernity demonstrates the human as the hero of our current situation and will discover all the secrets and truths in the world, allowing us to invent ourselves and produce who we want to be (Rabinow 1984, p. 42). In HCAI, the human is the hero that will bring us toward the future we desire, allowing us to ‘co-evolve’ alongside AI (Herrmann and Pfeiffer 2022, p. 12). [SEP] Justify:

Output: The text contrasts the claim by expressing skepticism towards the notion that AI can address all human challenges and emotions uniformly. It highlights a perspective where AI is seen more as a tool for human progress, rather than suggesting it can perfectly understand or respond to human emotions. The reference to historical determinism about AI suggests that reliance on AI involves acceptance of its limitations rather than an assertion of its infallibility in emotional understanding.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: Interessant sind auch die VENN-Diagramme (S. 137, 138, 142, 143, 186), da man auf sie einen einfachen wahrscheinlichkeitstheoretischen Test anwenden kann,[5] der z. B. deutlich zeigt, dass in der wie Computers und Physics in der natürlichen Sprache der Dokumente deutlich seltener als bei einer Zufallsverteilung gemeinsam vorkommen, Physics = 0,33 + 0,04 + 0,20 + 0,13 = 0,70 Computers = 0,13 + 0,04 + 0,20 + 0,13 = 0,50 Physics ∩ Computers = 0,04 + 0,20 = 0,24 0,70 x 0,50 0,24 = 1,46 während die Indexer diese scheinbare Zufälligkeit wieder herstellen. Physics = 0,09 + 0,02 + 0,62 + 0,15 = 0,88 Computers = 0,01 + 0,02 + 0,62 + 0,08 = 0,73 Physics ∩ Computers = 0,02 + 0,62 = 0,64 0,88 x 0,73 0,64 = 1,00 geht es bei der Arbeit um den Einsatz von „search term recommenders“ und das Verhältnis der zur Precision. Dabei zeigt sich, dass die alte Erkenntnis von F. Lancaster (vor ~40 Jahren an gewonnen) bis heute noch sehr stabil ist, bei der sich das Verhältnis zwischen Precision und Recall als etwa linear erweist. von 20% – 60% beträgt die Steigung meist etwas weniger als -1. Mit zunehmendem Recall fällt somit die Precision direkt proportional. [SEP] Justify:

Output: The text does not address the claim about dialects reflecting social and geographic backgrounds. Instead, it discusses the application of Venn diagrams and certain probability theories in analyzing search terms and the relationship between precision and recall in information retrieval. There is no relevant information to either corroborate or contrast the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: Therefore, the project comprises two parts. Firstly, the proposed algorithm estimates depth from monocular camera measurements. Secondly, the proposed algorithm uses depth information along with conventional color image features to improve object classification performance. The reason for fusing depth and color information is because it is expected that color and depth information contain useful complementary characteristics that can be utilized for improved object recognition. The following section describes the proposed approach in the next. For the first part of the research, two state of the art methods are utilized and compared its performance to select the best method for use in depth estimation. For the second part, extracted relevant depth and RGB features are used from the fused RGB and depth images together to classify objects. The following subsections describe used approaches in detail. 3. 1 from Liu et al. [13] shows that the property of continuous characteristic of depth values can be used to formulate the problem of depth estimation as a continuous conditional random field (CRF) learning problem. A deep convolutional neural field model is proposed which can jointly explore the capacity of deep CNN and continuous CRF. The structured inference model (CRF model) can learn the unary and pairwise potentials of that model jointly in a deep CNN framework. CNN predicts the unary and pairwise potentials on given superpixels which are input to the CRF loss layer. The predicted depth map is then backpropagated to the loss to the CNN network see Fig. 1. It showed that the integral of the partition function in a CRF can be calculated in closed form, allowing an exact solution to the maximum-likelihood estimation problem. As a result, no approximate inferences are required. To go at the problem, Liu et al. [13] proposes over segmenting the image into plenty of small regions called superpixels. Each superpixel defines a homogeneous region where depth is assumed to be constant. Based on this assumption, the CRF model is built by defining unary and pairwise potentials on superpixels and its neighbors. A rectangular patch is extracted from each superpixel so that it can capture the local context of the superpixel, and then the patch is fed into a deep network for estimating depth of each superpixel. The final depth map needs to be very close to the estimated depth of the superpixel. The rectangular patch is then fed into the AlexNet architecture for deep learning to estimate the depth of the superpixel. [SEP] Justify:

Output: The text corroborates the claim by detailing how an algorithm employs depth estimation from monocular camera measurements and how this depth information, combined with conventional color image features, enhances object classification performance. The focus on improving object recognition through the fusion of depth and color information supports the idea that AI techniques are applied to process data from cameras, which is integral for the navigation capabilities required in autonomous vehicles.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: The human is constantly developing and progressing to a more evolved state and any disturbance is seen as simply a glitch or deviation from where we are destined (Rabinow 1984, p. 39). This constant progress towards a more evolved state is something that should be developed and further exploited, albeit with a (re)emphasis on the importance of placing humans at the centre of AI development. In contrast, Foucault attempted to show how history is discontinuous (Rabinow 1984, p. 49). History has ruptures, changes, and regressions, and it is not a ‘continuous path of progress toward the Truth’ (May 2014, p. 29). In the context of HCAI, Foucault’s work reflects that we should get away from the idea that if we simply implement greater human control (to the right degree) and automation (to the right extent), we will continue on the path to progress (Shneiderman 2022). We must be aware of the temptation that AI can bring us closer to the truth of everything, and that ‘one day we will know ourselves, that nothing will escape our [AI hybridised] intellectual gaze’ (May 2014, p. 29). We should be sceptical about the goal of placing humans at the centre of everything and that this will lead to desirable outcomes. For Foucault, each historical period is not necessarily a development and refinement of the one that went before it (May 2014, p. 29). History is made of junctures and ruptures (Foucault 1972), and AI development and the placement of human at the centre of AI research (and AI research, generally) may not necessarily lead us closer to this ideal of progress and improvement that is promised in HCAI. Human-centring in AI development, and AI development itself, is not necessarily something we should aspire toward in the hope that it will bring us to a more enlightened state: History is not of its nature progressive; it does not necessarily move from the more primitive to the more enlightened, from the barbaric to the civilized. History has neither telos—a goal—nor a structured movement. [SEP] Justify:

Output: The text contrasts the claim by emphasizing skepticism towards the belief that placing humans at the center of AI development and automation will lead to desirable outcomes. It argues that AI development may not necessarily move us closer to an enlightened state, and suggests that history is not inherently progressive. This challenges the idea that AI assistants can understand and respond perfectly to human emotions, as it implies that such advancements may not lead to genuine understanding or progress.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: ist insofern besonders bemerkenswert, weil er in erster Näherung besagt, dass professionelle Rechercheure seit Jahrzehnten das Optimum anstreben, bei dem etwa 50% Recall auch 50% Precision gegenüberstehen. Auch bei Volltextrecherchen hat sich daran kaum etwas geändert, obwohl die dort nicht selten auf siebzig oder achtzig Prozent erhöht werden kann. Dabei fällt aber die Precision oft auf störend kleine Werte ab, wenn man nicht sehr lang und intensive recherchiert. Erfahrung (von den vor vierzig Jahren bis heute) lässt sich erkennen, dass das Verhältnis von Recall und Precision weniger ein Zeichen für die Güte einer Datenbank ist, als vielmehr ein Optimum das Rechercheure grundsätzlich anstreben. Sie wissen, dass sie sich dann etwa im Optimum befinden, wenn jedes zweite Dokument das sie in der Recherche herausfiltern relevant ist, und wenn sie feststellen, dass sie etwa jedes zweite Dokument das auf dem recherchierten Gebiet relevant ist, gefunden haben. ließ sich bei erfahrungsgemäß annähernd erreichen, unterliegt aber selbstverständlich erheblichen Streuungen. haben gezeigt, dass die einer Datenbank um 40% pendelt. Durch einen guten Thesaurus verbessert sich dieser Wert bemerkenswerter Weise nicht. Er verringern lediglich die Schwankungen. Dies wird durch das oben erwähnte Ergebnis neu belegt. [SEP] Justify:

Output: The text is unrelated to the claim concerning dialects reflecting social and geographic backgrounds. Instead, it discusses the concepts of recall and precision in professional research, which does not address the topic of dialects or their connections to social or geographic factors.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 3 from RGBD image Neural networks have a proven record of excellence in object classification. For the proposed algorithm, the rich semantic feature information obtained via the best performance pre-built network is used. Currently, one the best pre-built object classifiers available through MatConvNet is the ResNet-50 [15]. The ResNet-50 is not a conventional neural network, but a Neural Network. Typical, combined feature level information extracted at multiple levels of the network to attempt to combine high, mid and low-level feature information. Continuously adding layers to a network to make it deeper is only expected to work up to a point due to vanishing gradients and no guarantee that the additional layers learn anything new of value. Residual networks solve both problems. To ensure that an added layer will learn new information in the network, the layer output will have access to the input before transformation. The intuition behind this type of architecture is that it is easier to optimize the residual mapping than the original mapping. have been shown to outperform Neural Networks, where the best performing network has 152 layers. The proposed algorithm uses extracted information from the final fully connected layer of the ResNet-50 model architecture and uses this as feature input for SVM. This ensures that the RGB features to classify vehicles and pedestrians are rich and discriminative. Each training image is provided with 2048 meaningful RGB feature descriptors. From the depthmap, the proposed algorithm generates 3D point clouds and extracts two sets of features: depth features and normal features. For the depth features, the proposed algorithm finds the covariance matrix of the point-cloud at the bounded/masked regions and uses them as features. For the features, the proposed algorithm calculates the mean and the variance of the local surface normals calculated for each pixel based on the 3D point-cloud. Surface normals are calculated by extracting a rectangular box and selecting points having estimated depth within a given threshold. The proposed algorithm uses those points for calculating the minimum eigen-vector of the point-cluster. The minimum eigen-vector then represents the surface normal for those selected points. In training each SVM, it standardizes the feature values. Tests are performed using k-Validation to ensure that there is no overfitting occurring in the proposed model. In experiments, parameter k is selected as 10. 4. DatasetsPublicly available KITTI dataset is used for training, testing and evaluation purposes. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of neural networks, particularly the ResNet-50 model, for object classification in autonomous vehicles. It explains how rich semantic features from RGB images are utilized to classify vehicles and pedestrians, which directly relates to the processing of data from cameras necessary for safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: We can see this clearly if we abandon our commitment to historical continuity and allow ourselves to see breaks instead, moments in which one way of looking is replaced by another, not as an improvement on or a refinement of the earlier way, but simply as a new framework or perspective (May 2014, p. 35). Foucault does not embrace the viewpoint that history is constantly progressive, nor does he view history and society in decline (Rabinow 1984). Likewise, if we view AI through a Foucauldian lens, AI will not help humanity achieve all of its wildest dreams nor is it focused on some kind of doomsday vision of AI superintelligence enslaving us. Instead, history flows and ebbs, sometimes taking sharp junctures that are either progressive or the opposite: ‘This does not reverse the assumption of historical progress; it complicates it’ (May 2014, p. 68). Historical progress is discontinuous with sudden transformations or accelerations, rather than the view that knowledge and human development are consistent and constant: My problem was not at all to say, "Voila, long live discontinuity, we are in the discontinuous and a good thing too," but to pose the question, "How is it that at certain moments and in certain orders of knowledge, there are these sudden take-offs, these hastening of evolution, these transformations which fail to correspond to the calm, continuist image that is normally accredited? " (Foucault 1984a, p. 54). AI should not simply be seen as another tool to fulfil the goal of human progress. AI also holds the possibility of causing serious ruptures in how we interpret the world, our basis for knowledge, and how we view ourselves. This fact has also been demonstrated in a recent PEW Research study of ‘979 technology pioneers, innovators, developers, business and policy leaders, researchers and activists’; where 37% said that AI will not make us better off by 2030 (Anderson and Rainie 2018). Therefore, even the experts developing, implementing, and policing AI are uncertain if it will lead to progress (Li and Du 2007). These issues have also become evident in the recent open letter calling for the pause of large-scale AI experiments (more advanced than GPT4) for 6 months (Future of 2023). [SEP] Justify:

Output: The text contrasts the claim by suggesting that AI should not be viewed as a perfect tool for understanding human emotions or as a means of achieving human progress. Instead, it argues that AI has the potential to disrupt our interpretations of the world and implies that even experts express uncertainty regarding the benefits of AI, as evidenced by the PEW Research study indicating that 37% of experts believe AI will not improve our situation by 2030.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: In den Grafiken der vorliegenden Arbeit liegen die Recall-Werte bei einer Precision von rund 50% meist erheblich unter 40%. (S. 157) beispielsweise sogar weit unter 10%, da erhöhte nur bei entsprechend hoher Indexierungsbreite (im Extremfall beim Volltext) erreichbar sind. zeigen daher auch, dass es die Aufgabe eines search term recommenders sein muss, stärker auf die Precision als auf die zu achten, da Endnutzer ein System eher als positiv bewerten, das ihnen von 100 Treffern 50 Relevante erbringt, als solche, die von 100 Treffern nur einen relevanten bzw. alle hundert relevante herausfiltern. Im letzten Fall hätte man zwar als Endnutzer das Gefühl sehr viel relevante Information bekommen zu haben, wüsste aber nicht, wie viel weitere Treffer möglich gewesen wären, da das Umfeld (mit ähnlichen aber nichtrelevanten Treffern) zum Vergleich fehlt. eines leistungsfähigen search term recommenders zeigt sehr schön, dass auch er mit wachsender Leistungsfähigkeit das Verhältnis zwischen Precision und Recall gegen -1 führt, wobei der Schnittpunkt mit der y-Achse (Precision) möglichst hoch sein sollte. Eine geringere Steigung würde zwar bei leichtem Precision-Verlust zu rascherem Anstieg der führen, aber dies auf niedrigem Niveau (S. 237). (W. Umstätter) [1] Petras, V. und Bank, M. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the performance metrics of a search term recommender system, including precision and recall values. There is no mention of dialects or their relationship to social or geographic backgrounds.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: dataset contains images from 4 different cameras at fixed baselines between them. They can be used as a stereo camera system. In addition, it has a 64 channel Velodyne lidar that can be used for ground-truth or evaluation purposes. For estimating depth, the proposed algorithm uses 56 different video scenes, belonging to categories ’city’, ’road’, ’residential’. Frames can be extracted from videos for training and testing. Lidar measurements are used for evaluation purposes only. KITTI also provides a subset database meant specifically for object detection. This database contains 7481 training, and 7518 test scenes extracted from their videos, these images are independent meaning they come from various videos and various points in time from each video. Each scene contains a variety of vehicles, pedestrians, and other points of interest. The ground truth files for this dataset contain tight bounding boxes for each object, as well as a label for the object at that location. Officially, there are 8 labeled classes in the dataset;. and Don't Care. Both the misc. and tram class types contain very few examples within the database, and so it has been ignored. Various vehicles such as Car, Truck and Van classes bundled into a single Vehicle class. Likewise, the Pedestrian, and Cyclist classes are combined to represent a singular Pedestrian class. The Don’t care objects are those regions of interest which can’t yet be labeled, typically these are vehicles or pedestrians which are too far from the camera or lidar sensor to obtain meaningful information from the vehicle. For algorithm development, the proposed algorithm used a database of 500 samples per class; vehicles, pedestrians, and a class of miscellaneous objects which are referred to as the negative class ’other’. The subregions in a scene of vehicles and pedestrians are provided to use by the ground truth file from the KITTI dataset. Meaningful negative examples of varying sizes were obtained by guaranteeing that negatives shared anywhere between a 5 to 25 percent region overlap with a ground truth area. Therefore, it has been ensured that negative examples are difficult and relevant. They each contain fragments which belong to either a vehicle or pedestrian, presumably this should help classification performance on objects which are occluded in part. Additionally, this can make the classification of true vehicle and pedestrian samples more challenging and therefore the feature descriptors that define them will be more robust. 5. [SEP] Justify:

Output: The text corroborates the claim by describing the use of a dataset that includes images from cameras and lidar data, which are essential for developing algorithms for autonomous vehicles. It details how these cameras and sensors provide data needed for depth estimation and object detection, which are critical for navigating streets safely.