Input: 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.

Output: pass


Input: 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.

Output: contrast


Input: 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?

Output: pass


Input: 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.

Output: corroborate


Input: 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)?

Output: contrast


Input: 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.

Output: pass


Input: 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?

Output: pass


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: pass


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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).

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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).

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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).

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: This has received over 25,000 signaturesFootnote 18 and support from ‘co-founder Steve Wozniak, and Pinterest co-founder Evan Sharp’ (Sivakumar 2023), as well as many pertinent figures that are developing (Bengio 2023) and investing in AI (Hogarth 2023). Footnote 19 The general public also shares these concerns, with a recent survey showing that 46% of US adults were highly, or somewhat, concerned that AI will wipe out the human species (Bialik and Orth 2023). Therefore, we should be sceptical that AI will lead us on a path toward progress. With such concern around AI, it is important to re-evaluate what we actually mean by progress and how AI can be incorporated into this vision if we want to incorporate it at all. Unfortunately, the answer is not so simple as signing a digital letter to freeze AI for 6 months or, the opposite, continue business as usual in the hope that AI companies will self-regulate themselves. Foucault’s work may offer some valuable insights about this without necessarily providing a clear-cut solution. It is worth looking back to other junctures in history to look for insights into how analogous situations developed in the past and how these have impacted our present. Perhaps, we should look at certain practices, technologies, and applications that were banned or seriously limited in certain instances rather than only focusing on those that were permitted. Despite the deterministic view of many HCAI researchers and AI advocates generally, a future with ubiquitous AI is not necessarily inevitable and may not be completely desirable. Foucault’s approach emphasises the importance of looking at history for insights into how we have come to be in our present, but also to ensure that we do not make the same mistakes of the past. We should try to understand our current practices based on an understanding of our history (Paden 1987). This is not meant as a way to return to, or strive towards, a relic from the past. It is meant as a way that informs us that there are different ways of living now, as there have been different ways of living in the past. The reason for examining history is to postulate who we are and who we might become (May 2014, p. 99).

Output: contrast


Input: Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: : Vergleich der Suchmaschinen AltaVista und HotBot bezüglich Treffermengen und Aktualität. nfd 98 (6) (1998) [2] [3] 50% der Dokumente haben nur 2-6 Deskriptoren [4] [5] Umstätter, W. und Rehm, M. : Einführung in die Literaturdokumentation und Informationsvermittlung. Saur. Verl. München S. 8-10 (1981) dies mit:FacebookXGefällt mir Wird geladen … Ähnliche Beiträge 1 comment « Wencel/Telefonie in konvergenten Netzen. . Body and Seoul – aus ganzer Kehle und (sehr) hart an der Grenze » Subscribe to comments with RSS. am Dienstag: Impulse von NY. Times, BBK und JDoc «. said, on 10. Januar 2012 at 22:10 […] Vivien Petras, die am IBI (auch) auf diesem Gebiet forscht, wird sich freuen, dass sich ihr kalifornischer PhD-Betreuer mit Dank zitiert wird. (und zwar mit der hier annotierten Arbeit) […] einen abbrechen Δ bei mastodon bei bluesky bei bei bei Twitter (seit Herbst 2023 geschlossener Account) nach: August 2006 123456 78910111213 14151617181920 21222324252627 28293031 « » Bibliothek in der Bibliothek in Film und aktuell for on tour preprints. . . . . . . . . . . . . to read Zitiert in. . ankündigung berlin Berufsbild bibliothek bibliothek 2.

Output: pass


Input: Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: g height above ground, angle of the surface normal with the inferred gravity direction), this can uplift the performance significantly. Li et al. developed AFI-NET for object detection [18] that is developed on image features as the proposed method. Additionally, the dataset that has been studied with contained many ground truth samples of vehicles and pedestrians which were a good distance away from the camera which may have impacted the significance of the depth information. One of the reasons that depth information can be bad at far ranges is because the proposed algorithm used the principle of stereo to train the CNN architecture. Stereo depth error quadratically increases with far-ranges and it was also evident with Fig. 1 where at ranges beyond 50m, the unsupervised learning approach fails to compete with the supervised learning approach on depth estimation. The purpose of the research is to explore the limits of monocular cameras in estimating depths and perceive the 3D environment based on enhanced prediction. This research concentrated on the object classification performances using RGB-D information. The depth-map prediction based on monocular camera and fusion with RGB imagery improved object classification, and it has been concluded that depth information improves object classification performance. In doing so, RGBD camera sensor-based algorithms can replace lidar based algorithms which are expensive and need lots of object scans that makes it much harder to generalize autonomous driving. In the future, it has been planned to build more meaningful depth features as opposed to statistical measures of the point-cloud to see if that can improve performance further. Also, it has been seen that the evaluation performance goes down with far-away objects because the current method is bad at depth-prediction at far away ranges. It has been planned to develop an algorithm that can work for short-range and long-range distances as well. Also, it has been planned to deploy a neural network based on RGB-D channels for the neural network to learn the combined feature information. This can help to improve the classification performance further. This can also help on segmenting ground-planes (road surface), walls, and buildings by clustering and analyzing the surface normal and help to give important insight into the complex field of scene understanding. Analyzing RGB images based on depth maps from a monocular camera alone contains a research opportunity that can be exploited for improved perception of the 3D environment. DeclarationsConflict of is no conflict of interest.

Output: corroborate


Input: Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: The rules that we use to structure meaning and create order are not ‘inevitable, natural or—in any simple sense, ‘true’, but are wholly socially and historically constructed according to unconscious sets of governing rules’ (Downing 2008, p. 46). They stem from an incalculable number of consequences and interactions throughout history. Therefore, our knowledge, ideas, and beliefs are fundamentally dependent upon our particular place and time in history (Downing 2008, p. 39). We need to be aware of this and the impact our AI-related choices will have on the future. Instead of adopting the view that AI will solve the world’s challenges, we need to be more critical about how we define ‘progress’ and what this will mean for us and the future of the planet. 3. 5 Increasing human control over AI will reduce harm (Claim 5)One of the underpinning goals of AI development is to transfer greater independence to AI because of the supposed benefits it could bring. The commonly held assumption within the AI field is that to get greater automation, we must give away control to AI. Conversely, higher levels of control hinder automation (Pacailler et al. 2022). HCAI states that this is misleading and we can have high levels of automation and control (Shneiderman 2022). We need more human control over AI decision-making, not less (this is also reflected strongly as 'human oversight' in High-Group on AI 2019). HCAI advocates state that AI can and should be developed with high levels of human control and high levels of automation (Shneiderman 2022, p. 9). We do not need to ‘sacrifice human control when incorporating higher levels of automation’ (Pacailler et al. 2022, p. 471). We need to place a greater emphasis on the ability and skills of humans, particularly, when we are the ones most impacted by AI decision-making (Shneiderman 2022). Being in control of AI is one of the foundational goals of HCAI (Schmidt 2020). Fundamentally, HCAI promotes AI that is humanly controlled (Xu and Dainoff 2023). Humans should be included throughout the AI process (He et al. 2021) to ensure people ‘feel safe and have self-determination’ (Schmidt 2020, p. 1).

Output: contrast


Input: Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: 0 bibliothekartag Bibliothekartag 2011 Bibliotheken Bibliotheks- und bibliothekspop bibliothekswesen bibliothekswissenschaft Call for Papers CfP Debatte digitale bibliothek ät Frankreich frei frei-tag 2011 frei-tag 2012 frei13 geisteswissenschaften Gesellschaft google iwp Kritik libreas libreas audio for methodologie open access Open Access-Zeitschriften podcast potsdam soziale software Unkonferenz urheberrecht usa visualisierung web 2. 0 wissenschaft wissenschaftliches publizieren wissenschaftskommunikation Wissenschaftssoziologie zitation Zukunft Öffentliche Bibliothekender Einträge Kommentare-Feed WordPress.

Output: pass


Input: Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: Sensors in Autonomous Vehicles: of Math and 61201 Email: rayalaguerrero18@augustana. edu Search for other works by this author on: PubMed 10. 1115/1. 4052991 Published Online: December 27, 2021 1003. Download citation file: Ris (Zotero) toolbar search Menu toolbar search search input Search input auto suggest filter your search All ContentJ. Auton. Veh. Sys. Research and technology in autonomous vehicles is beginning to become well recognized among computer scientists and engineers. Autonomous vehicles contain a combination of global positioning system (GPS), light detection and ranging (LIDAR), cameras, radio detection and ranging (RADAR), and ultrasonic sensors (which are hardly ever included). These autonomous vehicles use no less than two sensing modalities and usually have three or more. The goal of this research is to determine which sensor to use depending on the functionality of the autonomous vehicle and analyze the similarities and differences of sensor configurations (which may come from different industries too). This study summarizes sensors in four industries: personal vehicles, public transportation, smart farming, and logistics. In addition, the paper includes advantages and disadvantages of how each sensor configuration are helpful by taking into consideration the activity that has to be achieved in the autonomous vehicle. A table of results is incorporated to organize most of the sensors’ availability in the market and their advantages and disadvantages. After comparing each sensor configuration, recommendations are going to be proposed for different scenarios in which some types of sensors will be more useful than others. Issue Section: Keywords: autonomous vehicles, sensors, camera, ultrasonic sensors, artificial intelligence, inputs/outputs, vehicle autonomy Topics: Autonomous vehicles, Sensors, Vehicles 1 vehicles are getting noticed in the technology industry. More companies are designing self-driven cars and/or other vehicles/products to facilitate certain tasks for humans or even replace interaction with such activity. These vehicles are being used for personal use, transportation, automation processes, and production in the farming industry. Technology companies are engaged in automated vehicles projects and work with specific types of sensors, which compose the model of these vehicles to interact with the environment and make the automation process possible. Each one of the technology companies strives to achieve the desired outcome for designing an autonomous vehicle, with a selection of what types of sensors and systems are needed.

Output: corroborate


Input: Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: One of the ways to do this is by ensuring a human-in-the-loop process for better accountability and to reduce harmful biases (Mhlanga 2022). The human-in-the-loop approach is the process of involving humans in the intensive training, testing, and tuning of machine learning models. Humans can categorize the training data to help the model understand which characteristics to identify, for example. People may, again, evaluate the quality of the model’s prediction, as well as provide input to the algorithm when it gets something wrong, implying that people are a component of the model’s continuous feedback loop (Mhlanga 2022, p. 15). We must be able to benefit from AI, while still maintaining control of it (Shneiderman 2022). Because AI cannot understand humans as well as we understand ourselves (Riedl 2019, p. 34), AI will make errors along the way; for example, causing harmful biases against people and groups (He et al. 2021). These errors are usually caused by harmful biased data and how the algorithms are designed or interpreted/used (Lepri et al. 2017). HCAI aims to ensure that humans are not harmed by AI through harmful biases (Xu and Dainoff 2023). HCAI advocates claim that AI may cause harm to humans if there is not enough adequate human supervision (Xu et al. 2022). Therefore, we need to increase human oversight of AI to reduce these harmful biases (Shneiderman 2022). If humans are involved in the process, they can better monitor AI and reduce harmful biases (Pacailler et al. 2022). HCAI proponents state that harmful AI bias results from bias in (human) data being fed to the AI, the way that algorithms are designed (by humans), or how AI’s results are understood and implemented (by humans) (Lepri et al. 2021; et al. 2023; van Berkel et al. 2022). There seems to be a logical inconsistency within the HCAI mode of thought here: AI will cause harmful biases because of biases in human data, or from the human AI engineers’ biases when developing it, or how humans interpret its results; but, we should increase human involvement in the AI process to reduce these harmful biases (which are largely caused by humans).

Output: contrast


Input: Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: com 2024 (1) Mai 2024 (1) Januar 2024 (1) November 2023 (1) Juli 2023 (1) Mai 2023 (1) Januar 2023 (1) November 2022 (1) Juli 2022 (1) November 2021 (1) September 2021 (1) Januar 2021 (1) Oktober 2020 (1) Juli 2020 (1) Juni 2020 (1) März 2020 (1) Januar 2020 (1) Dezember 2019 (1) November 2019 (1) September 2019 (2) Juli 2019 (1) April 2019 (2) März 2019 (2) Februar 2019 (4) Dezember 2018 (2) November 2018 (2) Juni 2018 (2) Mai 2018 (1) April 2018 (2) März 2018 (4) Februar 2018 (2) Januar 2018 (1) November 2017 (2) Oktober 2017 (2) September 2017 (2) Juli 2017 (7) Juni 2017 (4) Mai 2017 (1) April 2017 (2) März 2017 (2) Februar 2017 (4) Januar 2017 (1) Dezember 2016 (2) November 2016 (3) Oktober 2016 (2) September 2016 (1) August 2016 (1) Juni 2016 (1) Mai 2016 (1) März 2016 (1) Februar 2016 (3) Januar 2016 (3) Dezember 2015 (1) November 2015 (1) Oktober 2015 (2) August 2015 (1) Juli 2015 (2) Juni 2015 (3) Mai 2015 (2) April 2015 (1) März 2015 (5) Februar 2015 (1) Januar 2015 (1) Dezember 2014 (2) Oktober 2014 (2) September 2014 (2) August 2014 (3) Juli 2014 (3) Juni 2014 (2) Mai 2014 (4) April 2014 (3) März 2014 (4) Februar 2014 (1) Januar 2014 (2) Dezember 2013 (2) November 2013 (5) Oktober 2013 (1) September 2013 (1) August 2013 (2) Juli 2013 (3) Juni 2013 (3) Mai

Output: pass


Input: Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: Although there are numerous levels of automation ranging from one to four, examples include simple driving assistance to keep the vehicle in the lane or autopilot in other cars similar to the recently popular Tesla company. Several types of automatic technology are being used in various technologies, which need some assistance from software to increase productivity. Some companies are constantly innovating to create better autonomous vehicles that could be at some point fully automatic without any aid from a driver. In order to obtain the desired results, autonomous devices and vehicles need to contain specific systems and sensors that will adapt to the requirements of that specific industry. In this paper, we will review and compare the different sensors used in this type of technology and assign each one of them to specific companies that use such devices in their autonomous projects. We will create a report to easily visualize patterns of which types of sensors are used in unique settings, focusing on reasons why these types of sensors are better for that particular job. In addition, if the devices present some limitations, there will be a study explaining the disadvantages that could be assigned to it. We are going to focus on five categories where sensors can be used in automated vehicles, which include Types of Sensors, Levels of Autonomy, Personal Vehicles, Public Transportation, and Smart Farming. We will talk about personal vehicles, which is a very attractive area for companies to develop their projects. Innovation is constantly taking place in this area to provide a fully autonomous vehicle in the near future. Public transportation is also experimenting with a significant impact with autonomous technology, and it is important to analyze how companies are trying to adapt the systems to this area. Then, we will review the farming industry, where many farming vehicles and devices are now using automation to boost production and minimize human interaction. Then, we will focus on the medical field analyzing how ambulances are implementing these devices. And finally, we will study unmanned aerial vehicles (UAVs ) which are used for flight and navigation purposes. 2 As artificial intelligence started becoming more present in the industry, developers, and researchers started thinking about automation for vehicles. In order to build such projects, there was an extensive process of research for sensors and which types perform better in different situations. Sensors are in charge of most of the processes for automation in a vehicle, and in order to successfully perform, there must be a link, or what researchers call a fusion, between the different sensors to create a fully automated automobile. 2. 1 for Safety Basis.

Output: corroborate


Input: Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Instead of simply repeating our mistakes, maybe we should take a step back and identify some of the underlying causes of these biases in the first place. One of the underpinning reasons for many of these biases is the effort and push to categorise, classify, and pin down human subjects within an array of clusters and nodes of what defines the human or a particular type of human. This was an issue that Foucault tried to address in his work as he challenged those trying to distinguish the human through a series of characteristics. For Foucault, we are much more than just the composition of certain traits or behaviours; as every person is the entire collection of feelings, emotions, and experiences, as well as one’s body and its flow and interaction within the world (May 2014, p. 10). Within these algorithmic approaches to defining the human, they implicitly attach some idea or definition of the human and some unchanging feature(s) that defines it (Paden 1987; Sutherland and Patsoura 2015). However, Foucault would reject such approaches that claim that human has some essential boundless characteristics that define them (Chomsky and Foucault 2015; Lightbody 2010; Wilkin 1999). We need to be critical of approaches that limit the subject to human universals or assumptions about the inner essences of humankind (Pyyhtinen and Tamminen 2011). Foucault stated that modernity’s focus on the human sciences is mainly used as a way to categorise and classify the human, focusing on human life (biology), labour (economics), and speech (linguistics) (Foucault 1994; Pyyhtinen and Tamminen 2011). However, Foucault could not have foreseen the sheer extent of algorithmic datafication that humans are witnessing today. The mode of classifying and evaluating the human within AI processes has become vastly more dense and all-encompassing since Foucault’s time. AI has accelerated and amplified these classifications as a result of the abundance of data available on individuals [for example, ‘Facebook [alone] can classify roughly 52,000 traits of each of its users’ (Green 2018)]. The eagerness to increase algorithmic subjectification creates specific boundaries, sub-categories, and silos, of different characteristics, habits, and traits that define us and divide us (cf. Zuboff 2019).

Output: contrast


Input: Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: about navigation American Speech-Language-Making effective communication, a human right, accessible and achievable for all. Type your search query here -Academic & & Committee on the Status of About this by the Committee on the Status of Racial Minorities: Dolores E. Battle (chair), Maureen E. . Harris, Nilda Morgenstern-Lopez, Gloria M. Smith, Sandra L. Terrell, and (ex officio). Table of of the Position on the close of the 1970s, this country has seen language rights come to be regarded as civil rights. With court cases such as Larry P. v. Riles and the Decision, the role of the speech-language pathologist relative to social dialects needed resolution. In 1982, the unanimously approved the position paper on social dialects prepared by the Committee on the Status of Racial Minorities. The development of such a position paper required an in-depth examination of the controversial social issues that have been debated by many professions over the past two decades. Three different philosophical approaches to social dialects prevailed: (1) no intervention, (2) promotion of bidialectalism, and (3) eradication of nonstandard usage. For two years, the pros and cons of each philosophy were studied by the Committee on the Status of Racial Minorities. The initial draft of the paper was submitted to selected ASHA members for comment, each chosen on the basis of his or her research or clinical backgrounds or other professional interest in the area of social dialects. Sixty-three percent of those contacted responded, most with cogent comments that reflect the current controversy and the need for direction and resolution on this topic by the Association. From this peer review, the final draft was developed. The members of the Committee on the Status of who were instrumental in the completion of the project were: Maureen E. Aldes, Dolores E. Battle (chair), (ex officio),. Harris, Nilda Morgenstern-Lopez, Gloria M. Smith, and Sandra L. Terrell. The following individuals are gratefully acknowledged for their contributions to the final draft of the position paper: Sol Adler, M. Parker Anderson, Donn F. Bailey, Nick Bountress, Faye Vaughn-. Miller, Howard Mims, Joan Payne-Johnson, Nevis Phillips, Altheria C. .

Output: pass


Input: Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: The first condition for autonomous car build-out is to guarantee the safety of passengers, pedestrians, and the environment itself. There are several studies on sensor performance involving safety protocols, which involve object detection, weather condition scenarios, lane support systems, and assistance systems that provide safety to the driver. The different types of sensors used include and Ranging (RADAR), and Ranging (LIDAR), ultrasonic sensors, System (GPS), cameras, and vehicle communication. The results of such research summarize the advantages and disadvantages of the equivalent sensors and systems to conclude that an optimal self-driven vehicle must fusion those sensors [1]. The approach proposed focuses on the overall performance of these sensors and presents their advantages and disadvantages. It emphasizes comparison between different types of sensors and narrows the overall category of autonomous vehicles to subcategories or industries that operate this technology. This entails areas such as personal and public transportation and farming. Some of these industries recently developed their technologies, which will reform the study, as it was already considered outdated. 2. 2 Sensor Technology. Moreover, there is extensive research about how the different types of sensors work and how they are used together in order to make autonomous vehicles perform. However, it is necessary to combine the data of multiple sensors because there are no sensors capable of doing all the work on their own. Some of them specialize in providing visual information and others providing measures of distance between the vehicle and a pedestrian. Studies were done providing information about the called fusion of data provided by the numerous sensors and trying to optimize the system for autonomous vehicles [2]. Nevertheless, the different technologies used by specific companies are not provided. This survey will analyze the different projects available, sort the types of sensors used, and then compare which scenarios these sensors best perform in. With this, there is an organization about patterns or purposes regarding sensors and how they are used depending on the circumstances. 2. 3 Based on Weather Conditions. Safety is one of the most important features in autonomous vehicles because human interaction with the vehicle is not present. The technology manipulated needs to adapt to any situation in order to guarantee the safety of a driver, pedestrian or the environment to avoid any kind of mishap. Studies have been done about problems that the sensors encounter when dealing with difficult weather conditions such as severe rain, snowstorms, and fog [3].

Output: corroborate


Input: Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: The human is categorised, classified, and boxed (as the result of AI) into countless categories, sub-categories, and sub-sub-categories, of what it means to be a particular type of human. While it is claimed that this brings many benefits, such as greater identification of individual needs, tailoring relevant ads to individuals, and providing more suitable online content, it also poses great threats and harms to individuals, such as causing harmful bias/discrimination, harmful nudging, and creating digital echo chambers or filter bubbles (Benkler et al. 2018; Möller 2022; Scharkow et al. 2020). These issues are fundamentally based on trying to define, narrow down, and categorise the human. Harmful biases are often caused by trying to identify and group individuals and by allocating them to specific classifications or segments of humanity, rather than by an insufficient level of human control in the process (as HCAI proponents claim). Foucault examined different types of oppositions to identify common struggles in society between individuals and groups. These were the ‘opposition to the power of men over women, of parents over children, of psychiatry over the mentally ill, of medicine over the population, of administration over the ways people live’ (Foucault 1982a, b, p. 781). These struggles can also be contrasted with some of the oppositions seen in the field of AI ethics: the opposition to the power of AI companies over how to classify groups of people, the opposition that states use AI to surveil and control citizens, and (in the context of HCAI) the opposition that AI developers do not give enough attention to the human. An underpinning theme of the oppositions that Foucault analysed is that they ‘are a refusal of these abstractions, of economic and ideological state violence, which ignore who we are individually, and also a refusal of a scientific or administrative inquisition which determines who one is’ (Foucault 1982a, b, p. 781). While written forty years ago, this analysis can also be compared to the foundational issues underpinning AI debates; namely, the refusal to permit economic and ideological abstractions of individuals, and the scientific and administrative inquisitions of AI that attempt to determine and classify who one is.

Output: contrast


Input: Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: If courses on social dialects or sociolinguistics are not offered by the training programs in your local area, it is incumbent upon communicative disorders professionals to seek such training through continuing education activities and independent study. The following publications should be helpful: Dillard, J. L. (1973). Black English: and Usage in America. New York: Vintage Books. Erickson, J. G. , & Omark, D. (1981). of the Child: Issues and Guidelines. Baltimore, MD: Press. Jeter, I. K. (Ed. ). (1977). Social Dialects: Differences vs. Disorders. Rockville, MD: American Speech-Language-Hearing Association. Omark, D. , & Erickson, J. G. (1983). Exceptional Child. San Diego: Press. Stockwell, R. P. , Bowen, J. , & Martin, J. (1963). of English and Spanish. Chicago, IL: University of Chicago Press. Stockwell, R. P. , Bowen, J. , & Martin, J. (1975). Structures of English and Spanish. Chicago, IL: of Chicago Press. Wolfram, W. , & Fasold, R. (1974). of in American English. Englewood Cliffs, NJ: Prentice-Hall, Inc. Wolfram, W. , & Christian, D. (1976). Appalachian Speech. Washington, DC: Center for Applied Linguistics. Q: We are taught that test results are invalid when the test taker comes from a background other than that of the population on which the test was standardized. Since few tests are designed for dialects other than standard English, how can I do a valid speech and language assessment of a social dialect speaker? A: It is stressed in the position paper that knowledge of nondiscriminatory testing procedures is required to distinguish between dialect differences and communicative disorders. There are a variety of alternatives to the inappropriate use of tests developed and standardized on standard English speakers.

Output: pass


Input: Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: However, it is likely for sensors to experience difficulties dealing with environmental factors, and it is important to identify all the possible threats that could interfere with the execution of these devices to gather the required data. Still, there is not an in-depth comparison between the features previously mentioned, sensors limitations, and differentiation among the technologies developed by the most advanced technology companies. It will be beneficial to have a strict comparison of what types of sensors are being used in each scenario and label them down into categories to accentuate not only the limitations of those but also the advantages and the reason of their usage for that particular case setting. 3 Study 3. 1 Types of Sensors. For autonomous vehicles being able to function properly, they need some sensors that are grouped into three main categories: and cameras. It is important to explain how these sensors work and why developers are using them to perform these difficult tasks. In addition, GPS/GNSS technology is used as a position sensor that is relevant for these types of vehicles. Other relevant sensors such as inertial measurement units (IMU’s) and odometry sensors are going to be reviewed RADAR: and Ranging (RADAR) sensors send radio waves to gather the distance and speed of objects related to the vehicle. They are used for obstacle detection in autonomous vehicles, and their main function is to detect the objects in the environment and around the car. They are designed to identify any type of vehicles such as cars, motorcycles, and trucks. These sensors provide an environmental perception that it is useful by getting data about the surroundings and then providing it to the driver and other internal systems of the vehicle. The data collected help to provide parking assistance, blindspot detection, and rear collision warnings. RADAR sensors have the advantage of being efficient even with fog, rain, or snow, which is solving the limitation of other visual sensors. As mentioned in Ref. [4], RADAR sensors are getting cheaper and it is easier to implement those in common cars. However, there are not enough if we are trying to develop a high-end fully autonomous car. LIDAR: and Ranging (LIDAR) sensors are very similar to RADAR sensors, but use lasers to map the environment rather than radio waves. According to the study in Ref. [5], LIDAR sensors can detect objects 200–300 m away and emit short pulses to measure more than a million points per second.

Output: corroborate


Input: Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Claiming that AI development is not human-centred enough perpetuates the abstraction of the individual within the bounds of what is defined as human. It fails to tackle some of the underlying problems within AI development. HCAI proponents find fault with AI companies, developers, or the industry for not placing humans at the centre, but they fail to look at or challenge the root cause of many of these issues. While asking more from particular groups in AI; we also need to look at the ideologies underpinning AI in a critical way. This point can be illustrated in a quotation from Foucault (with my inclusion of how it can be applied to the AI debate in square brackets): ‘…the main objective of these struggles is to attack not so much "such or such" an institution of power [e. g. , the AI industry], or group [e. g. , AI developers], or elite [e. g. , AI companies], or class but rather a technique, a form of power [e. g. , the classification and categorisation of individuals through the use of AI]’ (Foucault 1982a, b, p. 781). The technique or form of power in the context of AI attempts to classify, categorise, and define individuals as a way of dictating what that individual clicks on, likes, buys, thinks, and votes. It is not confined to one particular application of AI. Still, instead, the underlying mentality pervasive within the AI field is that individuals are simply the identification and categorisation of their behaviours and patterns that are defined by the data points that they digitally interact with within the world. This technique of power ‘applies itself to immediate everyday life which categorizes the individual, marks him by his own individuality, attaches him to his own identity, imposes a law of truth on him which he must recognize and which others have to recognize in him’ (Foucault 1982a, b, p. 781). The individual is categorised and grouped through AI classifications. They are marked by their individuality, captured by AI through our constant use of search engines, social media, and online purchases; this is then attached to our digital identity, which is then imposed back upon us by telling us who we are (or who we should be) through our algorithmically-defined life.

Output: contrast


Input: Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: A: of identified and listed numerous multicultural tests and materials in the September 1981 issue of Asha. A supplement to that listing appears in this issue of Asha. Unfortunately, only two programs were identified that were described by their authors as bidialectal training programs. A program designed for grades K through 6 was developed for the by Wilbur Goodseal. The program manual and curriculum guide, entitled for Populations, can be obtained by contacting Emogene Lewis, 4108. The other program, designed for preschool children, can be found in by (Grune and Stratton). Other programs undoubtedly exist. But many multicultural products are either published by small publishers with limited dissemination or unpublished and used by their authors for their local populations. of would welcome information about such programs, particularly for older children and adults. Q: The administration in my school district and most of the classroom teachers firmly believe in the eradication philosophy. They are pressuring me to regard normal nonstandard English differences as if they were speech and language disorders. Being outnumbered, how can I convince them that they are wrong. A: There is no right or wrong on this very controversial issue. Rather, there are differing opinions. But the opinion of those who believe in eradication is not consistent with the policy of the communicative disorders profession. The position statement on social dialects represents the official policy of the American Speech-Language- Hearing Association. Thus, there is an official statement that can be brought to the attention of administrators and teachers to support your professional philosophy on this issue. Return to of is now the ASHA Office of now mandates that “Some coursework must address issues pertaining to normal and abnormal human development and behavior across the life span and to culturally diverse populations. ” Notes [*] professionals prefer to use the term Ebonics instead of the more popularly used term Black English. Derived from the words ebony and phonics, the term Ebonics is intended to avoid the focus on race and emphasize the ethnolinguistic origin and evolution of this variety of the English language. Return to terms: dialects Reference this material as: American Speech-Language-Hearing Association. (1983). Social dialects [Position Statement]. Available from www. asha. org/policy. © Copyright 1983 American Speech-Language-Hearing Association. All rights reserved.

Output: pass


Input: Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: The advantage of LIDAR sensors compared to RADAR sensors is that they allow to create three-dimensional (3D) maps of the environment to visualize the objects around the environment. They assist to cover a 360-deg view and are used to complement RADAR sensors and give more sophisticated tools and data with 3D mapping. For autonomous vehicles, it is crucial to have a clear sight of what is happening around the vehicle and make it capable of “seeing. ” In fact, years ago, it was an expensive technology, but nowadays the costs are getting lower for being used in the vehicle mass market. Cameras: Thus, autonomous vehicles need to have some visual data and information in order to respond to events such as reading the traffic lights and interpreting their colors to stop or move. Usually, autonomous vehicles are designed with cameras in different angles to give a 360-deg view around the vehicle. These cameras can easily recognize cars and pedestrians. However, cameras have many downsides because the data they receive from outside depends on a clear sight to get the appropriate visual information. Bad weather conditions could affect the view of these cameras, where rain, fog, and snow could be potential threats. Cameras depend on illumination, and darkness can affect performance which could lead to an accident if a pedestrian or a car are not identified. Some researchers have proposed solutions to improve visibility in these devices and minimize side effects that could be affecting safety [6]. Ultrasonic Sensors: Lastly, ultrasonic sensors are becoming more popular in the autonomous driving community as a cheap alternative to LIDAR sensors to incorporate obstacle detection systems and algorithms inside a self-driven vehicle. These sensors send ultrasonic impulses that are then reflected by the obstacle, and the signal is processed by the internal system of the vehicle to show the data of the obstacle perform accordingly. They are categorized in the type of range that could reach 5. 5 m, but are designed for specific tasks that involve obstacle detection and assistance in a short range such as implementing a parking assistance system and detecting objects near the car. There are some other limitations concerning the short field of view and the difficulty for the sensor to detect objects going at a fast speed. Additionally, they are vulnerable to jamming and spoofing attacks leaving the sensor physically unable to function and creating false positives, which could lead to a potential incident without the user supervision [7].

Output: corroborate