Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: 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). [SEP] Justify:

Output: The text contrasts the claim by expressing skepticism about the overall progress achieved with AI, which implies that AI may not fully understand human emotions. It highlights public concerns regarding AI, stating that a significant portion of US adults is worried about AI's potential negative impacts, which could suggest a limitation in AI's understanding and capability to respond to human emotions adequately.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim about dialects reflecting social and geographic backgrounds. Instead, it discusses unrelated topics concerning search engines, literature documentation, and various public comments. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 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. [SEP] Justify:

Output: The text corroborates the claim by discussing the application of AI algorithms, specifically in object classification and depth estimation using RGB-D information from cameras. It highlights that improving depth perception through monocular cameras can enhance the classification performance of objects such as vehicles and pedestrians, which is essential for safe navigation in autonomous vehicles. The development of algorithms that utilize sensor data aligns directly with the claim about AI's role in processing data from these sensors for safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: 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). [SEP] Justify:

Output: The text contrasts the claim by discussing the importance of human control over AI and expressing skepticism about AI solving challenges or understanding nuanced human emotional responses. It emphasizes a critical view on the autonomy of AI and highlights the need for human oversight, implying that AI may not fully grasp, nor adequately respond to, human emotions in a perfect manner.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not address dialects, social backgrounds, or geographic influences in any way. Instead, it appears to be a collection of terms related to library science and digital publishing without specific content related to dialects.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 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. [SEP] Justify:

Output: The text corroborates the claim by discussing how autonomous vehicles utilize various sensors, including cameras, to interact with their environment and navigate effectively. It mentions that technology companies are engaged in automated vehicle projects, implying that artificial intelligence and sensor data processing are crucial for the functionality of these vehicles.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: 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). [SEP] Justify:

Output: The text contrasts the claim by stating that AI cannot understand humans as well as we understand ourselves, indicating that AI will make errors due to harmful biases and limitations in understanding emotions. This is highlighted by the statement, 'Because AI cannot understand humans as well as we understand ourselves (Riedl 2019, p. 34),' which directly rebutts the idea that AI can perfectly respond to all human emotions.


Input: Task: pass the claim using the given text. 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 [SEP] Justify:

Output: The text does not provide any information or context that relates to the claim regarding the relationship between dialects and social or geographic backgrounds. It appears to be a listing of dates rather than a discussion about dialects.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 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. [SEP] Justify:

Output: The text corroborates the claim by detailing how artificial intelligence is becoming integrated into the industry for the development of autonomous vehicles. It mentions that 'sensors are in charge of most of the processes for automation in a vehicle,' indicating the essential role sensors play in automation, which aligns with the claim about AI processing data from these sensors. Additionally, it highlights the need for a 'link' or 'fusion' between sensors for successful automation, underscoring the process of collecting and interpreting data necessary for navigating streets safely.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: 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). [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and implications of AI in understanding human emotions and characteristics. It highlights Foucault's critique of categorizing humans and suggests that AI processes are even more complex and less capable of truly capturing the essence of human individuality and emotions. The mention of algorithmic subjectification indicates that AI may classify humans into traits and categories but does not imply a perfect understanding or response to human emotions.


Input: Task: pass the claim using the given text. 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. . [SEP] Justify:

Output: The text is largely irrelevant to the claim about dialects reflecting social and geographic backgrounds. Instead, it discusses the position paper on social dialects prepared by the Committee on the Status of Racial Minorities and various philosophical approaches to social dialects without addressing how dialects specifically reflect social and geographic backgrounds.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 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]. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of various sensors, including cameras, in ensuring the safety of autonomous vehicles. It highlights the importance of combining data from different types of sensors (RADAR, LIDAR, ultrasonic, GPS, and cameras) to optimize the performance of autonomous vehicles. The emphasis on safety protocols and the need for effective data fusion from multiple sensors aligns well with the claim that AI processes data from these sensors to navigate safely.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: The human is 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. [SEP] Justify:

Output: The text contrasts the claim by highlighting the limitations of AI in accurately categorizing and understanding human individuals. It discusses the dangers of categorization and classification by AI, which can lead to harmful biases and discrimination. This suggests that AI does not perfectly understand human emotions but rather attempts to box individuals into predefined categories, thus undermining the assertion that AI can fully grasp and respond to human emotional complexity.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim directly regarding how dialects reflect social and geographic backgrounds. Instead, it focuses on the need for professionals to seek training in social dialects and nondiscriminatory testing procedures, emphasizing the importance of distinguishing between dialect differences and communicative disorders. Thus, the text is irrelevant to the specific claim made.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 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. [SEP] Justify:

Output: The text corroborates the claim by discussing various types of sensors used in autonomous vehicles, such as RADAR and LIDAR, which are crucial for gathering environmental data necessary for navigation. It highlights the importance of these sensors in detecting objects and providing data to internal systems, thereby supporting the claim that AI utilizes this data for safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: 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. [SEP] Justify:

Output: The text contrasts the claim by arguing that AI development often abstracts individuals and categorizes them based on behaviors rather than understanding their emotions. It emphasizes the limitations of AI in genuinely comprehending human identity and emotions by describing how AI techniques classify and impose identities rather than facilitate an empathetic understanding of the individual.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text discusses programs related to bidialectal training and highlights differing opinions on whether nonstandard English differences should be regarded as disorders. While dialects are mentioned, the focus is on educational programs and philosophical disagreements rather than directly supporting or contradicting the claim about dialects reflecting social and geographic backgrounds. Therefore, the relevance to the claim is minimal.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: 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]. [SEP] Justify:

Output: The text supports the claim by discussing how sensors, such as LIDAR and cameras, are essential for autonomous vehicles to perceive their environment, interpret situations such as traffic signals, and navigate safely on the streets. It explains the functionality and importance of various sensors, stating, 'For autonomous vehicles, it is crucial to have a clear sight of what is happening around the vehicle.' Additionally, it mentions the role of AI in processing information from these sensors to enhance navigation and safety, confirming the use of AI in this context.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: While HCAI proponents promote the view that adopting AI is inevitable and ‘resistance is futile’ (which, as seen above, also extends to the adoption of the HCAI framework), we should be wary of such positions as they restrict our decision-making capacities and ability to counter, resist, and refrain from certain courses of action. While many technologies have brought great benefits to society, there are also many examples of the opposite (think and the hydrogen bomb), and there is no guarantee that AI, in all its compositions, applications, and uses, will automatically bring a more significant net benefit to humanity. Therefore, we should be skeptical toward the idea (as found with HCAI) that AI will automatically lead to ‘progress’. However, if one still wants to accept the deterministic view of our future illustrated in HCAI (that AI is here to stay, and we have to get used to it), this does not necessarily mean we should endorse a view that only cares for humans. Marx once commented, ‘History repeats itself, first as a tragedy, second as a farce’ (Marx 1963). Maybe we need to take a step back and ask ourselves: do we need a more human-centred approach to anything when we see the catastrophic events caused by taking such an anthropocentric stance throughout history (e. g. , environmental destruction (2019), biodiversity loss (2023), and global climate change (IPCC 2020; Pachauri and Reisinger 2007)). Shneiderman was right—we do need another Copernican Revolution. However, perhaps a reconsiders our place in the world and asks if AI can be used as a source of good, to right some of the wrongs we have caused, and to include considerations for other species and the planet as well as ourselves. 4. 1 Limitations and future researchA criticism against Foucault is that he provides no appealing alternative to humanism (Paden 1987). While his work in of Things can provide interesting insights into how we can critique HCAI and identify many flaws within such an approach, there is little clear-cut guidance on where we go from here. Foucault's characterisation of how humans have tried to modify themselves throughout history, which he discussed in Technologies of the Self (Foucault 1988b, 1990), may provide a more concrete approach for developing a Foucauldian ethics of AI. [SEP] Justify:

Output: The text contrasts the claim by suggesting that we should be skeptical about the deterministic view that AI will bring automatic progress or benefits to humanity. It emphasizes the potential limitations and risks of AI, indicating that such technology may not inherently lead to a better understanding or response to human emotions. Instead, it argues for a more critical approach to the adoption of AI, stating that while AI can be beneficial, there is no guarantee that it will perfectly understand human emotions or lead to positive outcomes.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: Our automated method works as follows: for any language we write down a Swadesh list, then we measure the distance between words with the same meaning belonging to different languages and, finally, we obtain the distance between two languages by an average. This approach is motivated by the analogy with genetics: vocabulary has the role of DNA and the comparison is simply made by measuring the differences between the DNA of the two languages. There are various advantages: the first is that, at variance with previous methods, it avoids subjectivity; the second is that results can be replicated by other scholars assuming that the database is the same; the third is that it does not require specific expertise in linguistics; and the last, but surely not the least, is that it allows for a rapid comparison of a very large number of languages (or dialects). More precisely, our measure of lexical distance between two words is a variant of the Levenshtein distance [15]. distance is simply the minimum number of insertions, deletions, or substitutions of a single character needed to transform one word into the other. Our distance is obtained by a normalization. Given two words and our measure of distance is(1)where is their standard Levenshtein distance and is the number of characters in the longer of the two words and. Therefore, the distance can take any value between 0 and 1. The reason for the normalization can be understood by the following example. Consider the case of two words of the same length in which a single substitution transforms one word into the other. If they are short, let's say 2 characters, they are very different. On the contrary, if they are long, let's say 8 characters, it is reasonable to say that they are very similar. Without normalization, their distance would be the same, equal to 1, regardless of their length. Instead, introducing the normalization factor, in the first case the distance is whereas in the second, it is much smaller and equal to. We use distance between pairs of words, as defined above, to construct the lexical distances between languages. For any language we prepare a list of words associated with the same meanings (we adopt the original Swadesh choice of ). Assume that the number of languages is and any language in the group is labeled by a Greek letter (say ) and any word of that language by with. The same index corresponds to the same meaning in all languages, i. e. [SEP] Justify:

Output: The text does not address the claim about dialects reflecting social and geographic backgrounds. Instead, it focuses on a method for measuring lexical distance between languages and dialects, primarily concerning technical aspects of linguistic comparison without touching upon their social or geographic implications.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: (GPS/GNSS): System (GPS), it is frequently used by autonomous vehicles to help them with navigation and give them positioning data. These systems use data that are received from several GPS satellites. With the current navigation technology, vehicles are incorporating these systems to create routes, analyze traffic, and possess information about the position of the vehicle in real time. This is then connected with the three major sensors for autonomous vehicles in order to create an innovative navigation experience without human interaction [8]. GPS connects with the (GNSS) which is giving more data to the vehicle that guarantees the actual position. This technology is mostly used for ground vehicles that are constantly on the road surrounded by other cars and pedestrians. IMU Sensors: IMU is a specific type of sensor that is used to measure angular rates, force, and sometimes magnetic fields. This sensor is used most of the time as a backup sensor that needs to function properly in case of any accident or error that can cause the other sensors to stop working. It also plays an important role in sensor fusion because it is related on the other sensors to provide data about acceleration, this is crucial to protect the drive at all times and the vehicle to be designed to respond to any kind of harmful situation that could put the driver’s life in danger. Odometry Sensors: Odometry sensors use the data from motion sensors to monitor the change of position over time. This particular sensor is linked with position sensors but focuses on the change of position. It is commonly used in robotics to estimate the position change from the starting point to the endpoint. However, it is now incorporated in many autonomous vehicles to keep track of the position of the vehicle from a specific location. 3. 2 Levels of Autonomy. In order to identify a vehicle as autonomous, the Society of created five different levels to classify any intended autonomous vehicle or device based on the different technologies and differentiate minor automation such as parking assistance from more major autonomous features like driving capability without human interaction. It is important for the purpose of this research to mention and understand the different levels of automation to properly categorize the different technologies that are going to be analyzed extensively. It is easy to identify two different main categories containing the five different levels: regarding support for the driver and automated driving features. From level 0 to level 2, the driver is involved during all types of support systems and needs to be aware of everything at all times. [SEP] Justify:

Output: The text corroborates the claim by explaining how autonomous vehicles utilize various sensor technologies, including GPS and IMU sensors, to gather data necessary for navigation. It details how GPS systems provide positioning data, while IMU sensors play crucial roles in enhancing safety by measuring various environmental factors. The integration of these sensors allows for real-time analysis and responsive navigation, aligning with the claim that AI processes this data for safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: PostLog inSign upPost8PostConversation2GrantsPeer ReviewsDocumentFlag content 3,000 Tipped ≈ $0. 008Dr. Barrett: How to 18, 2023DOI10. 55277/ResearchHub. fke8bs6uSaveTipDocumentFlag content8TipSaveDocumentFlag contentPostConversation2GrantsPeer ReviewsKey takeawaysExplore the challenge of defining the fundamental components of emotions and the absence of exclusive physical indicators. (Segment 1)Dispute the notion that emotions universally manifest on people's faces, highlighting the significant influence of cultural and environmental factors. (Segment 3)Demonstrate the impact of culture on the interpretation of facial expressions and raise questions regarding its legal implications. (Segment 5)Investigate how language constrains our ability to express emotions and advocate for embracing ethnic diversity for better understanding. (Segment 6)Examine the evolving role of the brain in labeling emotions and illustrate how this evolution impacts our perception. (Segment 8)Advocate for flexible emotional regulation and underscore the value of embracing pain for personal growth. (Segment 14)Discuss strategies to enhance sensory and emotional experiences, with a particular emphasis on improving understanding and perception. (Segment 16)Explore the intricate connection between the body and mind, examining sensory systems, motor control, and emotional regulation. (Segment 18)Delve into the relationship between digestive factors, SSRIs, and mental health, with the insights of Andrew Huberman. (Segment 19)SummaryDr. Barrett, obtained a Doctor of Philosophy degree and currently occupies the esteemed position of of Psychology at Northeastern University. Dr. Barrett possesses a high level of experience in the domain of emotional science, which has garnered her widespread acclaim and international recognition for her extensive knowledge and significant achievements in this subject. The author provides a comprehensive explanation of the notion of emotions and delves into the brain's function in processing and integrating signals derived from both internal physiological sensations and external surroundings, hence resulting in the formation of unique emotional experiences for each individual. The forthcoming discourse will center on the analysis of the relationship between emotions and language, the impact of linguistic specificity on the processing of emotions, the importance of facial expressions in the domain of emotions, and the interconnectedness of emotions with other factors such as sleep, movement, nutrition, and the formation and reinforcement of social connections. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity of emotions and the various factors that influence human emotional expression and understanding. For instance, it highlights the impact of culture and environment on the interpretation of emotions, stating, 'Dispute the notion that emotions universally manifest on people's faces...' This indicates that emotions do not have a single, universal expression, which challenges the idea that AI assistants can perfectly understand human emotions.


Input: Task: pass the claim using the given text. Claim: Dialects can reflect social and geographic backgrounds. [SEP] Text: , two words and in the languages and have the same meaning if. The lexical distance between two languages is then defined as(2)It can be seen that is always in the interval [0,1] and obviously. The result of the analysis described above is a upper triangular matrix whose entries are the non-trivial lexical distances between all pairs of languages. If a family of languages is considered, all the information about their relationships is encoded in the associated matrix, but this information is not manifest and it has to be extracted. The ubiquitous approach to this problem is to transform the matrix information into a phylogenetic tree. Nevertheless, in this transformation, part of the information may be lost because transfer among languages is not exclusively vertical (as in mtDNA transmission from mother to child) but it also can be horizontal (borrowings and, in extreme cases, creolization). In previous papers we proposed a complementary geometric approach [16], [5] (Analysis). This approach encodes the matrix information into the positions of the languages in a -dimensional space. For large one recovers all the matrix content, but a low dimensionality, typically = 2 or = 3, is sufficient to grasp all the relevant information. However, results shown in the last two figures of this paper mostly rely on a direct investigation of the entries of the matrix and to simple averages over them. Supporting InformationTable S1. This table provides information on the people who furnished the data collected by the author at the beginning of 2010. For any dialect two consultants have been independently interviewed. Their names and birth dates follow each of the dialect names. Table S2. This table contains the upper triangular matrix which is the result of the analysis described in the Methods section of the paper. The entries of the matrix are the non-trivial lexical distances between all pairs of languages. 1. Dahl OC (1951) Malgache et Maanjan: une comparaison linguistique. Egede Instituttet, Oslo, Norway. View 2. (1995) Borneo as a cross-roads for comparative Austronesian linguistics. In: Tryon editor. in History. . 72. In J. F. Bellwood and D. 3. (2009) Loanwords in Malagasy. In: Tadmor editor. Loanwords in the World's Languages: A Comparative Handbook. Berlin: Mouton. 746. [SEP] Justify:

Output: The text does not address the claim about dialects reflecting social and geographic backgrounds. Instead, it discusses lexical distances between languages, matrix analysis, and data collection methods, none of which directly relate to how dialects represent social or geographic factors.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: The automated features are implemented to support and make the user more comfortable during the ride, but the act of driving is controlled by the user. Some examples of features that fall between these levels are parking assistance features, adaptive cruise control, blindspot warning, etc. On the other hand, between levels 3 and 5, vehicles start taking partial control where the user does not need to drive. Levels 3 and 4 are being developed currently by implementing characteristics such as traffic jamming, chauffeur, or driver-less taxi services in some smart cities where the car is fully autonomous. Level 5 is still far from the current technology because it requires the vehicle to perform effectively by itself in any given condition which is hard to achieve at the moment. 3. 3 Personal Vehicles. To initiate the study, we will first evaluate the category of personal vehicles. Currently, many technological companies are innovating to make personal autonomous vehicles better, while new methods and improvements are getting released on a daily basis. Most of the features in personal vehicles involving automation are comfort-related improvements, including adaptive cruise control, parking assistance, blindspot detection, lane centering, etc. But some companies are taking it one step further by developing autopilot systems and aspire to reach more capacity of automation aiming to a fully autonomous car. The sensors previously described are becoming more accessible to mass-market vehicles, giving auto firms a wide variety of alternatives to build cars with sensors that can perform effectively in multiple scenarios. X and Model S: Tesla autonomous vehicles are one of the most famous in the current market. The company recently announced the “Autopilot” mode integrated into the. This gives the vehicle the capability to change, and fully self-drive long distances on highways for a significant period of time without human assistance. Tesla autonomous cars are equipped with three types of sensors: cameras with a 360-deg range of vision, RADAR sensors providing long-range object detection, and 12 ultrasonic sensors used for car detection and responsible for a collision prevention system. Surprisingly to experts in the industry, Tesla does not use LIDAR sensors to develop their products. Instead, they use ultrasonic sensors, which are building the same measure function to get the distance between objects and the vehicle, but employing ultrasonic waves. However, the primary reason Tesla does not use LIDAR sensors is that these sensors can cost 75, 000 dollars per unit. [SEP] Justify:

Output: The text corroborates the claim by detailing how automated features in vehicles, particularly Tesla’s 'Autopilot,' utilize various sensors including cameras and RADAR for navigation and collision prevention. It emphasizes the role of technology in enabling partial and full automation, aligning with the claim that AI processes sensor data for safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: The attendees will also partake in a discussion concerning practical approaches for coping with feelings of uncertainty, as well as methods for improving one's understanding of the emotional encounters of others. This specific episode is anticipated to engage persons who possess a curious disposition toward the complex interplay between neuroscience and psychology that underlies the phenomenon of emotions. Furthermore, it possesses significance for those who want to augment their self-awareness, interpersonal connections, and overall capacity to navigate the world with increased flexibility. 1. 00:05:46 of EmotionsDr. Barrett explores the complex endeavor of distinguishing and detecting emotions across the span of the last 150 years. This observation underscores the difficulty in establishing a precise and exclusive set of fundamental elements for emotions. These elements, such as physiological changes in the body, brain activity, and facial expressions, are not inherently unique to emotions and can occur in various situations. Researchers have consistently exerted considerable efforts in their endeavor to establish a comprehensive description of emotions, employing diagnostic patterns as a basis for their investigations. These patterns entail the identification of unique physiological signs, such as increased heart rate and distinctive facial expressions, which were hypothesized to be linked to particular emotions, such as fear. However, the assertions put forth lacked empirical support, therefore leading to perplexity around the perception of different emotions in the absence of observable physical cues. Researchers have been intrigued by this enigma for a significant period [1]. Figure 1. to Expressions of Fear and Surprise [1]. 1. Zhao, k. , et al. , to Expressions of Fear and Surprise. Frontiers in Psychology, 2017. 8. 2. 00:10:42 & Interpretation, Emotion Dr. Barrett offers a critical analysis of the conventional conceptualization of an emotion system and a facial expression system. Dr. Barrett argues that while there are processes that enable the movement of facial muscles, it is crucial to acknowledge that not all facial movements may be considered as indications of internal emotional states. The author pays much attention to the attribution of significance to facial movements, underscoring the importance of interpretation and contextual elements. Furthermore, the author emphasizes the role of the human brain in the cognitive processing of various sensory inputs, which is crucial for the understanding and interpretation of emotions. The analysis of facial expressions relies on inferential reasoning within the wider framework of an individual's specific circumstances, rather than being solely reliant on direct observation. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities involved in understanding and interpreting human emotions. It highlights that identifying emotions is challenging due to the lack of a clear and unique set of elements inherent to emotions, noting that physiological changes can occur in various situations and are not exclusive to emotions. Further, it emphasizes the need for contextual understanding and cognitive processing, which suggests that AI assistants, which may not possess the capability for such nuanced understanding and interpretation, cannot perfectly respond to all human emotions.


Input: Task: pass the claim using the given text. Claim: Language acquisition in children follows predictable stages. [SEP] Text: > <modsCollection xmlns=" <mods ID="evanson-etal-2023-language"> <titleInfo> <title>Language acquisition: do children and language models follow similar learning stages? [SEP] Justify:

Output: The text provides a title that suggests a comparison between children's language acquisition and language models but does not provide any specific evidence or information regarding the claim that language acquisition in children follows predictable stages. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: The cost collides with Tesla’s purpose of developing mass-market autonomous vehicles. For instance, the CEO and founder of the company, Elon Musk, guaranteed to have achieved results nearly as adequate as LIDAR sensors using just cameras. As presented in Ref. [9], it is possible to represent the same type of images and 3D modeling implementing an image-based approach, which is significantly less expensive than LIDAR sensors. These types of approaches are being taken to reduce the cost of production and have made some developers come up with new ways of creating 3D modeling similar to the way that LIDAR sensors perform. However, the use of ultrasonic sensors instead of the common implementation of LIDAR sensors can be harmful for the execution of autonomous vehicles. In fact, a recent study [10] presented some limitations regarding ultrasonic sensors that can cause an autonomous vehicle to misbehave. The researchers conducted an experiment where they performed various attacks including random and adaptive spoofing attacks that could force the vehicle to move in an undesired moment and jamming attacks preventing proper detection capabilities leading to collision possibilities. Therefore, even with the advantages that can provide ultrasonic sensors with cost-related issues can be harmful to society and a threat to pedestrians and customers with the possibilities of collision due to the malfunction of the sensors. Google Waymo: Google also is getting involved in the autonomous vehicles industry. They created a company called Waymo in 2009, which will exclusively develop to produce an autonomous car that can guarantee safe mobility for their customers with the innovation of automation technology. At first, the company started using LIDAR sensors developed by Velodyne, which is a technology company that provides LIDAR technology to different self-driving car programs. However, later on Waymo started designing and producing their own LIDAR sensors because the cost was too high and is a handicap to introduce Waymo technology to a mass market. These sensors are called which has an unmatched design and provides a 95-deg field of view. This is helpful compared to the other sensors that have a 30-deg field of view because it gives more coverage and is able to detect objects at a closer range without having to put many sensors in the vehicle. It also has the capability of detecting objects at a very close range compared to other types of LIDAR and different sensors that have a significantly greater minimum range. [SEP] Justify:

Output: The text corroborates the claim as it discusses how technologies involving cameras and LIDAR sensors are being utilized in autonomous vehicles for navigation. It mentions that approaches similar to LIDAR can be achieved using just cameras, which aligns with the claim about AI processing data from sensors and cameras. Additionally, it references companies like Tesla and Waymo, which are actively working to improve the safety and efficiency of autonomous navigation through innovative sensor technology, supporting the broader idea that AI plays a crucial role in these processes.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Dr. Barrett also acknowledges the presence of a mutual connection between the face and the brain, albeit this interaction is not limited to emotional functions but rather forms part of the broader communication network between the brain and the body. This essay presents a counterargument against the concept of a reductionist and mechanistic relationship, suggesting that specific facial expressions do not directly elicit or generate distinct emotional states. This is supported by the absence of a segregated emotional system within the brain [2]. Figure 2. Functional brain networks associated with emotion processing [2]. 2. Canu, E. , et al. , Resting state functional brain networks associated with emotion processing in frontotemporal lobar degeneration. Molecular Psychiatry, 2022. 27(11): p. 4809-4821. 3. 00:19:33 & Emotion, IndividualizationDr. Barrett offers a comprehensive examination of her scientific trajectory, while also expressing her reservations regarding the conventional perspective on the relationship between facial expressions and emotions. Dr. Barrett elucidates that the concept of universal facial expressions associated with specific emotional states is predominantly grounded in Western stereotypes that have been solidified as empirical truths. The initiation of her scientific endeavor occurred when she endeavored to use this information in a controlled experimental context, only to confront a disparity between the theoretical understanding and her empirical findings. Within the discipline of psychology, there has been a continuous discourse about the fundamental characteristics of facial expressions and their potential universality. Consequently, scholars have endeavored to establish a consensus by synthesizing the existing body of literature related to this topic. Dr. Barrett, together with a team including four esteemed senior scientists, undertook a comprehensive examination of a substantial compilation consisting of more than one thousand scientific 3. to Learning [3]. 3. Hooker, C. I. , et al. , to Learning. 2006. 26(35): p. 8915-8922. 4. 00:31:03 Emotion Categories, Culture & Dr. Barrett's in podcast with primarily focuses on the complicated dynamics of emotions, facial expressions, and their complex interaction with cultural factors. Dr. Barrett highlights the concept that emotions should not be regarded as distinct entities, but rather as categorizations or groupings of events, and even as behaviors underlining the dynamic and changeable nature of emotional experiences. In his scholarly discourse, Dr. Barrett offers an alternate viewpoint that challenges the prevailing consensus on the inherent capacity of newborns to see and comprehend facial expressions. [SEP] Justify:

Output: The text contrasts the claim by providing a nuanced examination of the relationship between emotions and facial expressions as discussed by Dr. Barrett. It emphasizes that specific facial expressions do not directly elicit distinct emotional states, challenging the idea that AI assistants can perfectly understand all human emotions. The mention of the complexities of emotions and their interaction with cultural factors further implies that emotions are not universally expressed or understood, which undermines the claim that AI can perfect responses to them.


Input: Task: pass the claim using the given text. Claim: Language acquisition in children follows predictable stages. [SEP] Text: </title> </titleInfo> <name type="personal"> <namePart type="given">Linnea</namePart> <namePart type="family">Evanson</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Yair</namePart> <namePart type="family">Lakretz</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Jean</namePart> <namePart type="given">Rémi</namePart> <namePart type="family">King</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2023-07</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Findings of the Association for Computational Linguistics: ACL 2023</title> </titleInfo> <name type="personal"> <namePart type="given">Anna</namePart> <namePart type="family">Rogers</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Jordan</namePart> <namePart type="family">Boyd-Graber</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Naoaki</namePart> <namePart type="family">Okazaki</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>Association for Computational Linguistics</publisher> <place> <placeTerm type="text">Toronto, Canada</placeTerm> </place> </originInfo> <genre authority=" [SEP] Justify:

Output: The text does not address the claim regarding language acquisition in children or its stages. Instead, it contains bibliographic information about authors and editors, publication details, and the resource type, which are irrelevant to the claim itself.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: In this case, the has a minimum range of zero making the vehicle possible of avoiding objects that are really close to the car. The vehicle is also implementing RADAR systems for general obstacle detection such as pedestrians and vehicles to also detect their speed, cameras are placed to get visual information, as well as microphones to hear audio cues such as ambulance and police sirens. In addition, one of the biggest advantages that Waymo has over the different technologies is their data set, specifically for testing autonomous vehicle performance. Waymo owns one of the biggest free data sets concerning autonomous vehicles and provide information and data about five different LIDAR sensors that are used in their vehicles and being tested every day around the world. The perk of having the data set available to any researcher is that experts and enthusiasts can create their own tests to prove the limits of the sensors and cameras from Google to try to improve the technology. Some companies are expanding the data set even more to make it more efficient and use different types of sensors from cameras to RADARs and LIDARs [11,12]. Google and Waymo are currently developing one of the most efficient and advanced technologies for sensors to implement in autonomous vehicles. One is one of the most famous auto-taxi services being tested in the to keep improving the function of the vehicle. Even though the company affirms to have significant amounts of data and testing for hazardous conditions such a rain, fog, and extreme climates; some locations cannot be tested with these vehicles due to lack of technology in the area. Smart cities are the main focus for these types of vehicles, in some other conditions the precision of the sensors will certainly not be the same, and this can lead to a lack of safety and performance for the vehicle. Baidu Apollo: Baidu, which is one of the largest internet and technology companies in the world has owned, a self-driving car project following the name of Apollo. Currently, they are deploying prototypes of autonomous cars, which are planned to be implemented in different Chinese cities as a Robo-Taxi. vehicle is built with the Puck series of the newest and most efficient short- and long-range LIDAR sensors in the market provided by the company. sensors are the most advanced in the industry for autonomous vehicles and are used for many self-driving programs for 3D mapping and retrieving data from the environment to create collision avoidance systems and object detection for safety measures. [SEP] Justify:

Output: The text corroborates the claim by detailing how autonomous vehicles utilize various sensors, including RADAR, cameras, and LIDAR systems, to detect obstacles like pedestrians and vehicles, as well as environmental conditions. It mentions the processing of visual data and audio cues to enhance navigation and safety. Additionally, it highlights the development of advanced technologies by companies like Waymo and Baidu Apollo, which support the use of AI in processing this sensor data for safe navigation.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: According to the author, it is proposed that infants demonstrate an inherent inclination toward specific facial patterns. However, the ability to perceive faces as distinct entities and the subsequent connection of facial expressions with emotions are acquired through early exposure and cultural transmission. Dr. Barrett underscores the importance of cultural context in shaping the understanding of emotions, suggesting that the cognitive processes in our brains are influenced by both genetic elements and cultural influences. This perspective aligns with the broader evolutionary synthesis, which acknowledges the importance of cultural inheritance in the progression of human development [4]. Figure 4. of Hippocampus [4]. 4. Zhu, Y. , et al. , of Real-Time fMRI Neurofeedback in Healthy Human. 2019. 13. 5. 00:37:50 Legal System, ‘Universal’ Emotions & Caution Dr. Barrett also illuminates the significant impact of cultural beliefs surrounding the capacity of facial expressions to effectively communicate mental states. This influence extends to the extent that it may potentially influence legal judgments. Dr. Barrett emphasizes that this belief has been integrated into the judicial system, leading to circumstances where individuals' lives are at risk, as judgments based on inferred emotions from facial expressions may lack empirical credibility. In his discourse, Dr. Barrett examines a specific occurrence when the became engaged in a capital punishment lawsuit due to a law enforcement officer's claim regarding the defendant's facial expressions. Dr. Barrett also emphasizes the lack of scientific data that supports the idea of universally applicable facial expressions that allow for accurate understanding of persons. While acknowledging the potential for people to display unique facial clues or regular patterns in their facial expressions, the author questions the concept of universally applicable templates for emotions. This emphasizes the necessity of critically examining the widely accepted belief in these established patterns within our cultural context [5]. 5. Dailey, M. N. , et al. , Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion, 2010. 10(6): p. 874-93. 6. 00:41:07 Language Descriptions, Differences & Emotion Dr. Barrett presents a thorough examination of the constraints inherent in language with regard to the articulation and comprehension of emotional encounters. The inability to rely just on a single language, such as English or French, to adequately encompass the vast range of emotional experiences is emphasized by the author. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the lack of scientific support for the notion of universally applicable facial expressions that allow accurate understanding of emotions. Dr. Barrett points out that while there may be unique facial cues, the concept of universally applicable templates for emotions is questionable. This perspective suggests that AI assistants, which may rely on interpreting emotional expressions, might not perfectly understand human emotions due to cultural influences and varying individual expressions.


Input: Task: pass the claim using the given text. Claim: Language can serve as a marker of identity and cultural heritage. [SEP] Text: Інститут українознавства ім. І. Крип'якевича Національної академії наук України м. Львів, вул. Козельницька, 4 Email: inukr@inst-ukr. lviv. [SEP] Justify:

Output: The text provides a contact address for an institution and does not discuss or relate to the claim about language as a marker of identity and cultural heritage. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: However, they are still employing some other LIDAR sensors from the same company that could be more expensive but perform very well for object detection purposes. A recent study [13] compared these two sensors and how their resolution could influence detecting objects while the vehicle is driving. It concludes that the researchers worked on an imaging system for multi-object tracking using both high-resolution and a low-resolution sensor. With regard to the cheaper sensor, there is a precision of 69. 3% using two different scenarios, vehicle and no vehicle. There is a difference for detecting objects within a significant range that cannot be seen in the system compared to the high-resolution Velodyne HDL-64 sensor, but the difference in cost is enough to start considering using these types of sensors more frequently and implementing them into different systems. Tests were done with sensors in different weather conditions [14]. Rain has a very limited influence on visibility for performance of the but fog is somewhat important and it is crucial to consider this scenario. More power is needed when dealing with fog because it has a significant impact in terms of vision, using the 905 nm LIDAR pedestrians, reflectors, traffic signs are not visible at all or reaching the 50–70% visibility. Similarly, the company is using two different types of RADAR sensors in their Apollo vehicles. ARS408-21 is provided by Continental, which is implemented for developing and Assist. In addition, they use the B01HC from Racobit for vehicle detection in mid-long range in different work environments. However, according to the previously mentioned study, RADAR sensors are not efficient for detecting non-metal objects in hazardous weather conditions, so LIDAR sensors are needed to provide more data and combine both to guarantee a high performance of the vehicle. With regard to cameras, they are currently using the MARS system which is an imaging solution for imaging applications. This system is also supported by two other cameras designed by Inc. BMW: BMW car manufacturer has also invested in autonomous driving technology. Currently, the company provides limited self-driving features such as park assistance, adaptive cruise control, and some blindspot detection systems in many of their different series of luxury cars. In the future, the automobile giant is planning on deploying a level 3–5 autonomous project in 2021 called iNext series. [SEP] Justify:

Output: The text corroborates the claim as it discusses the use of various sensors, including LIDAR and RADAR, in autonomous vehicles. It mentions that these sensors, along with cameras, are crucial for object detection and navigation while driving. Specifically, it highlights that LIDAR sensors can be essential for providing more data, which contributes to the high performance of the vehicle, aligning with the claim about AI processing data from sensors in autonomous vehicles.


Input: Task: contrast the claim using the given text. Claim: AI assistants are able to understand and perfectly respond to all human emotions. [SEP] Text: Various civilizations possess distinct vocabularies and conceptual frameworks that incorporate specific emotional states, which may not be frequently encountered in other linguistic systems. Dr. Barrett offers supplementary illustrations, including a German lexical item that connotes the notion of facial expression warranting physical aggression, a Polynesian term denoting a collective's fervent assertiveness, and a Japanese expression encapsulating the sentiment of melancholy subsequent to an unsatisfactory haircut. Dr. Barrett places significant emphasis on the need to embrace cultural variations in the emotional lexicon as a means to enrich our comprehension of the intricate and multifaceted range of human emotional experiences [6]. 6. Dzokoto, V. A. , et al. , Emotion Norms, Display Rules, and Regulation in the of Ghana: Using Proverbs. 2018. 9. 7. 00:48:18 Questions & Assumptions; Language, Emotions & Dr. Barrett also explores the fundamental assumptions that form the basis of inquiries into the impact of language on the cognitive mechanisms involved in the categorization and evaluation of emotional circumstances. Dr. Barrett presents a scholarly examination of the predominant approach employed in the fields of psychology and neuroscience. This approach typically involves initiating research by exploring commonly acknowledged constructs such as "anger" or "memory" and subsequently attempting to ascertain the corresponding physiological underpinnings within the brain or body. Dr. Barrett posits that the methodology under consideration presents inherent difficulties due to its reliance on subjective experiences, which may not be universally shared, and its susceptibility to biases that may be introduced during the scientific publication process. Dr. Barrett highlights the significance of taking into account the diverse pathways through which individuals navigate emotional experiences. Furthermore, she proposes that the psychological interpretation of unique biological indicators plays a crucial role in determining their importance. Dr. Barrett's presents an illustrative case in which serotonin and cortisol are shown to exert influence on metabolic control, rather than functioning as direct markers of emotional states such as "happiness" or "stress. " The author emphasizes the significance of establishing accurate semantic differences to aid in the examination and elucidation of the genuine functions of different biological signals [7]. Figure 05. Emotions and [8]. 7. Ueyama, T. , [Emotion, amygdala, and autonomic nervous system]. Brain Nerve, 2012. 64(10): p. 1113-8. Taggart, P. and P. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity and variability of human emotional experiences across different cultures and languages. It emphasizes the challenges in categorizing and interpreting emotions based on subjective experiences, stating that the methodology in psychology and neuroscience often relies on constructs that may not be universally understood. This suggests that AI assistants, which are programmed based on these constructs, may struggle to accurately comprehend and respond to the full spectrum of human emotions.


Input: Task: pass the claim using the given text. Claim: Language can serve as a marker of identity and cultural heritage. [SEP] Text: ua Телефон: +38 (032) 270-70-22 Домашня » Наші видання » Наукові збірники » Україна: культурна спадщина, національна свідомість, державність Укр En Про журнал Положення про політику відкритого доступу Цей журнал практикує політику негайного відкритого доступу до опублікованого змісту, підтримуючи принципи вільного поширення наукової інформації та глобального обміну знаннями задля загального суспільного прогресу Рік заснування 1992 Галузь та проблематика Міждисци [SEP] Justify:

Output: The text provided is largely irrelevant to the claim. It consists of administrative information about a journal and does not address the relationship between language, identity, or cultural heritage.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to process data from sensors and cameras in autonomous vehicles to navigate streets safely. [SEP] Text: In terms of hardware, they will implement solid-state LIDAR from the startup Innoviz, which is cheaper, faster, and provides higher resolution when imaging the environment around a vehicle. They have no spinning features compared to mechanical LIDARs, are placed in the front, back, and sides to then fuse the data, and a field of view is created that can compete with mechanical sensor’s performance at a low cost [15]. The startup confirms that the sensor is resilient to sunlight and adverse weather conditions and capable of delivering a clear 3D point cloud up to 250 m. This project is intended to be launched in 2021, and because of that, there are some research limitations due to the lack of current information publicly accessible. However, years ago, the BMW group tested and developed autonomous features of Levels 2 and 3 that are now in some way being implemented in their latest cars. Highly automated driving (HAD) systems are now being perfected after introducing some minor features such as traffic jam assistance and lane support. The vehicle being modified is a BMW series 5, which is implementing environmental perception sensors, cameras, and software features to link the autonomous actions. The system used to create the HAD is using 12 sensors, specifically, four laser scanners, three RADARs, four ultrasonic sensors, and a mono camera. A fusion of this sensor creates the ability to perceive objects at a close and long range in every direction to behave consequently depending on the situation. The process of creating 3D maps scanning the environment to have a visual representation is important. With the technology provided by sensor fusion and implementation of a system for HAD, 3D mapping is becoming possible to enhance the performance of autonomous features. However, this aspect can still be improved, as one of the biggest disadvantages is the lack of testing for this specific vehicle. There is a significant gap between research findings and the validation of a real product that will be customer-ready [16]. Other Technologies: Some other companies are also very involved in the process of creating their own version of autonomous vehicles. is getting into the autonomous vehicle industry, partnered with Argo AI, a company responsible for setting up and developing the hardware and the software for autonomous vehicles. Their project is based on the Hybrid car, and they adapted Argo AI’s technology into their project to develop an autonomous car that could be deployed to a mass market. [SEP] Justify:

Output: The text corroborates the claim by detailing how sensors, cameras, and software features are linked to the autonomous actions of vehicles, specifically mentioning the integration of environmental perception sensors and data fusion techniques. It explains that various types of sensors, including laser scanners, RADARs, and cameras, work together to enhance the vehicle's ability to perceive and navigate its environment safely, aligning with the claim about AI's role in processing data from sensors and cameras.