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#selfdrivingcars_2022-06-01_05-29-21.xlsx

#artificialintelligence

The graph represents a network of 1,612 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 01 June 2022 at 12:38 UTC. The requested start date was Wednesday, 01 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 19-day, 11-hour, 44-minute period from Thursday, 12 May 2022 at 09:04 UTC to Tuesday, 31 May 2022 at 20:48 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#selfdrivingcars_2022-04-20_05-36-02.xlsx

#artificialintelligence

The graph represents a network of 1,840 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 20 April 2022 at 12:47 UTC. The requested start date was Wednesday, 20 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 25-day, 6-hour, 8-minute period from Friday, 25 March 2022 at 15:16 UTC to Tuesday, 19 April 2022 at 21:24 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


The Self-Driving Car: Crossroads at the Bleeding Edge of Artificial Intelligence and Law

arXiv.org Artificial Intelligence

Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.


A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method

arXiv.org Artificial Intelligence

In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios of regular, sloped and rough. The ground surface behavior is assumed to only demonstrate local input-dependent smoothness. kernel's length-scales are obtained. Bayesian inference is implemented sing \textit{Maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame because adjacent segments may not have similar ground structure in an uneven scene while having shared hyper-parameter values. Simulation results shows the effectiveness of the proposed method in uneven and rough scenes which outperforms similar Gaussian process based ground segmentation methods.


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.


Autonomous delivery picking up in US

#artificialintelligence

Autonomous vehicles (AV) play an increasingly important role in food and parcel deliveries. In early December, Silicon Valley-based startup Nuro announced that it was launching the first commercial autonomous delivery in California. Partnering with 7-Eleven, the company provides the service for residents of Mountain View, where the business is located. According to a blog post from Nuro's co-founder Zhu Jiajun, customers can access the autonomous delivery through 7-Eleven's 7NOW delivery app. Nuro currently offers the service with its Prius vehicles in fully autonomous mode, expecting to replace them with its R2 autonomous cars later.


Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021

arXiv.org Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.


Robo-taxis are headed for a street near you

MIT Technology Review

In the coming years, mobility solutions--or how we get from point A to point B--will bridge the gap between ground and air transportation--yes, that means flying cars. Technological advancements are transforming mobility for people and, leading to unprecedented change. Nand Kochhar, vice president of automotive and transportation for Siemens Software says this transformation extends beyond transportation to society in general. "The future of mobility is going to be multimodal to meet consumer demands, to offer a holistic experience in a frictionless way, which offers comfort, convenience, and safety to the end consumer." Thinking about transportation differently is part of a bigger trend, Kochhar notes: "Look at few other trends like sustainability and emissions, which are not just a challenge for the automotive industry but to society as a whole." The advances in technology will have benefits beyond shipping and commute improvements--these technological advancements, Kochhar argues, are poised to drive an infrastructure paradigm shift that will bring newfound autonomy to those who, today, aren't able to get around by themselves. Kochhar explains, "Just imagine people in our own families who are in that stage where they're not able to drive today. Now, you're able to provide them freedom." Laurel Ruma: From Technology Review, I'm Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is the future of mobility. In 2011, Marc Andreessen famously said, "Software is eating the world."


Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety

arXiv.org Artificial Intelligence

Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.


What Is Artificial Intelligence and it's Future

#artificialintelligence

As it stands out today,Artificial intelligence elucidates simulation of human intelligence bymachines, particularly computer systems. AI programming focuses on three basiccognitive skills which are learning, reasoning and self-correction. Learning processes is theaspect of AI programming which focuses on acquiring data and creating rules forhow to turn the data into actionable information. These rules are calledalgorithms, and they provide the computing devices stepwise instructions on howto complete a specific task. Reasoning processes is theaspect of AI programming that focuses on choosing the right algorithm to reacha desired outcome. Typically, AI systems demonstrate at least some behaviours which are associated with human intelligence; thesebehaviours are planning,learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesserextent, social intelligence and creativity. The roots of computing dates back to the Logic Theoristprogram which was presented at the Dartmouth Summer scientific research onArtificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in1956.