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Artificial Intelligence's Environmental Costs and Promise

#artificialintelligence

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.


Top tweets: NASA's Modular Robotic Vehicle (MRV) and more

#artificialintelligence

Verdict lists five of the top tweets on robotics in Q1 2022 based on data from GlobalData's Technology Influencer Platform. The top tweets are based on total engagements (likes and retweets) received on tweets from more than 380 robotics experts tracked by GlobalData's Technology Influencer platform during the first quarter (Q1) of 2022. Massimo, an engineer, shared an article on the NASA Johnson Space Centre building the MRV in collaboration with an automotive partner. The fully electric vehicle is being regarded as suitable for busy urban environments, large resort areas, and industrial complexes, the article detailed. The MRV has no mechanical connections to the steering, propulsion, or brake actuators.


#selfdrivingcars_2022-02-23_05-36-01.xlsx

#artificialintelligence

The graph represents a network of 1,543 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, 23 February 2022 at 13:47 UTC. The requested start date was Wednesday, 23 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 13-day, 5-hour, 31-minute period from Wednesday, 09 February 2022 at 14:35 UTC to Tuesday, 22 February 2022 at 20:06 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#selfdrivingcars_2022-02-02_05-36-01.xlsx

#artificialintelligence

The graph represents a network of 1,623 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, 02 February 2022 at 13:49 UTC. The requested start date was Wednesday, 02 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 17-day, 22-hour, 51-minute period from Friday, 14 January 2022 at 22:39 UTC to Tuesday, 01 February 2022 at 21:31 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#selfdrivingcars_2022-01-19_05-36-01.xlsx

#artificialintelligence

The graph represents a network of 1,583 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, 19 January 2022 at 13:47 UTC. The requested start date was Wednesday, 19 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 14-day, 1-hour, 46-minute period from Tuesday, 04 January 2022 at 19:34 UTC to Tuesday, 18 January 2022 at 21:20 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

arXiv.org Artificial Intelligence

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.


smartcity OR smartcities_2022-01-13_17-24-59.xlsx

#artificialintelligence

The graph represents a network of 4,670 Twitter users whose tweets in the requested range contained "smartcity OR smartcities", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 14 January 2022 at 01:42 UTC. The requested start date was Friday, 14 January 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 5-day, 6-hour, 33-minute period from Friday, 07 January 2022 at 20:15 UTC to Thursday, 13 January 2022 at 02:48 UTC.


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.


Neural Myerson Auction for Truthful and Energy-Efficient Autonomous Aerial Data Delivery

arXiv.org Artificial Intelligence

A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction-based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of these SPA-based operations, a deep learning-based framework is additionally designed for delivery performance improvements.


#selfdrivingcars_2021-12-22_05-36-02.xlsx

#artificialintelligence

The graph represents a network of 1,627 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, 22 December 2021 at 13:46 UTC. The requested start date was Wednesday, 22 December 2021 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 15-day, 4-hour, 55-minute period from Saturday, 04 December 2021 at 15:55 UTC to Sunday, 19 December 2021 at 20:50 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.