TS may look like a simple data object and easy to deal with, but the reality is that for someone new it can be a daunting task just to prepare the dataset before the actual fun stuff can begin. Every single time series (TS) data is loaded with information; and time series analysis (TSA) is the process of unpacking all of that. However, to unlock this potential, data needs to be prepared and formatted appropriately before putting it through the analytics pipeline. TS may look like a simple data object and easy to deal with, but the reality is that for someone new it can be a daunting task just to prepare the dataset before the actual fun stuff can begin. So in this article we will talk about some simple tips and tricks for getting the analysis-ready data to potentially save many hours of one's productive time.

China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

NASA wants to make sure we don't unknowingly take organisms or other contaminants from Earth to other worlds (and vice-versa) when humans start exploring space beyond Low Earth Orbit. In a tweet, NASA Administrator Jim Brindestine has announced that the agency has updated its policies to reflect that commitment ahead of the upcoming Artemis missions. "We will protect scientific discoveries and the Earth's environment, while enabling dynamic human exploration and commercial innovation on the Moon and Mars," he wrote. While the space agency has been sending rovers and other unmanned spacecraft to the Moon and Mars, it's concerned about the biological contaminants associated with human presence. If we unknowingly take contaminants to other worlds when we start human exploration, we risk compromising the search for extraterrestrial life. At the same time, NASA wants to ensure its crewed missions don't cause adverse changes to Earth's environment with the introduction of contaminants from outer space.

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

Welcome to June 2020 edition of top 10 #technology and #innovation tweets: 1) Indian government authorities gave permission to test cargo delivery using #drones. We are investigating this issue and trying to have it resolved.

Artificial intelligence (AI) is expected to play a major role in shaping global competitiveness and productivity over the next couple of decades, granting early adopters significant societal, economic, and strategic advantages. As the pace of AI innovation and development picks up--underpinned by advancements in big data and high-performance computing--the United States and China are both in the driver's seat.

Scientists have built an AI tool that finds drone pilots flying dangerously close to airports or protected airspace. The system aims to reduce the risks drones pose to aircraft. Not only can they collide with planes, but they can also interfere with radio signals, causing a pilot to lose control of the aircraft. These risks have already caused chaos at a number of airports. Most notoriously, London's Gatwick airport was forced to shut down in December 2018 after drones were spotted near the runway.

The Ministry of Information and Communications (MIC) has launched an artificial intelligence (AI)-based Vietnamese-language speech-to-text generator, VAIS, and a text-to-speech engine, Vbee, during a ceremony in Hanoi. The launch formed part of a series of events hosted by the Ministry to introduce a selection of Made-in-Vietnam digital platforms contributing to the country's digital transformation and e-government building. According to a media report, addressing the event, the Deputy Minister of MIC, Nguyen Thanh Hung, said VAIS and Vbee are the two pioneering digital platforms in Vietnam that use AI to convert speech to text and vice versa. The applications are sponsored by the Ministry. VAIS can recognise various Vietnamese accents from all northern, central, and southern regions with an accuracy rate of up to 95% and immediately produce results at an exceptional speed.

This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, raise open questions, and help suggest the building blocks and areas where AI could play a role in mixed autonomy. We divide the stage of autonomous vehicle (AV) deployment into four phases: the pure HVs, the HV-dominated, the AVdominated, and the pure AVs. This paper is primarily focused on the latter three phases. It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also reach out to other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.

As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable. Providing useful explanations requires careful consideration of the needs of stakeholders, including end-users, regulators, and domain experts. Despite this need, little work has been done to facilitate inter-stakeholder conversation around explainable machine learning. To help address this gap, we conducted a closed-door, day-long workshop between academics, industry experts, legal scholars, and policymakers to develop a shared language around explainability and to understand the current shortcomings of and potential solutions for deploying explainable machine learning in service of transparency goals. We also asked participants to share case studies in deploying explainable machine learning at scale. In this paper, we provide a short summary of various case studies of explainable machine learning, lessons from those studies, and discuss open challenges.