Since Korea has a limited ICT R&D fund compared to other IT global countries, its strategy was essential to achieve its global competence in each generation of mobile communication. Just after the rollout of the world's first 5G service, the government took the next step by announcing the 5G strategy to promote the 5G application to a wide-ranging industry and create a sustainable 5G ecosystem leading to new growth engines. In this article, we focus on the government-industry 5G collaborations, including the R&D roadmap and promotion to the 5G commercialization, the global collaboration, the first 5G experience, and 5G vertical trials to make the 5G-enabled industrial transformation take place in Korea. The development of an electronic digital switching system called TDX in the 1980s, the world's first CDMA mobile service in the 1990s, and the nationwide wired and mobile broad Internet networks in the 2000s are the key advances that made it possible for Korean consumers to easily adopt new technologies such as LTE and 5G. In 2018, the handset penetration rate of South Korea was similar to western Europe, where LTE adaption was 84% with 99.95% coverage and 65Mbps downlink capacity.4

A successful digital economy requires cybersecurity to be a vital enabler, protecting the interests of individuals and businesses and enabling the resilience of businesses and services. Since 2013, Singapore's medium- to long-term directions for cybersecurity is to develop R&D expertise and capabilities to improve the trustworthiness of cyber infrastructures and systems with an emphasis on security, reliability, resilience, and usability among government agencies, academia, and industry. Various initiatives to support research, innovation, and enterprise have been implemented under the Whole-of-Government National Cybersecurity R&D (NCR) Programme.8 The program supports a synergistic range of initiatives to advance technological state-of-the-art in thematic National Satellites of Excellence in universities, grants for local research projects, international research collaborations, and joint technology developments with industry. Innovation is fostered through cross-sector R&D discussions and partnerships and fast-tracked by national testbeds for safe and repeatable cybersecurity experiments.

The 2020 LHC Olympics challenged teams to develop a machine learning code to find a hidden signal in particle-collision data. This image shows particle-collision data captured by the ATLAS detector at CERN's Large Hadron Collider. In searching for new particles, physicists can lean on theoretical predictions that suggest some good places to look and some good ways to find them: It's like being handed a rough sketch of a needle hidden in a haystack. But blind searches are a lot more complicated, like hunting in a haystack without knowing what you are looking for. To find what conventional computer algorithms and scientists may overlook in the huge volume of data collected in particle collider experiments, the particle physics community is turning to machine learning, an application of artificial intelligence that can teach itself to improve its searching skills as it sifts through a haystack of data.

White House economic adviser Larry Kudlow joins Sean Hannity on'Hannity.' I'm starting to get really scared ... and not of the coronavirus. I'm scared about the loss of liberty people around the world are experiencing as normal life grinds to a halt and we hunker down and keep our distance from each other to stop the spread of this microscopic terror. Three weeks ago you would think I was crazy if I told you that U.S. borders would be closed; many stores, restaurants, bars, and factories would be shut down; office workers would be teleworking from home; millions of children would be out of school; and many of us would be told to stay in our homes as much as possible and only leave when absolutely necessary. If an imaginative scriptwriter pitched a movie with this plot just a few weeks ago he might have been told by a movie studio that the idea was too wild and unbelievable even for a fantasy film.

College students are enrolled at each semester with either part time or full time status. While most of the students keep an overall constant enrollment status during their education period, some of them may frequently change their status between full time and part time from one semester to the next. The goal of this research is to exploit the historic patterns to estimate and categorize students$'$ strategy in three different groups of part time, full time and mixed, investigate the educational features of each group and compare their performance. Enrollment strategy refers to the student$'$s mindset for enrollment plan and in one way can be captured from the student$'$s historic enrollment status. Data is collected from the University of Central Florida from 2008 to 2017 and Hidden Markov Model is applied to identify different types of student strategy. Results show that students with Mixed Enrollment Strategy (MES) have features (ex. time to graduation and graduation and halt enrollment ratio) and performances (ex. cumulative GPA) relatively between students with Full time Enrollment Strategy (FES) and students with Part time Enrollment Strategy (PES).

On December 10, 2019, Phillip Goter and Joseph Herriges hosted the webinar "Autonomous Vehicles: Technical Advancements and Legal Considerations." If you were not able to attend the webinar, you can find a partial summary of its contents in the Q&A below. Transportation system elements in this context include other vehicles, pedestrians, and cyclists, as well as the vehicle's environment, such as roadway infrastructure, buildings, signs, pavement markings, and weather conditions. The safe operation of an AV requires connectivity between the vehicle and other elements of the transportation system. AVs are enabled by artificial intelligence systems and connectivity.

These are just a few examples of projects that suffered severe schedule delays and cost overruns, or that were unable to deliver on their promised scope. Planning projects accurately is notoriously difficult, whether they're publicly or privately funded, or in domains like construction, technology, pharma, or infrastructure. According to the 2018 "Pulse of the Profession" study conducted by the Project Management Institute, between 2011 to 2018 only about 50% of projects where completed on time and approximately 55% were within budget. Even though firms have been investing in project management techniques since the 1970s, the accuracy of project plans has not improved much. Inaccurate forecasts involving durations, costs, resources, and benefits are clearly major source of risk for leaders' careers and organizations' growth opportunities.

New research from AI cybersecurity firm Darktrace revealed that most security leaders are preparing for AI-powered cyberattacks. According to the research paper titled, "The Emergence Of Offensive AI," conducted by Forrester Consulting on behalf of Darktrace, 88% of decision makers in the security industry believe offensive AI is inevitable, with 50% of them expecting the industry to see these attacks in coming years. The research also highlighted that 77% of respondents expect weaponized AI to lead to an increase in the scale of cyberattacks, while 66% of them felt that it would lead to new attacks. Over 80% of security decision-makers opined that organizations require advanced cybersecurity defenses to combat offensive AI, and 75% of security leaders are concerned over business disruption. The findings are based on the responses from security leaders across different industries, including retail, financial services, and manufacturing sectors.

After the surprising results of the 2016 presidential election, I wanted to better understand the socio-economic and cultural factors that played a role in voting behavior. With the election results in the books, I thought it would be fun to reverse-engineer a predictive model of voting behavior based on some of the widely available county-level data sets. For example, if you want to answer the question "how could the election have been different if the percentage of people with at least a bachelor's degree had been 2% higher nationwide?" you can simply toggle that parameter up to 1.02 and click "Submit" to find out. The predictions are driven by a random forest classification model that has been tuned and trained on 71 distinct county-level attributes. Using real data, the model has a predictive accuracy of 94.6% and an ROC AUC score of 96%.

Late last year, the European Commission's Expert Group on Liability and New Technologies – New Technologies Formation (NTF) released a report on Liability for Artificial Intelligence. The report focuses on liability regimes across European Union (EU) member states and offers high-level recommendations on how those liability regimes can be adapted to meet challenges posed by artificial intelligence (AI) and other digital technologies. Insights from this report may inform legislative and regulatory changes in the EU and elsewhere, including in Canada. Here's what you need to know. The NTF first convened in June 2018.