This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether urban sprawl is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work.

Experts advocate robust regulation of facial recognition technology to reduce discriminatory outcomes. After Detroit police arrested Robert Williams for another person's crime, officers reportedly showed him the surveillance video image of another Black man that they had used to identify Williams. The image prompted him to ask the officers if they thought "all Black men look alike." Police falsely arrested Williams after facial recognition technology matched him to the image of a suspect--an image that Williams maintains did not look like him. Some experts see the potential of artificial intelligence to bypass human error and biases.

PCA vs Linear Regression – Two statistical methods that run very similarly. However, they differ in one important respect. What the two methods actually are and what this difference is, we explain to you in the following article. Principal Component Analysis (PCA) is a multivariate statistical method for structuring or simplifying a large data set. The main goal here is the discovery of relationships in 2 or 3 dimensional domain.

TensorFlow vs Theano – TensorFlow, along with PyTorch, is currently the best known and most widely used machine learning framework. However, the choice of tool should never depend on one's own preferences, but should be adapted to the data to be examined. Especially in the Big data area, this can prevent a decisive loss of performance. It is therefore also worthwhile to look off the beaten track and to look at other frameworks and libraries in addition to the top dogs. Theano is one such open source Python library.

The ability for marketers to gauge intent these days is spooky. Performing a simple Google search for "hotels in Angeles City" while sitting in a cafe in Manila will suddenly surface "cheapest transport from Manila to Angeles City" ads in your Facebook stream. It knows you'll need cheap transport to get there so you can spend your money on other things. What you may find even more surprising is when you're talking to a mate on the phone about the carnal pleasures of Angeles City and suddenly STD test ads start appearing in your Twitter feed. Is your phone really listening to what you're saying?

Biometric surveillance is coming for you, even if you have'nothing to hide' Clare Garvie is a Senior Associate at Georgetown University's Center on Privacy and Technology, where she has dedicated her work to studying law enforcement's use of face recognition technology on the American public. She is considered the foremost expert on face recognition technology; last year she testified in front of Congress. She writes extensively on its use in law enforcement investigations. As well, she brings to light the worrying ways the technology disrupts privacy, circumvents judicial norms and legal precedents, and promotes chilling effects on free speech and civil liberties. All of this happens under a veil of secrecy, without public consent and largely outside of the purview of American lawmakers. Garvie's research spotlights the ways these technologies are disproportionately used on Black and Brown communities and the failures of face recognition algorithms when deployed on people of color and women. The technology's efficacy, already cause for concern, is further problematized by law enforcement's cavalier practices.

For example, San Jose-based RPA firm Automation Anywhere recently worked with a pharmaceutical company in Europe to accelerate the research and approval of COVID-19 vaccines by augmenting reporting. RPA startup UiPath has also assisted with efforts around the pandemic, for instance helping the U.S. Department of Homeland Security use software bots to perform coronavirus-related data analysis. Deloitte reports that organizations that have implemented and scaled RPA see a return on investment within 12 months. And according to Everest Group, top performers earned nearly four times on their RPA investments while other enterprises earned nearly double. This isn't to suggest that RPA is without its challenges.

As we approach one year since the first national lockdown in the UK, it is clear that Covid-19 is still putting enormous pressures on our healthcare system. Indeed, the NHS reported in January that a record 4.46 million people were on the waiting list for routine treatments and operations, and a recent study by the British Medical Association found that almost 60% of doctors are suffering from some form of anxiety or depression. The path to recovering from this healthcare fallout will not be easy, however, when thinking about how we could alleviate this pressure in the future, emerging artificial intelligence (AI) technologies may be the answer. The World Health Organisation (WHO) predicts that there will be a shortfall of around 9.9 million healthcare professionals worldwide by 2030, despite the economy being able to create 40 million new health sector jobs by the same year. With larger, aging populations and increasingly complex healthcare demands, there will continue to be strain on health workers for the foreseeable future – so how can AI alleviate this?

In fiction, artificial intelligence is often associated with intelligent androids or dystopian futures. But in reality, the AI market mainly revolves around crunching large amounts of data to make quick decisions. Demand for these services -- which power analytics tools, driverless cars, voice assistants, and more -- is climbing. The global AI market was already worth $39.9 billion in 2019, according to Grand View Research, but could still grow at a compound annual growth rate of 42.2% between 2020 and 2027. That's why many companies are jumping aboard the AI bandwagon.

IMAGE: A barren plateau is a trainability problem that occurs in machine learning optimization algorithms when the problem-solving space turns flat as the algorithm is run. LOS ALAMOS, N.M., March 19, 2021--Many machine learning algorithms on quantum computers suffer from the dreaded "barren plateau" of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied--until now. Rigorous theoretical work has established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers. "The work solves a key problem of useability for quantum machine learning. We rigorously proved the conditions under which certain architectures of variational quantum algorithms will or will not have barren plateaus as they are scaled up," said Marco Cerezo, lead author on the paper published in Nature Communications today by a Los Alamos National Laboratory team.