Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods. Machine learning is a branch of computer science that broadly aims to enable computers to "learn" without being directly programmed (1). It has origins in the artificial intelligence movement of the 1950s and emphasizes practical objectives and applications, particularly prediction and optimization. Computers "learn" in machine learning by improving their performance at tasks through "experience" (2, p. xv). In practice, "experience" usually means fitting to data; hence, there is not a clear boundary between machine learning and statistical approaches. Indeed, whether a given methodology is considered "machine learning" or "statistical" often reflects its history as much as genuine differences, and many algorithms (e.g., least absolute shrinkage and selection operator (LASSO), stepwise regression) may or may not be considered machine learning depending on who you ask. Still, despite methodological similarities, machine learning is philosophically and practically distinguishable. At the liberty of (considerable) oversimplification, machine learning generally emphasizes predictive accuracy over hypothesis-driven inference, usually focusing on large, high-dimensional (i.e., having many covariates) data sets (3, 4). Regardless of the precise distinction between approaches, in practice, machine learning offers epidemiologists important tools. In particular, a growing focus on "Big Data" emphasizes problems and data sets for which machine learning algorithms excel while more commonly used statistical approaches struggle. This primer provides a basic introduction to machine learning with the aim of providing readers a foundation for critically reading studies based on these methods and a jumping-off point for those interested in using machine learning techniques in epidemiologic research.
Kumamoto – Startup companies are acquiring a growing presence in the field of disaster prevention and reduction, leveraging their strength in technology and their ability to quickly develop goods and services responding to actual needs in afflicted areas. Wota Corp. released a portable recycled water treatment device in 2019. Called Wota Box, it is capable of making 98% of the water that is discharged after showers, handwashing and laundry reusable. With the quality of water managed by artificial intelligence technology, Wota Box makes potable water available when the supply of water is cut off. More than 20 local governments have introduced the device for use at times of disaster.
Since the coronavirus pandemic took hold of our lives, virtual events have taken centre stage. With evolving time, virtual events are becoming the new normal, and AI is no longer something that business owners, marketers or organisers can afford to ignore. There is no denying that AI is proving to be one of the most important resources available for creating top-notch, successful events. But before we go any further, let's understand, what is AI? Most of us relate Artificial intelligence (AI) to sci-fi movies like Matrix or Inception.
Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. You might consider trying a dating app. That's what 26-year-old Gaby Deimeke did after she moved to Austin, Texas, in 2019. After hearing about Bumble BFF at a music festival, Deimeke download the app and gave it a try.
Facial recognition tech linked to personal health codes has been rolled out in a Chinese city bordering Myanmar as authorities seek to squash a coronavirus outbreak. China is one of the world's most surveilled countries, with the government rushing to install more than 200 million CCTV cameras to "cover all public spaces" in the past five years. Surveillance has been widely used to combat Covid-19 in China, which was the first country to adopt a QR code system to log test results and track contacts. But this is the first publicly reported instance of facial recognition being used to track a person's movements and health status as they enter and exit residential areas, supermarkets, transport hubs and other public places. "Everyone who comes in and out needs to have their (health) code and face scanned to pass," officials in Ruili, in Yunnan province, told reporters on Saturday. Ruili discovered 155 cases over the past week in one of the worst virus flareups in recent months to hit China, according to data published Tuesday.
Walsh Middle School seventh grader Aariv Modi has always had a fascination for technology, especially his parent's Alexa device. "I loved the idea of just speaking to a device that it could allow you to play music, listen to the news, and it seemed futuristic to me," Aariv said. In April, Aariv was recognized as the Voice/AI Pioneer of the Year by Project Voice for his contributions to the conversational artificial intelligence industry. During the COVID-19 lockdown, he taught hundreds of kids how to make Alexa do things it isn't programmed to do through webinars, camps and posts on his blog that is available online. "In this generation, as kids are growing up, they are being exposed to technology and learning things in ways that we never thought was possible," said Bradley Metrock, CEO of Score Publishing, which organizes the Project Voice conference.
Dear friends, Welcome to the July 4th issue of the Sunday Briefing. This week we're happy to announce the second post of "Visualization for Science" substack: Time Series State Map so check it out and don't forget to Subscribe to V4Sci so you never miss a post! You can also checkout the latest post at G4Sci: Network Motifs: Frequent patterns in Graphs where we introduce the ESU algorithm for exhaustive enumeration of all subgraphs of a given size. You should Subscribe to G4Sci to make sure you never miss a post! Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to work on the particularly long section 3.8 of the Primer.
The number of US adults teleworking due to the pandemic fell by 30% between January and May 2021 (from 23% to 16%), with the biggest drop in May. As more employees return to their offices, new and unexpected challenges hidden within the new hybrid work model threaten to severely disrupt safety and security. Another added complication is that newly relaxed CDC (Centers for Disease Control) guidelines do not apply to unvaccinated people or account for variations in local or state health guidelines that may be in place, potentially creating a need for organizations to identify and track different groups of employees and visitors. Thankfully, AI-based solutions exist to bolster the security of in-person workplaces and enhance key elements, from the sign-in process to restricted area enforcement, while also allowing for frictionless adherence to health guidelines. As the most accurate, turnkey biometric solution, facial recognition has the potential to solve many of the looming challenges offices will soon face as employees return to work.
Microsoft's Skype Desktop app is getting an upgrade, as it will have a new feature that would augment its voice and video call experience for its users. Microsoft has announced that it will be integrating an artificial intelligence or AI-enabled noise cancellation feature into Skype's Desktop app for both Mac and Windows OS. This feature is not yet available on the mobile and web versions of Skype. In order to activate the background noise cancellation feature, users can click on "Settings" and select the audio tab. According to Gadgets Now, the noise cancellation feature will then pop up, with the options auto, low and high to choose from. Also Read: Skype For Web Is Now Just As Good As The App, But There's A Huge Catch In a blog post, Skype has mentioned that the noise cancellation technology was made for Microsoft Teams.