In this special guest feature, Wilson Pang, CTO of Appen, offers a few quality controls that organizations can implement to allow for the most accurate and consistent data annotation process possible. Wilson joined Appen in November 2018 and is responsible for the company's products and technology. Wilson has over seventeen years' experience in software engineering and data science. Prior to joining Appen, Wilson was Chief Data Officer of CTrip in China, the second largest online travel agency company in the world where he led data engineers, analysts, data product managers and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering in eBay in California and provided leadership to various domains including data service and solutions, search science, marketing technology and billing systems. Wilson obtained his Masters and Bachelor's degrees of Electric Engineering from Zhejiang University in China.

In this special guest feature, Wilson Pang, CTO of Appen, offers a few quality controls that organizations can implement to allow for the most accurate and consistent data annotation process possible. Wilson joined Appen in November 2018 and is responsible for the company's products and technology. Wilson has over seventeen years' experience in software engineering and data science. Prior to joining Appen, Wilson was Chief Data Officer of CTrip in China, the second largest online travel agency company in the world where he led data engineers, analysts, data product managers and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering in eBay in California and provided leadership to various domains including data service and solutions, search science, marketing technology and billing systems. Wilson obtained his Masters and Bachelor's degrees of Electric Engineering from Zhejiang University in China.

Every part of our daily lives can play a role in causing it, from electricity, to transportation, the homes we live in, the food we eat, even the healthcare services we rely on. And all of those aspects of our lives are also affected by climate change. Much of what we know about the impacts of climate change comes from sophisticated computer models. Now, a group of computer scientists is calling on their colleagues to put advanced computing and artificial intelligence to work to solve the climate problem. "I can help pinpoint where deforestation is happening using satellite imagery or aerial imagery," said David Rolnick, lead author of a new study outlining how artificial intelligence could help with climate change.

Due to the increasing involvement of state players in automation warfare, when AI-driven automation is on its way to becoming a war weapon, what will it mean for an enterprise to stay competitive for survival? Artificial intelligence is redefining the very meaning of being an enterprise. The rapidly advancing artificial intelligence (AI) capability is on its way to revolutionizing every aspect of an enterprise. The ability to access data has leveled the playing field and brought every enterprise a unique possibility of progress. What needs to be seen is in this level playing field, which enterprises will be able to compete and lay a new foundation for fundamental transformation and which ones will decline.

As the chief technology officer and assistant dean of the Stanford Graduate School of Education, Paul Kim spends more time than most pondering how artificial intelligence (AI) can impact education. He believes most educators don't think about it enough, and those who do worry too much about it. "We're at a very early stage of understanding what AI can possibly do for us, especially in the education teaching system," he says. "I think the possibilities are huge." One thing he doesn't see as a possibility?

The San Francisco chapter of the Iron Order Motorcycle Club doesn't usually concern itself much with self-driving cars, but autonomous vehicle company Aurora recently spent the day driving around with the club's bikers. Aurora, the company co-founded by former Tesla Autopilot head Sterling Anderson, is developing an autonomous driving system it calls Aurora Driver. That system, like all self-driving programs, needs practice on the road, whether that's in autonomous mode logging real-world miles on public roads, in a computer simulation, or being manually driven. Its perception system is taking in everything around it: pedestrians, bicycles, other cars, trucks, delivery vans, e-scooters, errant shopping carts, construction crews, and, yes, motorcycles. That data is used to predict and react to future scenarios on the road.

While neural networks seem to get all the glory, data is the unsung hero of AI projects – data lies at the heart of everything from model training to tuning to selection to validation. No matter how compelling the business case, or talented the team, without high-quality data, AI projects are doomed to fail. An example from the field of computer vision illustrates the challenge. While we marvel at the accuracy of image classification models such as vgg16 and ResNet[2], we may take it for granted that a database with over 14,000,000 hand-annotated images exists to train these models. These are hardly random images – rather, they are organized based on a similarly expansive effort called WordNet, an effort to build a lexical database for the English language started in 1985[3].

AI is the new electricity, and data is the new oil. These words are often quoted during conference keynotes and on social media. Thomas Edison invented the electric bulb in 1878 and fast forward to 2019 – we cannot imagine our life without electricity. It has become an essential part of our life. Along the same lines, the very first AI applications were simple applications such as weather forecasting.

The creator of a hyper-realistic "deepfake" video of "Boris Johnson" endorsing Jeremy Corbyn today warned they could become a fixture in British politics. Earlier this week, the fake video of the "Prime Minister" backing the Labour leader in next month's general election was released online. The video was made by Future Advocacy, an artificial intelligence think tank, in a bid to pressurise MPs to address the spread of deepfakes online. Areeq Chowdhury, its leader, told the Standard: "The reason we are raising awareness of it now is we have time - it's in its infancy." In the video, the fake Mr Johnson tells the camera: "Hi folks, I am here with a very special message. Since that momentous day in 2016, division has coursed through our country as we argue with fantastic passion, vim and vigour about Brexit. "My friends, I wish to rise above this divide and endorse my worthy opponent, the Right Honourable Jeremy Corbyn, to be Prime Minister of our United Kingdom.

Deep neural networks identified potential adverse outcomes and atrial fibrillation from 12-lead ECGs that were originally interpreted as normal, according to new research presented at the American Heart Association Scientific Sessions. "Applications of machine learning and artificial intelligence techniques to problems in health care are increasingly common, but generally focus on diagnostic problems such as detecting features in an image of classifying a current diagnosis based on present features," Christopher M. Haggerty, PhD, assistant professor in the department of imaging science and innovation, and Brandon K. Fornwalt, MD, PhD, associate professor and director of the department of imaging science and innovation, both at Geisinger in Danville, Pennsylvania, told Healio. "Few studies have been able to apply machine learning to the task of predicting future events or patient outcomes. This work is among the first to demonstrate proof of concept for predicting a future patient event -- 1-year mortality -- with good performance based solely on 12-lead electrocardiography data." Sushravya M. Raghunath, PhD, math and computational scientist in the department of imaging science and innovation at Geisinger, and colleagues analyzed 1,775,926 12-lead resting ECGs of 397,840 patients from 34 years of archived medical records.