This interview is part of a Machine Learning series. We invited Adam Geitgey, Director of Software Engineering at Groupon, to talk about the difference between machine learning and the older artificial intelligence effort and the progress we've made so far. JAXenter: How are you involved in machine learning? Adam Geitgey: My professional background is primarily in traditional software development, not machine learning. I've worked on scaling large-scale websites, building backend systems, building mobile apps and other things like that.

A panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city – in areas diverse as transportation, healthcare and education – and spur discussion of how to ensure the safe, fair and beneficial development of these rapidly emerging technologies. Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford University to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at The University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford's School of Engineering.

In partnership with The Relevancy Group and CEO David Daniels, Persado today announced the findings of a new research report entitled The Science Behind Customer Engagement: How Machine-Learning Drives Action. The survey assesses the importance of understanding how machine-learning is helping marketers communicate with a growing online audience of 3.6 billion, roughly 50% of the world's population. Surveying 301 advertising and marketing executives on their experience with machine-learning, automation, and predictive platforms across marketing and advertising practices, The Relevancy Group found that incorporating cognitive or "smart" content generated through machine-learning platforms is an effective practice that resonates with a customer's emotional decision-making. In fact, the study found that early adopters of this technology who were able to leverage the right message and deliver on emotional personalization could drive a 3-5x increase in revenue. The findings indicate the market's need for better understanding and adoption of these technologies with most marketers showing willingness to adopt content created via machine learning.

Big urban cities in North America will be transformed by 2030 by artificial intelligence technologies, as self-driving cars, package-delivering robots and surveillance drones become commonplace, according to a new report. The report, produced by an international panel of artificial intelligence experts convened by Stanford University, takes an in-depth look at how artificial intelligence is already impacting society and how it will continue to evolve over the next 14 years. It is the first report in a planned series of studies looking at artificial intelligence, or AI, that will continue for at least 100 years. "Until now, most of what is known about AI comes from science fiction books and movies," said Peter Stone, a computer science professor at the University of Texas who chaired the panel that produced the report. "This study provides a realistic foundation to discuss how AI technologies are likely to affect society."

Impaired glucose tolerance (IGT) is a risk factor for the development of type 2 diabetes mellitus (T2DM) [1], and both IGT and T2DM are associated with increase in cardio-cerebrovascular related mortality [2, 3]. The Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe (DECODE) [4] study showed a tight correlation between IGT and cardiovascular mortality, and IGT is a known risk factor for early-stage atherosclerosis [5]. In the Actos Now for Prevention of Diabetes (ACT NOW) study, it was shown that pharmacotherapy with pioglitazone in IGT subjects resulted in reduced development of T2DM [6] as well as reduced progression of atherosclerosis [7]. Therefore, identification of IGT subjects who are at risk for rapid atherosclerosis progression, and understanding the important characteristics that affect the identification process, may be beneficial in risk stratification and early intervention. Machine learning (ML) methods have been widely used to learn complex relationships or patterns from data to make accurate predictions [8] and are usually applied in the setting of massive datasets ("big data").

In the Editorial, Editor-in-Chief Tom Baranowski, PhD, Professor of Pediatrics, Baylor College of Medicine, Houston, TX, identifies many lessons that can be learned from the success of the popular AVG Pokémon Go(tm), in which players can log hours of walking as they physically chase the animated Pokémon creatures based on video images displayed on their smart phones. Dr. Baranowski encourages researchers to conduct well-designed studies to assess the health effects of AVGs. Game developers could then use this information to create new games that would be both fun to play and promote beneficial physical activity. "Pokémon Go(tm) stimulated substantial amounts of physical activity in many likely-otherwise sedentary game players without intending to! We might call this stealth exergame programming, and we have a lot to learn about how to achieve this!" says Dr. Baranowski.

If you want drones to deliver packages and food to your doorstep in the future, they'll need to break free of the limitations of WiFi. That's one major reason why AT&T is gearing up to test drones on its LTE 4G network, which are powered by Qualcomm's Snapdragon Flight platform. As you can imagine, it'll involve figuring out how to ensure drones stay connected to AT&T's network, even when they're beyond operators' line of sight. On top of deliveries, the carrier says its testing could also let drones tackle things like inspections, rescues and exploration. "Not only do we aim to analyze wide-scalable LTE optimization for safe, legal commercial SUAS use cases with beyond line-of-sight connectivity, but the results can help inform positive developments in drone regulations and 5G specifications as they pertain to wide-scale deployment of numerous drone use cases," said Qualcomm CTO Matt Grob in a statement.

A panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city – in areas as diverse as transportation, health care and education - and to spur discussion about how to ensure the safe, fair and beneficial development of these rapidly emerging technologies. Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at the University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford.

How much of our lives will be changed by artificial intelligence? According to a new report, by 2030, large urban cities will be transformed as self-driving cars, package-delivering robots and surveillance drones become a common phenomenon of the streets. Produced by an international panel of artificial intelligence experts, convened by Stanford University, the report takes an in-depth look at how artificial intelligence is already impacting society, and how it will continuously evolve over the next 14 years. "Until now, most of what is known about AI comes from science fiction books and movies," said Peter Stone, a computer science professor at the University of Texas who chaired the panel that produced the report. "This study provides a realistic foundation to discuss how AI technologies are likely to affect society."

I guess it could be an older version, but here's a single PDF: https://www.dgp.toronto.edu/