If you drive in the United States, chances are you can't remember the last time you bought a paper map, printed out a digital map, or even stopped to ask for directions. Thanks to Global Positioning System (GPS) and the mobile mapping apps on our smartphones and their real-time routing advice, navigation is a solved problem. If you live in a place like Doha, Qatar, where the length of the road network has tripled over the last five years, commercial mapping services from Google, Apple, Bing, or other providers simply can't keep up with the pace of infrastructure change. "Each one of us who grew up in Europe or the US probably cannot understand the scale at which these cities grow," says Rade Stanojevic, a senior scientist at the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, a Qatar Foundation university, in Doha. "Pretty much every neighborhood sees a new underpass, new overpass, new large highway being added every couple of months." As Qatar copes with this rapid growth--and especially as it prepares to host the FIFA World Cup in 2022--the bad routing advice and accumulating travel delays from outdated digital maps is increasingly costly. That's why Stanojevic and colleagues at QCRI decided to try applying machine learning to the problem. A road network can be interpreted as a giant graph in which every intersection is a node and every road is an edge, says Stanojevic, whose specialty is network economics. Road segments can have both static characteristics, such as the designated speed limit, and dynamic characteristics, such as rush-hour congestion. To see where traffic really is going--rather than where an old map says it should go--and then predict the best routes through an ever-changing maze, all a machine-learning model would need is lots of up-to-data data on both the static and dynamic factors. "Fortunately enough, modern vehicle fleets have these monitoring systems that produce quite a lot of data," says Stanojevic.

We all know that there are troves of data that exist online about us and our browsing, clicking, and spending habits. However, given all that information and the people that spend their lives on the internet, how do those who tailor the ads we see parse that information? As with many things these days, it's useful to have machines to help. RJ Talyor is the CEO and founder of Pattern89, an Indianapolis-based marketing firm using the power of artificial intelligence (AI) to help advertisers figure out what works and what doesn't when it comes to the ads we see everyday. I spoke with him about how AI is helping marketers figure out not only who to target, but what elements to include in those ads.

A project using artificial intelligence to develop digital triage tools for mental health clinicians (CHRONOS) has been successful in the latest round of the Artificial Intelligence in Health and Care Award. Oxford Health BRC was one of a handful of organisations to receive the funding, which was announced by the Health Secretary Matt Hancock on June 16. The AI Award is making £140 million available over four years to accelerate the testing and evaluation of artificial intelligence technologies which meet the aims set out in the NHS Long Term Plan. The Award means that Oxford Health BRC will be able to develop digital tools to make it easier for clinicians in secondary mental health care to rapidly identify the most appropriate treatments for their patients. This is the first time a mental health project has received the AI Award.

If you've applied for a job lately, it's all but guaranteed that your application was reviewed by software--in most cases, before a human ever laid eyes on it. In this episode, the first in a four-part investigation into automated hiring practices, we speak with the CEOs of ZipRecruiter and CareerBuilder, and one of the architects of LinkedIn's algorithmic job-matching system, to explore how AI is increasingly playing matchmaker between job searchers and employers. But while software helps speed up the process of sifting through the job market, algorithms have a history of biasing the opportunities they present to people by gender, race...and in at least one case, whether you played lacrosse in high school. This miniseries on hiring was reported by Hilke Schellmann and produced by Jennifer Strong, Emma Cillekens, and Anthony Green with special thanks to Karen Hao. Jennifer: Searching for a job can be incredibly stressful, especially when you've been at it for a while. Anonymous Jobseeker: At that moment in time I wanted to give up, and I was like, all right, maybe this, this industry isn't for me or maybe I'm just dumb. And I was just like, really beating myself up. I did go into the imposter syndrome, when I felt like this is not where I belong. Jennifer: And this woman, who we'll call Sally, knows the struggle all too well. She's a black woman with a unique name trying to break into the tech industry. Since she's criticizing the hiring methods of potential employers, she's asked us not to use her real name. Anonymous Jobseeker: So, I use Glassdoor, I use LinkedIn, going to the website specifically, as well as other people in my networks to see, hey, are they hiring? And yeah, I think in total I applied to 146 jobs. Jennifer: And.. she knows that exact number, because she put every application in a spreadsheet.

Let's make the relevant question more personal: will machines replace me? I'm a mathematician; my profession is often seen from the outside as a very complicated but ultimately purely mechanical game played with fixed rules, like checkers, chess, or Go. These are activities in which machines have already demonstrated superhuman ability. But for me, math is different: it is a creative pursuit that calls on our intuition as much as our ability to compute.

"The thing that really attracted me to Formula 1 is that it's always been about data and technology," says Graeme Hackland, Williams Group IT director and chief information officer of Williams Racing. Since joining the motorsport racing team in 2014, Hackland has been putting that theory into practice. He is pursuing what he refers to as a data-led digital transformation agenda that helps the organization's designers and engineers create a potential competitive advantage for the team's drivers on race day. Hackland explains to VentureBeat how Williams F1 is looking to exploit data to make further advances up the grid and how emerging technologies, such as artificial intelligence (AI) and quantum computing, might help in that process. This interview has been edited for clarity.

But wait, hasn't there been a mathematical method for optimizing portfolios around for some years? Right, it's called the Modern portfolio theory (MPT) by economist Harry Markowitz, introduced in a 1952 essay, for which he was later awarded a Nobel Memorial Prize in Economic Sciences. The simple idea of the model is diversification in investing: owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. And how can we make it AI?

Machine learning is gradually becoming critical part of life. From recommending movies to self driving cars, AI is making its presence felt in all walks of life. As ML models are taking critical decision, gradual need was felt to explain the decision taken by these models. Most of these model tend to be black box. While accurate perdition helps, answer to'why it was decided the way it was' is equally important .

Modeling and simulating a power distribution network (PDN) for printed circuit boards (PCBs) with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept of using deep learning for PDN impedance prediction. A boundary element method (BEM) is applied to efficiently calculate the impedance for arbitrary board shape and stackup. Then over one million boards with different shapes, stackup, IC location, and decap placement are randomly generated to train a deep neural network (DNN). The trained DNN can predict the impedance accurately for new board configurations that have not been used for training. The consumed time using the trained DNN is only 0.1 seconds, which is over 100 times faster than the BEM method and 5000 times faster than full-wave simulations.

Negin Ashouri is on a mission to elevate women's quality of life, one medical device at a time. Even the challenges of a global pandemic haven't stopped the up-and-coming entrepreneur from advancing a first-of-its-kind technology that is enabling her to do just that. Ashouri's made-to-measure, biodegradable and disposable intravaginal prosthetic for women suffering from pelvic organ prolapse has earned her a prestigious award from Mitacs. Ashouri was presented the Mitacs Change Agent Entrepreneur Award at a virtual awards ceremony on June 10. She was one of five Mitacs Entrepreneur Award winners recognized for their efforts to turn their research into an innovative business that impacts the lives of Canadians.