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.

Sustainable development goals (SDGs) are becoming more and more important for companies of all shapes and sizes. Put simply, SDGs are a collection of 17 interlinked goals designed to help companies achieve a more sustainable future. Set in 2015 by the UN General Assembly, these goals aim to support such efforts as making processes more efficient, reducing waste, creating diversity, and improving education. Artificial intelligence is one way that these sustainable development goals can be achieved, but leveraging the technology is no simple task. To learn more about the use of machine learning and AI technology for SDGs, we talked to Tiago Ramalho, the founder of Recursive.

Originally, Maysam Moussalem dreamed of being an architect. "When I was 10, I looked up to see the Art Nouveau dome over the Galeries Lafayette in Paris, and I knew I wanted to make things like that," she says. "Growing up between Austin, Paris, Beirut and Istanbul just fed my love of architecture." But she found herself often talking to her father, a computer science (CS) professor, about what she wanted in a career. "I always loved art and science and I wanted to explore the intersections between fields. CS felt broader to me, and so I ended up there."

Is an AI decision ethical or legal, or even risky? Most of us do not consider such a question if your smart TV decides what movies you should see at 9 p.m. Or when you open your laptop or iPhone to Facebook only to find that there is a great advertisement popping up for that art course you were talking about to a friend just the other day. Or you might see a pop-up for a new flower to purchase online to complement the one that just arrived this morning. All of these are not just people in some marketing department trying to sell you something new. Every keystroke you make on your internet-connected phone or computer is being tracked and your data being categorized into some kind of pattern to know what you like and what you might like to purchase next.