Using a modified GAN architecture, they can move objects in the image without affecting the background or the other objects!

As part of the lead-up to Transform 2021 coming up July 12-16, we're excited to put a spotlight on some of our conference speakers who are leading impactful diversity, equity, and inclusion initiatives in AI and data. We were lucky to land a conversation with Huma Abidi, senior director of AI software products and engineering at Intel. She spoke about her DE&I work in her private life, including her support for STEM education for girls in the U.S. and all over the world, founding the Women in Machine Learning group at Intel, and more. HA: This one is easy. I lead a globally diverse team of engineers and technologists responsible for delivering world-class products that enable customers to create AI solutions.

AI-powered job interview software may be just as bullshit as you suspect, according to tests run by the MIT Technology Review's "In Machines We Trust" podcast that found two companies' software gave good marks to someone responding to an English-language interview in German. Companies that advertise software tools powered by machine learning for screening job applicants promise efficiency, effectiveness, fairness, and the elimination of shoddy decision-making by humans. In some cases, all the software does is read resumes or cover letters to quickly determine if an applicant's work experience appears right for the job. But a growing number of tools require job-seekers to navigate a hellish series of tasks before they even come close to a phone interview. These can range from having conversations with a chatbot to submitting to voice/face recognition and predictive analytics algorithms that judge them based on their behavior, tone, and appearance.

IMAGE: As a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence, Regenstrief Institute Research Scientist Suranga Kasthurirathne, PhD, is studying the role of operationalizing artificial intelligence (AI) within... view more INDIANAPOLIS -- Regenstrief research scientist and Indiana University School of Medicine faculty member Suranga Kasthurirathne, PhD, has been selected as a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence. He and the other outstanding early and midcareer researchers chosen as fellows will work together on cross-disciplinary projects while being mentored by some of the most renowned scientists from around the world, including Nobel Prize winners. Through its fellowship program, the ICA seeks to create a global network of future research leaders. Each fellow proposes a project. Dr. Kasthurirathne's focuses on the role of operationalizing artificial intelligence (AI) within learning health systems.

Arthur, the AI monitoring & governance company, has been named a "Cool Vendor" by Gartner in their newly released report titled "Cool Vendors in AI Governance and Responsible AI." According to the report, Gartner predicts that "Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance." "We consider it a great honor to be named a Gartner'Cool Vendor,'" says Adam Wenchel, CEO of Arthur. "We strongly believe in the importance of AI governance and responsibility and are glad to see Gartner highlighting trends in this industry. We believe this designation recognizes our commitment to creating the most comprehensive set of model monitoring tools possible to enable any company to operationalize AI governance seamlessly."

In the past, hiring decisions were made by people. Today, some key decisions that lead to whether someone gets a job or not are made by algorithms. The use of AI-based job interviews has increased since the pandemic. As demand increases, so too do questions about whether these algorithms make fair and unbiased hiring decisions, or find the most qualified applicant. In this second episode of a four-part series on AI in hiring, we meet some of the big players making this technology including the CEOs of HireVue and myInterview--and we test some of these tools ourselves. This miniseries on hiring was reported by Hilke Schellmann and produced by Jennifer Strong, Emma Cillekens, Karen Hao and Anthony Green with special thanks to James Wall. Jennifer: Work… is a big part of our lives. It's how most of us pay our bills, feed our families… and put a roof over our heads. Michelle Rogers: "A permanent job would mean stability. You need something to keep you going and to keep you fresh." Dora Lespier: "Like being able to take my daughter being able to get whatever she needs. Henry Claypool: "You know, it's, it's a big part of my identity. It's what I do a lot.

Resident Moz search scientist Dr. Pete Meyers returns to the MozCon stage this year, and we're so excited for his presentation: Rule Your Rivals: From Data to Action. In our last interview before the show, we talked with Dr. Pete about 2020, the trends he's seeing in the SERPs, and what makes competitive analysis effective. Read the full interview below, and don't forget to grab your ticket to see Dr. Pete and our other amazing speakers at MozCon Virtual 2021 (ticket sales end Friday, July 9!): Question: 2020 was quite a year, how was this year for you? Did you have any favorite projects? Dr. Pete: Honestly, there were a lot of days this past year when it felt like just staying alive and sane were our main project (and I'm not sure I completed the sane part).

Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Submit it here or post it in the Slate Parenting Facebook group. My nephew Nicolas is 9 and only plays video games. He wakes up around 2:00 pm doesn't brush his teeth or shower and begins playing games.

The goal of Question Answering over Knowledge Graphs (KGQA) is to find answers for natural language questions over a knowledge graph. Recent KGQA approaches adopt a neural machine translation (NMT) approach, where the natural language question is translated into a structured query language. However, NMT suffers from the out-of-vocabulary problem, where terms in a question may not have been seen during training, impeding their translation. This issue is particularly problematic for the millions of entities that large knowledge graphs describe. We rather propose a KGQA approach that delegates the processing of entities to entity linking (EL) systems. NMT is then used to create a query template with placeholders that are filled by entities identified in an EL phase. Slot filling is used to decide which entity fills which placeholder. Experiments for QA over Wikidata show that our approach outperforms pure NMT: while there remains a strong dependence on having seen similar query templates during training, errors relating to entities are greatly reduced.