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.

Indicators that writers will need to scramble lest they find themselves replaced by a robot in coming months or years were out in full force in Q2. Those included a warning that some big news outlets are simply dying to replace writers with robots. Says Dan Kennedy, a journalism professor at Northeastern University: "Let me introduce you to the two most bottom line-obsessed newspaper publishers in the United States: Alden Global Capital and Gannett. "If they could, they'd unleash the algorithms to cover everything up-to-and-including city council meetings, mayoral speeches and development proposals. "And if they could figure-out how to program the robots to write human-interest stories and investigative reports, well, they'd do that too."

The last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are now being incorporated into commercial products. With this comes an additional challenge: how can we build AI systems that solve tasks where there is not a crisp, well-defined specification? While multiple solutions have been proposed, in this competition we focus on one in particular: learning from human feedback. Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve. The MineRL BASALT competition aims to spur forward research on this important class of techniques. We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions. These tasks are defined by a paragraph of natural language: for example, "create a waterfall and take a scenic picture of it", with additional clarifying details. Participants must train a separate agent for each task, using any method they want. Agents are then evaluated by humans who have read the task description. To help participants get started, we provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline that leverages these demonstrations. Our hope is that this competition will improve our ability to build AI systems that do what their designers intend them to do, even when the intent cannot be easily formalized. Besides allowing AI to solve more tasks, this can also enable more effective regulation of AI systems, as well as making progress on the value alignment problem.

Will you lose your job because of AI? These two are some of the questions I have seen online for a long time. However, they intensified even more after GitHub released the Copilot. Thus, in this article, you will see an overview of GitHub Copilot and my thoughts about it. According to GitHub, their Copilot application is an artificial intelligence pair programmer that "helps you write code faster and with less work".