Election deniers are mobilizing their supporters and rolling out new tech to disrupt the November election. These groups are already organizing on hyperlocal levels, and learning to monitor polling places, target election officials, and challenge voter rolls. And though their work was once fringe, its become mainstreamed in the Republican Party. Today on WIRED Politics Lab, we focus on what these groups are doing, and what this means for voters and the election workers already facing threats and harassment. Write to us at politicslab@wired.com. Our show is produced by produced by Jake Harper. Jake Lummus is our studio engineer and Amar Lal mixed this episode. Jordan Bell is the Executive Producer of Audio Development and Chris Bannon is Global Head of Audio at Conde Nast. Also be sure to subscribe to the WIRED Politics Lab newsletter here. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link. Leah Feiger: Welcome to WIRED Politics Lab, a show about how tech is changing politics. Today, we're going to talk about how election deniers are mobilizing their supporters and rolling out new tech to disrupt November.

According to the USCO: "This initiative is in direct response to the recent striking advances in generative AI technologies and their rapidly growing use by individuals and businesses." It is also a response to requests from Congress and the public. A summary of this guidance is here. The Guide provides a convenient collection of relevant materials in one document for your convenience. We are also planning a webinar on legal issues with Generative AI, generating employee guidance on the use of AI and dealing with contractors that produce content for you.

TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra's suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.

Large-scale language models like ChatGPT have taken the world by storm, dazzling users with their ability to pen convincing marketing copy, suggest recipes, and converse a lot like humans. But these models, for all their strengths, have some hefty (and concerning) limitations. And what other kinds of AI could be on the horizon? In this complimentary webinar, Kellogg faculty David Ferrucci, the AI researcher who started and led the IBM Watson team from its inception through its landmark Jeopardy success in 2011, and Brian Uzzi, a professor of management and organizations, will walk us through the inner workings and social ramifications of today's AI--and tomorrow's.

Activity Description Artificial intelligence (AI) and machine learning (ML) are increasingly being used in healthcare settings with applications in decision support, patient care, and disease management. If the underlying data on which AI depends is inherently biased or lacks a diverse representation of populations, the algorithms cannot produce accurate outputs and will further widen the gap of equitable care. This activity will discuss how AI and ML can be used to address social determinants of health and create more equitable healthcare solutions and improve health outcomes. Learning Objectives 1. Identify approaches to create inclusive data sets that produce positive health outcomes for all patients.

DataHour sessions are an excellent opportunity for aspiring individuals looking to launch a career in the data-tech industry, including students and freshers. Current professionals seeking to transition into the data-tech domain or data science professionals seeking to enhance their career growth and development can also benefit from these sessions. In this blog post, we will introduce you to some of the upcoming DataHour sessions, including contrastive learning for image classification, feature engineering, POS tagging, document segmentation using Layout Parser, and many more. Each session is designed to provide you with insights into various data tech topics, techniques, and methods. Attendees will learn from experts in the field, gain practical knowledge, and get to ask questions to clear their doubts.

Travis Jefferies is a Staff AI Research Engineer in the Lockheed Artificial Intelligence Center (LAIC). In this role, he is an individual contributor on an AI consulting team that is tasked with solving problems and proliferating AI adoption to Lockheed Martin business areas. Prior to his role in the LAIC, Travis worked in Sustainment where he used analytics and machine learning to help automate manual processes, reduce uncertainty, and achieve higher availability and mission capability for the warfighter. Travis is committed to lifelong learning and holds a Masters degree in Analytics from Georgia Tech and a Bachelors degree in Engineering Management from the University of Arizona. On the weekends he can be found barbequing, dancing, or summiting mountains in southern Arizona where he grew up and currently lives.

Interest in AI / ML is exploding, but these new techniques and technologies present some unique challenges that can result in suboptimal results if not addressed correctly. Dysfunctional AI / ML efforts can be characterized by high costs, an inability to scale, and slow or unnecessarily limited outcomes -- but it doesn't have to be that way. In a recent webinar, MLOps in Action: Real-World Examples for Establishing Best Practices, the Maven Wave / Atos team delivered a comprehensive look at how to diagnose problems and improve on the AI / ML efforts by focusing on ten facets in an MLOps assessment. During the discussion, six takeaways emerged that illuminate what to expect from an MLOps approach and how to best proceed. A common problem with any new technology is the wishful thinking that it will be a panacea for whatever challenges the enterprise faces.

MobiSpaces delivers an end-to-end mobility-aware and mobility optimised data governance platform, that concerns the offerings of data acquisition, in-situ processing and all the security- and privacy-related operations. MobiSpaces envisions a set of toolboxes, suites, and tools that implement the MobiSpaces concept. MobiSpaces identifies the AI-based Data Operations Toolbox, including an additional list of tools, namely the Declarative querying, Decentralized Data Management, and Online Data Aggregator. In addition MobiSpaces utilises the Edge Analytics Suite, including a further list of tools, namely the XAI Prediction Modelling, Edge-driven Federated Learning, and Visual Analytics. MobiSpaces currently has five use cases utilising these tools including; iRoute, SmartSense, MarineTrafficTracker, Vessel Edge, and CrowdSeaMapping. Join us in our first introductory webinar " Meet the MobiSpaces Use Cases: Innovations for Urban and Maritime Domains " on 31 January 2023, 11:00-12:15 CEST where you can hear directly from the project.

To receive new posts every Sunday, consider becoming a free or paid subscriber. Let's connect on Twitter or Instagram or LinkedIn! For any business related issues or collaborations, feel free to write me an email to support@jousefmurad.com!