Page 19 – Advance Aii

Your Essential Guide to Compliance in...

In an era where AI and machine learning have streamlined everything, including hiring processes, the balance between efficiency and equity comes into question. With algorithms, machine learning, and statistical modeling defining who gets hired or promoted, are these decisions genuinely fair? How can we ensure these technological advances don’t harbor biases or injustices? New York City has responded to these concerns by introducing new regulations on using Automated Employment Decision Tools (AEDTs) in 2023. But what exactly does this mean for you as a software vendor, employer, HR professional, or job seeker in NYC? In this article, we delve into what constitutes an AEDT, who this law affects, and what signs indicate a violation of the law. We’ll also outline how to ensure compliance and familiarize you with some of the AEDTs currently available in the market. Read on to ensure your recruitment tools and hiring processes remain fair, transparent, and, above all, compliant. What are AEDTs? Let’s start by explaining what exactly Automated Employment Decision Tools (AEDTs) are. Under New York City’s new regulations, AEDTs refer to a broad spectrum of automated tools that utilize AI, algorithms, machine learning, or statistical models to assist or even make hiring and...

Healthcare data extraction remains a significant hurdle, with the sector requiring 7.7x more administrative workers than other industries. Automating healthcare data extraction can help organizations reduce operational spending and streamline their processes while improving patient care. Healthcare data extraction systems capture and extract crucial information from a range of healthcare documents—patient registration forms, insurance forms, lab results, billing information, regulatory compliance documents, and more. The extracted data is processed and neatly organized into structured formats. The result? Everyone in the healthcare ecosystem benefits: Doctors, nurses, administrative staff, billing departments, et al. Plus, being able to quickly access critical data will lead to smarter decisions across clinical, operational, and financial domains and help offer a better patient experience. This guide will help you quickly get up to speed with healthcare data extraction. We’ll show you how it’s transforming the entire healthcare ecosystem, its benefits, and practical steps to implement it in your organization. The current state of healthcare documentation Healthcare documentation is the backbone of patient care and organizational operations, but it’s also become a monster that’s eating up valuable time and resources. Over 71% of clinicians report feeling overwhelmed by the sheer volume of information available....

NVIDIA has unveiled NVIDIA Cosmos, an innovative platform designed to accelerate the development of physical AI – the artificial intelligence behind robots, autonomous vehicles (AVs), and other real-world automated systems. By combining state-of-the-art world foundation models (WFMs), advanced video processing tools, and an AI-driven data pipeline, Cosmos enables developers to create, train, and optimize AI models more efficiently than ever before. Developing physical AI has traditionally required massive amounts of real-world data, making it a costly and time-intensive process. NVIDIA Cosmos aims to change that by offering physics-based synthetic data generation, allowing developers to create photorealistic 3D environments that mimic real-world conditions. These simulated environments help train AI models without relying entirely on expensive, manually collected data. NVIDIA describes world foundation models as fundamental to the next wave of AI, much like large language models (LLMs) revolutionized natural language processing. WFMs use a combination of text, images, video, and sensor data to simulate real-world interactions, making them essential for robotics and autonomous systems that need to navigate complex environments. Cosmos includes a range of advanced AI tools tailored for the development of robotics and AVs: Synthetic Data Generation – Using Cosmos, developers can create high-fidelity, physics-aware video simulations of industrial...

What is this Every Thursday, I will share a dev diary about what we’ve been working on over the past few weeks. I’ll focus on the interesting challenges and solutions that I encountered. I won’t be able to cover everything, but I’ll share what caught my interest. Why am I doing it I want to bring our community along on this journey, and I simply love writing about things I’m passionate about! This is my unfiltered dev journal, so please keep in mind that what I write here are my thoughts and will be outdated by the time you read this, as so many things change quickly. Any plans I mention aren’t set in stone and everything is subject to change. Also, if you don’t like spoilers, then don’t read this. Space Engineers 1 Update 206 is nearing completion. Here is a little sneak peek from yesterday’s team presentation: Space Engineers 2 VS 1.1 is almost finished and we are testing it now, release will be soon. Flora impostors for VS 2 – fendering all visible trees as real geometry would be too GPU intensive, so a good practice is to render distant trees as billboard impostors, which are not...

In the past few weeks, we’ve discussed several types of metadata that benefit from automated labelling: the CEFR, keywords, topic, and learning objectives. Of course, there are many other types of metadata. But the ones we’ve addressed have a few things in common. It’s difficult to use them efficiently, and they’re usually not created in an objective, consistent manner. That is why it’s so interesting to automate these labels. Not only is automation innovative and impactful, but it also significantly enriches learning materials. Let’s do a recap and consider what the future has in store! Improved availability of information With the demand for personalisation and new business models increasing, publishers find themselves faced with new challenges. Filtering and labelling learning materials is an intricate, time-consuming endeavour that is prone to inconsistency. If done manually, it’s impossible to create, manage, and distribute personalised learning materials at scale. And that’s a problem. Because today, nearly every organisation that deals with large quantities of digital or e-learning materials aims to provide a content aggregation and distribution platform. Their goal is to create a one-stop shop that enables users to find personalised or targeted teaching materials quickly and easily. Such companies are built on three pillars. They all have...

Analytics powers your marketing program, but how much value are you really getting out of your data? Artificial intelligence can help. AI is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information. That includes your analytics data from places like Google Analytics, automation platforms, content management systems, CRMs, and more. In fact, AI exists today that can help you get much more value out of the data you already have, unify that data, and actually make predictions about customer behaviors based on it. That sounds great. But how do you actually get started? This article is here to help you take your first step. At Marketing AI Institute, we’ve spent years researching and applying AI. Since 2016, we’ve published more than 400 articles on the subject. And we’ve published stories on 50+ AI-powered vendors with more than $1 billion in total funding. We’re also tracking 1,500+ sales and marketing AI companies with combined funding north of $6.2 billion. This article leans on that expertise to demystify AI. And, it’ll give you ideas on how to use AI for analytics and offer some tools to explore further....

Global law firm Herbert Smith Freehills has launched an AI-powered tool to help clients keep on top of the swiftly evolving field of AI policy and regulation – which is all very meta. It has been built by the firm’s Emerging Technology Group. The HSF AI Tracker will ‘capture the core AI principles and regulations released by governments, alongside the broader spectrum of existing laws that apply to AI technology’. It has been used over 5,500 times in its pilot phase across all industry sectors, the firm said. It builds upon the group’s development of the firm’s ‘digital marketplace’ – see here, which offers a range of six tools, including ones for privilege, ESG, dawn raids, and now AI policy. The AI-developed database will be ‘updated regularly, allowing users to monitor key legal developments, including case law and court decisions which transform existing legal frameworks and establish new precedents….across 12 jurisdictions; beginning with the UK, Australia and countries in Europe, Asia, and the Middle East’. There is a roadmap to add others, they added. It will cover: ‘AI policy, capturing the principles, guidance, and documents that governments release to articulate and advance their AI goals and ambitions AI regulation, covering...

Posted On: April 1, 2020 Digital transformation is a process and a revolutionary change. But also it is an outcome and an evolutionary phenomenon. Digital transformation has changed the outlook on Human Resource Management. In the contemporary world, people apply for online jobs, go through digital interview processes, and even work remotely as well. As an HR manager, it’s pivotal to acknowledge that technology has become an integral part of today’s workforce management. According to a report by Deloitte, digital transformation budgets are going to increase by 25 % by the year 2020. HR teams are moving from excel sheets to Artificial intelligence, Machine learning, blockchains, and automated performance management. Digital Transformation of HR services will improve the workflow of the organization and provide a transparent employee engagement. It empowers employees to make necessary decisions at the workplace or any organization tasks without any supervision. The organizations are providing AI and ML-powered tools/resources to employees in order to make a long-term digital HR strategy. In a recent Gartner survey, two-thirds of business leaders said that if their company does not digitalize more by 2020, it will no longer be competitive. In HR, digitalization is changing everything, from hiring to developing...

🔘 Paper page: arxiv.org/abs/2108.10921 Abstract Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples. Hand-labelling vast amounts of data may be tedious, expensive, and error-prone. Recently, some studies have explored the use of diverse sources of weak supervision to produce competitive end model classifiers. In this paper, we survey recent work on weak supervision, and in particular, we investigate the Data Programming (DP) framework. Taking a set of potentially noisy heuristics as input, DP assigns denoised probabilistic labels to each data point in a dataset using a probabilistic graphical model of heuristics. We analyze the math fundamentals behind DP and demonstrate the power of it by applying it on two real-world text classification tasks. Furthermore, we compare DP with pointillistic active and semi-supervised learning techniques traditionally applied in data-sparse settings. Authors Chufan Gao, Mononito Goswami Liked this post? Follow this blog to get more.

Build Custom AI Agents for Workflow Automation Build Custom AI Agents for Workflow Automation to eliminate repetitive tasks, improve operational efficiency, and scale business operations without exponentially increasing costs. If you’re an entrepreneur, business leader, or IT professional, you know how time-consuming everyday workflows can drain resources and slow growth. Imagine transforming static tasks into dynamic, autonomous processes that work around the clock. In this guide, you’ll learn how to create your own team of AI agents, each with specialized roles, to enhance productivity and redefine how your organization operates. Also Read: Understanding AI Agents: The Future of AI Tools Understanding AI Agents: What They Are and Why They Matter AI agents are designed to perform human-like tasks with little-to-no supervision. These agents operate on predefined instructions and are trained to handle specific responsibilities, from managing calendar invites, answering customer emails, generating reports, or automating sales outreach. Each AI agent can be trained on a unique dataset or workflow, which allows it to execute complex tasks in real-time. Unlike single-purpose automation tools that often require manual triggers, AI agents use contextual awareness and can interact with other systems. This allows not just seamless automation, but also enhanced decision-making. In short,...