Role requiring'No experience data provided' months of experience in Houston About VLink: Started in 2006 and headquartered in Connecticut, VLink is one of the fastest-growing digital technology services and consulting companies. Since its inception, our innovative team members have been solving the most complex business, and IT challenges of our global clients. Client is looking for a Data Architect who is primarily an individual contributor but can be responsible for a small team. Main scope of work is to provide solution architecture development, consultancy and assurance to projects, making sure applications are well designed and conform to client standards and reference/segment architectures. Translates the guidelines and standards into practice and solves common technical challenges and provides technical recommendations which have a perceptible impact on local business performance; actively drives the identification, development and implementation of new technologies and opportunities to optimise technology/IT systems. May represent the Company externally as a subject matter expert with suppliers, customers and external agencies. Empowered to make decisions on solutions within guidelines. Applies TOE standards and raises step-outs if needed. Understands the IT Strategic Roadmap and applies within the context of their organisational assignment.

Artificial intelligence (AI) is a rapidly growing part of our daily (business) life. As exciting and groundbreaking its possibilities are, the technology can also come with major risks. To protect citizens against misuse, the EU this spring proposed a draft legislation impacting basically every party that develops AI-applications. Our daily life is becoming more and more intertwined with AI, a catch-all term for a machine or system that makes decisions, based on large amounts of data, and improves itself while learning. The algorithms that recommend new information based on your search behaviour on social media, the face recognition on photos on your smartphone, or computers that select job applicants.

Technologies like artificial intelligence (AI), machine learning (ML), and automation in all of its forms can augment human workers and enable them to pivot to more valuable work, and perform their jobs with more efficiency, safety, and ease. Yet there's justifiable concerns emerging regarding the potential of these technologies to displace human workers. Ronald van Loon is working in partnership with Protiviti, and was able to examine their recent study, Future of Work Top Risks Survey brief, which was conducted as a joint effort with NC State University, and lend his point of view as an industry analyst about the evolving dynamic between technology and the future of work. How we work changed dramatically over the course of the past year, leading to new remote and hybrid work models, changing workforce and employment trends, and ubiquitous technology adoption to accelerate the necessary transformation to sustain operations. Protiviti's findings indicate that the future of work is shaping up to be one of the most disruptive and definitive business challenges of the next decade.

Technologies like artificial intelligence (AI), machine learning (ML), and automation in all of its forms can augment human workers and enable them to pivot to more valuable work, and perform their jobs with more efficiency, safety, and ease. Yet there's justifiable concerns emerging regarding the potential of these technologies to displace human workers. Ronald van Loon is working in partnership with Protiviti, and was able to examine their recent study, Future of Work Top Risks Survey brief, which was conducted as a joint effort with NC State University, and lend his point of view as an industry analyst about the evolving dynamic between technology and the future of work. How we work changed dramatically over the course of the past year, leading to new remote and hybrid work models, changing workforce and employment trends, and ubiquitous technology adoption to accelerate the necessary transformation to sustain operations. Protiviti's findings indicate that the future of work is shaping up to be one of the most disruptive and definitive business challenges of the next decade.

Internal audit departments are slowing down the adoption of next-generation technologies, such as artificial intelligence and machine learning, according to a new survey. The survey, conducted by the consulting firm Protiviti, found the number of internal audit organizations undertaking digital transformation initiatives declined to 60 percent, compared to 76 percent in a survey a year ago. Only 7 percent of the nearly 780 chief audit executives and internal audit leaders polled indicated they are currently implementing machine learning and AI updates. Over half the survey respondents (53 percent) said they have no current plans to adopt machine learning or AI at all. The study was finished in the first quarter of this year, but was based on a poll conducted in the fourth quarter of last year, before the novel coronavirus pandemic began spreading across the U.S. The release of the study was delayed until now as businesses have reacted and adjusted to the new environment.

For many internal auditors, artificial intelligence (AI) may seem like a daunting topic to tackle -- but that shouldn't stop them from considering how they can apply it to their work. Tools and techniques exist that can provide auditors with powerful, straightforward techniques to enhance their work. With an increased focus and urgency around the use of data to support internal audit activities, the time for next-generation pursuits, such as use of AI, is now. Following up on a previous blog post discussing the basics of AI for auditors, here we offer our thoughts on how internal audit organizations can get started with AI methods, such as machine learning (ML), to increase efficiency and coverage, better assign resources to areas that matter most, deliver more insight and even help identify leading indicators of risk. We also offer a specific example of ML applied to internal audit. Machine Learning Doesn't Have to Be Complex ML is an application of AI in which the system itself is designed with the ability to learn and improve from experience.

CAIRO - 12 February 2020: "We are entering the cognitive age. Over the next 25 years, advanced AI [Artificial Intelligence] will be the central element of digital transformation that fundamentally changes how businesses operate," Executive Vice President of global consulting firm Protiviti, Cory Gunderson, once said. Protiviti argues in a report that artificial Intelligence (AI) and Machine Learning (ML) are poised to help companies make dramatic shifts in performance, shareholder value and business development within the next two years. "AI opens the door to analyse massive amounts of data and deliver critical insights that organisations across a wide variety of industries can use to improve processes, drive profitability, and increase their competitive advantage," it stated. The research concluded that companies leading the charge with advanced AI are finding that it is a real game changer, while companies that are still lagging behind will soon experience a major disadvantages.

Following the financial crisis of 2007-2008, regulators issued specific guidance to help banks reduce the risk of financial losses or other adverse consequences stemming from decisions based on incorrect or misused financial models. Since then, the guidance has become the model risk management bible for financial institutions. It is used to ensure that model validation, typically performed annually, can identify vulnerabilities in the models and manage them effectively. Recently, the rapid advance and broader adoption of machine learning (ML) models have added more complexity and time to the model validation process. Specifically, ML models have highlighted expertise gaps in in-house model validation teams trained in traditional modeling techniques.

The AI World Conference & Expo is packed few days with news emanating from the Expo floor, the plenary sessions, a hackathon and tracks. There's more good stuff than a writer can possibly fit into post-event coverage. Our Reporters' Notebook comprises some of the bits and pieces that we collected over the three days in Boston. In an address to attendees of AI World 2019 in Boston recently, Paul F. Nemitz, principal Advisor, Directorate General and Justice and Consumers, European Commission, issued a warning about privacy. In a talk entitled, "Democracy, Ethics and the Rule of Law in the Age of AI," Nemitz provided the European view of privacy, calling the GDPR (General Data Protection Regulation, in effect May 2018) "the most sophisticated system for protecting personal data."

The annual cost of cybercrime is estimated to rise to $6 trillion by 2021.[1] Artificial intelligence (AI), frequently mentioned for its potential to accelerate innovation, boost performance and improve decision-making, is already being applied to defend against cybercrime. Because AI works well with functions that use massive amounts of data and require analysis and judgment, integrating AI-based cybersecurity technology with other defenses is a natural choice for cybersecurity professionals. Today, AI is used more extensively in cybersecurity than in any other function, with 75% of companies using AI technology to detect and ward off cyberthreats, according to the results of a recent global executive survey on AI conducted by Protiviti. Cybersecurity usage of AI is expected to grow nearly 20% by 2021.[2] AI's significant and compelling benefits come with new risks that need to be managed.