Are we using #AI to detect anomalies, or are we detecting anomalies in AI itself? Tune in to this thought-provoking discussion on the evolving landscape of anomaly detection in this episode of The AI Fundamentalists podcast. Discover: ?? Effective anomaly detection approaches ?? Challenges in AI implementation ?? Best practices for building resilient models that withstand anomalies From enhancing cybersecurity to ensuring ethical AI deployment, discover the critical role of anomaly detection in shaping the future of intelligent systems. Tune in here: https://hubs.li/Q02Ys4GY0 #AIEthics #ModelGovernance #AIGovernance
关于我们
Monitaur is an AI governance software platform helping companies build, manage and automate responsible and ethical governance across consequential modeling systems. As companies accelerate their use of big data and AI to transform their business and services, they are increasingly aware of the operational, regulatory, financial and legal risks involved. Monitaur provides customers with a comprehensive and turnkey solution for model risk management and governance that spans policy to proof. Its software establishes a system of record for model governance where cross-functional stakeholders can align and collaborate to build and deploy AI that is fair, robust, transparent, safe and compliant. Founded in 2019 by a team of deep domain experts in the areas of corporate innovation, machine learning, assurance, and software development, Monitaur is committed to improving people’s lives by providing confidence and trust in AI.
- 网站
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https://www.monitaur.ai
Monitaur的外部链接
- 所属行业
- 软件开发
- 规模
- 11-50 人
- 总部
- Boston，MA
- 类型
- 私人持股
- 创立
- 2019
- 领域
- machine learning、artificial intelligence、assurance、governance、compliance、audit、transparent AI、responsible AI、ethical AI、ml monitoring、ml ops、model risk management、AI governance和model audit
产品
![点击此处查看Monitaur Model Governance Software]()
Monitaur Model Governance Software
治理、风险管理与合规 (GRC) 软件
To succeed with AI, model governance and model risk management take on a new urgency. Monitaur meets this urgency. And we have the most comprehensive and effective AI and model governance solution available. Our software meets you where you are in your governance journey from defining policies and practices to managing actionable governance processes that can be rolled out at scale with automation. You focus on building the best model, we'll deliver the best governance with alignment on definition and well-managed collaboration. - Avoid expensive mistakes by running objective model validations before they go live - Catch wayward models through continuous testing for fairness, data quality, and robustness. - See issues and remediate them quickly thanks to real-time alerts - Empower business and risk stakeholders to understand model performance for themselves - Automate the collection of technical evidence through our robust API.
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主要
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US，MA，Boston，02111
Monitaur员工
动态
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Don’t fear AI - learn how to govern it responsibly! ChatGPT has everyone talking about the risks of generative AI. “Should they be regulated?" "Do we really need #AIGovernance?" Before getting lost in the weeds of how to manage generative AI, get to know more about existing risk management frameworks, and how to adapt them to achieve responsible AI governance. https://hubs.li/Q02Ys7XX0
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Great summary and thanks for your insights! We're glad the information was helpful.
Just wrapped up reading "The Essential Guide to #AI #Governance" by OCEG , co-authored by Carole Switzer and Lee Dilmar, with sponsorship from Monitaur . This resource is a game-changer for understanding how to harness #AI's potential while maintaining robust governance, risk management, and compliance (GRC) practices. #Key insights I found particularly valuable: 1. Strategic Alignment and Oversight: The guide emphasizes that AI governance begins with aligning AI initiatives with organizational strategies. Leaders need to identify all units employing AI, the purpose of their use, and integrate these into a cohesive, monitored strategic roadmap. This ensures that AI doesn’t operate in silos but as a well-integrated driver of business objectives. 2. Comprehensive Risk Management: AI's rapid advancement brings unique risks—from operational disruptions to ethical breaches and reputational damage. The guide lays out a rigorous risk management protocol that includes regular audits, stress tests, and interdisciplinary reviews to proactively identify and mitigate potential risks, ensuring that AI remains a tool for progress, not liability. 3. Ethical and Transparent Development: AI must be explainable and accountable. The guide advocates for creating transparent models, ensuring decisions made by AI can be understood by stakeholders. It highlights the importance of ethical principles like fairness and bias mitigation to avoid perpetuating inequalities—a must for building public and regulatory trust. 4. Data Security and Model Assurance: Protecting the quality and integrity of data is foundational. This resource details best practices for robust data governance and continuous model assurance, including real-time performance monitoring and explainability metrics. These are crucial to maintain the trustworthiness and reliability of AI systems. 5. Training and Workforce Education: The guide stresses that successful AI integration is as much about people as it is about technology. Investing in AI literacy across the organization and continuous training programs ensures teams are well-equipped to interact with AI responsibly, fostering a culture that values ongoing learning and ethical use. 6. Compliance and Regulatory Adaptation: With laws like the EU AI Act setting new precedents, the guide outlines how organizations must stay agile to meet compliance. This includes embedding compliance checks throughout the AI lifecycle, preparing for audits, and maintaining thorough documentation. Have a read if you are a C-suite executive, risk management leader, IT strategists, and policymaker striving for responsible AI adoption. Credit to Carole Switzer ,@Lee Dilmar , and the OCEG team for this essential, forward-thinking contribution to the AI community.
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Is your organization struggling to manage AI risks effectively? Manual processes and fragmented tools won't cut it anymore. Join analyst Michael Rasmussen of GRC 20/20 Research and Anthony Habayeb, CEO of Monitaur for a webinar to learn how AI GRC solutions can transform your approach to AI governance. Discover how to automate oversight, streamline compliance, and build a bulletproof business case for change. Presented by OCEG. ?? Date: December 5th ?? Time: 11 AM EST ?? Register Here: https://hubs.li/Q02YrWpW0 #AIGovernance #AIGRC #AIModelRisk
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Let's face it. Documentation in any profession gets a bad reputation for being a chore. Still, it is a necessary one. For AI models, documentation can be the key to success for your projects. This is a great episode with resources for getting started in the show notes.
Could the secret to successful AI governance lie in understanding the critical role of model documentation? In this episode, we challenge the common belief that model cards marked the start of documentation in AI. We explore model documentation practices, from their crucial beginnings in fields like finance to their adaptation in Silicon Valley. Our discussion also highlights the important role of early modelers and statisticians in advocating for a complete approach that includes the entire model development lifecycle. Key takeaways: - ? The origins and best practices of model documentation - ? Model cards: Their pros and cons - ? System cards: Taking documentation further - ? Balancing automation and the value of human expertise in documentation #AIGovernance #ModelDocumentation Listen now: https://hubs.li/Q02XDj4K0
Model documentation: Beyond model cards and system cards in AI governance
monitaur.ai
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Without clear objectives and success criteria, AI investments can be costly—and failures can impact more than just your bottom line. As more organizations embrace AI, how can you ensure your projects deliver real value while managing risk? We've outlined 7 essential actions that differentiate successful AI implementations: 1. Define clear goals with measurable outcomes 2. Foster cross-functional collaboration 3. Drive best practices with holistic governance 4. Prioritize high-quality data throughout the lifecycle 5. Test rigorously in production environments 6. Uphold continuous governance 7. Stay vigilant and adapt to emerging risks The good news? You don't have to navigate this journey alone. Our latest article breaks down each step and shows you how to increase your likelihood of success while mitigating common AI project risks. Read the full guide here: https://hubs.li/Q02XJ3M60 Monitaur can help align your organization around these critical success factors! https://hubs.li/Q02XJgZ00 #AIGovernance #RiskManagement #AIRisk
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Monitaur转发了
Applied-AI Investor, co-founder milemark?capital | M&A Board Member | MIT Sloan/Visiting Fellow, deltav & Lecturer | Carlyle, Citi & Salomon Alumni | 8 Boards | YPO G1
Very excited for this week's BIG.AI@MIT Conference! We are featuring 10 hand selected startups that are deploying proprietary AI models/systems to reshape their respective industries Monitaur StackAI Ikigai Vertical Horizons Lamarr.AI Unbox AI catalan.ai Klarity Atacama Biomaterials Aptamino https://lnkd.in/gCHF75t6
The Business Implications of Generative AI @ MIT (BIG.AI@MIT) - MIT Initiative on the Digital Economy
https://ide.mit.edu
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Are you keeping up with the rapid evolution of Large Language Models? The latest episode of The AI Fundamentalists podcast looks back at Large Language Models (LLMs). It looks at how quickly the field has progressed and how difficult it has been over the past year. Highlights... Technological Advancements: The hosts explore how "models which far exceed GPT-2 now fit on your phone," highlighting the remarkable progress in model efficiency. They also discuss exciting developments in multimodal modeling, where unified models can now process text, images, and video simultaneously. Practical Applications: The podcast looks at how LLMs are making work more efficient in different industries. It warns against thinking of them as a one-size-fits-all solution. As one host notes, "LLMs are great for productivity enhancement... However, we need to stop thinking they're this panacea that are going to do things that are not built to do." Ethical Considerations: The episode talks about the huge energy costs involved in training large models. It also talks about tech giants reopening nuclear power plants to meet these needs. This raises important questions about the environmental impact of AI developments. Governance and Risk: The hosts discuss the evolving landscape of AI governance, emphasizing that while the core principles remain the same, LLMs present unique challenges. They also stress the importance of human oversight in high-risk applications. Future Directions: The talk looks at new trends like agent-based modeling with LLMs and the possibility of new ways to study AI that could fix current limitations. Tune in to gain a deeper understanding of where we stand with LLMs and where we might be heading. These insights could inspire your next breakthrough! ??https://hubs.li/Q02VnQM50 #LLMs #DataScience #AIEthics #AIGovernance #AIModelRisk
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?? The AI revolution isn't coming—it's here. Are you prepared? If you missed our recent webinar on building future-proof AI governance frameworks, the replay is now available. And trust us, you'll want to watch this one. Anthony Habayeband Lee Dittmar delivered a master class on tackling the hard questions many organizations are avoiding about AI implementation. Beyond just bias and transparency concerns, they discuss risks that could make or break your AI strategy. What you'll learn: ?? Why waiting to establish AI governance could cost you more than you think ?? How to navigate the maze of emerging global AI regulations ?? Essential steps to future-proof your governance framework ?? Keys to building an effective AI governance team to drive success Don't let AI transform your industry without you. Watch the replay here: https://hubs.li/Q02WT_T00 #AIGovernance #AIModelRisk OCEG
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Curious about the rapid advancements in artificial intelligence? The latest episode of The AI Fundamentalists podcast gives a fascinating look at where we are with AI, especially Large Language Models (LLMs). This is one year after the hosts’ last deep dive on the topic. Key revelations from this discussion: ?? AI's productivity paradox: LLMs are great for enhancing efficiency, but are we overlooking their limitations in mission-critical applications? ?? The energy dilemma: Tech giants are reactivating nuclear plants to power AI. Is it worth it? ?? AI ethics and governance: As models get more complex, so do the challenges of keeping them in check. ?? The future of AI: Exciting developments in multimodal modeling and agent-based systems. It's a balanced look at AI's promises and pitfalls, perfect for anyone wondering how these technologies might shape our future. Tune in to expand your understanding of AI's current capabilities, limitations, and potential future directions. ?? https://hubs.li/Q02VnpC70 #AIModelRisk #LLMs #AIGovernance