Women on Boards’ Post

Another great on-demand #Webinar from #Gartner on #AI The future of AI will see a shift from responsible AI to AI risk management. Gartner's recommendation: Deploy regulations and practical guidelines for responsible AI & risk management now. Watch our on-demand #webinar to explore Gartner's predictions on the future of AI. #GartnerIT #AI #ArtificialIntelligence

💡 Step up your compliance game with today's tip on leveraging Artificial Intelligence (AI) and Machine Learning (ML) in risk assessments! Discover how these cutting-edge technologies can enhance accuracy, efficiency, and predictive insights in evaluating risks within your organization. From identifying emerging threats to streamlining compliance processes, AI and ML are revolutionizing risk management. Stay ahead of the curve and tune in to our latest episode for practical strategies on integrating AI and ML into your risk assessment toolkit. Don't miss out on this essential knowledge! 🎧 Dive deeper: https://bit.ly/4eUdcRV #ComplianceTips

The future of AI will see a shift from responsible AI to AI risk management. Gartner's recommendation: Deploy regulations and practical guidelines for responsible AI & risk management now. Watch our on-demand webinar to explore Gartner's predictions on the future of AI. #GartnerIT #AI #ArtificialIntelligence

How can boards position themselves to guide their companies in the era of Generative AI? Explore 5 ways board members can prepare.

In my experience, the best way to design an algorithm for unsupervised learning in AI is to prepare the results of some mathematical equation and wait until the program/app comes up with the formula. For example input a=3 b=4 c=5 We wait until an unsupervised learning program comes with the formula a square plus b square equals c square. My colleagues developed an unsupervised program and the input was the distance to the planets in our solar system. The program came up with the second Kepler law a planet covers the same area of space in the same amount of time no matter where it is in its orbit. If you need help with your AI project book a call at https://askfaleskini.as.me Upvote my contribution here https://lnkd.in/ePpxYXYW #ai #PeterFaleskini #unsupervisedlearning

Contextual Analysis and Prediction: ML algorithms, trained on historical project data, can predict the likelihood and impact of potential risks with a nuanced understanding of context. Read more 👉 https://lttr.ai/ARNvU #ai #artificialintelligence #pmp #projectmanagement #risk #lessonslearned

After a considerable hiatus, here is my latest article on Towards Data Science. This piece marks the start of an exciting series where I’ll delve into the realm of #AIRisk Management practices. This initial article centers on an important aspect: fostering real accountability within organizations for AI incidents. The primary objective of this series is to serve as a reader-friendly companion to our O'Reilly book, ‘Machine Learning for High-Risk Applications,’ co-authored by Patrick Hall, James Curtis, and myself. In each article, my aim will be to distill the critical insights, concepts, and practical strategies from the book into easily digestible portions. The ultimate goal is to make this valuable knowledge accessible to a broader audience. Stay tuned for more articles in this series. #AI #ResponsibleAI #MachineLearning

As organizations begin to adopt AI tools, it's important for risk management teams and leaders understand the potential threats and risks. Check out our 4 steps to govern AI at your organization. https://ow.ly/nwto50Q9RA8 #AI #GenerativeAI #riskmanagement #GenAI

I often get asked about the different uses of AI in supply chain. A good example is where large amounts of [often poor quality] data are used to create actionable insights. A good example of this is supply chain risk analytics. This is well explained here by one of the leading thinkers in this space, James Hayden. Worth reading... #ai #generativeai #supplychain #riskmanagement