CybAI news

Cyber threats are on the rise worldwide. Every year, such online attacks become more severe and complex. Cybersecurity infrastructure is the most effective antidote to this critical ailment. It provides protection for the critical infrastructure and secures sensitive information. While the number and impact of cyber attacks keep on expanding, the solutions to deal with these incidents are also becoming innovative. Artificial intelligence (AI) is the modern addition to this security landscape. It helps in increasing the level of protection for sensitive data and infrastructure in multiple ways. Faster threat detection, decision-making, and automated operations are some examples of AI…

Written in collaboration with Navin Sharma and Joe Pindell, Stardog Across industries, the impact of post-delivery failure costs (recalls, warranty claims, lost goodwill after delivery) can be enormous. In the food industry, the average cost can be over $10M per event in direct costs. The impact is often multi-dimensional, due to lost sales and brand reputation damages. According to some estimates, the lost sales from post-delivery failures is equivalent to over 2,000 times the average revenue per customer. Inadequate resolutions and slow responsiveness to customers, particularly in high-stakes situations like this, can make them churn to competition, cutting off the oxygen…

arXiv:2407.13896v1 Announce Type: new Abstract: As Artificial Intelligence (AI) increasingly integrates into our daily lives, fairness has emerged as a critical concern, particularly in medical AI, where datasets often reflect inherent biases due to social factors like the underrepresentation of marginalized communities and socioeconomic barriers to data collection. Traditional approaches to mitigating these biases have focused on data augmentation and the development of fairness-aware training algorithms. However, this paper argues that the architecture of neural networks, a core component of Machine Learning (ML), plays a crucial role in ensuring fairness. We demonstrate that addressing fairness effectively requires a holistic approach…

arXiv:2407.13862v1 Announce Type: new Abstract: Geolocating images of a ground-level scene entails estimating the location on Earth where the picture was taken, in absence of GPS or other location metadata. Typically, methods are evaluated by measuring the Great Circle Distance (GCD) between a predicted location and ground truth. However, this measurement is limited because it only evaluates a single point, not estimates of regions or score heatmaps. This is especially important in applications to rural, wilderness and under-sampled areas, where finding the exact location may not be possible, and when used in aggregate systems that progressively narrow down locations. In…