CybAI news

AI is everywhere. In just a couple of years, this technology has evolved significantly and is transforming the way most of us do business. And yet, many organizations continue to grapple with how they can really integrate AI into their daily operations. It’s critical that this changes soon. To thrive in the age of AI, companies must do more than simply adopt AI. They must embrace an iterative approach, continuously learning and adapting as the technology evolves. In this article, I’ll share four commitments that companies should make to transition to full AI adopters. Understand Your Business Challenges AI for…

arXiv:2411.14468v1 Announce Type: new Abstract: In the previous article, we introduced a neural network framework based on symmetric differential equations. This novel framework exhibits complete symmetry, endowing it with perfect mathematical properties. While we have examined some of the system's mathematical characteristics, a detailed discussion of the network training methodology has not yet been presented. Drawing on the principles of the traditional backpropagation algorithm, this study proposes an alternative training approach that utilizes differential equation signal propagation instead of chain rule derivation. This approach not only preserves the effectiveness of training but also offers enhanced biological interpretability. The foundation of…

arXiv:2411.14498v1 Announce Type: new Abstract: Neural Architecture Search (NAS) continues to serve a key roll in the design and development of neural networks for task specific deployment. Modern NAS techniques struggle to deal with ever increasing search space complexity and compute cost constraints. Existing approaches can be categorized into two buckets: fine-grained computational expensive NAS and coarse-grained low cost NAS. Our objective is to craft an algorithm with the capability to perform fine-grain NAS at a low cost. We propose projecting the problem to a lower dimensional space through predicting the difference in accuracy of a pair of similar networks.…

arXiv:2411.14469v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly applied in areas influencing societal outcomes, it is critical to understand their tendency to perpetuate and amplify biases. This study investigates whether LLMs exhibit biases in predicting human mobility -- a fundamental human behavior -- based on race and gender. Using three prominent LLMs -- GPT-4, Gemini, and Claude -- we analyzed their predictions of visitations to points of interest (POIs) for individuals, relying on prompts that included names with and without explicit demographic details. We find that LLMs frequently reflect and amplify existing societal biases. Specifically, predictions…