The approach means practically a proactive commitment to excellence in uncertainty conditions. A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.doi:10.30564/aia.v1i2.1195Andrey Ivanovich Kostogryzov人工智能进展(...
One example of such an advanced platform which is worth mentioning here isDarktracewhich uses “self-learning AI to identify and respond to in-progress cyber-threats”. This proactive cyber risk management approach in an enterprise can reduce the risk of attacks and its consequences dramatically by...
The new method is based on a mathematical approach called sequential Monte Carlo (SMC). SMC algorithms are an established set of algorithms that have been widely used for uncertainty-calibrated AI, by proposing probable explanations of data and tracking how likely or unlikely the proposed explanation...
In this paper, we aim to unleash the power of generative AI for PHY design of constellation symbols in communication systems. Although the geometry of constellations is predetermined according to networking standards, e.g., quadrature amplitude modulation (QAM), probabilistic shaping can design the ...
To use this approach, we need to write a PPL program so that the distribution over outcomes corresponds to the posterior probability distribution of interest. This is straightforward if we understand how to simulate from the model, and how to insert the constraints given by the observed data. ...
Generative AI models, especially Large Language Models (LLMs), have demonstrated remarkable general-purpose capabilities and the ability to perform few-shot or zero-shot learning, enabling accurate predictions with little or no annotated data10,11. These models are particularly advantageous in clinical...
Given their genuine interpretability, the idea of learning scoring systems from data is obviously appealing from the perspective of explainable AI. In this paper, we propose a practically motivated extension of scoring systems called probabilistic scoring lists (PSL), as well as a method for ...
A probabilistic approach for multi-objective clustering using game theorydoi:10.1109/AISP.2012.6313779ABSTRACT Multi-Objective clustering as the most important and fundamental unsupervised learning has been in the gravity of focus of quite a lot numbers of researchers over several ...
We present both the theoretical foundation and a detailed implementation framework for this new approach to information retrieval. Traditional search engines operate with time complexities ranging from O(log n) to O(n), depending on the implementation and index structure. While considerable improvements...
We start by defining an approach to non-monotonic probabilistic reasoning in terms of non-monotonic categorical (true-false) reasoning. We identify a type of non-monotonic probabilistic reasoning, akin to default inheritance, that is commonly found in practice, especially in "evidential" and "...