Centuries-old Islamic medical texts like Avicenna's Canon of Medicine and the Prophetic Tibb-e-Nabawi encode a wealth of preventive care, nutrition, and holistic therapies, yet remain inaccessible to many and underutilized in modern AI systems. Existing language-model benchmarks focus narrowly on factual recall or user preference, leaving a gap in validating culturally grounded medical guidance at scale. We propose a unified evaluation pipeline, Tibbe-AG, that aligns 30 carefully curated Prophetic-medicine questions with human-verified remedies and compares three LLMs (LLaMA-3, Mistral-7B, Qwen2-7B) under three configurations: direct generation, retrieval-augmented generation, and a scientific self-critique filter. Each answer is then assessed by a secondary LLM serving as an agentic judge, yielding a single 3C3H quality score. Retrieval improves factual accuracy by 13%, while the agentic prompt adds another 10% improvement through deeper mechanistic insight and safety considerations. Our results demonstrate that blending classical Islamic texts with retrieval and self-evaluation enables reliable, culturally sensitive medical question-answering.

Learning time-series models is useful for many applications, such as simulation and forecasting. In this study, we consider the problem of actively learning time-series models while taking given safety constraints into account. For time-series modeling we employ a Gaussian process with a nonlinear exogenous input structure. The proposed approach generates data appropriate for time series model learning, i.e. input and output trajectories, by dynamically exploring the input space. The approach parametrizes the input trajectory as consecutive trajectory sections, which are determined stepwise given safety requirements and past observations. We analyze the proposed algorithm and evaluate it empirically on a technical application. The results show the effectiveness of our approach in a realistic technical use case.

Tech research company OpenAI has just released an updated version of its text-generating artificial intelligence program, called GPT-4, and demonstrated some of the language model's new abilities. Not only can GPT-4 produce more natural-sounding text and solve problems more accurately than its predecessor. It can also process images in addition to text. But the AI is still vulnerable to some of the same problems that plagued earlier GPT models: displaying bias, overstepping the guardrails intended to prevent it from saying offensive or dangerous things and "hallucinating," or confidently making up falsehoods not found in its training data. On Twitter, OpenAI CEO Sam Altman described the model as the company's "most capable and aligned" to date.

The question of whether an artificial general intelligence will be developed in the future--and, if so, when it might arrive--is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology. Some futurists point to Moore's Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a "general intelligence." But evolution has produced minds like the human mind at least once.