**TL;DR: ChatGPT 5 is giving me wrong information on basic facts over half the time. Back to Google/Wikipedia for reliable information.** I've been using ChatGPT for a while now, but lately I'm serio...
We study a stochastic scheduling on an unreliable machine with general up-times and general set-up times which is described by a group of partial differential equations with Dirac-delta functions in the boundary and initial conditions. In special cas...
Haven’t manufacturers improved them to the point where the difference between naturally aspirated engines and turbos in terms of reliability is so marginal that we can just feel free to neglect it?...
The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm divides nodes into leaders and followers, to satisfy read/write requests spanning geo-diverse sites. With the increase of workload, Raft shall provide scale...
In a subjective experiment to evaluate the perceptual audiovisual quality of multimedia and television services, raw opinion scores collected from test subjects are often noisy and unreliable. To produce the final mean opinion scores (MOS), recommend...
Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To...
Unreliable fading wireless channels are the main challenge for strict performance guarantees in mobile communications. Diversity schemes including massive number of antennas, huge spectrum bands and multi-connectivity links are applied to improve the...
Deep learning could be prone to learning shortcuts raised by dataset bias and result in inaccurate, unreliable, and unfair models, which impedes its adoption in real-world clinical applications. Despite its significance, there is a dearth of research...
Deep neural networks (DNNs) are known for their high prediction performance, especially in perceptual tasks such as object recognition or autonomous driving. Still, DNNs are prone to yield unreliable predictions when encountering completely new situa...
Grove is a concurrent separation logic library for verifying distributed systems. Grove is the first to handle time-based leases, including their interaction with reconfiguration, crash recovery, thread-level concurrency, and unreliable networks. Thi...
[**Reference: Full blog post**](https://www.kadoa.com/blog/ai-agents-hype-vs-reality) There has been a lot of hype about the promise of autonomous agent-based LLM workflows. By now, all major LLMs ar...
Hallucination detection in text generation remains an ongoing struggle for natural language processing (NLP) systems, frequently resulting in unreliable outputs in applications such as machine translation and definition modeling. Existing methods str...
Session types are formal specifications of communication protocols, allowing protocol implementations to be verified by typechecking. Up to now, session type disciplines have assumed that the communication medium is reliable, with no loss of messages...
un′re•li′a•bil′i•ty, un′re•li′a•ble•ness, n. undependable, irresponsible, untrustworthy. In Lists: Undesirable personal qualities, more... Synonyms: fickle, irresponsible, fly-by-night, untrustworthy, not to be …
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Stringent demands for timely information delivery, driven by the widespread adoption of real-time applications and the Internet of Things, have established the age of information (AoI) as a critical metric for quantifying data freshness. Existing AoI...
I know this is really easy stuff, but I just bombed a critical essay about it and my teacher doesn't really speak English well. As I see it, the most important part when determining the reliability o...
Future digital signal processing (DSP) systems must provide robustness on algorithm and application level to the presence of reliability issues that come along with corresponding implementations in modern semiconductor process technologies. In this p...
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Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-truth label is always among the ca...