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Category: information science – Page 23

While classical physics presents a deterministic universe where cause must precede effect, quantum mechanics and relativity theory paint a more nuanced picture. There are already well-known examples from relativity theory like wormholes, which are valid solutions of Einstein’s Field Equations, and similarly in quantum mechanics the non-classical state of quantum entanglement—the “spooky action at a distance” that troubled Einstein—which demonstrates that quantum systems can maintain instantaneous correlations across space and, potentially, time.

Perhaps most intriguingly, the protocol suggests that quantum entanglement can be used to effectively send information about optimal measurement settings “back in time”—information that would normally only be available after an experiment is complete. This capability, while probabilistic in nature, could revolutionize quantum computing and measurement techniques. Recent advances in multipartite hybrid entanglement even suggest these effects might be achievable in real-world conditions, despite environmental noise and interference. The realization of such a retrocausal quantum computational network would, effectively, be the construction of a time machine, defined in general as a system in which some phenomenon characteristic only of chronology violation can reliably be observed.

This article explores the theoretical foundations, experimental proposals, significant improvements, and potential applications of the retrocausal teleportation protocol. From its origins in quantum mechanics and relativity theory to its implications for our understanding of causality and the nature of time itself, we examine how this cutting-edge research challenges our classical intuitions while opening new possibilities for quantum technology. As we delve into these concepts, we’ll see how the seemingly fantastic notion of time travel finds a subtle but profound expression in the quantum realm, potentially revolutionizing our approach to quantum computation and measurement while deepening our understanding of the universe’s temporal fabric.

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In 1956, a group of pioneering minds gathered at Dartmouth College to define what we now call artificial intelligence (AI). Even in the early 1990s when colleagues and I were working for early-stage expert systems software companies, the notion that machines could mimic human intelligence was an audacious one. Today, AI drives businesses, automates processes, creates content, and personalizes experiences in every industry. It aids and abets more economic activity than we “ignorant savages” (as one of the founding fathers of AI, Marvin Minsky, referred to our coterie) could have ever imagined. Admittedly, the journey is still early—a journey that may take us from narrow AI to artificial general intelligence (AGI) and ultimately to artificial superintelligence (ASI).

As business and technology leaders, it’s crucial to understand what’s coming: where AI is headed, how far off AGI and ASI might be, and what opportunities and risks lie ahead. To ignore this evolution would be like a factory owner in 1900 dismissing electricity as a passing trend.

Let’s first take stock of where we are. Modern AI is narrow AI —technologies built to handle specific tasks. Whether it’s a large language model (LLM) chatbot responding to customers, algorithms optimizing supply chains, or systems predicting loan defaults, today’s AI excels at isolated functions.

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Quantum computing and networking company IonQ has delivered a data center-ready trapped-ion quantum computer to the uptownBasel innovation campus in Arlesheim, Switzerland.

The IonQ Forte Enterprise quantum computer is the first of its kind to operate outside the United States and Switzerland’s first quantum computer designed for commercial use.

According to IonQ, Forte Enterprise is now online, servicing compute jobs while performing at a record algorithmic qubit count of #AQ36. The number of algorithmic qubits (#AQ) is a tool for showing how useful a quantum computer is at solving real problems for users by summarizing its ability to run benchmark quantum algorithms often used for applications.

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A review of syntheticapertureradar image formation algorithms and implementations: a computational perspective.

✍️ Helena Cruz et al.

Designing synthetic-aperture radar image formation systems can be challenging due to the numerous options of algorithms and devices that can be used. There are many SAR image formation algorithms, such as backprojection, matched-filter, polar format, Range–Doppler and chirp scaling algorithms. Each algorithm presents its own advantages and disadvantages considering efficiency and image quality; thus, we aim to introduce some of the most common SAR image formation algorithms and compare them based on these two aspects. Depending on the requisites of each individual system and implementation, there are many device options to choose from, for instance, FPGAs, GPUs, CPUs, many-core CPUs, and microcontrollers. We present a review of the state of the art of SAR imaging systems implementations.

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To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model.

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Precision agriculture leverages cutting-edge machine learning algorithms to transform farming, boosting productivity and sustainability. From Random Forest for crop classification to CNNs for high-resolution imagery analysis, these tools optimize resources, detect diseases early, and improve yield prediction. Discover the top algorithms shaping modern agriculture and how they empower smarter, data-driven decisions.

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Artificial intelligence is no longer just a buzzword; it’s a transformative force reshaping industries, from healthcare to finance to retail. However, behind every successful AI system lies an often-overlooked truth: AI is only as good as the data that powers it.

Organizations eager to adopt AI frequently focus on algorithms and technologies while neglecting the critical foundation—data. Even the most advanced AI initiatives are doomed to fail without a robust data strategy. I’ll explore why a solid data strategy is the cornerstone of successful AI implementation and provide actionable steps to craft one.

Imagine building a skyscraper without solid ground beneath it. Data plays a similar foundational role for AI. It feeds machine learning models, drives predictions and shapes insights. However, as faulty materials weaken a structure, poor-quality data can derail an AI project.

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Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.

Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his work on blending traditional physics-based scientific models with to accurately predict hypotheses in the journal Nature Chemical Engineering.

Kwon’s research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact , smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”

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