Lifeboat Foundation

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.

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.

Anchored by next-generation IBM Quantum System Two in Illinois Quantum and Microelectronics Park, new initiative will advance useful quantum applications as industries move towards quantum-centric supercomputing.

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.

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.

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.

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.

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 experimental data 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 renewable energy, smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”

An Iranian cosmologist has recently suggested another way we could look for extraterrestrial life in our universe. Could it be, he wonders in a new paper (which appears now on the preprint site arXiv), that these advanced alien civilizations are using Dyson spheres around primordial black holes as a way to gather energy? And, if so, how could we look for the signs? His work makes some big assumptions that may not be justified, but this specific type of cosmology has always been a little far out—and it’s where the biggest insights can sometimes lie.

Shant Baghram is a physicist at the Sharif University of Technology in Tehran. His new paper, which is an unusual solo work in a long career of collaboration with colleagues and graduate students, is a quick-and-dirty introduction to ideas like SETI (the Search for Extraterrestrial Intelligence), the Drake equation, and the Dyson sphere—all hallmarks of those who theorize about alien civilizations.