Humans are still learning from nature.
Researchers mimicked ancient fish scales for a 3D-printed concrete structure:
Concrete is a ubiquitous building material, but over time, it may become prone to cracking. In order to ensure the long-term durability and safety of concrete structures, it is essential to prevent or minimize cracking.
Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel approach to overcome these limitations.
As reported in Advanced Photonics Nexus, researchers from BU’s Computational Imaging Systems Lab have introduced a local conditional neural field (LCNF) network, which they use to address the problem. Their scalable and generalizable LCNF system is known as “neural phase retrieval”—” NeuPh” for short.
NeuPh leverages advanced DL techniques to reconstruct high-resolution phase information from low-resolution measurements. This method employs a convolutional neural network (CNN)-based encoder to compress captured images into a compact latent-space representation.
🚀 LumenOrbit (YC S24) is building a network of megawatt-scale data centers in space, scalable to gigawatt capacity.
Why we should train AI in space.
To keep pace with AI development, vast new data centers and many gigawatts of new energy projects to power them will need to be deployed around the…
Tiny robots much smaller than blood cells could deliver clot-forming drugs where they’re needed most, a study in rabbits suggests. The tech has yet to be tested in humans.
A new study, published in “Nature Communications” this week, led by Jake Gavenas PhD, while he was a PhD student at the Brain Institute at Chapman University, and co-authored by two faculty members of the Brain Institute, Uri Maoz and Aaron Schurger, examines how the brain initiates spontaneous actions. In addition to demonstrating how spontaneous action emerges without environmental input, this study has implications for the origins of slow ramping of neural activity before movement onset—a commonly-observed but poorly understood phenomenon.
In their study, Gavenas and colleagues propose an answer to that question. They simulated spontaneous activity in simple neural networks and compared this simulated activity to intracortical recordings of humans when they moved spontaneously. The study results suggest something striking: many rapidly fluctuating neurons can interact in a network to give rise to very slow fluctuations at the level of the population.
Imagine, for example, standing atop a high-dive platform and trying to summon the willpower to jump. Nothing in the outside world tells you when to jump; that decision comes from within. At some point you experience deciding to jump and then you jump. In the background, your brain (or, more specifically, your motor cortex) sends electrical signals that cause carefully coordinated muscle contractions across your body, resulting in you running and jumping. But where in the brain do these signals originate, and how do they relate to the conscious experience of willing your body to move?
This article delves into a transformative shift in the criminal justice system brought on by the use of AI-assisted police reports.
Police reports play a central role in the criminal justice system. Many times, police reports exist as the only official memorialization of what happened during an incident, shaping probable cause determinations, pretrial detention decisions, motions to suppress, plea bargains, and trial strategy. For over a century, human police officers wrote the factual narratives that shaped the trajectory of individual cases and organized the entire legal system.
All that is about to change with the creation of AI-assisted police reports. Today, with the click of a button, generative AI Large Language Models (LLMS) using predictive text capabilities can turn the audio feed of a police-worn body camera into a pre-written draft police report. Police officers then fill-in-the blanks of inserts and details like a “Mad Libs” of suspicion and submit the edited version as the official narrative of an incident.
From the police perspective, AI-assisted police reports offer clear cost savings and efficiencies from dreaded paperwork. From the technology perspective, ChatGPT and similar generative AI models have shown that LLMs are good at predictive text prompts in structured settings, exactly the use case of police reports. But hard technological, theoretical, and practice questions have emerged about how generative AI might infect a foundational building block of the criminal legal system.
Can automated restaurants still be community and cultural spaces, or will they become feeding stations for humans? These and other questions loom as new food tech reaches the market.
Here’s who made the 2024 TIME100 AI list of the most influential people in artificial intelligence.
NASA and the Defense Advanced Research Projects Agency (DARPA) have signed an interagency agreement to collaborate on a satellite servicing demonstration in geosynchronous Earth orbit, where hundreds of satellites provide communications, meteorological, national security, and other vital functions.
Under this agreement, NASA will provide subject matter expertise to DARPA’s Robotic Servicing of Geosynchronous Satellites (RSGS) program to help complete the technology development, integration, testing, and demonstration. The RSGS servicing spacecraft will advance in-orbit satellite inspection, repair, and upgrade capabilities.
