Lifeboat Foundation

Wetware computing and organoid intelligence is an emerging research field at the intersection of electrophysiology and artificial intelligence. The core concept involves using living neurons to perform computations, similar to how Artificial Neural Networks (ANNs) are used today. However, unlike ANNs, where updating digital tensors (weights) can instantly modify network responses, entirely new methods must be developed for neural networks using biological neurons. Discovering these methods is challenging and requires a system capable of conducting numerous experiments, ideally accessible to researchers worldwide. For this reason, we developed a hardware and software system that allows for electrophysiological experiments on an unmatched scale. The Neuroplatform enables researchers to run experiments on neural organoids with a lifetime of even more than 100 days. To do so, we streamlined the experimental process to quickly produce new organoids, monitor action potentials 24/7, and provide electrical stimulations. We also designed a microfluidic system that allows for fully automated medium flow and change, thus reducing the disruptions by physical interventions in the incubator and ensuring stable environmental conditions. Over the past three years, the Neuroplatform was utilized with over 1,000 brain organoids, enabling the collection of more than 18 terabytes of data. A dedicated Application Programming Interface (API) has been developed to conduct remote research directly via our Python library or using interactive compute such as Jupyter Notebooks. In addition to electrophysiological operations, our API also controls pumps, digital cameras and UV lights for molecule uncaging. This allows for the execution of complex 24/7 experiments, including closed-loop strategies and processing using the latest deep learning or reinforcement learning libraries. Furthermore, the infrastructure supports entirely remote use. Currently in 2024, the system is freely available for research purposes, and numerous research groups have begun using it for their experiments. This article outlines the system’s architecture and provides specific examples of experiments and results.

The recent rise in wetware computing and consequently, artificial biological neural networks (BNNs), comes at a time when Artificial Neural Networks (ANNs) are more sophisticated than ever.

The latest generation of Large Language Models (LLMs), such as Meta’s Llama 2 or OpenAI’s GPT-4, fundamentally rely on ANNs.

I’m sure some of you have looked at robo mowers as Roombas for your yard but, sadly, many of them require you to install a boundary wire around the perimeter of your lawn. And any product that requires you to dig a trench is the opposite of what “low effort” means to me. That’s why I was interested in trying Segway’s Navimow i105, its £945 (around $1,200) GPS-equipped mower which eliminates that busywork. And keeping your lawn neat and tidy is a job that’s all busywork.

Ask a gardener and they’ll tell you the secret to a great lawn is to seed a piece of flat land and then mow it into submission. Regular, militant mowing kills off all the other flora, ensuring only grass can grow until everything looks well-manicured. But that relentless mowing requires a lot of time, a luxury I’ve never had. It’s the sort of job a robot mower was born to do, given it can scuttle around and trim grass without you there.

Segway’s i Series is the company’s latest, more affordable offering compared to its pricier S Series. The new units have a smaller battery and range, with the i105 able to handle areas up to 500 square meters. Unlike some GPS mowers, the i105 is equipped with a forward facing HD camera with a 180-degree field of vision. So while it relies on satellites for positioning, it’ll have enough sense to stop before it clatters into an obstacle. It’s not packing sophisticated computer vision smarts, but it’ll play safe lest it charge into a pet, inattentive family member or prized flower.

One of the main goals of the LHC experiments is to look for signs of new particles, which could explain many of the unsolved mysteries in physics. Often, searches for new physics are designed to look for one specific type of new particle at a time, using theoretical predictions as a guide. But what about searching for unpredicted—and unexpected—new particles?

Researchers have developed a new intelligent photonic sensing-computing chip that can process, transmit and reconstruct images of a scene within nanoseconds. Credit: Wei Wu, Tsinghua University.

Researchers have created a photonic chip capable of processing images at nanosecond speeds, significantly faster than current methods. This chip enhances edge intelligence by integrating AI analysis directly into optical processing, potentially transforming applications such as autonomous driving.

Researchers have demonstrated a new intelligent photonic sensing-computing chip that can process, transmit and reconstruct images of a scene within nanoseconds. This advance opens the door to extremely high-speed image processing that could benefit edge intelligence for machine vision applications such as autonomous driving, industrial inspection and robotic vision.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001124S0029) for the Artificial Intelligence Quantified (AIQ) project.

AIQ seeks to find ways of assessing and understanding the capabilities of AI to enable mathematical guarantees on performance. Successful use of military AI requires ensuring safe and responsible operation of autonomous and semi-autonomous technologies.

Learn about Sleepy Pickle, a new threat exploiting machine learning models via pickle files. Protect your data now!

Researchers have developed a technique that allows artificial intelligence (AI) programs to better map three-dimensional spaces using two-dimensional images captured by multiple cameras. Because the technique works effectively with limited computational resources, it holds promise for improving the navigation of autonomous vehicles.

Brighter with Herbert.

The discovery process involved extensive fieldwork and the use of advanced technology. Researchers utilized high-resolution aerial photographs and an optimized artificial intelligence model to accurately map the habitat and health of trees across the region, indirectly leading to the identification of the new viper species. This method allowed the researchers to cover a vast area with unprecedented precision, enhancing their understanding of the ecosystem and its inhabitants.

One of the most intriguing aspects of the Ovophis jenkinsi is its behavior. Unlike many snakes that prefer to flee when threatened, this viper exhibits aggressive defensive tactics.

“It is usually slow-moving but shows great aggression when disturbed,” the researchers wrote. “When threatened, these snakes inflate their bodies to make themselves appear larger and strike quickly.”