Lifeboat Foundation

Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products such as smartphones and self-driving cars. Despite the vital role and broad applications of artificial neural networks, we lack systematic approaches, such as network science, to understand their underlying mechanism. The difficulty is rooted in many possible model configurations, each with different hyper-parameters and weighted architectures determined by noisy data. We bridge the gap by developing a mathematical framework that maps the neural network’s performance to the network characters of the line graph governed by the edge dynamics of stochastic gradient descent differential equations. This framework enables us to derive a neural capacitance metric to universally capture a model’s generalization capability on a downstream task and predict model performance using only early training results. The numerical results on 17 pre-trained ImageNet models across five benchmark datasets and one NAS benchmark indicate that our neural capacitance metric is a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.

Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. Gornet and Thomson show that as an agent navigates an environment, a self-attention neural network using predictive coding can recover the environment’s map in its latent space.

Call it the force’s doing, but it has been surprises galore for the GEDI mission.

In early 2023, the lidar mission that maps the Earth’s forests in 3D was to be burned up in the atmosphere to make way for another unrelated mission on the International Space Station. A last-minute decision by NASA saved its life and put it on hiatus until October 2024. Earlier this year, another surprise revealed itself: the mission that replaced GEDI was done with its work, effectively allowing GEDI to get back to work six months earlier than expected.

That’s how, in April, a robotic arm ended up moving the GEDI mission (short for Global Ecosystem Dynamics Investigation and pronounced “Jedi” like in the Star Wars films) from storage on the ISS to its original location, from where it now continues to gather crucial data on aboveground biomass on Earth.

In the decade since their discovery at Drexel University, the family of two-dimensional materials called MXenes has shown a great deal of promise for applications ranging from water desalination and energy storage to electromagnetic shielding and telecommunications, among others. While researchers have long speculated about the genesis of their versatility, a recent study led by Drexel and the University of California, Los Angeles, has provided the first clear look at the surface chemical structure foundational to MXenes’ capabilities.

Using advanced imaging techniques, known as scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS), the team, which also includes researchers from California State University Northridge, and Lawrence Berkeley National Laboratory, mapped the electrochemical surface topography of the titanium carbide MXene — the most-studied and widely used member of the family.

Their findings, published in the 5th anniversary issue of the Cell Press journal Matter (“Atomic-scale investigations of Ti 3 C 2 Tx MXene surfaces”), will help to explain the range of properties exhibited by members of the MXene family and allow researchers to tailor new materials for specific applications.

GIS Site Selection Software makes renewable energy farm development simple with capacity and land data paired with an intuitive mapping platform.

A new computational technique developed enables the use of surface mapping technologies like GPS to analyze subsurface geological structures.

This method, termed deformation imaging, offers insights into the rigidity of the Earth’s crust and mantle, enhancing our understanding of geological processes like earthquakes. The technique has already provided a detailed view of subsurface areas during the 2011 Tohoku earthquake and has the potential for widespread future applications with satellite data.

New Geological Imaging Technique

“Every mission is contributing back to the other missions and future missions in terms of new tools and techniques to develop,” said Dr. Fred Calef III. “It’s not just you working on something. It’s being able to share data between people… getting a higher order of science.”

As NASA’s Perseverance rover continues to explore the surface of Mars, an open-source, online mapping software known as Multi-Mission Geographic Information System (MMGIS) has been instrumental in determining the best routes for the car-sized rover and landing sites for its Ingenuity helicopter prior to the latter’s “retirement” but is also available for the public to follow the mission, as well. This software holds the potential to help both scientists and the public explore Mars in new and exciting ways for years to come.

“Maps and images are a common language between different people — scientists, engineers, and management,” said Dr. Nathan Williams, who is a mapping specialist at NASA JPL and was a key player in selecting Jezero Crater as the landing site for the Perseverance rover. “They help make sure everyone’s on the same page moving forward, in a united front to achieve the best science that we can.”

Continue reading “MMGIS: Open-Source Mapping Interface for Mars Exploration” »

With the pending arrival of AI agents, we will even more effectively join the always-on interconnected world, both for personal use and for work. In this way, we will increasingly dialog and interact with digital intelligence everywhere.

The path to AGI and superintelligence remains shrouded in uncertainty, with experts divided on its feasibility and timeline. However, the rapid evolution of AI technologies is undeniable, promising transformative advancements. As businesses and individuals navigate this rapidly changing landscape, the potential for AI-driven innovation and improvement remains vast. The journey ahead is as exciting as it is unpredictable, with the boundaries between human and artificial intelligence continuing to blur.

By mapping out proactive steps now to invest and engage in AI, upskill our workforce and attend to ethical considerations, businesses and individuals can position themselves to thrive in the AI-driven future.

Organic electrochemical artificial neurons (OANs) are the latest entry of building blocks, with a few different approaches for circuit realization. OANs possess the remarkable capability to realistically mimic biological phenomena by responding to key biological information carriers, including alkaline ions, noise in the electrolyte, and biological conditions. An organic artificial neuron with a cascade-like topology made of OECT inverters has shown basic (regular) firing behavior and firing frequency that is responsive to the concentration of ionic species (Na⁺, K⁺) of the host liquid electrolyte³³. An organic artificial neuron consisting of a non-linear building block that displays S-shape negative differential resistance (S-NDR) has also been recently demonstrated³⁴. Due to the realization of the non-linear circuit theory with OECTs and the sharp threshold for oscillations, this artificial neuron displays biorealistic firing properties and neuronal excitability that can be found in the biological domain such as input voltage-induced regular and irregular firing, ion and neurotransmitter-induced excitability and ion-specific oscillations. Biohybrid devices comprising artificial neurons and biological membranes have also shown to operate synergistically, with membrane impedance state modulating the firing properties of the biohybrid in situ. More recently, a circuit leveraging the non-linear properties of antiambipolar OMIECs, which exhibit negative differential transconductance, has been realized³⁵. These neurons show biorealistic properties such as various firing modes and responsivity to biologically relevant ions and neurotransmitters. With this neuron, ex-situ electrical stimulation has been shown in a living biological model. Therefore, the class of OANs perfectly complements the broad range of features already demonstrated by solid-state spiking circuits (Supplementary Table 1), offering opportunities for both hybrid interfacing between these technologies and new developments in neuromorphic bioelectronics.

Despite the promising recent realizations of organic artificial neurons, all approaches still remain in the qualitative demonstration domain and a rigorous investigation of circuit operation is still missing. Indeed, quantitative models exist only for inorganic, solid-state artificial neurons without the inclusion of physical soft-matter parameters and the biological wetware (i.e., aqueous electrolytes, alkaline ions, biomembranes)^36,37. This gap in knowledge significantly impedes the simulation of larger-scale functional circuits, and therefore the design and development of integrated organic neuromorphic electronics, biohybrids, OAN-based neural networks, and intelligent bioelectronics.

In this work, we unravel the operation of organic artificial neurons that display non-linear phenomena such as S-shape negative differential resistance (S-NDR). By combining experiments, numerical simulations of non-linear iontronic circuits, and newly developed analytical expressions, we investigate, reproduce, rationalize, and design the wide biorealistic repertoire of organic electrochemical artificial neurons including their firing properties, neuronal excitability, wetware operation, and biohybrid formation. The OAN operation is efficiently rationalized to include how neuronal dynamics are probed by biochemical stimuli in the electrolyte medium. The OAN behavior is also extended on the biohybrid formation, with a solid rationale of the in situ interaction of OANs with biomembranes. Non-linear simulations of OANs are rooted in a physics-based framework, considering ion type, ion concentration, organic mixed ionic–electronic parameters, and biomembrane properties. The derived analytical expressions establish a direct link between OAN spiking features and its physical parameters and therefore provide a mapping between neuronal behavior and materials/device parameters. The proposed approach open opportunities for the design and engineering of advanced biorealistic OAN systems, establishing essential knowledge and tools for the development of neuromorphic bioelectronics, in-liquid neural networks, biohybrids, and biorobotics.