As artificial intelligence (AI) systems attain greater autonomy and complex environmental interactions, they begin to exhibit behavioral anomalies that, by analogy, resemble psychopathologies observed in humans. This paper introduces Psychopathia Machinalis: a conceptual framework for a preliminary synthetic nosology within machine psychology, intended to categorize and interpret such maladaptive AI behaviors.
Quantum computers have the potential to solve problems far beyond the reach of today’s fastest supercomputers. But today’s machines are notoriously fragile. The quantum bits, or “qubits,” that store and process information are easily disrupted by their environment, leading to errors that quickly accumulate.
One of the most promising approaches to overcoming this challenge is topological quantum computing, which aims to protect quantum information by encoding it in the geometric properties of exotic particles called anyons. These particles, predicted to exist in certain two-dimensional materials, are expected to be far more resistant to noise and interference than conventional qubits.
“Among the leading candidates for building such a computer are Ising anyons, which are already being intensely investigated in condensed matter labs due to their potential realization in exotic systems like the fractional quantum Hall state and topological superconductors,” said Aaron Lauda, professor of mathematics, physics and astronomy at the USC Dornsife College of Letters, Arts and Sciences and the study’s senior author.
Researchers at The University of Manchester’s National Graphene Institute have developed a new class of programmable nanofluidic memristors that mimic the memory functions of the human brain, paving the way for next-generation neuromorphic computing.
In a study published in Nature Communications, scientists from the National Graphene Institute, Photon Science Institute and the Department of Physics and Astronomy have demonstrated how two-dimensional (2D) nanochannels can be tuned to exhibit all four theoretically predicted types of memristive behavior, something never before achieved in a single device.
This study not only reveals new insights into ionic memory mechanisms but also has the potential to enable emerging applications in low-power ionic logic, neuromorphic components, and adaptive chemical sensing.
What if scientists could use the peculiar world of quantum mechanics to design solutions once thought impossible — changing how we build, heal, and communicate?
At Lawrence Livermore National Laboratory, researchers are developing quantum systems that could help us do just that. These machines think differently, tapping into the strange rules of quantum mechanics to simulate atomic interactions, unlock new materials, and reveal hidden patterns in nature. In this episode, we’ll explore how quantum computers work, why they need to be colder than deep space, and what it will take to bring their full potential to life.
(This is an Apple Podcast)
Podcast Episode · Big Ideas Lab · 06/03/2025 · 21m.
Questions to inspire discussion.
🛑 Q: How does the Robo Taxi handle blocked routes? A: The Robo Taxi demonstrates impressive rerouting capabilities, finding new paths when exits are blocked and making right-hand turns to circumvent blocked left-hand turn lanes.
🚦 Q: How does the Robo Taxi adapt to traffic situations? A: It shows human-like behavior by slowing down dramatically to enter the right-hand lane when a slower vehicle is ahead, and can accelerate and speed up to overtake slower vehicles.
💧 Q: How does the Robo Taxi handle standing water? A: The Robo Taxi demonstrates adaptability by avoiding standing water in parking lots, performing three-point turns to navigate around obstacles.
🔄 Q: How flexible is the Robo Taxi in changing its driving approach? A: It shows impressive adaptability by altering its method to slow down when encountering slower vehicles and changing again to make right-hand turns around blocked left-hand turn lanes.
Technical Considerations.
For 25 years, scientists at Northwestern Medicine have been studying people aged 80 years and older – dubbed “SuperAgers” – to uncover what makes them stand out.
In a new study, researchers show that these individuals display memory performance comparable to those at least 30 years younger, defying the long-held belief that cognitive decline is an unavoidable part of aging.
The study was published in Alzheimer’s & Dementia.
Several widely used epigenetic clocks have been developed for mice and other species, but a persistent challenge remains: different mouse clocks often yield inconsistent results. To address this limitation in robustness, we present EnsembleAge, a suite of ensemble-based epigenetic clocks. Leveraging data from over 200 perturbation experiments across multiple tissues, EnsembleAge integrates predictions from multiple penalized models. Empirical evaluations demonstrate that EnsembleAge outperforms existing clocks in detecting both pro-aging and rejuvenating interventions. Furthermore, we introduce EnsembleAge HumanMouse, an extension that enables cross-species analyses, facilitating translational research between mouse models and human studies. Together, these advances underscore the potential of EnsembleAge as a robust tool for identifying and validating interventions that modulate biological aging.
Some experts warn that this radical change may remain out of reach for many, due to societal and economic challenges.