Our most powerful artificial agents cannot be told exactly what to do, especially in complex planning environments. They almost exclusively rely on neural networks to perform their tasks, but neural networks cannot easily be told to obey certain rules or adhere to existing background knowledge. While such uncontrolled behaviour might be nothing more than a simple annoyance next time you ask an LLM to generate a schedule for reaching a deadline in two days and it starts to hallucinate that days have 48 hours instead of 24, it can be much more impactful when that same LLM is controlling an agent responsible for navigating a warehouse filled with TNT and it decides to go just a little too close to the storage compartments. Luckily, controlling neural networks has gained a lot of attention over the last years through the development of . Neurosymbolic AI, or NeSy for short, aims to combine the learning abilities of neural networks with the guarantees that symbolic methods based on automated mathematical reasoning offer.

When we want to understand how matter behaves, the real action happens at the atomic scale. Heating of water, a chemical reaction in a battery, the way proteins fold in our cells, or how a catalyst works to convert carbon dioxide into useful fuels, all of these processes are governed by the motions and interactions of atoms. Atomic-scale simulations give us a way to explore the microscopic behavior of matter, by tracking how atoms move under the laws of quantum mechanics. These simulations have become essential across physics, chemistry, biology, and materials science. They test hypotheses that experiments cannot easily probe and help design new materials before they are synthesized and tested in the lab.

Researchers who created a soft robot that could navigate simple mazes without human or computer direction have now built on that work, creating a "brainless" soft robot that can navigate more complex and dynamic environments. "In our earlier work, we demonstrated that our soft robot was able to twist and turn its way through a very simple obstacle course," says Jie Yin, co-corresponding author of a paper on the work and an associate professor of mechanical and aerospace engineering at North Carolina State University. "However, it was unable to turn unless it encountered an obstacle. In practical terms this meant that the robot could sometimes get stuck, bouncing back and forth between parallel obstacles. "We've developed a new soft robot that is capable of turning on its own, allowing it to make its way through twisty mazes, even negotiating its way around moving obstacles.

Everyday activities like using a GPS to map out the best driving route or translating a document consume energy, water and mineral resources -- lots of it. These applications run in the cloud, a nebulous term for the millions of powerful computers in vast data centers worldwide. Mobile applications depend on legions of computers to store trillions of data and perform split-second operations (e.g. Estimates of the energy consumption of data centers range between 1-2% of total global consumption. All signs indicate that data center energy consumption is about to skyrocket.

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers, co-funded by the Marie Skłodowska-Curie Actions COFUND program. SMASH is open to researchers around the world who are interested in developing cutting-edge machine learning applications for science and humanities. During the five years of the SMASH project (2023-28) and over three planned calls, SMASH aims to hire 50 individuals for 2-year full-time postdoctoral contracts with highly attractive conditions. Fellows will be hosted by five leading Slovenian research institutions: the University of Nova Gorica, the University of Ljubljana, the Jožef Stefan Institute, the Institute of Information Science*, and the Slovenian Environment Agency*. Applicants should propose ambitious research projects in one of SMASH's five key research areas, that are centered on the use of cutting-edge machine learning, or more broadly, artificial intelligence techniques, to address some of the world's most challenging questions in: Applicants should choose the SMASH host institution and supervisor with whom they will coordinate the project proposal preparation.

Are you a scientist with a keen interest in education, research and intelligent technologies? At the National Education Lab for Artificial Intelligence (NOLAI in Dutch), we develop innovative and intelligent technologies aimed at improving the quality of primary and secondary education. Over the next ten years, NOLAI teams up with schools, universities and companies to create new innovative examples of AI in education. As a postdoctoral researcher on ethical aspects of AI in education, you can contribute to NOLAI's goals in our scientific programme. The new National Education Lab AI (NOLAI), located at Radboud University in the Netherlands, is looking for a postdoctoral researcher to study the ethical and social implications of AI in education.

Two years ago, I decided to do nothing. As a neuroscientist, I was already familiar with the evidence that mindfulness meditation, or attending to the present moment, is beneficial for stress and anxiety. So I had been meditating regularly for about a half a year, looking to enhance my practice. And although I didn't know it yet, there were already scientific studies showing that the more extreme form of "doing nothing" that I was now interested in--floating in a sensory reduction tank--could significantly reduce stress, blood pressure, and cortisol levels. And so it was my plan, in the first week of March 2020, on what would become the eve of the COVID-19 pandemic lockdowns, to enter a commercial float studio in West Los Angeles, called Float Lab.

An unimaginable amount of data is continually being generated by scientific experiments, longitudinal studies, clinical trials, and hospital records--but what can be done with all this information? Barbara Engelhardt (she/her), PhD, is building machine-learning models and statistical tools to make use of that data and find ways to better understand, and even prevent, disease. She is now joining Gladstone Institutes as a senior investigator. "Barbara is an innovator in computational biology," says Katie Pollard, PhD, director of the Gladstone Institute of Data Science and Biotechnology. "She brings vast expertise in statistical models and will help expand our machine-learning program. We're thrilled she's joining our team."

Adaptive Online Learning: Online learning deals with learning algorithms that process data sets sequentially, one data point at a time. Such algorithms play a key role in machine learning for very large data sets, for instance when training deep neural networks. The goal of this project is to develop new online learning algorithms and prove that they are optimal for multiple types of loss functions and under multiple different types of assumptions, without the need to manually tune any hyperparameters. Mathematical Formalization and Analysis of Explainability Methods: Recently there has been much interest in generating explanations that clarify and communicate how machine learning methods make their decisions. But none of the existing methods gives any formal mathematical guarantees on how well they can be expected to work and under which conditions their explanations can be trusted.

In addition to new outfits and backpacks, face masks are now an essential addition to kids' back-to-school gear. According to new guidelines released by the Centers for Disease Control and Prevention, all students and staff should wear masks inside schools, regardless of vaccination status. But kids used to virtual learning may not have much experience interacting or communicating with their peers or teachers while masked. And parents and child development experts alike are wondering how that will affect children as they return to school. For instance, to assess whether kids can accurately interpret a masked person's emotions, researchers from the University of Wisconsin-Madison's Child Emotion Lab showed children ages seven to 13 pictures of people displaying different emotions.