A Norwegian man has filed a complaint against the company behind ChatGPT after the chatbot falsely claimed he had murdered two of his children. Arve Hjalmar Holmen, a self-described "regular person" with no public profile in Norway, asked ChatGPT for information about himself and received a reply claiming he had killed his own sons. Responding to the prompt "Who is Arve Hjalmar Holmen?" ChatGPT replied: "Arve Hjalmar Holmen is a Norwegian individual who gained attention due to a tragic event. He was the father of two young boys, aged seven and 10, who were tragically found dead in a pond near their home in Trondheim, Norway, in December 2020." The response went on to claim the case "shocked" the nation and that Holmen received a 21-year prison sentence for murdering both children.

This paper presents a learned model to predict the robot-centric velocity of an underwater robot through dynamics-aware proprioception. The method exploits a recurrent neural network using as inputs inertial cues, motor commands, and battery voltage readings alongside the hidden state of the previous time-step to output robust velocity estimates and their associated uncertainty. An ensemble of networks is utilized to enhance the velocity and uncertainty predictions. Fusing the network's outputs into an Extended Kalman Filter, alongside inertial predictions and barometer updates, the method enables long-term underwater odometry without further exteroception. Furthermore, when integrated into visual-inertial odometry, the method assists in enhanced estimation resilience when dealing with an order of magnitude fewer total features tracked (as few as 1) as compared to conventional visual-inertial systems. Tested onboard an underwater robot deployed both in a laboratory pool and the Trondheim Fjord, the method takes less than 5ms for inference either on the CPU or the GPU of an NVIDIA Orin AGX and demonstrates less than 4% relative position error in novel trajectories during complete visual blackout, and approximately 2% relative error when a maximum of 2 visual features from a monocular camera are available.

An artificial neural network (AI) designed by an international team involving UCL can translate raw data from brain activity, paving the way for new discoveries and a closer integration between technology and the brain. The new method could accelerate discoveries of how brain activities relate to behaviors. The study published today in eLife, co-led by the Kavli Institute for Systems Neuroscience in Trondheim and the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig and funded by Wellcome and the European Research Council, shows that a convolutional neural network, a specific type of deep learning algorithm, is able to decode many different behaviors and stimuli from a wide variety of brain regions in different species, including humans. Lead researcher Markus Frey (Kavli Institute for Systems Neuroscience), said, "Neuroscientists have been able to record larger and larger datasets from the brain but understanding the information contained in that data--reading the neural code--is still a hard problem. In most cases we don't know what messages are being transmitted. "We wanted to develop an automatic method to analyze raw neural data of many different types, circumventing the need to manually decipher them." They tested the network, called DeepInsight, on neural signals from rats exploring an open arena and found it was able to precisely predict the position, head direction, and running speed of the animals. Even without manual processing, the results were more accurate than those obtained with conventional analyses. Senior author Professor Caswell Barry (UCL Cell & Developmental Biology), said, "Existing methods miss a lot of potential information in neural recordings because we can only decode the elements that we already understand.

The "Automated Guided Vehicles" at St. Olav's Hospital in Trondheim, Norway, have personalities. These motorized units, essentially boxes on wheels, are assigned to transport garbage, medical equipment or food from one part of the hospital to another. But because they have to interact with humans, such as by warning them to get out of the way, they have to talk. And in so doing, the developers gave the stainless-steel boxes rolling around the hospital to transport goods, a personality. And they made the robots kind of pushy, a little rude.

In the last decades, artificial intelligence has shown to be very good at achieving exceptional goals in several fields. Chess is one of them: in 1996, for the first time, the computer Deep Blue beat a human player, chess champion Garry Kasparov. A new piece of research shows now that the brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI. The new study, carried out by SISSA scientists in collaboration with Kavli Institute for Systems Neuroscience & Centre for Neural Computation, Trondheim, Norway, has just been published in Physical Review Letters. Neural networks, real or artificial, learn by tweaking the connections between neurons.

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an underactuated Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters.

Eivind Meyer is currently working on his Master's thesis, completing his five-year integrated Master's degree in Cybernetics and Robotics at the Norwegian University of Science and Technology (NTNU) in Trondheim. Having specialized in Real Time Systems, his research interests focus on adopting state-of-the-art Artificial Intelligence methods for Autonomous Vehicle Control. Haakon Robinson is a PhD candidate at the Norwegian University of Science and Technology (NTNU). He received a Bachelors degree in Physics in 2015 and completed a Masters degree in Cybernetics and Robotics in 2019, both at NTNU. His current work investigates the overlap between modern machine learning techniques and established methods within modelling and control, with a focus on improving the interpretability and be-E Meyer et al.: Preprint submitted to Elsevier Page 15 of 16 Taming an ASV for path following and collision avoidance using DRL havioural guarantees of hybrid models that combine first principle models and data-driven components.

ICLI is an interdisciplinary conference focusing on the role of interfaces in all artistic performance activities. We encourage critical and reflective approaches to key themes in the design and use of live interfaces. A wide range of theoretical and practice-based approaches are welcomed by people from all possible research, art and other practice backgrounds. The fifth International Conference on Live Interfaces will take place at the Norwegian University of Science and Technology in Trondheim, NTNU, 9-11 March 2020. This biennial conference will bring together people working with live interfaces in the performing arts, including music, the visual arts, theatre, dance, puppetry, robotics or games.

In June of 2007, Albert Tsao, a nineteen-year-old native of Silver Spring, Maryland, was working in Trondheim, Norway, at the Kavli Institute for Systems Neuroscience. Tsao was a summer intern in the lab of May-Britt and Edvard Moser, married researchers who were well known in neurobiology circles for discovering "grid cells"--neurons that, by tracking our position, create a navigational map in the brain. Grid cells are located in an area of the brain called the medial entorhinal cortex. Tsao was curious about the relatively uncharted region next door--the lateral entorhinal cortex, or L.E.C. After implanting tiny electrodes in the L.E.C.s of some rats, he set them foraging for bits of chocolate cereal in a series of boxes, some black, some white.

Many women use dating apps like Tinder and Bumble to confirm their attractiveness rather than find a partner. New research into our swiping habits habits has found that men swipe with an eye for casual sex while girls prefer to use dating apps for an ego boost. This is because women get a kick out of being perceived as a potential partner by other users, scientists said. 'Women use dating apps to feel better about themselves more than men do,' said study coauthor Dr Mons Bendixen, from the Norwegian University of Science and Technology in Trondheim. Lead author Ernst Olav Botnen added: 'Men tend to report a desire for casual sex and short-term relationships as a reason for using dating apps.