Breakthroughs, discoveries, and DIY tips sent every weekday. For some adventurers, scaling Mount Everest represents the ultimate test of grit and determination: a visual signifier of humanity's epic struggle to overcome the elements. For others, the peak can seem more like a really tall trash can. Every year, around 600 climbers make the trek from the mountain's base camp to the summit. During their time on Everest, each person produces an estimated 18 pounds of waste, most of which is left behind.

Helicopters and drones have been used to try and trace the location of the three climbers, who set out to climb Mt Cook on Saturday. Ms Walker said drone footage showed evidence of where the climbers had begun to cross the slopes below the Zurbriggen Ridge. This included footprints and equipment, including clothes and energy gels, which are thought to have belonged to the men. Scaling Mt Cook via the Zurbriggen Ridge is a Grade Four climb, according to New Zealand alpine group Climb NZ. This mean that it requires "sound mountaineering judgement and experience". Both Blair and Romero are said to have been experienced climbers.

Using data from professional bouldering competitions from 2008 to 2022, we train a logistic regression to predict climber results and measure climber skill. However, this approach is limited, as a single numeric coefficient per climber cannot adequately capture the intricacies of climbers' varying strengths and weaknesses in different boulder problems. For example, some climbers might prefer more static, technical routes while other climbers may specialize in powerful, dynamic problems. To this end, we apply Probabilistic Matrix Factorization (PMF), a framework commonly used in recommender systems, to represent the unique characteristics of climbers and problems with latent, multi-dimensional vectors. In this framework, a climber's performance on a given problem is predicted by taking the dot product of the corresponding climber vector and problem vectors. PMF effectively handles sparse datasets, such as our dataset where only a subset of climbers attempt each particular problem, by extrapolating patterns from similar climbers. We contrast the empirical performance of PMF to the logistic regression approach and investigate the multivariate representations produced by PMF to gain insights into climber characteristics. Our results show that the multivariate PMF representations improve predictive performance of professional bouldering competitions by capturing both the overall strength of climbers and their specialized skill sets. We provide our code open-source at https://github.com/baronet2/boulder2vec.

This paper presents an argument that certain AI safety measures, rather than mitigating existential risk, may instead exacerbate it. Under certain key assumptions - the inevitability of AI failure, the expected correlation between an AI system's power at the point of failure and the severity of the resulting harm, and the tendency of safety measures to enable AI systems to become more powerful before failing - safety efforts have negative expected utility. The paper examines three response strategies: Optimism, Mitigation, and Holism. Each faces challenges stemming from intrinsic features of the AI safety landscape that we term Bottlenecking, the Perfection Barrier, and Equilibrium Fluctuation. The surprising robustness of the argument forces a re-examination of core assumptions around AI safety and points to several avenues for further research.

Bouldering is a sport where athletes aim to climb up an obstacle using a set of defined holds called a route. Typically routes are assigned a grade to inform climbers of its difficulty and allow them to more easily track their progression. However, the variation in individual climbers technical and physical attributes and many nuances of an individual route make grading a difficult and often biased task. In this work, we apply classical and deep-learning modelling techniques to the 2016, 2017 and 2019 Moonboard datasets, achieving state of the art grade prediction performance with 0.87 MAE and 1.12 RMSE. We achieve this performance on a feature-set that does not require decomposing routes into individual moves, which is a method common in literature and introduces bias. We also demonstrate the generalization capability of this model between editions and introduce a novel vision-based method of grade prediction. While the generalization performance of these techniques is below human level performance currently, we propose these methods as a basis for future work. Such a tool could be implemented in pre-existing mobile applications and would allow climbers to better track their progress and assess new routes with reduced bias.

In this paper, we bridge work in rock climbing route generation and grading into the computational creativity community. We provide the necessary background to situate that literature and demonstrate the domain's intellectual merit in the computational creativity community. We provide a guiding set of desiderata for future work in this area. We propose an approach to computational route grading. Finally, we identify important gaps in the literature and consider how they may be filled. This paper thus also serves as a pilot study, planting a flag for our ongoing research in this domain.

For those whose feel the call of the mountains, video games have proved abundant recently: Death Stranding, The Legend of Zelda: Breath of the Wild, and Sable all feature enticing summits, enveloped in clouds, with makeshift rock-paths towards them. Now there is Jusant, the latest title to turn vertiginous traversal into a puzzle, inspiring wanderlust from the comfort of the sofa. It is the new game from Don't Nod, the French studio behind the hit adventure series Life Is Strange. Rather, all its talking is done through dizzying, gravity-defying action. Co-creative director Mathieu Beaudelin wants to give players a taste of being an elite climber, he says, to become one with the massif.

CBP's Air and Marine Operations launched a rescue operation upon request by the Cochise County Sheriff's Office. A college student was located Thursday after he fell several hundred feet while climbing an Oregon mountain. Joel Tranby was climbing North Sister in the Cascade Mountains with his girlfriend early Monday afternoon when he fell about 300 to 500 feet and was severely injured. While Tranby's girlfriend was able to use her phone to call for help, she could not see where Tranby had landed, authorities said. "Unfortunately, he stopped responding verbally before searchers arrived," Lane County Sheriff's Office Sgt.

Tracking climbers' activity to improve services and make the best use of their infrastructure is a concern for climbing gyms. Each climbing session must be analyzed from beginning till lowering of the climber. Therefore, spotting the climbers descending is crucial since it indicates when the ascent has come to an end. This problem must be addressed while preserving privacy and convenience of the climbers and the costs of the gyms. To this aim, a hardware prototype is developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called quickdraw, that connects the climbing rope to the bolt anchors. The corresponding sensors are configured to be energy-efficient, hence become practical in terms of expenses and time consumption for replacement when using in large quantity in a climbing gym. This paper describes hardware specifications, studies data measured by the sensors in ultra-low power mode, detect sensors' orientation patterns during lowering different routes, and develop an supervised approach to identify lowering.

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.