The exponential growth in data sizes and storage costs has brought considerable challenges to the data science community, requiring solutions to run learning methods on such data. While machine learning has scaled to achieve predictive accuracy in big data settings, statistical inference and uncertainty quantification tools are still lagging. Priority scientific fields collect vast data to understand phenomena typically studied with statistical methods like regression. In this setting, regression parameter estimation can benefit from efficient computational procedures, but the main challenge lies in computing error process parameters with complex covariance structures. Identifying and estimating these structures is essential for inference and often used for uncertainty quantification in machine learning with Gaussian Processes. However, estimating these structures becomes burdensome as data scales, requiring approximations that compromise the reliability of outputs. These approximations are even more unreliable when complexities like long-range dependencies or missing data are present. This work defines and proves the statistical properties of the Generalized Method of Wavelet Moments with Exogenous variables (GMWMX), a highly scalable, stable, and statistically valid method for estimating and delivering inference for linear models using stochastic processes in the presence of data complexities like latent dependence structures and missing data. Applied examples from Earth Sciences and extensive simulations highlight the advantages of the GMWMX.

Despite their remarkable advancement in locomotion and manipulation, humanoid robots remain challenged by a lack of synchronized loco-manipulation control, hindering their full dynamic potential. In this work, we introduce a versatile and effective approach to controlling and generalizing dynamic locomotion and loco-manipulation on humanoid robots via a Force-and-moment-based Model Predictive Control (MPC). Specifically, we proposed a simplified rigid body dynamics (SRBD) model to take into account both humanoid and object dynamics for humanoid loco-manipulation. This linear dynamics model allows us to directly solve for ground reaction forces and moments via an MPC problem to achieve highly dynamic real-time control. Our proposed framework is highly versatile and generalizable. We introduce HECTOR (Humanoid for Enhanced ConTrol and Open-source Research) platform to demonstrate its effectiveness in hardware experiments. With the proposed framework, HECTOR can maintain exceptional balance during double-leg stance mode, even when subjected to external force disturbances to the body or foot location. In addition, it can execute 3-D dynamic walking on a variety of uneven terrains, including wet grassy surfaces, slopes, randomly placed wood slats, and stacked wood slats up to 6 cm high with the speed of 0.6 m/s. In addition, we have demonstrated dynamic humanoid loco-manipulation over uneven terrain, carrying 2.5 kg load. HECTOR simulations, along with the proposed control framework, are made available as an open-source project. (https://github.com/DRCL-USC/Hector_Simulation).

There are more than 30 species of dolphins in the world, the Māui dolphin, which lives off the west coast of the North Island, New Zealand, faces a threat of extinction. To save this rarest of the world's dolphins, a nonprofit organization has been formed called MAUI63 (Marine Animal Unmanned Identification, with 63 representing the estimated number of Māui dolphins when this initiative began in 2018). The team's scientists and conservationists use an AI-powered drone to locate, track, identify, and ultimately protect these and Hector's dolphins. The Māui dolphin population has declined further since the project began, as a 2021 survey counted only 54. Hector's and Māui dolphins are small coastal dolphins found only in New Zealand.

Drone footage of endangered dolphins swimming with paddleboarders in Southland could help artificial intelligence, which is being used for conservation. Rodd Trafford filmed the playful pod in Te Waewae Bay, about an hours drive west of Invercargill, on January 9. "I thought it was a once-in-a-lifetime opportunity. I'll never get a chance to film that again," Trafford said. The footage appears to show at least 16 dolphins. READ MORE: * Dolphin advocates say Government's proposed protections are fundamentally flawed * 'It's right in the middle of their hood': Dolphin researcher fearful old dumpsite could spell disaster for Hector's * DOC proposal could cut red tape to building cycle trails on conservation land Trafford said he technically broke drone-use rules when he got the footage, but the Department Of Conservation had since given him the OK because the department would use the footage to enhance its artificial technology work.

Description logics are a powerful tool for describing ontological knowledge bases. That is, they give a factual account of the world in terms of individuals, concepts and relations. In the presence of uncertainty, such factual accounts are not feasible, and a subjective or epistemic approach is required. Aleatoric description logic models uncertainty in the world as aleatoric events, by the roll of the dice, where an agent has subjective beliefs about the bias of these dice. This provides a subjective Bayesian description logic, where propositions and relations are assigned probabilities according to what a rational agent would bet, given a configuration of possible individuals and dice. Aleatoric description logic is shown to generalise the description logic ALC, and can be seen to describe a probability space of interpretations of a restriction of ALC where all roles are functions. Several computational problems are considered and model-checking and consistency checking algorithms are presented. Finally, aleatoric description logic is shown to be able to model learning, where agents are able to condition their beliefs on the bias of dice according to observations.

Frame semantic parsing is a complex problem which includes multiple underlying subtasks. Recent approaches have employed joint learning of subtasks (such as predicate and argument detection), and multi-task learning of related tasks (such as syntactic and semantic parsing). In this paper, we explore multi-task learning of all subtasks with transformer-based models. We show that a purely generative encoder-decoder architecture handily beats the previous state of the art in FrameNet 1.7 parsing, and that a mixed decoding multi-task approach achieves even better performance. Finally, we show that the multi-task model also outperforms recent state of the art systems for PropBank SRL parsing on the CoNLL 2012 benchmark.

Two brothers whose grandmother was left unaided for hours after a fall, have developed AI technology to help local authorities reach hundreds of shielding, isolated or digitally excluded residents. Monty and Hector Alexander are working with Hammersmith and Fulham council to pilot their automated voice call system that phones households every fortnight to ask whether they need help during the Covid-19 pandemic. At the ages of 24 and 26, the brothers said they decided to harness their tech expertise to find solutions to social issues rather than joining a big corporate machine or tech giant. The brothers, who live in White City, founded the start-up Yokeru while Monty was still studying mechanical engineering at Imperial College London last year in an attempt to "improve communication between vulnerable people and caregivers". Much of their motivations were personal after their grandmother was left stranded outside her residential home for eight hours after falling over, even with the home's 24/7 care.

Customer support services are regularly confronted with availability issues to answer their users. Many human resources are deployed to handle very similar requests, which is a daunting task, and which penalizes the processing of more complex customer requests. Artificial Intelligence provides a quick solution to implement through tailored Virtual Assistants.

There are many things that Westworld is. Its scenes are sculpted with the same beauty and care that went into building the robots' exquisite bodies. It explores with delicacy the adjacent modes of consciousness that accompany sophisticated artificial intelligence. And then there are the things that Westworld is not. There is, for starters, the total absence of meaningful relationships.

If this robot reminds you of Scorponok from the "Transformers" film series, you wouldn't be far off. Designed by researchers at the University of Bielefeld, this advanced walking robot is based on a rather simple creature: an insect. HECTOR, short for Hexapod Cognitive autonomously Operating Robot, has six legs and elastic joints that allow its motions to mimic muscle movement. This construction allows the robot to navigate over uneven terrain. At a little more than three feet long and weighing in at 26 pounds, this robot probably won't be involved in any world domination schemes anytime soon.