Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

These robots can clean, exercise - and care for your elderly parents. Would you trust them to? Hidden away in a lab in north-west London three black metal robotic hands move eerily on an engineering work bench. We're not trying to build Terminator, jokes Rich Walker, director of Shadow Robot, the firm that made them. Bespectacled, with long hair and a beard and moustache, he seems more like a latter-day hippy than a tech whizz, and he is clearly proud as he shows me around his firm.

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

It looks faintly like one half of a small pair of very fluffy slippers. It squeaks and wriggles and nestles in the palm of my hand, black eyes hidden beneath a mop of silvery-white fur. It weighs about the same as a tin of soup. It doesn't need to be fed or walked and it doesn't use a litter tray; it's guaranteed not to leave "gifts" on my doorstep. Which is just as well, because Moflin is about to become my pet.

Physical interactions with socially assistive robots (SARs) positively affect user wellbeing. However, haptic experiences when touching a SAR are typically limited to perceiving the robot's movements or shell texture, while other modalities that could enhance the touch experience with the robot, such as vibrotactile stimulation, are under-explored. In this exploratory qualitative study, we investigate the potential of enhancing human interaction with the PARO robot through vibrotactile heartbeats, with the goal to regulate subjective wellbeing during stressful situations. We conducted in-depth one-on-one interviews with 30 participants, who watched three horror movie clips alone, with PARO, and with a PARO that displayed a vibrotactile heartbeat. Our findings show that PARO's presence and its interactive capabilities can help users regulate emotions through attentional redeployment from a stressor toward the robot. The vibrotactile heartbeat further reinforced PARO's physical and social presence, enhancing the socio-emotional support provided by the robot and its perceived life-likeness. We discuss the impact of individual differences in user experience and implications for the future design of life-like vibrotactile stimulation for SARs.

The Bhutanese government is increasing its utilization of technological approaches such as including Remote Sensing-based knowledge in their decision-making process. This study focuses on crop type and crop extent in Paro, one of the top rice-yielding districts in Bhutan, and employs publicly available NICFI high-resolution satellite imagery from Planet. Two Deep Learning (DL) approaches, point-based (DNN) and patch-based (U-Net), models were used in conjunction with cloud-computing platforms. Three different models per DL approaches (DNN and U-Net) were trained: 1) RGBN channels from Planet; 2) RGBN and elevation data (RGBNE); 3) RGBN and Sentinel-1 (S1) data (RGBNS), and RGBN with E and S1 data (RGBNES). From this comprehensive analysis, the U-Net displayed higher performance metrics across both model training and model validation efforts. Among the U-Net model sets, the RGBN, RGBNE, RGBNS, and RGBNES models had an F1-score of 0.8546, 0.8563, 0.8467, and 0.8500 respectively. An independent model evaluation was performed and found a high level of performance variation across all the metrics. For this independent model evaluation, the U-Net RGBN, RGBNE, RGBNES, and RGBN models displayed the F1-scores of 0.5935, 0.6154, 0.5882, and 0.6582, suggesting U-Net RGBNES as the best model. The study shows that the DL approaches can predict rice. Also, DL methods can be used with the survey-based approaches currently utilized by the Bhutan Department of Agriculture. Further, this study demonstrated the usage of regional land cover products such as SERVIR's RLCMS as a weak label approach to capture different strata addressing the class imbalance problem and improving the sampling design for DL application. Finally, through preliminary model testing and comparisons outlined it was shown that using additional features such as NDVI, EVI, and NDWI did not drastically improve model performance.

That's how I discovered the Golden Pup from Joy for All. It sports a jaunty red bandana. It barks when you talk. It wags when you touch it. It has a realistic heartbeat.

Imagine activating new robots meant to aid staff in an elder care facility, only to discover the robots are counterproductive. They undermine the most meaningful moments of the jobs and increase staff workloads, because robots demand care too. Eventually, they're returned. This vignette captures key elements of James Adrian Wright's ethnography, "Robots Won't Save Japan", an essential resource for understanding the state of elder care robotics. Wright's rich ethnographic interviews and observations challenge the prevailing funding, research, and development paradigms for robotics. Elder care residents tend to be Disabled, so this review article augments Wrights' insights with overlooked perspectives from Disability and Robotics research. This article highlights how care recipients' portrayal suggests that Paro, a plush robot seal, might perform better than the care team and author indicated -- leading to insights that support urgent paradigm shifts in elder care, ethnographic studies, and robotics. It presents some of the stronger technical status quo counter-arguments to the book's core narratives, then confronts their own assumptions. Furthermore, it explores exceptional cases where Japanese and international roboticists attend to care workers and recipients, justifying key arguments in Wright's compelling book. Finally, it addresses how "Robots won't save Japan" will save Robotics.

Japan has been developing robots to care for older people for over two decades, with public and private investment accelerating markedly in the 2010s. By 2018, the national government alone had spent well in excess of $300 million funding research and development for such devices. At first glance, the reason for racing to roboticize care may seem obvious. Almost any news article, presentation, or academic paper on the subject is prefaced by an array of anxiety-inducing facts and figures about Japan's aging population: birth rates are below replacement levels, the population has started to shrink, and though in 2000 there were about four working-age adults for every person over 65, by 2050 the two groups will be near parity. The number of older people requiring care is increasing rapidly, as is the cost of caring for them.

Japan is flourishing with all the successes in the field of robotics with multi-functional as well as humanoid robots. The Japanese domestic market has already started using these innovative and coolest robots across all industries to boost productivity and enhance customer engagement. Robotics in Japan has successfully left the world in awe. Let's explore some of the top coolest robots in Japan that have impressed the global tech market in these few years. Short for "Child-Robot with Biometric Body", it has mesmerized the world with its ability to respond to sounds and react by wiggling and changing facial expressions.