In these politically divisive times, there's one thing we all agree on--we don't want a giant data center in our backyard. Behold, the hyperscale data center! Massive structures, with thousands of specialized computer chips running in parallel to perform the complex calculations required by advanced AI models. A single facility can cover millions of square feet, built with millions of pounds of steel, aluminum, and concrete; feature hundreds of miles of wiring, connecting some hundreds of thousands of high-end GPU chips, and chewing through hundreds of megawatt-hours of electricity. These facilities run so hot from all that computing power that their cooling systems are triumphs of engineering complexity in themselves. But the star of the show are those chips with their advanced processors.

Hyundai Motor Group says it will roll out human-like robots in its factories from 2028, as major companies race to use the new technology. The South Korean firm showed off Atlas, a humanoid robot developed by Boston Dynamics, at the Consumer Electronics Show (CES) in Las Vegas on Monday. Hyundai says it plans to integrate Atlas across its global network, including a plant in the US state of Georgia that was involved in a massive immigration raid in 2025 . Other firms that have said they will use humanoid robots in their operations include Amazon, Tesla and Chinese car making giant BYD. The Atlas robots will gradually take on more tasks, said Hyundai.

MAGA diehard who was pardoned by Trump offers scathing assessment after his speech: 'He's stuttering... we can't lie anymore' Chilling new video of Nick Reiner making disturbing comments about murder... as friend reveals dad Rob's tragic failed attempt to save him: 'I'm going to kill that f***ing dog' The tiny diet change that brought down my sky-high cholesterol WITHOUT statins or drugs. Mike was told he risked a heart attack or stroke. Reiner family bombshell as insiders reveal who is paying for Nick's celebrity lawyer... their secret motive... and who will REALLY inherit $200m fortune Corey Feldman claims he was molested by the late Corey Haim while making 1987's The Lost Boys Knives out for Susie Wiles after Vanity Fair calamity... as insiders reveal full-blown MAGA meltdown: 'She might have lost one of her nine lives' I sneakily looked at my perfect son's phone... What a terrible mistake! HGTV star David Bromstad shares devastating substance abuse journey: 'I've had really hard times' Tara Reid speaks out for the first time since THAT video emerged... and tells KATIE HIND why she is convinced she was spiked after watching CCTV Brown's $3m-a-year president faces ire for'shifting blame' after shooter unleashed havoc on campus Cruel rosacea blighted Rebecca's life and made her nose swell. Her doctor said she just'had to just put up with it'.

Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learni ng (DL) methods. However, these approaches often face challenges such as limited real - time applicability, non - representative test c onditions, and the need for large datasets to achieve optimal performance. This paper presents a transfer - learning framework for knee joint angle prediction that requires only a few gait cycles from new subjects. Three datasets - Georgia Tech, the Universi ty of California Irvine (UCI), and the Sharif Mechatronic Lab Exoskeleton (SMLE) - containing four EMG channels relevant to knee motion were utilized. A lightweight attention - based CNN - LSTM model was developed and pre - trained on the Georgia Tech dataset, t hen transferred to the UCI and SMLE datasets. The proposed model achieved Normalized Mean Absolute Errors (NMAE) of 6.8 percent and 13.7 percent for one - step and 50 - step predictions on abnormal subjects using EMG inputs alone. Incorporating historical knee angles reduced the NMAE to 3.1 percent and 3.5 percent for normal subjects, and to 2.8 percent and 7.5 percent for abnormal subjects. When f urther adapted to the SMLE exoskeleton with EMG, kinematic, and interaction force inputs, the model achieved 1.09 p ercent and 3.1 percent NMAE for one - and 50 - step predictions, respectively. These results demonstrate robust performance and strong generalization for both short - and long - term rehabilitation scenarios . Keywords: EMG, Transfer Learning, Knee Angle Prediction, Attention Mechanism, Rehabilitation, Exoskeleton . 1 - Introduction Electromyography (EMG) measures electrical signals generated by contracting muscle fibers, reflecting neuromuscular activity. EMG is typically measured using electrodes placed on the skin's surface (surface Electromyography (sEMG)). Alternatively, electrodes may be inserted into the muscle tissue [2] . The frequency range of EMG signals is generally reported to be from 6 to 500 Hz, with most power concentrated between 20 and 250 Hz [3] . Analyzing EMG signals provides valuable information about muscle activation patterns, coordination, and fatigue levels.

Three Chinese nationals have been arrested in Georgia on suspicion of attempting to illegally purchase 2kg of uranium. Lasha Maghradze, deputy head of the nation's State Security Service (SSG), told a news briefing the group planned to pay $400,000 (£300,570) for the nuclear material in the capital, Tblisi, before transporting it to China via Russia. The alleged plot was unearthed by intelligence agents while one member of the group was attempting to buy the radioactive substance on the black market, he said. The three pleaded not guilty at a court in Tblisi and have been placed in custody to prevent them fleeing the country, according to public broadcaster Georgia Today. They face up to five years in prison under a provision of Georgia's criminal code banning the purchasing of nuclear material.

State's Republican-led public service commission to decide on power expansion and prices, as Democrats vie for voice Georgia is facing the largest demand for electricity in its history, driven by nation-leading datacenter construction. The Georgia Power company has made an unprecedented bid to the agency that oversees the utility for about 10 additional gigawatts of energy in the coming years - enough to power 8.3m homes, at an estimated cost of nearly $16bn, according to the Southern Environmental Law Center . But those huge numbers are not primarily for homes or local businesses in Georgia . Instead about 80% of the company's ask is driven by datacenters, primarily for artificial intelligence, according to Tom Krause, spokesperson for the state's public service commission, or PSC. It is the largest increase ever considered by the commission in a multiyear plan and comes as the Atlanta metro area led the nation in datacenter construction last year - a phenomenon playing out across the US and increasingly sparking protests and pushback.

In human society, trust is an essential component of social attitude that helps build and maintain long-term, healthy relationships which creates a strong foundation for cooperation, enabling individuals to work together effectively and achieve shared goals. As many human interactions occur through electronic means such as using mobile apps, the potential arises for AI systems to assist users in understanding the social state of their relationships. In this paper we investigate the ability of Large Language Models (LLMs) to reason about trust between two individuals in an environment which requires fostering trust relationships. We also assess whether LLMs are capable of inducing trust by role-playing one party in a trust based interaction and planning actions which can instil trust.

The accelerating uptake of battery-electric vehicles demands infrastructure planning tools that are both data-rich and geographically scalable. Whereas most prior studies optimise charging locations for single cities, state-wide and national networks must reconcile the conflicting requirements of dense metropolitan cores, car-dependent exurbs, and power-constrained rural corridors. We present DOVA-PATBM (Deployment Optimisation with Voronoi-oriented, Adaptive, POI-Aware Temporal Behaviour Model), a geo-computational framework that unifies these contexts in a single pipeline. The method rasterises heterogeneous data (roads, population, night lights, POIs, and feeder lines) onto a hierarchical H3 grid, infers intersection importance with a zone-normalised graph neural network centrality model, and overlays a Voronoi tessellation that guarantees at least one five-port DC fast charger within every 30 km radius. Hourly arrival profiles, learned from loop-detector and floating-car traces, feed a finite M/M/c queue to size ports under feeder-capacity and outage-risk constraints. A greedy maximal-coverage heuristic with income-weighted penalties then selects the minimum number of sites that satisfy coverage and equity targets. Applied to the State of Georgia, USA, DOVA-PATBM (i) increases 30 km tile coverage by 12 percentage points, (ii) halves the mean distance that low-income residents travel to the nearest charger, and (iii) meets sub-transmission headroom everywhere -- all while remaining computationally tractable for national-scale roll-outs. These results demonstrate that a tightly integrated, GNN-driven, multi-resolution approach can bridge the gap between academic optimisation and deployable infrastructure policy.

As Large Language Models (LLMs) gain expertise across diverse domains and modalities, scalable oversight becomes increasingly challenging, particularly when their capabilities may surpass human evaluators. Debate has emerged as a promising mechanism for enabling such oversight. In this work, we extend the debate paradigm to a multimodal setting, exploring its potential for weaker models to supervise and enhance the performance of stronger models. We focus on visual question answering (VQA), where two "sighted" expert vision-language models debate an answer, while a "blind" (text-only) judge adjudicates based solely on the quality of the arguments. In our framework, the experts defend only answers aligned with their beliefs, thereby obviating the need for explicit role-playing and concentrating the debate on instances of expert disagreement. Experiments on several multimodal tasks demonstrate that the debate framework consistently outperforms individual expert models. Moreover, judgments from weaker LLMs can help instill reasoning capabilities in vision-language models through finetuning.

The AI data center also promises to transform the state's energy future. Stretching in length for more than a mile, it will be Meta's largest in the world, and it will have an enormous appetite for electricity, requiring two gigawatts for computation alone (the electricity for cooling and other building needs will add to that). When it's up and running, it will be the equivalent of suddenly adding a decent-size city to the region's grid--one that never sleeps and needs a steady, uninterrupted flow of electricity. To power the data center, Entergy aims to spend 3.2 billion to build three large natural-gas power plants with a total capacity of 2.3 gigawatts and upgrade the grid to accommodate the huge jump in anticipated demand. In its filing to the state's power regulatory agency, Entergy acknowledged that natural-gas plants "emit significant amounts of CO2" but said the energy source was the only affordable choice given the need to quickly meet the 24-7 electricity demand from the huge data center.