The expectation--maximization (EM) algorithm combines global monotonicity, local linear convergence, and strong practical robustness, but these features are usually analyzed separately. Global descent is nonlinear, whereas local convergence is governed by the spectrum of the linearized EM map. How these two levels fit into a single dynamical picture has remained less transparent. We make explicit the latent-variable operator that connects them. Along the EM trajectory, the likelihood increment admits a global energy decomposition in terms of posterior-relative entropy. Linearization at a nondegenerate maximizer $θ^\ast$ then reveals the local operator \[ \mathcal G_{θ^\ast}=I-DT(θ^\ast), \] which coincides with both the missing-information ratio and the information-geometric Hessian of the observed likelihood. This operator provides a unified description of local contraction, posterior rigidity, and geometric curvature. Its spectrum yields a sharp characterization of local convergence and naturally leads to an optimal scalar relaxation rule for locally accelerated EM. These results place global descent, local spectral behavior, and optimal local relaxation within a common dynamical framework.

This paper proposes a data-driven learning framework for identifying governing laws of generalized diffusions with non-gradient components. By combining energy dissipation laws with a physically consistent penalty and first-moment evolution, we design a two-stage method to recover the pseudo-potential and rotation in the pointwise orthogonal decomposition of a class of non-gradient drifts in generalized diffusions. Our two-stage method is applied to complex generalized diffusion processes including dissipation-rotation dynamics, rough pseudo-potentials and noisy data. Representative numerical experiments demonstrate the effectiveness of our approach for learning physical laws in non-gradient generalized diffusions.

This paper introduces Energentic Intelligence, a class of autonomous systems defined not by task performance, but by their capacity to sustain themselves through internal energy regulation. Departing from conventional reward-driven paradigms, these agents treat survival-maintaining functional operation under fluctuating energetic and thermal conditions-as the central objective. We formalize this principle through an energy-based utility function and a viability-constrained survival horizon, and propose a modular architecture that integrates energy harvesting, thermal regulation, and adaptive computation into a closed-loop control system. A simulated environment demonstrates the emergence of stable, resource-aware behavior without external supervision. Together, these contributions provide a theoretical and architectural foundation for deploying autonomous agents in resource-volatile settings where persistence must be self-regulated and infrastructure cannot be assumed.

The LLM-as-judge paradigm is increasingly being adopted for automated evaluation of model outputs. While LLM judges have shown promise on constrained evaluation tasks, closed source LLMs display critical shortcomings when deployed in real world applications due to challenges of fine grained metrics and explainability, while task specific evaluation models lack cross-domain generalization. We introduce GLIDER, a powerful 3B evaluator LLM that can score any text input and associated context on arbitrary user defined criteria. GLIDER shows higher Pearson's correlation than GPT-4o on FLASK and greatly outperforms prior evaluation models, achieving comparable performance to LLMs 17x its size. GLIDER supports fine-grained scoring, multilingual reasoning, span highlighting and was trained on 685 domains and 183 criteria. Extensive qualitative analysis shows that GLIDER scores are highly correlated with human judgments, with 91.3% human agreement. We have open-sourced GLIDER to facilitate future research.

In this article we study virtual nonholonomic constraints, which are relations between the generalized coordinates and momenta of a mechanical system that can be enforced via feedback control. We design a constraint which emulates gymnastics giant motion in an acrobot, and prove that this constraint can inject or dissipate energy based on the sign of a design parameter. The proposed constraint is tested both in simulation and experimentally on a real-world acrobot, demonstrating highly effective energy regulation properties and robustness to a variety of disturbances.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' The top Republican on the Senate Energy Committee will warn Thursday against allowing U.S. artificial intelligence capabilities to fall into China's hands when the panel meets for a hearing on the topic. Senators returned to Capitol Hill just days ago after spending the month of August in their home states. AI is expected to be a prominent topic for lawmakers as they race to get ahead of the rapidly advancing technology. It's also the topic at the heart of Thursday's hearing led by Energy Committee Chair Joe Manchin, D-W.Va., and ranking member John Barrasso, R-Wyo., that aims to examine how AI has affected the U.S. energy sector and how the federal government can stay competitive in that lane.

As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than 5 .

EXECUTIVE SUMMARY With the growing number of energy regulations and certifications, the demand to reduce energy consumption has become an important aspect of building management. Advances in the internet of things (IoT) technologies and smart building strategies are playing a huge role in helping change the industry through a shift from energy management to enterprise management systems. Technologies like machine learning and artificial intelligence (AI) are making it possible for a smart building to forecast, predict and optimize its operations and increase energy efficiencies.

The amount of energy consumed by big data centers has always been a headache for tech companies. Keeping the servers cool as they crunch numbers is such a challenge that Facebook even built one of its facilities on the edge of the Arctic Circle. Well, Google has a different solution to this problem: putting its DeepMind artificial intelligence unit in charge and using AI to manage power usage in parts of its data centers. A 40 percent reduction in the amount of electricity needed for cooling, which Google describes as a "phenomenal step forward." After accounting for "electrical losses and other non-cooling inefficiencies," this 40 percent reduction translated into a 15 percent reduction in overall power saving, says Google.

Google's 500m purchase of DeepMind is paying off in more ways than one, helping the search firm slash the energy it uses cooling its datacenters by 40 percent. The DeepMind-powered savings on cooling have resulted in an overall 15 percent reduction in its datacenters' PUE (power usage effectiveness), a measure of how much energy is used by IT as a proportion of the total energy used by a building. How Google DeepMind's ant soccer skills can help improve your search results Google's artificial-intelligence researchers at DeepMind have developed a new algorithm that improves its game-play capabilities. With the shift to cloud computing, the tech sector has emerged as one of the world's biggest users of energy, much of it from dirty sources such as coal. To help cut its carbon footprint from the 4,402,836MWh Google's datacenters used in 2014, the company has, for example, built a datacenter in Finland where seawater is used for cooling equipment, and pays Swedish wind farms to compensate its other energy consumption. DeepMind offers yet another answer to this challenge by allowing Google to optimize its datacenters' efficiency with a series of deep neural networks that predict future energy requirements and provide recommended actions to achieve an optimal PUE.