We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy installations from satellite imagery, then deploy them on over 13 trillion pixels covering the world. For each detected feature, we estimate the construction date and the preceding land use type. This dataset offers crucial insights into progress toward sustainable development goals and serves as a valuable resource for policymakers, researchers, and stakeholders aiming to assess and promote effective strategies for renewable energy deployment. Our final spatial dataset includes 375,197 individual wind turbines and 86,410 solar PV installations. We aggregate our predictions to the country level -- estimating total power capacity based on construction date, solar PV area, and number of windmills -- and find an $r^2$ value of $0.96$ and $0.93$ for solar PV and onshore wind respectively compared to IRENA's most recent 2023 country-level capacity estimates.

Does seeing always mean knowing? Large Vision-Language Models (LVLMs) integrate separately pre-trained vision and language components, often using CLIP-ViT as vision backbone. However, these models frequently encounter a core issue of "cognitive misalignment" between the vision encoder (VE) and the large language model (LLM). Specifically, the VE's representation of visual information may not fully align with LLM's cognitive framework, leading to a mismatch where visual features exceed the language model's interpretive range. To address this, we investigate how variations in VE representations influence LVLM comprehension, especially when the LLM faces VE-Unknown data-images whose ambiguous visual representations challenge the VE's interpretive precision. Accordingly, we construct a multi-granularity landmark dataset and systematically examine the impact of VE-Known and VE-Unknown data on interpretive abilities. Our results show that VE-Unknown data limits LVLM's capacity for accurate understanding, while VE-Known data, rich in distinctive features, helps reduce cognitive misalignment. Building on these insights, we propose Entity-Enhanced Cognitive Alignment (EECA), a method that employs multi-granularity supervision to generate visually enriched, well-aligned tokens that not only integrate within the LLM's embedding space but also align with the LLM's cognitive framework. This alignment markedly enhances LVLM performance in landmark recognition. Our findings underscore the challenges posed by VE-Unknown data and highlight the essential role of cognitive alignment in advancing multimodal systems.

Artificial Intelligence is often said to be a "black box" -- an opaque, almost mystical thing that we don't really understand. Throw data into the black box, and out comes a prediction, or so they say. However, much of AI is not opaque, it's just a complex system that "reasons" differently than we (think we) do. For example, kids learn to write by first experimenting with letters, and finding patterns in words. GPT-3 learned to write by training a generative text algorithm on the entire Internet, yielding a model with 175 billion parameters that, essentially, predict how "the Internet" would complete a prompt.

At Altair, chief technology officer Sam Mahalingam is heads-down testing the company's newest software for designing cars, buildings, windmills, and other complex systems. The engineering and design software company, whose customers include BMW, Daimler, Airbus, and General Electric, is developing software that combines computer models of wind and fluid flows with machine design in the same process--so an engineer could design a turbine blade while simultaneously seeing its draft's effect on neighboring mills in a wind farm. What Altair needs for a job as hard as this, though, is a particular kind of computing power, provided by graphics processing units (GPUs) made by Silicon Valley's Nvidia and others. "When solving complex design challenges like the interaction between wind structures in windmills, GPUs help expedite computing so faster business decisions can be made," Mahalingam says. An aerodynamics simulation performed with Altair ultraFluidX on the Altair CX-1 concept design, modeled in Altair Inspire Studio. Altair, which offers its computational fluid dynamics software on Oracle Cloud Infrastructure and other cloud computing services, is testing new GPU capabilities to improve how its software solves the complex physics problems that simulate a car's behavior in a wind tunnel, see how windmills' turbulence affect each another, or understand wind flows around skyscrapers long before construction begins.

It was the childhood home where Sir Issac Newton would go on to split the sun's rays using a prism, altering the way we think about light forever. Now, cutting edge lighting technology based on his discovery has revealed drawings thought to have been hand-carved by the young scientist at Woolsthorpe Manor. Conservation experts found a depiction of a windmill, thought to have been inspired by the building of a nearby mill, etched into walls of the historic building. Lighting technology based on his discovery has revealed drawings thought to have been hand-carved by the young scientist. Newton exerted a profound influence on many aspects of science through his great mastery of precise experiments.

Capitalism is predicated on the production of goods and services. The difference between the cost of producing these goods and services and the revenue they generate is called profit, which is delivered back to shareholders. In order to increase profit, owners of capital try to innovate so that the same amount of goods and services can be created with fewer and fewer workers. However, if there are fewer and fewer workers, there is less cash in the economy for the purchase of goods and services. This is what Marx called an inherent contradiction of capitalism -- a contradiction that would one day lead to its replacement by socialism.

April Fools' Day brings out the pranksters and tomfoolery. Some companies (see: Google) step up their game each year! Here are a few robotics or techie related ones we spotted. Feel free to share in the comments! Google Netherlands can now harness the country's unused windmills to banish rain from the skies: "Google Wind uses Machine Learning to recognise cloud patterns and orchestrate the network of windmills when rain is approaching."

Holland is one of the greatest countries to live in, but the biggest downside is that it rains 145 days a year. That's why the Google Cloud Platform team in the Netherlands is launching Google Wind this Spring. We leveraged existing Dutch infrastructure to realize this moonshot in record time. We upgraded some historical windmills in Holland with control modules connected to Google Cloud Platform. Google Wind then uses Machine Learning to recognize cloud patterns and orchestrate the network of windmills when rain is approaching.

We then describe In this paper, we describe a user evaluation of a humanrobot a study which assessed the responses of naïve users dialogue system that is designed to enable a humanoid to output that varied along two dimensions: the robot to cooperate with a human partner on building wooden method of describing an assembly plan (pre-order construction toys. In the evaluation, we experimentally vary or post-order), and the method of referring to objects two aspects of the output generated by the system: the way in the world (basic and full). Varying both that it describes assembly plans to the user, and the way that of these factors produced significant results: subjects it refers to objects in the world. We then measure the impact using the system that employed a pre-order of varying each of these features on the users' objective success description strategy asked for instructions to be repeated at working with the system, as well as on their subjective significantly less often than those who experienced impressions of the interaction.

In salamander retina, the response of On-Off ganglion cells to a central flash is reduced by movement in the receptive field surround. Through computer simulation of a 2-D model which takes into account their anatomical and physiological properties, we show that interactions between four neuron types (two bipolar and two amacrine) may be responsible for the generation and lateral conductance of this change sensitive inhibition. The model shows that the four neuron circuit can account for previously observed movement sensitive reductions in ganglion cell sensitivity and allows visualization and prediction of the spatiotemporal pattern of activity in change sensitive retinal cells.