Today, we have more information readily available at our fingertips (or by simple voice command) than any other time in history. Whenever you pose a question to Amazon's Alexa or the Google Assistant, you're effectively asking an artificial intelligence (AI) search algorithm to cull the Internet for a brief sentence or two that will answer your question. Increasingly, architects are using AI-leaning software tools in a similar way, calling on algorithms to cull the world of architectural possibilities quickly and efficiently for design approaches that help to meet the growing demand for sustainable architecture and green technology. "In architecture, AI is generally synonymous with generative design -- or, as I like to call it, 'optioneering,'" explains Dan Stine, director of design technology at Lake Flato Architects, San Antonio, Texas. "Our software tools use algorithms that generate a large number of design options based on parameters we define, then rank those options according to how well they meet our criteria. Ultimately, we select the option that works best for a given project."

Roles that involve building relationships with clients, customers or patients can never be replaced by automation. Download The Economic Times News App to get Daily Market Updates & Live Business News. How will India achieve its steep renewable-energy goals? Solar plant that floats is one way. Unfriended: What Sheryl Sandberg's sign out from Facebook means for the tech giant in India

Aurora Solar's platform uses computer vision to help solar companies rapidly design systems tailored to customers' specific needs. Its proprietary measurement and modeling technologies help speed up and lower the cost of solar power installations.

Traversing intersections is a challenging problem for autonomous vehicles, especially when the intersections do not have traffic control. Recently deep reinforcement learning has received massive attention due to its success in dealing with autonomous driving tasks. In this work, we address the problem of traversing unsignalized intersections using a novel curriculum for deep reinforcement learning. The proposed curriculum leads to: 1) A faster training process for the reinforcement learning agent, and 2) Better performance compared to an agent trained without curriculum. Our main contribution is two-fold: 1) Presenting a unique curriculum for training deep reinforcement learning agents, and 2) showing the application of the proposed curriculum for the unsignalized intersection traversal task. The framework expects processed observations of the surroundings from the perception system of the autonomous vehicle. We test our method in the CommonRoad motion planning simulator on T-intersections and four-way intersections.

For years, full service refueling has been the most common choice when consumers visited a gas station, and it is still a great service. But how will that look in the future? Will it be possible and affordable to recruit people for these jobs? Will the demand for convenience grow as the world gets more digital? The full service concept is still a very common and popular service in the South European countries. With temperatures of up to 40 C in the summer and a well developed tradition of good service, it's clear why full service is still a competitive asset for the South European fuel retail market.

Inflation is a global problem, and it's one that is being exacerbated by climate change. This is because the increased frequency and severity of extreme weather events drive up prices for food, energy, and other necessities. But there is hope: AI can help us fight climate change by reducing emissions, improving energy efficiency, and increasing the use of renewable energy sources. Therefore, the Green transition is a key pillar in fighting inflation, and AI is an important tool in this effort. In fact, according to a 2022 BCG Climate AI Survey report (shown below), 87% of private and public sector CEOs with decision-making power in AI and climate believe AI is an essential tool in the fight against climate change.

There is an exponential increase in the size of machine learning models and the carbon footprint of AI systems is an important thing to consider in order to create a sustainable world. In order to train them to accurately process images, text, or video, they need more and more energy. Some conferences now request submissions of papers to include information on CO2 emissions as the AI community struggles with its environmental impact. A new study proposes a way for quantifying those emissions that is more precise. It also contrasts the elements that influence them and evaluates two strategies for lowering them.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The job landscape in the United States is dramatically shifting: The COVID-19 pandemic has redefined essential work and moved workers out of the office. New technologies are transforming the nature of many occupations. Globalization continues to push jobs to new locations. And climate change concerns are adding jobs in the alternative energy sector while cutting them from the fossil fuel industry.

Electricity is fundamental to our society. As climate change becomes more severe and demand for clean energy increases, the future is the electrification of everything and along with it, the need for reliable energy. The U.S. infrastructure spans over a vast 200,000 miles and inspecting all of it is a time-consuming and high-risk process that often calls for hanging from helicopters or climbing tall towers. It is inefficient, costly, and dangerous. According to T&D World, utility line work is one of the top 10 most dangerous jobs in America.

When dealing with real-world optimization problems, decision-makers usually face high levels of uncertainty associated with partial information, unknown parameters, or complex relationships between these and the problem decision variables. In this work, we develop a novel Chance Constraint Learning (CCL) methodology with a focus on mixed-integer linear optimization problems which combines ideas from the chance constraint and constraint learning literature. Chance constraints set a probabilistic confidence level for a single or a set of constraints to be fulfilled, whereas the constraint learning methodology aims to model the functional relationship between the problem variables through predictive models. One of the main issues when establishing a learned constraint arises when we need to set further bounds for its response variable: the fulfillment of these is directly related to the accuracy of the predictive model and its probabilistic behaviour. In this sense, CCL makes use of linearizable machine learning models to estimate conditional quantiles of the learned variables, providing a data-driven solution for chance constraints. An open-access software has been developed to be used by practitioners. Furthermore, benefits from CCL have been tested in two real-world case studies, proving how robustness is added to optimal solutions when probabilistic bounds are set for learned constraints.