If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
America's solar farms have a bird problem. Utility companies have been finding bird carcasses littering the ground at their facilities for years, a strange and unexpected consequence of the national solar boom. No one was quite sure why this was happening, but it was clearly a problem for a type of energy that was billed as being environmentally friendly. So in 2013, a group of utilities, academics, and environmental organizations came together to form the Avian Solar Working Group to develop strategies to mitigate avian deaths at solar facilities around the US. "There was very little research about the impacts of solar on birds," says Misti Sporer, the lead environmental scientist at Duke Energy, an electric utility in North Carolina, and the coordinator of the working group.
We face great challenges in a globalized and modern world that we, humanity, have built. The fulfillment of the 17 United Nations Sustainable Development Goals (SDGs) in 2030 is essential to make a planet with a viable future. Achieving them not only depends on the will of governments, institutions or people. The application of technologies that, with their multiplying effect, allow achieving the goals is extremely important. Technological innovation plays a decisive role in the evolution of changes towards a new model that involves improving development, without leaving anyone behind, and with the focus on avoiding inequality and injustice, ensuring better protection of the environment.
Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses. Artificial intelligence models continue to grow in sophistication and complexity, adding to the need for more data, computation, and energy. To help combat increasing energy costs, researchers at TU Graz's Institute of Theoretical Computer Science have developed a new algorithm, called e-propagation (e-prop for short). E-prop mimics how neurons send electrical impulses to other neurons in our brain, which massively reduces the amount of energy human brains use, in comparison to machine learning. Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses.
As created for AI4IMPACT's Deep Learning Datathon 2020, TEAM DEFAULT has created a neural-network-based deep learning model used for predicting energy production demand in France. The model was created using Smojo, on AI4IMPACT's innovative cloud-based learning and model deployment system. Our model was able to achieve a 0.131 test loss which beat persistence loss of 0.485 by a quite a fair margin. As the energy market becomes increasingly liberalized across the world, the free and open market has seen an uptick and importance for optimized energy demand. New and existing entrants turn to data and various methods to forecast energy consumption in hopes of turning over a profit.
Technological innovation plays a decisive role in the evolution of changes towards a new model that involves improving development, without leaving anyone behind, and with the focus on avoiding inequality and injustice, ensuring better protection of the environment. These are broadly the foundations of the 17 Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda. Technology with its multiplier effect can accelerate the achievement of objectives and goals. There are four technologies (based on AI) that allow addressing the five basic elements on which the 2030 Agenda is structured: people, prosperity, planet, peace and alliances. The interconnection of the five pillars of the 2030 Agenda and with four technological blocks that pivot on the Internet of Things (IoT), Automation, the Analysis of large volumes of data (Big Data) and Advanced Robotics is essential so that the developed world that we know is in balance and the current imbalances are corrected.
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research.
The combination of artificial intelligence and IoT has led to better predictive capabilities for devices, informed data storage, and enterprise machine optimization. The relatively recent combination of AI and IoT into the artificial intelligence of things (AIoT) has added a level of actionable insight to the traditional IoT. The internet of things, a system of interrelated computing devices and machines that can transfer data over a network without human interaction, has been used to enable new features, better functionality, and real-time status monitoring for consumers. Combining this with ever-developing AI advancements is allowing organizations to predict changes and optimize their devices. AIoT allows an algorithm to improve communication and apply predictive capabilities to give companies advantages over their competition.
In 2012 we started a project to allow our Home Control System to support natural language processing. This allows it to understand basic commands in English via the spoken voice or via a text interface (e.g. Our thinking back then and still is that a voice only interface is severely limiting. This approach also fits better with our user interface research and strategy. Since then we have been extending this to essentially be a narrow Artificial Intelligence (AI) capability.
Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.