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.
By using a combination of machines, sensory devices, embedded computational intelligence and various communication mechanisms, CPS monitor physical elements with computer-based algorithms tied to the internet. This means they are capable of autonomously functioning based on their physical surroundings. In light of the advancements in analytics, artificial intelligence (AI) and communications, there is an increased demand for intelligent machines that can interact with the environment around them, such as driverless cars which monitor and communicate with their surroundings, and smart appliances that optimise energy consumption. CPS are stimulating significant changes in quality of life and forming the basis of smart infrastructure, products, and services. As this kind of technology continues to become more integrated into our everyday lives, here are four areas of CPS we can expect to come to the fore.
Lime is an American transportation company that runs electric scooters, electric bikes, normal pedal bikes, and car-sharing systems in various cities around the world. The company has recently announced that it has reached 150 million rides powered with 100% renewable energy just three years after launching the company. But how are these new-age transportation vehicles built from an electrical standpoint? In this article, we'll try to introduce some basic technical concepts for designing e-bikes. The first step to designing an e-bike is to estimate the required motor power.
Smart cities aren't just sci-fi or cyberpunk dreams, but an actual solution based on Artificial Intelligence and the Internet of Things. But the question is, what is the mechanism that put it all in action? How far away humanity is from a futuristic picture of smart cities we saw in movies? To answer this question, I decided to shed some light on the current state of things for anyone interested both in existing possibilities and solutions we can track in the foreseeable future. For better or for worse, smart cities nowadays are less about flying cars, robots selling coffee, or other flashy visions from science fiction.
For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions – about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.
For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions--about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.
Amazon is reportedly on the verge of announcing a deal to acquire self-driving startup Zoox for over $1 billion. According to The Information, unnamed sources close to the matter say the deal could be announced as soon as Friday. If confirmed, the acquisition of the startup would give the e-commerce giant access to a pool of over 1,000 staff and additional talent in the self-driving space. California-based Zoox, which also has offices across the San Francisco Bay Area, was founded in 2014 by Tim Kentley-Klay and Dr. Jesse Levinson. The startup describes itself as a company focused on building "autonomous mobility from the ground up," which includes self-driving software for vehicles to safely navigate city streets. To date, the startup has raised $955 million over four funding rounds.
The construction site of 2050 will be human-free. Robots will work in teams to build complex structures using dynamic new materials. Elements of the build will self-assemble. Drones flying overhead will scan the site constantly, inspecting the work and using the data collected to predict and solve problems before they arise, sending instructions to robotic cranes and diggers and automated builders with no need for human involvement. The role of the human overseer will be to remotely manage multiple projects simultaneously, accessing 3D and 4D visuals and data from the on-site machines, ensuring the build is proceeding to specification.
This post was co-authored by the extended Azure Mobility Team. The past year has been eventful for a lot of reasons. At Microsoft, we've expanded our partnerships, including Volkswagen, LG Electronics, Faurecia, TomTom, and more, and taken the wraps off new thinking such as at CES, where we recently demonstrated our approach to in-vehicle compute and software architecture. Looking ahead, areas that were once nominally related now come into sharper focus as the supporting technologies are deployed and the various industry verticals mature. The welcoming of a new year is a good time to pause and take in what is happening in our industry and in related ones with an aim to developing a view on where it's all heading.
Advances in Data Science are lately permeating every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. The present work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within the Data Science realm that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.