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) …
Australian giant Downer has a 30-year contract with the New South Wales government to manage and maintain its fleet of 78 Waratah trains that operate in the greater Sydney metro area. With 2041 not approaching any time soon, the company recognised a perfect opportunity to maximise technology to make the most of its data and plan for proactive, rather than reactive, maintenance of Sydney's trains. In December 2016, the NSW government ordered 24 Waratah Series 2 trains under its Sydney Growth Trains Project and in February 2019, announced the decision to order 17 more trains. The new trains are touted as providing passengers with improved safety and comfort, fitted with air-con, more CCTV cameras, and improved accessibility. Downer general manager of Digital Technology and Innovation Mike Ayling said his company saw this as the perfect opportunity to leverage additional sensor data from the fleet.
Over more than a decade, self-driving vehicles have logged millions of miles on roadways across the globe. Despite all that driving, researchers say, the machines are still unable to replicate the sophisticated problem-solving and spontaneity human drivers employ each time they get behind the wheel. In their ambitious attempt to create an autonomous car service, companies like Waymo run their software through millions of potential scenarios, create three-dimensional maps using lasers, and outfit their vehicles powerful sensors like LIDAR that can cost more than the cars they guide. The goal is to prepare the vehicle for anything it might encounter before it touches the road, creating a system of rules that predetermine behavior. Now, an upstart British company called Wayve claims to have created a self-driving car using technology that almost sounds Stone Age compared to the competition.
The world's biggest carmaker Volkswagen has said it will use cloud computing and Internet of Things (IoT) technologies from Amazon Web Services (AWS) to connect and manage its manufacturing plants and supply chain. Infrastructure around the world is being linked together via sensors, machine learning and analytics. We examine the rise of the digital twin, the new leaders in industrial IoT (IIoT) and case studies that highlight the lessons learned from production IIoT deployments. The two companies said they have signed a multi-year deal to build what they are calling the'Volkswagen Industrial Cloud', which will manage the automotive giant's manufacturing and logistics. The aims of the project are to increase plant efficiency and uptime, improve production flexibility, and increase vehicle quality.
According to techopedia, a smart city is a city that utilizes information and communication technologies so that it enhances the quality and performance of urban services (such as energy and transportation) so that there's a reduction in resource consumption, wastage, and overall costs. In this article, we will look at components of a smart city and its AI-powered- IoT use cases, how AI helps with the adaption of IoT in Smart cities, and an example of AI-powered-IoT solution. Hence, a smart city would be a city that not only possesses ICT but also employs technology in a way that positively impacts the inhabitants. This article is an excerpt taken from the book'Hands-On Artificial Intelligence for IoT' written by Amita Kapoor. The book explores building smarter systems by combining artificial intelligence and the Internet of Things--two of the most talked about topics today.
This includes revolutionary tactics such as data patterns and trend spotting. In many cases, there is a lack of collaboration between a company's IT department that will analyse data, and those with real business insight who want to use it. It's important that companies look for new forms of data with which to create actionable results. This is a shift that we are already seeing and is going to continue throughout the year as businesses maintain their data education and practise. The next step in data collection is the use of video as a way of gathering information and analysing the state of the business via information from the physical world, and where in the workplace improvements can be made.
An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decision making, and allows downstream algorithms to deal with abstracted data without knowledge of the used sensor. In this work, we propose and evaluate a novel architecture that generates an egocentric, grid-based, predictive, and semantically-interpretable ER. In particular, we provide a proof of concept for the spatio-temporal fusion of multiple camera sequences and short-term prediction in such an ER. Our design utilizes a strong semantic segmentation network together with depth and egomotion estimates to first extract semantic information from multiple camera streams and then transform these separately into egocentric temporally-aligned bird's-eye view grids. A deep encoder-decoder network is trained to fuse a stack of these grids into a unified semantic grid representation and to predict the dynamics of its surrounding. We evaluate this representation on real-world sequences of the Cityscapes dataset and show that our architecture can make accurate predictions in complex sensor fusion scenarios and significantly outperforms a model-driven baseline in a category-based evaluation.
The TriRhenaTech alliance universities and their partners presented their competences in the field of artificial intelligence and their cross-border cooperations with the industry at the tri-national conference 'Artificial Intelligence : from Research to Application' on March 13th, 2019 in Offenburg. The TriRhenaTech alliance is a network of universities in the Upper Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
The year has just begun, and it's a good time to ponder the changes the previous year brought through IoT and related technologies. Let's look on toward what the new year may bring. Last year, we wrote so much about the Internet of Things (IoT). For us, it feels natural to begin the year with our views on IoT developments in 2019. Since the IoT is becoming not only a technical but also a social phenomenon, changing our perception of devices around us and the environment we live in, the top trends we selected speak to the intersection of machine and the human worlds.
If you just include "machine learning" in your pitch you can add a zero on to the end of your valuation. Machine Learning (ML) and the Internet of Things (IoT) have been huge buzzwords in the past few years, much of which has been hype and much of which reflects their profound potential. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given the hype around machine learning and IoT and the broad range of application to which they can be brought to bear, it can be difficult to cut through the noise and understand where the actual value lies. In this post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some machine learning applications and use cases currently out in the world today.
Big data applications have already driven the need for architectures that put memory closer to compute resources, but artificial intelligence (AI) and machine learning are further demonstrating how hardware and hardware architectures play a critical role in successful deployments. A key question, however, is where the memory is going to reside. Research commissioned by Micron Technology found that 89% of respondents say it is important or critical that compute and memory are architecturally close together. The survey, carried out by Forrester Research, also found that memory and storage are the most commonly cited concerns regarding hardware constraints limiting AI and machine learning today. More than 75% of respondents recognize a need to upgrade or re-architect their memory and storage to limit architectural constraints.