Collaborating Authors


ARK Invest's Big Ideas 2022: The 14 transformative technologies to watch this year


ARK Invest solely invests in disruptive innovations. ARK's thematic investment strategies span market capitalizations, sectors, and geographies to focus on public companies that we expect to be the leaders, enablers, and beneficiaries of disruptive innovation. ARK's strategies aim to deliver long-term growth with low correlation to traditional investment strategies. ARK Invest defines "disruptive innovation" as the introduction of a technologically enabled new product or service that potentially changes the way the world works. ARK focuses solely on offering investment solutions to capture disruptive innovation in the public equity markets.

Hitting the Books: What autonomous vehicles mean for tomorrow's workforce


In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies. In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions -- positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.

smartcity OR smartcities_2022-01-13_17-24-59.xlsx


The graph represents a network of 4,670 Twitter users whose tweets in the requested range contained "smartcity OR smartcities", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 14 January 2022 at 01:42 UTC. The requested start date was Friday, 14 January 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 5-day, 6-hour, 33-minute period from Friday, 07 January 2022 at 20:15 UTC to Thursday, 13 January 2022 at 02:48 UTC.

Forecasting: theory and practice Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

#CES2022 Twitter NodeXL SNA Map and Report for Monday, 03 January 2022 at 20:34 UTC


The graph represents a network of 6,360 Twitter users whose recent tweets contained "#CES2022", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Monday, 03 January 2022 at 21:39 UTC. The tweets in the network were tweeted over the 1-day, 4-hour, 48-minute period from Sunday, 02 January 2022 at 15:45 UTC to Monday, 03 January 2022 at 20:34 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

The rise of industrial AI and AIoT: 4 trends driving technology adoption


The AI adoption rate in industrial settings has increased from 19% to 31% in slightly more than two years, according to data from the recently released 252-page Industrial AI and AIoT Market Report 2021–2026. On top of the 31% of respondents that have fully or partially rolled out AI technology in their operations, an additional 39% are currently testing or piloting the technology. Increased AI adoption can be witnessed across the board but is especially strong in the energy vertical and in process industries, such as oil and gas or chemicals. The combination of high-value assets, large volumes of operational data, and processes that rely on hundreds of parameters contributes to the strong adoption in these industries. Common industrial AI applications include maintenance (e.g., predictive maintenance [PdM]), predictive quality control, the use of machine vision for fault detection, AI-optimized inventory management, and AI-based production planning and optimization.



The graph represents a network of 1,627 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 22 December 2021 at 13:46 UTC. The requested start date was Wednesday, 22 December 2021 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 15-day, 4-hour, 55-minute period from Saturday, 04 December 2021 at 15:55 UTC to Sunday, 19 December 2021 at 20:50 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. 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, Offenburg and Trier, 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 20 technologies that defined the first 20 years of the 21st Century

The Independent - Tech

The early 2000s were not a good time for technology. After entering the new millennium amid the impotent panic of the Y2K bug, it wasn't long before the Dotcom Bubble was bursting all the hopes of a new internet-based era. Fortunately the recovery was swift and within a few years brand new technologies were emerging that would transform culture, politics and the economy. They have brought with them new ways of connecting, consuming and getting around, while also raising fresh Doomsday concerns. As we enter a new decade of the 21st Century, we've rounded up the best and worst of the technologies that have taken us here, while offering some clue of where we might be going. There was nothing much really new about the iPhone: there had been phones before, there had been computers before, there had been phones combined into computers before. There was also a lot that wasn't good about it: it was slow, its internet connection barely functioned, and it would be two years before it could even take a video.