What makes the fourth industrial revolution so different from previous industrial revolutions is the convergence and interaction between multiple technology trends at once. In this article, I list the ten major technology trends that are driving the fourth industrial revolution – trends that I believe will forever alter how we do business and live our lives. As processing power has increased and the size of computer microchips has shrunk, we've quickly become used to computers and devices getting smaller, lighter, cheaper, more powerful, and more ubiquitous. Looking ahead, probably the next big leap in computing power will come from quantum computers – computers that are so fast and powerful, they could be used to complete new, previously impossible tasks that traditional computers aren't capable of. The IoT refers to the increasing number of intelligent, connected devices and objects that are capable of gathering and transmitting data.

As technological advancement is in full swing and making the shift towards digitization, IT technology developers are making efforts to develop software to ease the work which is being done by manually. In this regard, one such technological advancement is Intelligent Automation, a unification of artificial intelligence and automation. The eruption of intelligent automation has revolutionized the way data is handled and processed. Intelligent automation systems sense and synthesize huge volume of information and automate entire processes and operational workflows in an organization and adapt it. Already, it is assisting a large number of business enterprises in order to meet the demand, intensify efficiency and implement new business models.

It must be CES time! A few years ago, the only robots at CES were toys. And as the robot toy makers at Ologic can attest, having your robot featured as the leading image for CES was still no guarantee that your robot would make it into production (AMP is pictured above). Luckily Ologic have transferred their consumer electronics experience into building robots of every other kind. The 2022 CES Innovation Awards recognize a range of robotics technologies as Honorees, and feature three in the "Best of Innovation" category as well. See & Spray is a technologically advanced, huge robot for the agriculture industry that leverages computer vision and machine learning to detect the difference between plants and weeds, and precisely spray herbicide only on the weeds. This groundbreaking plant-level management technology gives a machine the gift of vision and reduces the use of herbicide by up to 80 percent, benefiting the farmer, the surrounding community and the environment.

We are currently witnessing an explosion of network traffic. Numerous emerging services and applications, such as cloud services, video streaming platforms and the Internet of Things (IOT), are further increasing the demand for high-capacity communications. Optical communication systems, technologies that transfer information optically using fibers, are the backbone of today's communication networks of fixed-line, wireless infrastructure and data centers. Over the past decade, the growth of the internet was enabled by a technique known as digital signal processing (DSP), which can help to reduce transmission distortions. However, DSP is currently implemented using CMOS integrated circuits (ICs), thus it relies heavily on Moore's Law, which has approached its limits in terms of power dissipation, density and feasible engineering solutions.

Standard deep learning algorithms are implemented using floating-point real numbers. This presents an obstacle for implementing them on low-end devices which may not have dedicated floating-point units (FPUs). As a result, researchers in TinyML have considered machine learning algorithms that can train and run a deep neural network (DNN) on a low-end device using integer operations only. In this paper we propose PocketNN, a light and self-contained proof-of-concept framework in pure C++ for the training and inference of DNNs using only integers. Unlike other approaches, PocketNN directly operates on integers without requiring any explicit quantization algorithms or customized fixed-point formats. This was made possible by pocket activations, which are a family of activation functions devised for integer-only DNNs, and an emerging DNN training algorithm called direct feedback alignment (DFA). Unlike the standard backpropagation (BP), DFA trains each layer independently, thus avoiding integer overflow which is a key problem when using BP with integer-only operations. We used PocketNN to train some DNNs on two well-known datasets, MNIST and Fashion-MNIST. Our experiments show that the DNNs trained with our PocketNN achieved 96.98% and 87.7% accuracies on MNIST and Fashion-MNIST datasets, respectively. The accuracies are very close to the equivalent DNNs trained using BP with floating-point real number operations, such that accuracy degradations were just 1.02%p and 2.09%p, respectively. Finally, our PocketNN has high compatibility and portability for low-end devices as it is open source and implemented in pure C++ without any dependencies.

In a modern power system, real-time data on power generation/consumption and its relevant features are stored in various distributed parties, including household meters, transformer stations and external organizations. To fully exploit the underlying patterns of these distributed data for accurate power prediction, federated learning is needed as a collaborative but privacy-preserving training scheme. However, current federated learning frameworks are polarized towards addressing either the horizontal or vertical separation of data, and tend to overlook the case where both are present. Furthermore, in mainstream horizontal federated learning frameworks, only artificial neural networks are employed to learn the data patterns, which are considered less accurate and interpretable compared to tree-based models on tabular datasets. To this end, we propose a hybrid federated learning framework based on XGBoost, for distributed power prediction from real-time external features. In addition to introducing boosted trees to improve accuracy and interpretability, we combine horizontal and vertical federated learning, to address the scenario where features are scattered in local heterogeneous parties and samples are scattered in various local districts. Moreover, we design a dynamic task allocation scheme such that each party gets a fair share of information, and the computing power of each party can be fully leveraged to boost training efficiency. A follow-up case study is presented to justify the necessity of adopting the proposed framework. The advantages of the proposed framework in fairness, efficiency and accuracy performance are also confirmed.

While CES 2022 wasn't quite the full-blown, in-person event organizers were hoping for, the massive electronics show in Las Vegas, Nevada, still managed to deliver a stellar line-up of exciting technology. There's so much happening at CES, but there are always some announcements that really grab our attention and for those products, a highly coveted Hottest of CES 2022 TechRadar award is in sight. The TechRadar team spent all week voting on and debating over our favorite CES 2022 launches. What you'll find below are the results: The 20 hottest products from this year's show. LG came out swinging at CES 2022 by announcing a number of 8K and 4K OLEDs, several new QNED mini-LED TVs, and even a MicroLED TV.

The metaverse as described to us by science fiction is a world of infinite possibilities. The easiest way to conceptualise it is by looking at Hollywood blockbusters such as Avatar and Ready, Player One. In the movies, the metaverse is a three-dimensional digital universe where players can escape physical reality, engage with each other as an avatar of their creation and experience anything they want, only limited by the human imagination and technology, says Selina Yuan, general manager of international business unit, Alibaba Cloud Intelligence. Apart from being a wondrous twin digital reality of our physical world, the metaverse's true potential lies in its ability to make better use of the digital intelligence we are already gaining and visualising it in a way that uncovers new insights that might have otherwise remain hidden. This could be the key to helping us solve real-world problems and building a greener, more inclusive, and technically advanced world.

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel. Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT's Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces -- factors that impact fusion reactor designs. To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior.

Closed-loop manufacturing is central to Manufacturing 4.0 automation, but it's also been in place on production floors for years. But can it be automated to work with little or no human intervention? A closed-loop system on a production floor is a set of machines utilized in manufacturing that communicate and coordinate with each other to get certain processes done. The only catch is when something goes wrong and an alert is issued. At that point, a human has to step in to resolve the issue.