Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework, but challenges remain in identifiability, interpretability, and integration with traditional nonlinear time series methods. We address these challenges through two contributions. First, we introduce a Grouped Sparse autoencoder that employs the Spike-and-Slab Lasso prior, with parameters under this prior being shared across variables of the same economic category, thereby achieving semi-identifiability and enhancing model interpretability. Second, we incorporate time-varying parameters into the VAR component to better capture evolving economic dynamics. Our empirical application to the US economy demonstrates that the Grouped Sparse autoencoder produces more interpretable factors through its parsimonious structure; and its combination with time-varying parameter VAR shows superior performance in both point and density forecasting. Impulse response analysis reveals that monetary policy shocks during recessions generate more moderate responses with higher uncertainty compared to expansionary periods.

This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. By means of synthetic and real data experiments it is established that the proposed estimator can achieve, in many cases, better accuracy than a recently proposed loss-based estimator. We contribute to the literature on measuring uncertainty by extracting new indexes of low, medium and high economic policy uncertainty, using the probabilistic quantile factor methodology. Medium and high indexes have clear contractionary effects, while the low index is benign for the economy, showing that not all manifestations of uncertainty are the same.

AI tools have become incredibly powerful and increasingly good at mimicking human writing. GPT-3 and related tools like Jasper AI can compose articles, blog posts, and even entire books at scale. For the first time in history, it's possible to create thousands of pages of text with almost no effort at all. But a new backlash against this content is already brewing. As AI continues to scale up, we're going to see a strange trend -- bad, flawed writing will become way more prominent and way more commercially valuable. That might seem strange or negative, but it's actually a wonderful thing.

Worldwide most factories aim for low-cost and fast production ignoring resources and energy consumption. But, high revenues have been accompanied by environmental degradation. The United Nations reacted to the ecological problem and proposed the Sustainable Development Goals, and one of them is Sustainable Production (Goal 12). In addition, the participation of lightweight robots, such as collaborative robots, in modern industrial production is increasing. The energy consumption of a single collaborative robot is not significant, however, the consumption of more and more cobots worldwide is representative. Consequently, our research focuses on strategies to reduce the energy consumption of lightweight robots aiming for sustainable production. Firstly, the energy consumption of the lightweight robot UR10e is assessed by a set of experiments. We analyzed the results of the experiments to describe the relationship between the energy consumption and the evaluation parameters, thus paving the way to optimization strategies. Next, we propose four strategies to reduce energy consumption: 1) optimal standby position, 2) optimal robot instruction, 3) optimal motion time, and 4) reduction of dissipative energy. The results show that cobots potentially reduce from 3\% up to 37\% of their energy consumption, depending on the optimization technique. To disseminate the results of our research, we developed an AR game in which the users learn how to energy-efficiently program cobots.

Virtual Reality (VR) makes a computer-generated world seem like the real thing. When in fact it is not. Consider the following example.Video games are a new way of presenting a virtual world and making it more lifelike.It's easy to use VR to enhance what we see in the real world. It is a substitute reality for the real world. What makes it more intriguing than just a video game is that this virtual reality will be the new reality.

SaaS, PaaS – and now AIaaS: Entrepreneurial, forward-thinking companies will attempt to provide customers of all types with artificial intelligence-powered plug-and-play solutions for myriad business problems. Industries of all types are embracing off-the-shelf AI solutions. According to industry experts, global AI software revenue -- most of it online artificial intelligence as a service software (AIaaS) -- is set to grow by an astounding annual rate of 34.9%, with the market reaching over $100 billion by 2025. It sounds like a great idea, but there is a caveat -- "one-size-fits-all" syndrome. Companies seeking to use AI as a differentiating technology in order to gain business advantages -- and not merely doing it because that's what everyone else is doing -- require planning and strategy, and that almost always means a customized solution.

The growing use of social networks, smartphones that collect and continuously generate data, the growing use of the Internet, the presence of sensors that measure and monitor everything, causes the volume of the produced data is growing exponentially, providing valuable information for society and for companies. All this is Big Data, defined as a large collection of data volume and variety can not be managed with traditional database management tools, but require the use of new technologies and adequate data management systems for storing and analysis, are able to extract their value quickly.[1] With Big Data are experiencing a new revolution, the large amount of data and information available to us, are considered "black gold" of the new millennium. They are fundamental to the predictive analysis and extrapolation of information (Data Mining) developed by research institutes and companies in support of their decision-making strategies. In business intelligence is changing the way to manage information for decision support, they are developing new tools, and down the costs of data collection systems, storage and processing.

At this point in history, all businesses need to transform to survive.

The Internet of Things (IoT) is poised to fundamentally change the way a wide range of industries approach the procurement, processing, and distribution of raw materials and finished products. New efficiencies based on the introduction of intelligent sensors, mission-critical communications, automation, and robotics will optimize industries ranging from mining and shipping to manufacturing verticals including electronics, automotive and petrochemical products. This emerging megatrend is alternatively called the Fourth Industrial Revolution and Industry 4.0, although these aren't interchangeable terms. Let's take a look at both. The First Industrial Revolution, which started in Britain around 1760 and ran until between 1820 and 1840, saw the mechanization of the textile industry via a transition from hand tools to machine tools.

Yuansong Liao and John Moody Department of Computer Science, Oregon Graduate Institute, P.O.Box 91000, Portland, OR 97291-1000 Abstract The committee approach has been proposed for reducing model uncertainty and improving generalization performance. The advantage of committees depends on (1) the performance of individual members and (2) the correlational structure of errors between members. This paper presents an input grouping technique for designing a heterogeneous committee. With this technique, all input variables are first grouped based on their mutual information. Statistically similar variables are assigned to the same group.