The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users' analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, and time-intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation. We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM-driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics.

Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted values remains a challenge, due to the inherent complexities in material modeling. This study introduces a novel approach for uncertainty quantification in fatigue life prediction of metal materials based on integrating knowledge from physics-based fatigue life models and machine learning models. The proposed approach employs physics-based input features estimated using the Basquin fatigue model to augment the experimentally collected data of fatigue life. Furthermore, a physics-informed loss function that enforces boundary constraints for the estimated fatigue life of considered materials is introduced for the neural network models. Experimental validation on datasets comprising collected data from fatigue life tests for Titanium alloys and Carbon steel alloys demonstrates the effectiveness of the proposed approach. The synergy between physics-based models and data-driven models enhances the consistency in predicted values and improves uncertainty interval estimates.

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output diversity. This trade-off between diversity and alignment quality remains a significant challenge. Drawing inspiration from curiosity-driven exploration in reinforcement learning, we introduce curiosity-driven RLHF (CD-RLHF), a framework that incorporates intrinsic rewards for novel states, alongside traditional sparse extrinsic rewards, to optimize both output diversity and alignment quality. We demonstrate the effectiveness of CD-RLHF through extensive experiments on a range of tasks, including text summarization and instruction following. Our approach achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. We make our code publicly available at https://github.com/ernie-research/CD-RLHF.

There's no doubt Great Britain lays claim to some of the greatest scientific discoveries and inventions that have changed the face of modern society. Now, MailOnline's interactive map reveals the birthplace of 30 of these famous British marvels, from stainless steel to the jet engine and the electric motor. Who can forget Alan Turing's Bombe machine, used to break Enigma-enciphered messages about enemy military operations during WWII? Turing developed the Bombe in 1939 at Bletchley Park in Buckinghamshire and hundreds were built, marking a crucial contribution to the war effort. Also on the map is the hovercraft invented by Christopher Cockerell in 1955 and first launched four years later on the the Isle of Wight.

To keep global temperatures from rising more than 1.5 degrees Celsius, we need to cut emissions in half by 2035--even as we will likely hit another record for burning fossil fuels this year. Still, the brilliant engineering demonstrated in this year's winning projects provides hope that we can rise to the challenge. A new kind of thermal battery will allow us to decarbonize the heat that powers the industrial processes behind everything from cement to chemicals. Newly inexpensive lasers are helping turn ore into pure iron for steelmaking using renewable electricity. Food challenges have generated different types of innovation: Instead of hauling agricultural waste to decompose in the dump, why not create a harvester-style robot that can process it into carbon-sequestering, soil-enriching biochar? To fight pests, a technique called mRNA interference allows bioengineers to create a precision poison for a particularly troublesome beetle.

In 1988, we launched the Best of What's New Awards. The original list highlighted "the very things that make our lives more comfortable, more rewarding, more exciting, and more fun," to quote then-Publisher Grant A. Burnett. Now, in 2024, we continue our decades-old tradition of honoring big ideas. We even see hints of our original honorees in this year's list: Sea-Doo and Ford made both lists, 36 years apart. We're proud to bring you promising innovations--from things that make life at home easier to literal out-of-this-world explorations. This is the Best of What's New 2024. Had you asked me at the beginning of 2024 what our best gadgets list would look like, I'd have guessed it would be filled with quirky AI-driven devices like the rabbit R1 or the Humane Ai Pin. "Now with AI" is a phrase that has dominated consumer electronics in the 2020s. These devices promised unadulterated access to the power of neural networks in ways that would seamlessly integrate into our lives without relying on phones or smart fridges. Then, the devices came out. The software is slow and buggy, and the hardware is clunky. Maybe the stand-alone AI device will still have its year, and we'll look back and chuckle at these humble beginnings. In reality, 2024's big breakthrough came from Apple in the form of its long-rumored Vision Pro headset. The device has its own hurdles to clear, but after just a few minutes of using it, it was clear that it's something different, important, and honestly pretty amazing. The list also includes Sony's innovative pro-grade camera, the most accessible drone we've ever used, and a no-fun phone--no fun in a good way, of course. Credible rumors of Apple's VR bounced around the gadget blogs and tech sites for nearly a decade. It was consumer tech's sasquatch in that people claimed to have seen it, but no one knew if it even existed. Then, the Vision Pro emerged from the proverbial forest in February with a surprising design and a massive 3,500 price tag. It also came toting a new R-series chip and a dedicated OS meant for spatial computing.

The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts through steel recycling. However, managing impurities, particularly phosphorus, remains a challenge. This study aims to develop a machine learning model to estimate the steel phosphorus content at the end of the process based on input parameters. Data were collected over two years from a steel plant, focusing on the chemical composition and weight of the scrap, the volume of oxygen injected, and process duration. After preprocessing the data, several machine learning models were evaluated, with the artificial neural network (ANN) emerging as the most effective. The best ANN model included four hidden layers. The model was trained for 500 epochs with a batch size of 50. The best model achieves a mean square error (MSE) of 0.000016, a root-mean-square error (RMSE) of 0.0049998, a coefficient of determination (R2) of 99.96%, and a correlation coefficient (r) of 99.98%. Notably, the model achieved a 100% hit rate for predicting phosphorus content within +-0.001 wt% (+-10 ppm). These results demonstrate that the optimized ANN model offers accurate predictions for the steel final phosphorus content.

In order to train agents that can quickly adapt to new objectives or reward functions, efficient unsupervised representation learning in sequential decision-making environments can be important. Frameworks such as the Exogenous Block Markov Decision Process (Ex-BMDP) have been proposed to formalize this representation-learning problem (Efroni et al., 2022b). In the Ex-BMDP framework, the agent's high-dimensional observations of the environment have two latent factors: a controllable factor, which evolves deterministically within a small state space according to the agent's actions, and an exogenous factor, which represents time-correlated noise, and can be highly complex. The goal of the representation learning problem is to learn an encoder that maps from observations into the controllable latent space, as well as the dynamics of this space. Efroni et al. (2022b) has shown that this is possible with a sample complexity that depends only on the size of the controllable latent space, and not on the size of the noise factor. However, this prior work has focused on the episodic setting, where the controllable latent state resets to a specific start state after a finite horizon. By contrast, if the agent can only interact with the environment in a single continuous trajectory, prior works have not established sample-complexity bounds. We propose STEEL, the first provably sample-efficient algorithm for learning the controllable dynamics of an Ex-BMDP from a single trajectory, in the function approximation setting. STEEL has a sample complexity that depends only on the sizes of the controllable latent space and the encoder function class, and (at worst linearly) on the mixing time of the exogenous noise factor. We prove that STEEL is correct and sample-efficient, and demonstrate STEEL on two toy problems. Code is available at: https://github.com/midi-lab/steel.

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research.

In the steel production domain, recycling ferrous scrap is essential for environmental and economic sustainability, as it reduces both energy consumption and greenhouse gas emissions. However, the classification of scrap materials poses a significant challenge, requiring advancements in automation technology. Additionally, building trust among human operators is a major obstacle. Traditional approaches often fail to quantify uncertainty and lack clarity in model decision-making, which complicates acceptance. In this article, we describe how conformal prediction can be employed to quantify uncertainty and add robustness in scrap classification. We have adapted the Split Conformal Prediction technique to seamlessly integrate with state-of-the-art computer vision models, such as the Vision Transformer (ViT), Swin Transformer, and ResNet-50, while also incorporating Explainable Artificial Intelligence (XAI) methods. We evaluate the approach using a comprehensive dataset of 8147 images spanning nine ferrous scrap classes. The application of the Split Conformal Prediction method allowed for the quantification of each model's uncertainties, which enhanced the understanding of predictions and increased the reliability of the results. Specifically, the Swin Transformer model demonstrated more reliable outcomes than the others, as evidenced by its smaller average size of prediction sets and achieving an average classification accuracy exceeding 95%. Furthermore, the Score-CAM method proved highly effective in clarifying visual features, significantly enhancing the explainability of the classification decisions.