In industrial manufacturing of glass bottles, quality control of bottle prints is necessary as numerous factors can negatively affect the printing process. Even minor defects in the bottle prints must be detected despite reflections in the glass or manufacturing-related deviations. In cooperation with our medium-sized industrial partner, two ML-based approaches for quality control of these bottle prints were developed and evaluated, which can also be used in this challenging scenario. Our first approach utilized different filters to supress reflections (e.g. Sobel or Canny) and image quality metrics for image comparison (e.g. MSE or SSIM) as features for different supervised classification models (e.g. SVM or k-Neighbors), which resulted in an accuracy of 84%. The images were aligned based on the ORB algorithm, which allowed us to estimate the rotations of the prints, which may serve as an indicator for anomalies in the manufacturing process. In our second approach, we fine-tuned different pre-trained CNN models (e.g. ResNet or VGG) for binary classification, which resulted in an accuracy of 87%. Utilizing Grad-Cam on our fine-tuned ResNet-34, we were able to localize and visualize frequently defective bottle print regions. This method allowed us to provide insights that could be used to optimize the actual manufacturing process. This paper also describes our general approach and the challenges we encountered in practice with data collection during ongoing production, unsupervised preselection, and labeling.

As large language models (LLMs) become increasingly advanced, their ability to exhibit compositional generalization -- the capacity to combine learned skills in novel ways not encountered during training -- has garnered significant attention. This type of generalization, particularly in scenarios beyond training data, is also of great interest in the study of AI safety and alignment. A recent study introduced the SKILL-MIX evaluation, where models are tasked with composing a short paragraph demonstrating the use of a specified $k$-tuple of language skills. While small models struggled with composing even with $k=3$, larger models like GPT-4 performed reasonably well with $k=5$ and $6$. In this paper, we employ a setup akin to SKILL-MIX to evaluate the capacity of smaller models to learn compositional generalization from examples. Utilizing a diverse set of language skills -- including rhetorical, literary, reasoning, theory of mind, and common sense -- GPT-4 was used to generate text samples that exhibit random subsets of $k$ skills. Subsequent fine-tuning of 7B and 13B parameter models on these combined skill texts, for increasing values of $k$, revealed the following findings: (1) Training on combinations of $k=2$ and $3$ skills results in noticeable improvements in the ability to compose texts with $k=4$ and $5$ skills, despite models never having seen such examples during training. (2) When skill categories are split into training and held-out groups, models significantly improve at composing texts with held-out skills during testing despite having only seen training skills during fine-tuning, illustrating the efficacy of the training approach even with previously unseen skills. This study also suggests that incorporating skill-rich (potentially synthetic) text into training can substantially enhance the compositional capabilities of models.

Automated persistent and fine-grained monitoring of orchards at the individual tree or fruit level helps maximize crop yield and optimize resources such as water, fertilizers, and pesticides while preventing agricultural waste. Towards this goal, we present a 4D spatio-temporal metric-semantic mapping method that fuses data from multiple sensors, including LiDAR, RGB camera, and IMU, to monitor the fruits in an orchard across their growth season. A LiDAR-RGB fusion module is designed for 3D fruit tracking and localization, which first segments fruits using a deep neural network and then tracks them using the Hungarian Assignment algorithm. Additionally, the 4D data association module aligns data from different growth stages into a common reference frame and tracks fruits spatio-temporally, providing information such as fruit counts, sizes, and positions. We demonstrate our method's accuracy in 4D metric-semantic mapping using data collected from a real orchard under natural, uncontrolled conditions with seasonal variations. We achieve a 3.1 percent error in total fruit count estimation for over 1790 fruits across 60 apple trees, along with accurate size estimation results with a mean error of 1.1 cm. The datasets, consisting of LiDAR, RGB, and IMU data of five fruit species captured across their growth seasons, along with corresponding ground truth data, will be made publicly available at: https://4d-metric-semantic-mapping.org/

More maddening, the argument suggests that the technology's massive consumption of electricity today doesn't much matter, since it will allow us to generate abundant clean power in the future. By all accounts, AI's energy demands will only continue to increase, even as the world scrambles to build larger, cleaner power systems to meet the increasing needs of EV charging, green hydrogen production, heat pumps, and other low-carbon technologies. Altman himself reportedly just met with White House officials to make the case for building absolutely massive AI data centers, which could require the equivalent of five dedicated nuclear reactors to run. It's a bedrock perspective of MIT Technology Review that technological advances can deliver real benefits and accelerate societal progress in meaningful ways. But for decades researchers and companies have oversold the potential of AI to deliver blockbuster medicines, achieve super intelligence, and free humanity from the need to work. To be fair, there have been significant advances, but nothing on the order of what's been hyped.

Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechanical properties from their internal lattice structures, which are often composed of multiple materials that repeat geometric patterns. While traditional inverse design approaches have shown potential, they struggle to map nonlinear material behavior to multiple possible structural configurations. This paper presents a novel framework leveraging video diffusion models, a type of generative artificial Intelligence (AI), for inverse multi-material design based on nonlinear stress-strain responses. Our approach consists of two key components: (1) a fields generator using a video diffusion model to create solution fields based on target nonlinear stress-strain responses, and (2) a structure identifier employing two UNet models to determine the corresponding multi-material 2D design. By incorporating multiple materials, plasticity, and large deformation, our innovative design method allows for enhanced control over the highly nonlinear mechanical behavior of metamaterials commonly seen in real-world applications. It offers a promising solution for generating next-generation metamaterials with finely tuned mechanical characteristics.

In recent years, the rapid development of artificial intelligence (AI) technology has enabled it to achieve, and in some cases surpass, top human performance in various high-intelligence tasks. These include recognition in speech [1], facial [2], and image [3], games such as Go [4], StarCraft [5], and Dota2 [6], as well as tasks related to text [7], image [8], and video generation, machine translation [9], knowledge-based question answering [10], debates, and solving advanced mathematical problems [11]. Science is one of the most important fields for the application of AI. As the crown jewel of human civilization and the cornerstone of various industries, science is a core driver of human progress, and its development can significantly accelerate and even revolutionize many fields. Historically, there have been three major research paradigms in science: the first paradigm, experiment, which emerged from Newtonian empiricism; the second paradigm, theory, born from Einstein's rationalism; and the third paradigm, simulation/computation, which arose from the third industrial revolution, the computation and information revolution.

In materials science, data-driven methods accelerate material discovery and optimization while reducing costs and improving success rates. Symbolic regression is a key to extracting material descriptors from large datasets, in particular the Sure Independence Screening and Sparsifying Operator (SISSO) method. While SISSO needs to store the entire expression space to impose heavy memory demands, it limits the performance in complex problems. To address this issue, we propose a RF-SISSO algorithm by combining Random Forests (RF) with SISSO. In this algorithm, the Random Forest algorithm is used for prescreening, capturing non-linear relationships and improving feature selection, which may enhance the quality of the input data and boost the accuracy and efficiency on regression and classification tasks. For a testing on the SISSO's verification problem for 299 materials, RF-SISSO demonstrates its robust performance and high accuracy. RF-SISSO can maintain the testing accuracy above 0.9 across all four training sample sizes and significantly enhancing regression efficiency, especially in training subsets with smaller sample sizes. For the training subset with 45 samples, the efficiency of RF-SISSO was 265 times higher than that of original SISSO. As collecting large datasets would be both costly and time-consuming in the practical experiments, it is thus believed that RF-SISSO may benefit scientific researches by offering a high predicting accuracy with limited data efficiently.

Important variables of processes are, in many occasions, categorical, i.e. names or labels representing, e.g. categories of inputs, or types of reactors or a sequence of steps. In this work, we use Large Language Models (LLMs) to derive embeddings of such inputs that represent their actual meaning, or reflect the ``distances" between categories, i.e. how similar or dissimilar they are. This is a marked difference from the current standard practice of using binary, or one-hot encoding to replace categorical variables with sequences of ones and zeros. Combined with dimensionality reduction techniques, either linear such as Principal Components Analysis (PCA), or nonlinear such as Uniform Manifold Approximation and Projection (UMAP), the proposed approach leads to a \textit{meaningful}, low-dimensional feature space. The significance of obtaining meaningful embeddings is illustrated in the context of an industrial coating process for cutting tools that includes both numerical and categorical inputs. The proposed approach enables feature importance which is a marked improvement compared to the current state-of-the-art (SotA) in the encoding of categorical variables.

As large language models (LLMs) rapidly evolve into large multimodal models (LMMs), the integration of these technologies into building information modeling (BIM) tasks to enhance work performance is signiLicantly increasing. The use of generative artiLicial intelligence (AI) during the conceptual design phase is particularly becoming a norm in industry and academia. A recent survey by the Royal Institute of British Architects (RIBA) reported that 68% of the responding architects are already using generative AI, such as text-to-image models, for early design visualization While the application of LLMs in BIM tasks beyond the early design phase is still in an early stage, it is foreseeable that BIM systems with natural language interfaces supported by LLMs will supplant BIM tools with traditional user interfaces in the near future. In this paper, we use the term "LLM-augmented BIM" as a general expression to indicate a task or a process of querying, generating, and managing BIM data and/or models via speech or text in natural language. We refer to the former as "speech-to-BIM" and the latter as "text-to-BIM" tasks.

This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a standard penetrate-drag-scoop motion pattern. Inspired by human excavation strategies, our approach incorporates oscillatory rotation elements -- including swivel, twist, and dive motions -- to break up compacted, tangled grains and reduce jamming. We also present an adaptive impedance control method, the Reactive Attractor Impedance Controller (RAIC), that adapts a motion trajectory to unexpected forces during loading in a manner that tracks a trajectory closely when loads are low, but avoids excessive loads when significant resistance is met. Our method is evaluated on four terrains using a robotic arm, demonstrating improved excavation performance across multiple metrics, including volume scooped, protective stop rate, and trajectory completion percentage.