by jsendak | May 9, 2024 | Computer Science
Expert Commentary: The Role of Large Language Models in Material Selection
Material selection is a critical part of the conceptual design process, as it has a profound impact on the functionality, aesthetics, manufacturability, and sustainability of a product. Traditionally, expert knowledge and experience have guided material selection decisions, but recent advancements in artificial intelligence have introduced the potential for using Large Language Models (LLMs) to assist in this process. This study explores the effectiveness of LLMs in material selection, comparing their performance against expert choices in various design scenarios.
The researchers began by collecting a dataset of expert material preferences, providing a solid foundation for evaluating how well LLMs align with expert recommendations. They then employed prompt engineering and hyperparameter tuning to enhance the LLMs’ performance. This comprehensive approach allowed for a detailed analysis of the factors influencing the effectiveness of LLMs in recommending materials.
The study uncovered two failure modes that highlight the challenges with using LLMs for material selection. First, there was a significant discrepancy between the recommendations of LLMs and human experts. This raises concerns about the reliability and accuracy of LLMs in replicating expert decision-making. Second, the study found that LLMs’ recommendations varied across different model configurations, prompt strategies, and temperature settings. This suggests that there is no universally optimal setting for LLMs in material selection and that careful customization is required to achieve satisfactory results.
However, the study also identified a promising approach to improving LLMs’ performance in material selection: parallel prompting. By providing multiple prompts simultaneously, the researchers were able to improve the alignment between LLM recommendations and expert choices. This finding demonstrates the importance of prompt engineering methods and the potential for tailoring LLMs to better replicate human decision-making processes.
While LLMs can provide valuable assistance in material selection, it is clear that they are not yet capable of fully replacing human experts. The significant divergence between LLM and expert recommendations highlights the need for further research to better understand how LLMs can be fine-tuned to replicate expert decision-making. Future studies should explore methods for incorporating domain-specific knowledge into LLMs and improving their understanding of nuanced material properties and design requirements.
This study contributes to the growing body of knowledge on integrating LLMs into the design process. It sheds light on the current limitations and potential for future improvements, emphasizing the need for continued research in this area. As LLMs continue to advance, their role in material selection may become more refined and accurate, offering designers an invaluable tool to enhance their decision-making process and create more innovative and sustainable products.
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by jsendak | May 7, 2024 | Computer Science
arXiv:2405.03500v1 Announce Type: new
Abstract: In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to optimize the trade-off between rate, distortion, and classification accuracy. The RDC model is extensively analyzed both statistically on a multi-distribution source and experimentally on the widely used MNIST dataset. The findings reveal that the RDC model exhibits desirable properties, including monotonic non-increasing and convex functions, under certain conditions. This work provides insights into the development of human-machine friendly compression methods and Video Coding for Machine (VCM) approaches, paving the way for end-to-end image compression techniques in real-world applications.
Analysis of the RDC Model for Lossy Image Compression
The concept of lossy image compression has been widely studied and utilized in various multimedia information systems. In this article, the authors propose a novel approach called the Rate-Distortion-Classification (RDC) model, which aims to optimize the trade-off between compression rate, distortion, and classification accuracy. This approach is particularly relevant in the field of visual analysis applications, where the accurate classification of compressed images is crucial.
The multi-disciplinary nature of this concept is evident in the integration of image compression techniques with visual analysis and classification tasks. By considering semantic distortion in compressed images, the RDC model provides a framework that takes into account the impact of compression on the accuracy of subsequent classification algorithms. This is an important step towards developing human-machine friendly compression methods, as it enables efficient and accurate analysis of compressed images.
The statistical analysis and experimental evaluation of the RDC model further validate its effectiveness. By demonstrating desirable properties such as monotonic non-increasing and convex functions, the authors show that the RDC model can effectively balance the trade-off between compression rate, distortion, and classification accuracy. These findings provide valuable insights into the optimization of lossy image compression techniques and highlight the potential for end-to-end image compression methods in real-world applications.
From a broader perspective, the concepts presented in this article are closely related to the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. In these domains, the efficient compression and analysis of visual data are essential for delivering immersive and interactive multimedia experiences. The RDC model contributes to this by offering a unified framework that enhances the trade-off between compression, distortion, and classification. This can lead to the development of more advanced and efficient compression methods for various multimedia applications.
Conclusion
The Rate-Distortion-Classification (RDC) model introduced in this article represents a significant advancement in lossy image compression. By integrating the considerations of compression rate, distortion, and classification accuracy, the RDC model provides a unified framework for optimizing the trade-off between these factors. The statistical analysis and experimental evaluation conducted demonstrate the effectiveness of this approach, highlighting its potential for real-world applications.
The concepts discussed in this article are highly relevant to the broader fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The RDC model contributes to these domains by offering a framework that enhances the compression and analysis of visual data. This can lead to the development of more efficient and accurate compression methods, ultimately improving the performance and user experience of multimedia applications.
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by jsendak | May 7, 2024 | Computer Science
With the rapid development of artificial intelligence and deep learning, large-scale Foundation Models (FMs) such as GPT and CLIP have shown remarkable achievements in various fields, including natural language processing and computer vision. The potential application of FMs in autonomous driving is an exciting prospect. FMs can play a significant role in enhancing scene understanding and reasoning in autonomous vehicles.
By pre-training on extensive linguistic and visual data, FMs can develop a deep understanding of the various elements present in a driving scene. This understanding allows FMs to interpret the scene and provide cognitive reasoning, enabling them to give linguistic commands and action plans for driving decisions and planning. This capability can greatly enhance the accuracy and reliability of autonomous driving systems.
One particularly intriguing aspect of FMs in autonomous driving is their ability to augment data based on their understanding of driving scenarios. FMs can generate feasible scenes of rare occurrences that may not be encountered during routine driving and data collection. This enhancement can allow autonomous driving systems to better handle the long-tail distribution – situations that occur infrequently but are still critical for safe driving.
The development of World Models, such as the DREAMER series, further demonstrates the potential of FMs in autonomous driving. World Models leverage massive amounts of data and self-supervised learning to comprehend physical laws and dynamics. By generating unseen yet plausible driving environments, World Models can contribute to improved predictions of road user behavior and the offline training of driving strategies.
In summary, the applications of FMs in autonomous driving are vast and promising. By harnessing the powerful capabilities of FMs, we can address potential challenges arising from the long-tail distribution in autonomous driving and significantly advance overall safety in this domain.
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by jsendak | May 6, 2024 | Computer Science
Alan Turing’s proposal of the imitation game in 1950 laid the foundation for exploring whether machines can exhibit human-like intelligence. This framework has been extensively studied since then, with various mathematical approaches being used to understand the concept of imitation games.
Category theory, a branch of mathematics that emerged later, provides a powerful tool to analyze a broader class of imitation games called Universal Imitation Games (UIGs). By applying category theory to UIGs, researchers have been able to dissect and classify different types of these games.
Static Games
One type of UIG is static games, where the participants reach a steady state. In these games, the focus is on the interactions and strategies employed by the participants. Category theory allows us to explore the structure and properties of these static games further.
Dynamic UIGs
In dynamic UIGs, the participants are divided into two groups: “learners” and “teachers.” The learners aim to imitate the behavior of the teachers over the long run. Category theory helps us analyze dynamic UIGs by characterizing them as initial algebras over well-founded sets.
By understanding the structure and properties of these dynamic UIGs, we gain insights into the learning process and the strategies employed by the participants to imitate the teachers. This analysis can have implications in fields such as artificial intelligence and machine learning.
Evolutionary UIGs
In evolutionary UIGs, the participants are engaged in a competitive game, where their fitness determines their survival. Participants can go extinct and be replaced by others with higher fitness. Category theory provides a framework to study these evolutionary games and analyze the dynamics of population changes.
By characterizing evolutionary UIGs, we can gain a deeper understanding of the principles that govern the emergence and persistence of certain strategies over others. This understanding can be beneficial in various fields, including evolutionary biology and economics.
Quantum Imitation Games
The discussion around UIGs does not end with classical computers. Researchers have also ventured into exploring imitation games on quantum computers. Extending the categorical framework for UIGs to quantum settings opens up new opportunities for research and applications.
By applying the principles of category theory to quantum imitation games, we can explore how quantum phenomena and quantum strategies shape the dynamics of these games. This research can contribute to the development of quantum algorithms and the understanding of quantum information processing.
Conclusion
Category theory provides a powerful framework to analyze and understand various types of Universal Imitation Games. By characterizing these games based on initial and final objects, we gain insights into the strategies, learning processes, and dynamics that govern the interactions between participants.
The application of category theory to imitation games has the potential to advance our understanding of intelligence, learning, and evolution. It bridges the gap between mathematics, computer science, and other fields, allowing for a comprehensive exploration of these fundamental concepts.
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by jsendak | May 3, 2024 | Computer Science
arXiv:2403.16071v2 Announce Type: replace-cross
Abstract: Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications. Deep learning approaches greatly improve current lip reading systems. However, lip reading in cross-speaker scenarios where the speaker identity changes, poses a challenging problem due to inter-speaker variability. A well-trained lip reading system may perform poorly when handling a brand new speaker. To learn a speaker-robust lip reading model, a key insight is to reduce visual variations across speakers, avoiding the model overfitting to specific speakers. In this work, in view of both input visual clues and latent representations based on a hybrid CTC/attention architecture, we propose to exploit the lip landmark-guided fine-grained visual clues instead of frequently-used mouth-cropped images as input features, diminishing speaker-specific appearance characteristics. Furthermore, a max-min mutual information regularization approach is proposed to capture speaker-insensitive latent representations. Experimental evaluations on public lip reading datasets demonstrate the effectiveness of the proposed approach under the intra-speaker and inter-speaker conditions.
Expert Commentary: Lip Reading in Cross-Speaker Scenarios
In recent years, lip reading has gained significant attention as a realistic and practical application in various fields. Deep learning approaches have revolutionized lip reading systems, but they still face challenges in cross-speaker scenarios where the speaker’s identity changes. This problem arises due to the inter-speaker variability, which makes it difficult for a well-trained lip reading system to handle new speakers effectively.
A crucial insight to overcome this challenge is to reduce visual variations across speakers in order to avoid overfitting the model to specific individuals. To achieve this goal, the authors of this work propose a novel approach that focuses on exploiting lip landmark-guided fine-grained visual clues instead of the commonly used mouth-cropped images as input features. By leveraging the lip landmarks, the system can effectively diminish speaker-specific appearance characteristics and capture more robust information for lip reading.
Furthermore, the authors introduce a max-min mutual information regularization approach to capture speaker-insensitive latent representations. This regularization technique helps the model capture shared characteristics among different speakers while suppressing speaker-specific information. By doing so, the system can generalize better to unseen speakers, resulting in improved performance in cross-speaker scenarios.
This work highlights the multi-disciplinary nature of the concepts involved in lip reading systems. It combines deep learning techniques, computer vision, and information theory to address the unique challenges of lip reading in cross-speaker scenarios. By integrating knowledge from multiple domains, the authors provide a comprehensive solution that tackles both the input visual clues and the latent representations to enhance the performance of lip reading systems.
From a broader perspective, this work is closely related to the field of multimedia information systems. Lip reading is a form of multimedia analysis that involves processing visual cues to extract meaningful information, in this case, speech. The proposed approach leverages deep learning techniques, which are widely used in multimedia information systems for tasks such as image and video analysis. By adapting these techniques to the specific challenges of lip reading, this work contributes to the advancement of multimedia information systems in the domain of speech analysis.
Additionally, the concepts and techniques discussed in this work have implications for other related fields such as animations, artificial reality, augmented reality, and virtual realities. Lip reading systems play a crucial role in enabling realistic and immersive interactions in these environments. The ability to accurately interpret silent speech can enhance the user experience and enable more natural communication in virtual and augmented reality applications. Therefore, the advancements in lip reading presented in this work can have a significant impact on the development of these technologies.
In conclusion, this work presents a novel approach to address the challenges of lip reading in cross-speaker scenarios. By leveraging lip landmark-guided visual clues and applying max-min mutual information regularization, the proposed system achieves improved performance in handling new speakers. The multi-disciplinary nature of the concepts and their relevance to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities make this work a valuable contribution to the lip reading research community.
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by jsendak | May 3, 2024 | Computer Science
Expert Commentary: Understanding Robustness in Neural Networks
Artificial neural networks have shown remarkable precision in various tasks but have been found lacking in robustness, which can lead to unforeseen behaviors and potential safety risks. In contrast, biological neural systems have evolved mechanisms to solve these issues and maintain robustness. Therefore, studying the biological mechanisms of robustness can provide valuable insights for building trustworthy and safe artificial systems.
One key difference between artificial and biological neural networks is how they adjust their connectivity based on neighboring cell activity. Biological neurons have the ability to adapt their connections, resulting in more robust neural representations. The smoothness of the encoding manifold has been proposed as a crucial factor in achieving robustness.
Recent studies have observed power law covariance spectra in the primary visual cortex of mice, which are believed to indicate a balanced trade-off between accuracy and robustness in neural representations. This finding provides an important clue for understanding the relationship between the geometry, spectral properties, robustness, and expressivity of neural representations.
The authors of this article have contributed to this field by demonstrating that unsupervised local learning models with winner takes all dynamics can learn power law representations. This provides a mechanistic model that captures the characteristic spectra observed in biological systems. By using weight, Jacobian, and spectral regularization, the researchers have investigated the link between representation smoothness and spectrum, while also evaluating performance and adversarial robustness.
The findings of this research serve as a foundation for future studies into the mechanisms underlying power law spectra and optimal smooth encodings in both biological and artificial systems. By understanding these mechanisms, we can unlock the secrets of robust neural networks in mammalian brains and apply them to the development of more stable and reliable artificial systems.
Key Takeaways:
- Artificial neural networks lack robustness, posing safety risks in certain scenarios.
- Biological neural systems offer insights into achieving robustness.
- Smoothness of the encoding manifold is crucial for robust neural representations.
- Power law covariance spectra may signify a trade-off between accuracy and robustness.
- Unsupervised local learning models can learn power law representations.
- Weight, Jacobian, and spectral regularization help understand the link between representation smoothness and spectrum.
- This research lays the foundation for future investigations into power law spectra and smooth encodings in both biological and artificial systems.
Overall, this research contributes to our understanding of robustness in neural networks and provides insights into the mechanisms that can enhance the stability and reliability of artificial systems. By studying the relationship between smoothness of neural representations and power law spectra, we can bridge the gap between artificial and biological neural networks, potentially leading to safer and more trustworthy artificial intelligence systems. Further research in this area can uncover even more intriguing findings, advancing our knowledge of neural processing and paving the way for future advancements in machine learning and artificial intelligence.
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