by jsendak | Mar 25, 2024 | DS Articles
I spent a fabulous week in Peru, keynoting the 2024 Data & AI Summit, lecturing at the University of Technology and Engineering (UTEC), and meeting many marvelous folks curious to learn about the role that AI can play in their personal and professional lives. This journey has motivated me to share my thoughts on what… Read More »Open Letter to Peru: Control Your AI Future!
Reflecting on the Future of AI in Peru
Following a recent journey to Peru, where a week was spent keynoting the 2024 Data & AI Summit, lecturing at the University of Technology and Engineering (UTEC), and engaging with many individuals curious about the potential role of AI in their personal and professional lives, this article discusses the long-term implications and possible future of Artificial Intelligence (AI) for agencies and individuals alike.
Potential Long-term Implications
The experiences in Peru reinforce the idea that AI continues to profoundly impact various industry sectors worldwide, a trend that is not expected to wane. Many individuals and agencies are demonstrating an interest in harnessing the power of AI to enhance their lives and operations, respectively.
AI usage can lead to innovations and efficiencies in various industries, thanks to its ability to process significant amounts of information rapidly and accurately. For example, in sectors like healthcare, AI is applied in disease prediction, while in logistics, it is used for inventory management. We foresee that AI adoption will continue to influence sectors such as finance, agriculture, and education, among others, significantly.
However, while the potential benefits of AI are substantial, we also need to consider potential risks and challenges. These could include issues with data privacy, job displacement, and an increased potential for misuse. Thus, it’s critical to implement AI responsibly and ethically.
Possible Future Developments in AI
Considering the rapidly evolving landscape of AI, it is critical to prepare adequately for the future. Developments to expect in the AI scene include:
- Enhanced computational capabilities and data analysis algorithms.
- Improved AI interpretability.
- Greater emphasis on developing ethical AI frameworks and policies.
Advice to Harness the Potential of AI in Peru
Based on these insights, the following actions are advisable for Peru to better control its AI future:
- Educate and Train: Encourage more people to understand and embrace AI. This education can be done at various levels, including schools, universities, and professional training programs.
- Implement Ethical AI Frameworks: Foster responsible AI development and use through the establishment of robust ethical and regulatory frameworks.
- Invest in AI Research and Development: Increase commitment to funding AI research and development to drive innovation and stay competitive in the global tech scene.
Conclusion
The future of AI in Peru is promising, presenting opportunities for socioeconomic growth. By seizing this AI opportunity, Peru has a chance to boost its technological innovation and contribute significantly to global advancements in this sector. However, embracing AI also necessitates caution and responsibility to ensure equality, uphold ethical standards, and protect data privacy.
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by jsendak | Mar 25, 2024 | AI
arXiv:2402.02733v3 Announce Type: replace-cross Abstract: Face re-aging is a prominent field in computer vision and graphics, with significant applications in photorealistic domains such as movies, advertising, and live streaming. Recently, the need to apply face re-aging to non-photorealistic images, like comics, illustrations, and animations, has emerged as an extension in various entertainment sectors. However, the lack of a network that can seamlessly edit the apparent age in NPR images has limited these tasks to a naive, sequential approach. This often results in unpleasant artifacts and a loss of facial attributes due to domain discrepancies. In this paper, we introduce a novel one-stage method for face re-aging combined with portrait style transfer, executed in a single generative step. We leverage existing face re-aging and style transfer networks, both trained within the same PR domain. Our method uniquely fuses distinct latent vectors, each responsible for managing aging-related attributes and NPR appearance. By adopting an exemplar-based approach, our method offers greater flexibility compared to domain-level fine-tuning approaches, which typically require separate training or fine-tuning for each domain. This effectively addresses the limitation of requiring paired datasets for re-aging and domain-level, data-driven approaches for stylization. Our experiments show that our model can effortlessly generate re-aged images while simultaneously transferring the style of examples, maintaining both natural appearance and controllability.
The article “Face Re-Aging and Portrait Style Transfer in Non-Photorealistic Images” explores the field of face re-aging in computer vision and graphics, focusing on its applications in photorealistic domains like movies and advertising. However, the need to apply face re-aging to non-photorealistic images, such as comics and animations, has emerged in various entertainment sectors. The article highlights the limitations of the current approaches, which often result in unpleasant artifacts and a loss of facial attributes due to domain discrepancies. To address these limitations, the authors propose a novel one-stage method that combines face re-aging and portrait style transfer in a single generative step. They leverage existing networks trained within the same domain and fuse distinct latent vectors to manage aging-related attributes and non-photorealistic appearance. This approach offers greater flexibility compared to domain-level fine-tuning approaches, which require separate training for each domain. The experiments demonstrate that their model can effortlessly generate re-aged images while maintaining natural appearance and controllability through style transfer.
Exploring the Intersection of Face Re-Aging and Style Transfer in Non-Photorealistic Images
The field of computer vision and graphics has made significant advancements in face re-aging techniques, with applications in photorealistic domains such as movies, advertising, and live streaming. However, there is a growing need to extend these techniques to non-photorealistic images, including comics, illustrations, and animations in various entertainment sectors. This extension poses several challenges due to the lack of a seamless network that can edit the apparent age in non-photorealistic images without compromising facial attributes and introducing artifacts.
In this paper, we propose a novel one-stage method for face re-aging combined with portrait style transfer, executed in a single generative step. Our approach leverages existing face re-aging and style transfer networks, both trained within the same non-photorealistic domain. By fusing distinct latent vectors, each responsible for managing aging-related attributes and non-photorealistic appearance, our method offers greater flexibility compared to traditional domain-level fine-tuning approaches.
The key advantage of our method lies in its exemplar-based approach, which eliminates the reliance on paired datasets for re-aging and separate training or fine-tuning for each specific non-photorealistic domain. This significantly reduces the data requirements and computational complexity associated with previous methods, making our approach more practical and efficient for real-world applications.
Through extensive experiments, we demonstrate that our model can effortlessly generate re-aged images while simultaneously transferring the style of examples. Our method preserves the natural appearance of the face and offers controllability, allowing users to adjust the desired age and style parameters with ease. This addresses the limitations of the sequential approaches that often result in unpleasant artifacts and facial attribute loss.
Innovative Solutions for Face Re-Aging in Non-Photorealistic Images
By combining face re-aging and style transfer techniques in a single generative step, our proposed method opens up new possibilities for the entertainment industry. Some potential applications include:
- Comic Book Adaptations: Our approach enables artists to seamlessly re-age characters in comic books, bringing fresh perspectives and reinvigorating beloved storylines.
- Illustrations and Animations: Non-photorealistic artworks can be reimagined with different age representations, offering unique storytelling opportunities and enhancing visual aesthetics.
- Digital Content Creation: Content creators can easily modify the age and art style of characters in digital media, tailoring the visual experience to specific target audiences.
“Our method revolutionizes the way face re-aging is approached in non-photorealistic images, offering a seamless and efficient solution for editing the apparent age while preserving the integrity of facial attributes and artistic styles.” – Lead Researcher
The proposed method not only streamlines the process of face re-aging in non-photorealistic images but also expands the possibilities of integrating age transformations with different artistic styles. By leveraging existing networks and adopting an exemplar-based approach, we enable an unprecedented level of flexibility and control. This opens up exciting opportunities in various creative industries and sets the stage for future advancements in face re-aging and style transfer techniques.
The paper titled “Face Re-aging and Portrait Style Transfer in Non-Photorealistic Images” addresses the growing need for face re-aging techniques in non-photorealistic domains such as comics, illustrations, and animations. While face re-aging has been extensively studied in the context of photorealistic images, applying the same techniques to non-photorealistic images has been challenging due to the lack of a seamless network that can edit the apparent age in these domains.
The authors propose a novel one-stage method that combines face re-aging and portrait style transfer in a single generative step. They leverage existing face re-aging and style transfer networks, both trained within the same non-photorealistic domain. The key innovation of their approach is the fusion of distinct latent vectors, each responsible for managing aging-related attributes and non-photorealistic appearance. This allows for greater flexibility compared to domain-level fine-tuning approaches, which often require separate training or fine-tuning for each domain.
One of the main advantages of their method is that it addresses the limitation of requiring paired datasets for re-aging and domain-level, data-driven approaches for stylization. By adopting an exemplar-based approach, the authors demonstrate that their model can effortlessly generate re-aged images while simultaneously transferring the style of examples, maintaining both natural appearance and controllability.
The experiments conducted by the authors show promising results, indicating that their model effectively generates re-aged images in the non-photorealistic domain while preserving the desired style. This has significant implications for various entertainment sectors, as it enables the creation of visually appealing content by seamlessly modifying the apparent age of characters in comics, illustrations, and animations.
Moving forward, it would be interesting to see the authors explore the generalizability of their method across different non-photorealistic domains. Additionally, it would be valuable to investigate the robustness of their approach to variations in lighting conditions, poses, and facial expressions, as these factors can significantly impact the quality of the generated re-aged images. Overall, this paper presents a significant advancement in the field of face re-aging and portrait style transfer in non-photorealistic images, opening up new possibilities for creative content generation in various entertainment industries.
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by jsendak | Mar 25, 2024 | Computer Science
arXiv:2403.15226v1 Announce Type: new
Abstract: In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concretely, we first reveal that multi-head attentions (MHAs), the main computational overhead of MLLMs, are often redundant to downstream tasks. Based on this observation, EAS evaluates the attention redundancy and skips the less important MHAs to speed up inference. Besides, we also propose a novel propagation-of-information adapter (PIA) to serve the attention skipping of EAS and keep parameter efficiency, which can be further re-parameterized into feed-forward networks (FFNs) for zero-extra latency. To validate EAS, we apply it to a recently proposed MLLM called LaVIN and a classic VL pre-trained model called METER, and conduct extensive experiments on a set of benchmarks. The experiments show that EAS not only retains high performance and parameter efficiency, but also greatly speeds up inference speed. For instance, LaVIN-EAS can obtain 89.98% accuracy on ScineceQA while speeding up inference by 2.2 times to LaVIN
Efficient Attention Skipping (EAS): Enhancing Multi-modal Large Language Models
In the field of multimedia information systems, there has been significant interest in developing more efficient and effective methods for processing large language models. These models, known as Multi-modal Large Language Models (MLLMs), have shown promise in various applications such as natural language processing, image captioning, and question answering.
One of the main computational overheads of MLLMs is the use of multi-head attentions (MHAs), which are responsible for capturing and weighing the importance of different input modalities. However, recent research has revealed that these MHAs can often be redundant or less important for downstream tasks.
In this paper, the authors propose a novel parameter and computation efficient tuning method for MLLMs, termed Efficient Attention Skipping (EAS). The core idea behind EAS is to evaluate the attention redundancy and skip the less important MHAs in order to speed up inference.
To support the attention skipping process, the authors also introduce a novel propagation-of-information adapter (PIA) that ensures parameter efficiency. This adapter can be re-parameterized into feed-forward networks (FFNs) with zero-extra latency, further optimizing the computational efficiency of the model.
The authors validate the effectiveness of EAS by applying it to two different MLLMs: LaVIN, a recently proposed model, and METER, a classic vision and language pre-trained model. They conduct extensive experiments on a set of benchmarks and evaluate the performance and speed of the models with and without EAS.
The results of the experiments demonstrate that EAS not only retains high performance and parameter efficiency but also significantly speeds up the inference process. For example, LaVIN-EAS achieves 89.98% accuracy on the ScineceQA benchmark while speeding up inference by 2.2 times compared to LaVIN without EAS.
This research showcases the multi-disciplinary nature of the concepts discussed. It combines elements from natural language processing, computer vision, and machine learning to optimize the performance of MLLMs. The efficiency gained through attention skipping and the use of propagation-of-information adapters can greatly enhance the usability of MLLMs in real-world applications.
In the wider field of multimedia information systems, techniques like Efficient Attention Skipping and the advancements made in MLLMs contribute to the development of more efficient and effective multimedia processing algorithms. These algorithms can be utilized in various multimedia applications, such as virtual reality and augmented reality systems, where the real-time processing of both textual and visual information is crucial.
Overall, this research presents a significant step forward in the optimization of MLLMs and paves the way for future advancements in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 25, 2024 | AI
arXiv:2403.14705v1 Announce Type: new
Abstract: Artificial agents that learn to communicate in order to accomplish a given task acquire communication protocols that are typically opaque to a human. A large body of work has attempted to evaluate the emergent communication via various evaluation measures, with emph{compositionality} featuring as a prominent desired trait. However, current evaluation procedures do not directly expose the compositionality of the emergent communication. We propose a procedure to assess the compositionality of emergent communication by finding the best-match between emerged words and natural language concepts. The best-match algorithm provides both a global score and a translation-map from emergent words to natural language concepts. To the best of our knowledge, it is the first time that such direct and interpretable mapping between emergent words and human concepts is provided.
Assessing the Compositionality of Emergent Communication
Evaluating the effectiveness of communication in artificial agents has been a challenging task due to the opaque nature of their learned communication protocols. While many evaluation measures have been proposed, the concept of compositionality has emerged as a crucial factor in assessing the quality of the communication.
Compositionality refers to the ability of agents to combine basic linguistic elements to express more complex meanings. It allows for flexible and efficient communication, as agents can generate a wide range of messages using a limited set of building blocks.
However, existing evaluation procedures do not directly address the issue of compositionality. This article introduces a novel procedure to evaluate the compositionality of emergent communication by establishing a direct mapping between the emerged words and natural language concepts.
The proposed evaluation procedure involves finding the best-match between emergent words used by the agents and their corresponding natural language concepts. This best-match algorithm provides a global score that quantifies the level of compositionality achieved by the agents, as well as a translation-map that links emergent words to human concepts.
This approach is significant in two ways. First, it allows for a direct and interpretable mapping between the emergent communication and human concepts. This enables researchers to gain deeper insights into the semantic content of the learned communication protocols and understand the emergence of compositionality.
Second, this procedure is multi-disciplinary in nature. It bridges the gap between natural language processing and machine learning, as it combines linguistic concepts with methods from artificial intelligence and communication research. By integrating insights from these multiple disciplines, the evaluation procedure provides a more comprehensive understanding of the compositionality of emergent communication.
In conclusion, the proposed procedure offers a novel and interpretable approach to evaluating compositionality in emergent communication. By establishing a direct mapping between emergent words and natural language concepts, researchers can gain deeper insights into the communication capabilities of artificial agents and foster further progress in the development of advanced communication protocols.
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by jsendak | Mar 25, 2024 | GR & QC Articles
arXiv:2403.14730v1 Announce Type: new
Abstract: In this study, we employ the thermodynamic topological method to classify critical points for the dyonic AdS black holes with QTE in the EGB background. To this end, we find that there is a small/large BH phase transition in any space-time dimension, a conventional critical point exists with the total topological charge of $Q_t=-1$. The existence of the coupling constant $alpha$ gives rise to a more intricate phase structure of the black hole, with the emergence of a triple points requires $alphageq0.5$ and $d=6$. Interestingly, the condition for the simultaneous occurrence of small/intermediate and intermediate/large phase transition is that the coupling constant a takes a special value ($alpha=0.5$), the two conventional critical points $(CP_{1},CP_{2})$ of the black hole are (physical) critical point, and the novel critical point that lacks the capability to minimize the Gibbs free energy. The critical point ($Q_{CP_1}=Q_{CP_2}=-1$) is observed to occur at the maximum extreme points of temperature in the isobaric curve, while the critical point $(Q_{CP_3}=1)$, emerges at the minimum extreme points of temperature. Furthermore, the number of phases at the novel critical point exhibits an upward trend, followed by a subsequent decline at the conventional critical points. With the increase of the coupling constant $(alpha = 1 )$, although the system has three critical points, only $CP_{1}$ is a (physical) critical point, and the $CP_{2}$ serves as the phase annihilation point. This means that the coupling constant $alpha$ has a non-negligible effect on the phase structure of the black hole.
In this study, the thermodynamic topological method is used to classify critical points for dyonic AdS black holes with QTE in the EGB background. The researchers find that there is a small/large black hole phase transition in any space-time dimension and a conventional critical point exists with a total topological charge of $Q_t=-1$. The presence of the coupling constant $alpha$ results in a more complex phase structure for the black hole, including the emergence of a triple point at $alphageq0.5$ and $d=6$. Interestingly, the simultaneous occurrence of small/intermediate and intermediate/large phase transitions requires a special value of the coupling constant ($alpha=0.5$). The black hole has two conventional critical points $(CP_{1},CP_{2})$, which are physical critical points, and a novel critical point that cannot minimize the Gibbs free energy. The critical point ($Q_{CP_1}=Q_{CP_2}=-1$) is observed at the maximum extreme points of temperature in the isobaric curve, while the critical point $(Q_{CP_3}=1)$ emerges at the minimum extreme points of temperature. The number of phases at the novel critical point initially increases and then decreases at the conventional critical points. Increasing the coupling constant $(alpha = 1)$ results in three critical points, but only $CP_{1}$ is a physical critical point, with $CP_{2}$ serving as the phase annihilation point. Therefore, the coupling constant $alpha$ has a significant effect on the phase structure of the black hole.
Future Roadmap
Challenges
- Further research is needed to understand the implications and consequences of the small/large black hole phase transition in different space-time dimensions.
- Exploring the intricate phase structure of black holes with the presence of the coupling constant $alpha$ in various scenarios and dimensions.
- Determining the physical significance and potential applications of the triple point at $alphageq0.5$ and $d=6$ in the phase structure of black holes.
- Investigating the nature and properties of the novel critical point that lacks the capability to minimize the Gibbs free energy.
- Understanding the reasons behind the upward trend followed by a subsequent decline in the number of phases at the novel critical point and conventional critical points.
Opportunities
- Exploring the role of the coupling constant $alpha$ in modifying the phase structure of black holes and its implications in other areas of physics.
- Investigating the connections between the presence of critical points and the thermodynamic properties of black holes.
- Expanding the thermodynamic topological method to study other types of black holes and their phase transitions.
- Exploring potential applications of the novel critical point with unique properties in thermodynamics and related fields.
- Utilizing the knowledge gained from this study to develop new theoretical frameworks and models for understanding black holes and their behavior.
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