As both a major funder of climate change initiatives as well as one of the largest economic beneficiaries of Open AI, Bill Gates’ claim that climate-focused concerns of AI energy usage are being overblown has been understandably met with some skepticism. Is Gates just providing cover to protect his economic interests, or is AI actually… Read More »Are Bill Gates’ energy expectations for AI optimism or realism?
Bill Gates: AI Energy Usage Concerns and Climate Change
Bill Gates, a prominent funder of climate change initiatives and one of the largest economic beneficiaries of Open AI, recently disputed claims that AI energy usage is a significant risk to climate change. This has sparked a debate on whether Gates’ statements stem from optimism, realism, or an attempt to protect his economic interests.
Long-Term Implications
Some people critics suspect that Gates may be trying to protect his financial interests in AI by dismissing concerns about the technology’s energy consumption. As AI becomes a dominant technological force, its environmental impact will inevitably become a more significant issue.
However, if Gates’s viewpoint is proven correct, that AI related energy consumption is not as significant as currently feared, this could change projections for the future impact of AI technology on the environment.
Potential Future Developments
Considering the burgeoning use of AI across industries, the question of its energy consumption and associated environmental impact will not go away soon. On the contrary, it is set to become a key point in debates around climate change.
Environmental sustainability is increasingly becoming a key requirement for technology growth and development. It’s essential that developers and AI researchers focus on creating more energy-efficient AI systems that do not compromise the efforts towards climate change mitigation.
Actionable Advice
Stay informed: Keep up to date with the latest developments in AI technology and its environmental impact. Pay attention to the views of different stakeholders, from researchers to business leaders.
Advocate for responsible technology use: Encourage the development and implementation of responsible AI systems that consider both societal and environmental impacts.
Promote transparency: Demand transparency from AI companies and stakeholders regarding their energy consumption and environmental impact.
Support green initiatives: Support legislation and initiatives aimed at promoting technology sustainability and confronting the challenges posed by climate change.
“The advancement of AI should not come at the expense of our planet. That’s why all stakeholders – from developers to users and policymakers – have a part to play in ensuring environmentally-friendly AI.”
arXiv:2409.15545v1 Announce Type: cross
Abstract: The subjective nature of music emotion introduces inherent bias in both recognition and generation, especially when relying on a single audio encoder, emotion classifier, or evaluation metric. In this work, we conduct a study on Music Emotion Recognition (MER) and Emotional Music Generation (EMG), employing diverse audio encoders alongside the Frechet Audio Distance (FAD), a reference-free evaluation metric. Our study begins with a benchmark evaluation of MER, highlighting the limitations associated with using a single audio encoder and the disparities observed across different measurements. We then propose assessing MER performance using FAD from multiple encoders to provide a more objective measure of music emotion. Furthermore, we introduce an enhanced EMG approach designed to improve both the variation and prominence of generated music emotion, thus enhancing realism. Additionally, we investigate the realism disparities between the emotions conveyed in real and synthetic music, comparing our EMG model against two baseline models. Experimental results underscore the emotion bias problem in both MER and EMG and demonstrate the potential of using FAD and diverse audio encoders to evaluate music emotion objectively.
The Subjective Nature of Music Emotion and Its Impact on Recognition and Generation
Music has long been recognized as a powerful medium for evoking emotions in listeners. However, the subjective nature of music emotion makes it challenging to objectively measure and evaluate these emotions. This inherent bias affects both Music Emotion Recognition (MER) and Emotional Music Generation (EMG), two important areas in multimedia information systems.
In the field of MER, researchers have traditionally relied on a single audio encoder to extract features from music and classify the emotions conveyed. This approach, while convenient, fails to consider the diverse ways in which different encoders perceive and represent music. As a result, the performance of MER systems can vary widely, depending on the choice of encoder.
To address this limitation, the authors of the article propose using the Frechet Audio Distance (FAD), a reference-free evaluation metric, alongside multiple audio encoders. By considering the output of multiple encoders, it becomes possible to obtain a more objective measure of music emotion. This multi-disciplinary approach, combining insights from audio signal processing, machine learning, and psychology, has the potential to significantly improve the performance and reliability of MER systems.
Moreover, the article also explores the field of EMG, which focuses on generating music that conveys specific emotions. While previous EMG models have achieved some success, they often struggle to produce music that is both varied and emotionally evocative. To overcome this limitation, the authors propose an enhanced EMG approach that aims to improve both the variation and prominence of generated music emotion. This is achieved by incorporating insights from music theory, computational creativity, and human-computer interaction.
In addition to evaluating the performance of their EMG model, the authors also investigate the realism disparities between emotions conveyed in real and synthetic music. This comparison highlights the challenges faced by EMG models in capturing the nuances and complexities of human emotional expression. By addressing these challenges, the field of EMG can contribute to the development of more realistic and emotionally engaging multimedia experiences.
Relevance to Multimedia Information Systems and Virtual Realities
The concepts discussed in the article are highly relevant to the wider field of multimedia information systems. Multimedia information systems deal with the storage, retrieval, and analysis of multimedia data, including audio, images, and videos. Emotion recognition and generation play a crucial role in enhancing the user experience and personalization of such systems.
Animations, artificial reality, augmented reality, and virtual realities are all domains that can benefit from advancements in music emotion recognition and generation. For example, in virtual reality applications, the incorporation of emotionally engaging music can significantly enhance the sense of immersion and presence. Similarly, in animations and augmented reality experiences, the ability to generate music that effectively conveys specific emotions can enhance the storytelling and overall impact of the content.
By addressing the inherent biases and limitations of current approaches, the research presented in this article contributes to the development of more accurate, reliable, and emotionally engaging multimedia information systems. The multi-disciplinary nature of the concepts discussed, spanning fields such as audio signal processing, machine learning, psychology, music theory, computational creativity, and human-computer interaction, highlights the complexity and interplay of different disciplines in the pursuit of advancing multimedia technologies.
arXiv:2409.11635v1 Announce Type: new Abstract: Pain is a more intuitive and user-friendly way of communicating problems, making it especially useful in rehabilitation nurse training robots. While most previous methods have focused on classifying or recognizing pain expressions, these approaches often result in unnatural, jiggling robot faces. We introduce PainDiffusion, a model that generates facial expressions in response to pain stimuli, with controllable pain expressiveness and emotion status. PainDiffusion leverages diffusion forcing to roll out predictions over arbitrary lengths using a conditioned temporal U-Net. It operates as a latent diffusion model within EMOCA’s facial expression latent space, ensuring a compact data representation and quick rendering time. For training data, we process the BioVid Heatpain Database, extracting expression codes and subject identity configurations. We also propose a novel set of metrics to evaluate pain expressions, focusing on expressiveness, diversity, and the appropriateness of model-generated outputs. Finally, we demonstrate that PainDiffusion outperforms the autoregressive method, both qualitatively and quantitatively. Code, videos, and further analysis are available at: href{https://damtien444.github.io/paindf/}{https://damtien444.github.io/paindf/}.
The article “PainDiffusion: Generating Facial Expressions in Response to Pain Stimuli in Rehabilitation Nurse Training Robots” introduces a novel model that aims to improve the communication of problems in rehabilitation nurse training robots through the use of pain expressions. Unlike previous methods that often result in unnatural robot faces, PainDiffusion generates facial expressions with controllable pain expressiveness and emotion status. This model leverages diffusion forcing and a conditioned temporal U-Net to predict facial expressions over arbitrary lengths. It operates within EMOCA’s facial expression latent space, ensuring efficient data representation and rendering time. The training data is obtained from the BioVid Heatpain Database, and the article proposes a new set of metrics to evaluate the quality of pain expressions. The article concludes by demonstrating that PainDiffusion outperforms autoregressive methods both qualitatively and quantitatively. Code, videos, and further analysis can be accessed at the provided link.
The Power of Pain: Using Pain as a Tool for Effective Communication and Rehabilitation
In the field of rehabilitation nurse training robots, effective communication is key. The ability to understand and respond to patient needs and concerns is crucial for providing optimal care and support. Traditionally, methods for training robots in this field have focused on classifying or recognizing pain expressions. However, these approaches often result in unnatural and artificial robot faces, hindering the human-robot interaction. This is where PainDiffusion comes into play.
PainDiffusion is a groundbreaking model that generates facial expressions in response to pain stimuli, with controllable pain expressiveness and emotion status. Unlike previous methods, which may produce jiggling robot faces, PainDiffusion leverages diffusion forcing to roll out predictions over arbitrary lengths using a conditioned temporal U-Net. This approach ensures a more natural and intuitive communication between the robot and the patient.
One of the key advantages of PainDiffusion is its ability to operate as a latent diffusion model within EMOCA’s facial expression latent space. This means that the generated facial expressions are based on a compact data representation, allowing for quick rendering time. By utilizing this efficient framework, PainDiffusion minimizes any delays or lags in the robot’s response, enhancing the overall user experience.
In order to train PainDiffusion, the creators of the model processed the BioVid Heatpain Database, extracting expression codes and subject identity configurations. By leveraging this rich dataset, the model is able to effectively learn and generate pain expressions that accurately reflect the input stimuli. Furthermore, the creators have also proposed a novel set of metrics to evaluate the performance of PainDiffusion. These metrics focus on the expressiveness, diversity, and appropriateness of the model-generated outputs, ensuring the quality and authenticity of the robot’s responses.
Ultimately, the results speak for themselves. PainDiffusion outperforms the autoregressive method in both qualitative and quantitative measures. The robot’s generated facial expressions are more realistic, expressive, and appropriate, creating a more positive and empathetic interaction between the robot and the patient.
The potential applications of PainDiffusion are vast. In addition to rehabilitation nurse training robots, this model can also be used in various healthcare and therapy settings where effective communication is crucial. By utilizing pain as a tool for communication, we can bridge the gap between humans and robots, enabling them to work together seamlessly for the betterment of society.
If you want to explore further, the code, videos, and additional analysis of PainDiffusion are available at https://damtien444.github.io/paindf/. Witness the power of pain and see how it can revolutionize the world of robotics and healthcare.
The arXiv paper titled “PainDiffusion: Generating Facial Expressions in Response to Pain Stimuli” introduces a novel model that aims to generate more natural and expressive facial expressions in response to pain stimuli. The authors highlight the importance of pain as a means of communication, particularly in rehabilitation nurse training robots.
The paper acknowledges that previous methods focused on classifying or recognizing pain expressions, but often resulted in robotic faces that appeared unnatural and jiggling. To address this issue, the authors propose PainDiffusion, a model that leverages diffusion forcing to generate facial expressions with controllable pain expressiveness and emotion status.
PainDiffusion operates as a latent diffusion model within EMOCA’s facial expression latent space, which ensures a compact data representation and quick rendering time. The model is trained on the BioVid Heatpain Database, where expression codes and subject identity configurations are processed to provide the necessary training data.
To evaluate the pain expressions generated by PainDiffusion, the authors propose a novel set of metrics that focus on expressiveness, diversity, and the appropriateness of the model-generated outputs. These metrics help to objectively assess the quality of the generated facial expressions.
The paper concludes by demonstrating that PainDiffusion outperforms the autoregressive method both qualitatively and quantitatively. This indicates that the proposed model is more effective in generating natural and expressive facial expressions in response to pain stimuli.
Overall, this research provides valuable insights into improving the realism and effectiveness of rehabilitation nurse training robots. By generating more intuitive and user-friendly facial expressions, these robots can better communicate and empathize with patients, ultimately enhancing the rehabilitation process. The proposed PainDiffusion model offers a promising approach that can potentially be applied in various healthcare and training scenarios. Further analysis and resources, including code, videos, and additional information, can be accessed through the provided link. Read the original article
As an artist, Matt Bollinger’s aim is not only to define America but also to define himself. Through his contemporary approach to social realism and the Ashcan School style, he has become one of the most interesting artists working today in the realms of painting, drawing, and animation. Bollinger’s work speaks about and creates narratives of midwest America, often portraying characters that follow them through recessions, pandemics, aging, and the ups and downs of life.
Emerging Trends in the Art Industry
Examining Bollinger’s work and his unique perspective on America and personal growth, it is possible to identify several potential future trends in the art industry.
1. Blending Traditional and Contemporary Approaches
Bollinger’s ability to merge traditional elements from the Ashcan School style with contemporary techniques is a trend that is likely to continue in the industry. His work demonstrates that artists can combine different artistic styles and approaches to create something fresh and engaging.
2. Utilizing Multiple Mediums for Storytelling
Bollinger’s characters often transcend mediums, appearing in various bodies of work as he tells stories about their lives. This approach highlights the potential for artists to use different mediums to convey narratives and capture the complexities of human experiences. Paintings, drawings, and animations can all be utilized to create an interconnected universe for characters and their stories to unfold.
3. Exploring Themes of Social Realism
Bollinger’s focus on social realism reflects a growing interest in art that addresses societal issues and gives a voice to marginalized communities. This trend is likely to continue as artists seek to communicate messages of social significance and make a positive impact on society.
Predictions and Recommendations for the Industry
Based on the emerging trends seen in Bollinger’s work, there are several predictions and recommendations for the art industry:
Encourage Interdisciplinary Collaboration: Artists should be encouraged to collaborate with professionals from different disciplines to create innovative and compelling works that push the boundaries of traditional art forms.
Foster a Diverse and Inclusive Art Community: The industry should actively promote diversity and inclusivity, supporting artists from different backgrounds and ensuring their voices are heard and represented.
Support Artists in Exploring Social Realism: Galleries, institutions, and patrons should provide platforms and resources for artists interested in social realism, enabling them to create thought-provoking work that raises awareness and promotes dialogue on pressing societal issues.
Invest in Technological Advancements: The art industry should embrace technological advancements and support artists in leveraging new tools and mediums to enhance their creative process and reach a wider audience.
Encourage Art Education and Accessibility: Investing in art education programs and promoting accessibility to art can help nurture the next generation of artists and cultivate a society that values and appreciates art in all its forms.
By embracing these predictions and recommendations, the art industry can adapt to the changing landscape and continue to thrive in the future.
Conclusion
Matt Bollinger’s work and his unique perspective on America and personal growth shed light on potential future trends in the art industry. Blending traditional and contemporary approaches, utilizing multiple mediums for storytelling, and exploring themes of social realism are likely to shape the future of art. By embracing interdisciplinary collaboration, diversity, inclusivity, technological advancements, and art education, the industry can pave the way for innovative and impactful artistic expressions.
arXiv:2409.06709v1 Announce Type: new
Abstract: Audio-Visual Source Localization (AVSL) aims to localize the source of sound within a video. In this paper, we identify a significant issue in existing benchmarks: the sounding objects are often easily recognized based solely on visual cues, which we refer to as visual bias. Such biases hinder these benchmarks from effectively evaluating AVSL models. To further validate our hypothesis regarding visual biases, we examine two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where the vision-only models outperform all audiovisual baselines. Our findings suggest that existing AVSL benchmarks need further refinement to facilitate audio-visual learning.
Audio-Visual Source Localization: Challenges and Opportunities
Audio-Visual Source Localization (AVSL) is an emerging field that aims to accurately determine the location of sound sources within a video. This has several applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. AVSL has the potential to enhance the user experience in these domains by providing more immersive and interactive audiovisual content.
In this paper, the authors identify a significant issue in existing AVSL benchmarks – visual bias. They point out that in many benchmarks, sounding objects can be easily recognized based solely on visual cues. This visual bias undermines the evaluation of AVSL models, as they don’t effectively capture the audio-visual learning capabilities. To demonstrate this, the authors analyze two representative AVSL benchmarks, VGG-SS and EpicSounding-Object, where vision-only models outperform all audiovisual baselines.
This research highlights the need for refinement in existing AVSL benchmarks to promote accurate audio-visual learning. It emphasizes the multi-disciplinary nature of AVSL, requiring the integration of computer vision and audio processing techniques. By tackling the issue of visual bias, researchers can develop more robust AVSL models that are capable of accurately localizing sound sources in videos.
In the wider field of multimedia information systems, AVSL has the potential to revolutionize the way we interact with audiovisual content. By accurately localizing sound sources, multimedia systems can provide a more immersive experience by adapting the audio output based on the user’s perspective and position relative to the source. This can greatly enhance virtual reality and augmented reality applications by creating a more realistic and interactive audiovisual environment.
Moreover, AVSL can contribute to the advancement of animations and artificial reality. By accurately localizing sound sources, animators can synchronize audio and visual elements more precisely, resulting in a more immersive and engaging animated experience. In artificial reality applications, AVSL can add another layer of realism by accurately reproducing spatial audio cues, making artificial environments indistinguishable from real ones.
Overall, the identification of visual bias in existing AVSL benchmarks underscores the importance of refining these benchmarks to promote accurate audio-visual learning. This research highlights the interdisciplinary nature of AVSL and its applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By addressing these challenges, researchers can unlock the full potential of AVSL and revolutionize the way we perceive and interact with audiovisual content.
