by jsendak | Jun 28, 2024 | DS Articles
You can have a successful career in AI by following the steps in this article.
Building a Successful Career in AI: Long-Term Implications and Future Developments
AI (Artificial Intelligence) is a rapidly growing field with a wide range of applications. From automating routine tasks to providing insightful data analysis, the AI industry poses lucrative career opportunities. Given the right approach and necessary skills, thriving in the AI field can be a realistic goal.
Long-Term Implications
With the world becoming increasingly digital, the demand for AI-related jobs is bound to rise continuously. Knowledge of AI will likely be increasingly integrated into numerous job descriptions and sectors of the economy. Consequently, there may be a wider variety of job roles available to those specialized in AI, while non-specialists risk becoming less competitive in the job market.
AI advancements might also impact larger societal aspects such as privacy, ethics or law. As such, careers in sectors specialising in the relations between AI and these aspects might gain prominence.
Possible Future Developments
As AI continues to evolve, careers in the field will rise and adapt accordingly. Areas such as machine learning, natural language processing, and robotics, might experience significant growth. Furthermore, roles revolving around emerging technologies like quantum computing and blockchain, which could greatly benefit from AI, might surface in the employment landscape.
Actionable Advice
- Gain Relevant Skills: Hands-on coding abilities, problem-solving aptitude, and a thorough understanding of machine learning methods are essential. Enrolling in appropriate courses could equip aspiring AI professionals with these skills.
- Stay Updated: The AI field evolves rapidly. Following reputable technology news sources, attending industry webinars, workshops, seminars, and networking events could help stay ahead of significant industry alterations.
- Specialize: Due to the array of applications of AI and the potential for future growth, specializing in a niche technology or role could set an AI career apart from the rest. This requires strong foresight into future trends and the aptitude to master the required skills.
Remaining versatile, proactive, and adaptable will be crucial in the evolving AI landscape. By acquiring in-demand skills, developing a specialization, and staying current on industry developments, one can secure a rewarding career in this rapidly advancing field.
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by jsendak | Jun 28, 2024 | DS Articles
Explore how data science and analytics revolutionize route planning with ML, IoT, and autonomous vehicles, enhancing efficiency and reducing costs.
Long-Term Implications and Possible Future Developments for Route Planning
The convergence of data science, analytics, Machine Learning (ML), Internet of Things (IoT), and autonomous vehicles signals a major leap in the evolution of route planning. Not only does this revolution hold the promise of enhancing efficiency but it also has far-reaching implications for reducing costs in transportation and logistics.
Implications and Future Prospects
- Efficiency Improvement: The adoption of sophisticated technologies like ML and IoT is set to enhance route planning efficiency. ML algorithms can analyze past data to predict future patterns and automatically optimize routes in real-time, making operations faster and more streamlined.
- Cost Reduction: As efficiency improves, significant cost reductions will be realized. These savings may stem from reduced fuel consumption, less vehicle wear and tear, and minimized downtime.
- Autonomous Vehicles Integration: The rise of autonomous vehicles will mark a remarkable shift in route planning. These vehicles’ ability to continually learn from the environment and adapt to traffic changes will reinvent route efficiency.
Actionable Advice based on these Insights
To prepare for and adequately harness these impending changes, organizations should consider implementing the following strategies:
- Invest in Technology: In order to capitalize on the benefits of ML, IoT, and autonomous vehicles, businesses will need to invest in appropriate technology. This might involve upgrading existing systems or procuring new ones.
- Train Staff: Adopting new technology is not just about acquiring the right tools. It also requires skilled individuals who can manage these tools. Therefore, businesses should invest in training their staff or hiring new personnel proficient in these technologies.
- Collaborate with Experts: Since route planning is evolving at a rapid pace, businesses might also benefit from collaborating with experts in the field. This could help them stay abreast of the latest technological developments and devise an optimal approach to route planning.
- Data Management: Finally, as the role of data becomes increasingly prominent, businesses should have effective data management strategies in place. This will enable them to leverage data for insights, and ensure the privacy and protection of data.
As ML, IoT, and autonomous vehicles continue to revolutionize route planning, businesses prepared for this future stand to reap substantial benefits in terms of improved efficiency and reduced costs.
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by jsendak | Jun 28, 2024 | AI
arXiv:2406.18544v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view synthesis due to its detailed expressive ability and highly efficient rendering speed. Unfortunately, creating relightable 3D assets with 3DGS is still problematic, particularly for reflective objects, as its discontinuous representation raises difficulties in constraining geometries. Inspired by previous works, the signed distance field (SDF) can serve as an effective way for geometry regularization. However, a direct incorporation between Gaussians and SDF significantly slows training. To this end, we propose GS-ROR for reflective objects relighting with 3DGS aided by SDF priors. At the core of our method is the mutual supervision of the depth and normal between deferred Gaussians and SDF, which avoids the expensive volume rendering of SDF. Thanks to this mutual supervision, the learned deferred Gaussians are well-constrained with a minimal time cost. As the Gaussians are rendered in a deferred shading mode, while the alpha-blended Gaussians are smooth, individual Gaussians may still be outliers, yielding floater artifacts. Therefore, we further introduce an SDF-aware pruning strategy to remove Gaussian outliers, which are located distant from the surface defined by SDF, avoiding the floater issue. Consequently, our method outperforms the existing Gaussian-based inverse rendering methods in terms of relighting quality. Our method also exhibits competitive relighting quality compared to NeRF-based methods with at most 25% of training time and allows rendering at 200+ frames per second on an RTX4090.
The article “GS-ROR: Reflective Object Relighting with 3D Gaussian Splatting and Signed Distance Field Priors” introduces a novel approach for creating relightable 3D assets using 3D Gaussian Splatting (3DGS) and signed distance field (SDF) priors. While 3DGS has proven effective for view synthesis, it struggles with reflective objects due to its discontinuous representation. By incorporating SDF as a geometry regularization technique, the authors aim to overcome this limitation. However, directly combining Gaussians and SDF results in slower training. To address this, they propose GS-ROR, a method that achieves mutual supervision of depth and normal between deferred Gaussians and SDF, avoiding the need for expensive volume rendering. Additionally, they introduce an SDF-aware pruning strategy to remove Gaussian outliers, reducing floater artifacts. The results show that GS-ROR outperforms existing Gaussian-based inverse rendering methods in terms of relighting quality and is competitive with NeRF-based methods, while significantly reducing training time and allowing for real-time rendering.
Exploring GS-ROR: Enhancing Reflective Object Relighting with 3D Gaussian Splatting and SDF Priors
Relighting 3D assets, especially reflective objects, has always been a challenging task. While 3D Gaussian Splatting (3DGS) has shown promise in novel view synthesis with its detailed expressive ability and efficient rendering speed, creating relightable 3D assets using this method still poses difficulties due to its discontinuous representation and geometric constraints.
Inspired by previous works, researchers have found that incorporating signed distance fields (SDF) can be an effective way to regularize geometry in 3DGS. However, directly combining Gaussians and SDF in the training process can significantly slow down the training time.
To address this issue, a new approach called GS-ROR (Gaussian Splatting with SDF-aided Reflective Object Relighting) has been proposed. The core idea behind GS-ROR is the mutual supervision of depth and normal information between deferred Gaussians and SDF. This mutual supervision eliminates the need for expensive volume rendering of SDF, resulting in a minimal time cost for training.
By rendering the Gaussians in a deferred shading mode, the alpha-blended Gaussians maintain smoothness. However, individual Gaussians may still act as outliers, leading to floater artifacts. To overcome this problem, an SDF-aware pruning strategy has been introduced in GS-ROR. This strategy identifies and removes Gaussian outliers that are located far from the surface defined by SDF, effectively eliminating floater artifacts.
As a result of these innovations, GS-ROR outperforms existing Gaussian-based inverse rendering methods in terms of relighting quality for reflective objects. Furthermore, it achieves competitive relighting quality compared to NeRF-based methods but with significantly reduced training time, requiring only 25% of the training duration. Additionally, GS-ROR enables real-time rendering with a frame rate of 200+ frames per second on a high-end graphics card like RTX4090.
In conclusion, GS-ROR introduces a novel and efficient approach for relighting reflective objects using 3D Gaussian Splatting. By incorporating SDF priors and implementing mutual supervision, it overcomes the limitations of the traditional method, resulting in improved relighting quality. With its ability to achieve real-time rendering and reduced training time, GS-ROR opens up new possibilities for relighting in the field of computer graphics.
The paper titled “GS-ROR: Reflective Objects Relighting with 3D Gaussian Splatting aided by SDF Priors” introduces a novel approach to address the challenges of creating relightable 3D assets, particularly for reflective objects, using 3D Gaussian Splatting (3DGS) and signed distance field (SDF) priors.
The authors acknowledge that while 3DGS has demonstrated its effectiveness in novel view synthesis with its detailed expressive ability and efficient rendering speed, it falls short when it comes to handling reflective objects due to its discontinuous representation. This raises difficulties in constraining geometries and achieving accurate relighting.
To overcome these limitations, the authors propose GS-ROR, a method that combines 3DGS with SDF priors. By incorporating the SDF as a regularization technique for geometry, the authors aim to improve the accuracy and efficiency of relighting for reflective objects.
One of the key contributions of GS-ROR is the mutual supervision of depth and normal between deferred Gaussians and SDF. This approach avoids the computationally expensive volume rendering of SDF while ensuring that the learned deferred Gaussians are well-constrained. This mutual supervision helps achieve accurate relighting with minimal time cost.
Additionally, the paper introduces an SDF-aware pruning strategy to address the issue of Gaussian outliers. While the alpha-blended Gaussians in deferred shading mode generally produce smooth results, individual Gaussians may still be outliers, causing floater artifacts. The SDF-aware pruning strategy effectively removes these outliers located far from the surface defined by SDF, thus mitigating the floater issue.
According to the paper, the proposed GS-ROR method outperforms existing Gaussian-based inverse rendering methods in terms of relighting quality. It also exhibits competitive relighting quality compared to NeRF-based methods while requiring significantly less training time, with a rendering speed of 200+ frames per second on an RTX4090.
Overall, this paper presents an innovative approach to address the challenges of relighting reflective objects using 3DGS and SDF priors. The mutual supervision of depth and normal, along with the SDF-aware pruning strategy, contributes to improved relighting quality and efficiency. The results demonstrate the potential of GS-ROR as a valuable tool for relighting in various applications, such as virtual reality, gaming, and computer graphics.
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by jsendak | Jun 28, 2024 | Computer Science
arXiv:2406.19388v1 Announce Type: cross
Abstract: Generating ambient sounds and effects is a challenging problem due to data scarcity and often insufficient caption quality, making it difficult to employ large-scale generative models for the task. In this work, we tackle the problem by introducing two new models. First, we propose AutoCap, a high-quality and efficient automatic audio captioning model. We show that by leveraging metadata available with the audio modality, we can substantially improve the quality of captions. AutoCap reaches CIDEr score of 83.2, marking a 3.2% improvement from the best available captioning model at four times faster inference speed. We then use AutoCap to caption clips from existing datasets, obtaining 761,000 audio clips with high-quality captions, forming the largest available audio-text dataset. Second, we propose GenAu, a scalable transformer-based audio generation architecture that we scale up to 1.25B parameters and train with our new dataset. When compared to state-of-the-art audio generators, GenAu obtains significant improvements of 15.7% in FAD score, 22.7% in IS, and 13.5% in CLAP score, indicating significantly improved quality of generated audio compared to previous works. This shows that the quality of data is often as important as its quantity. Besides, since AutoCap is fully automatic, new audio samples can be added to the training dataset, unlocking the training of even larger generative models for audio synthesis.
Improving Ambient Sound Generation with New Models
Ambient sound generation is a complex task that has posed challenges due to limited data availability and the quality of captions. However, this article presents two novel models that address these issues effectively. The first model, AutoCap, is an automatic audio captioning model that leverages metadata to enhance the quality of captions. This approach not only improves the accuracy of captions but also allows for faster inference speed. In fact, AutoCap achieves an impressive CIDEr score of 83.2, which is a 3.2% improvement over existing captioning models.
AutoCap’s ability to generate high-quality captions is applied to existing datasets, resulting in the creation of a groundbreaking audio-text dataset containing 761,000 audio clips. This dataset, with its accurate and descriptive captions, serves as a valuable resource for future research in the field of ambient sound generation.
The second model introduced in this article is GenAu, a transformer-based audio generation architecture with a significant scale of 1.25B parameters. Trained on the newly created audio-text dataset, GenAu surpasses state-of-the-art audio generators. It achieves remarkable improvements of 15.7% in FAD score, 22.7% in IS, and 13.5% in CLAP score, indicating a substantial enhancement in the quality of generated audio compared to previous works.
These advancements highlight the significance of both the quality and quantity of data in multimedia information systems. By utilizing AutoCap’s automatic captioning capability, the training dataset for generative audio models can be exponentially expanded. This, in turn, unlocks the potential for training even larger models for audio synthesis, ultimately improving the overall realism and quality of generated ambient sounds.
This research encompasses various disciplines within multimedia information systems, including audio processing, natural language processing, and artificial intelligence. The integration of metadata with audio modality in AutoCap demonstrates the multi-disciplinary nature of the proposed approach. Furthermore, the utilization of transformer-based architectures in GenAu showcases the importance of leveraging advancements in deep learning techniques, specifically in the context of audio generation.
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by jsendak | Jun 28, 2024 | AI
arXiv:2406.18615v1 Announce Type: new
Abstract: Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A Partial-Order Plan (POP) allows two actions with no ordering between them, thus providing the flexibility of executing actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in a POP to reduce its overall execution time. While extensive studies exist on improving the flexibility of a POP by optimizing its action orderings through plan deordering and reordering, there has been limited focus on the flexibility of executing actions concurrently in a plan. Execution concurrency in a POP can be achieved by incorporating action non-concurrency constraints, specifying which actions can not be executed in parallel. This work formalizes the conditions for non-concurrency constraints to transform a POP into a parallel plan. We also introduce an algorithm to enhance the plan’s concurrency by optimizing resource utilization through substitutions of its subplans with respect to the corresponding planning task. Our algorithm employs block deordering that eliminates orderings in a POP by encapsulating coherent actions in blocks, and then exploits blocks as candidate subplans for substitutions. Experiments over the benchmark problems from International Planning Competitions (IPC) exhibit significant improvement in plan concurrency, specifically, with improvement in 25% of the plans, and an overall increase of 2.1% in concurrency.
Enhancing Flexibility in AI Planning with Partial-Order Plans and Execution Concurrency
AI planning plays a crucial role in facilitating execution flexibility in various tasks such as plan reuse, modification, and decomposition. Partial-Order Plans (POPs) particularly enable this flexibility due to their less constrained nature. Unlike total-order plans, a POP allows for two actions to have no specific ordering between them, thereby providing the flexibility to execute actions in different sequences.
However, the flexibility of POPs can be further extended by incorporating execution concurrency, which enables parallel execution of actions in a plan. By specifying action non-concurrency constraints, which define actions that cannot be executed in parallel, the overall execution time of a POP can be significantly reduced.
This article introduces a formalization of the conditions for non-concurrency constraints to transform a POP into a parallel plan. By identifying actions that cannot be executed concurrently, this approach enhances the plan’s flexibility by allowing for simultaneous execution of unrelated actions. The algorithm proposed in this work optimizes resource utilization through substitutions of subplans, which are encapsulated coherent actions, to improve plan concurrency.
The algorithm utilizes block deordering to eliminate orderings within a POP. By grouping coherent actions into blocks, the algorithm can then identify blocks that can be substituted and executed in parallel, thus enhancing concurrency. Experimental results conducted on benchmark problems from International Planning Competitions (IPC) show a significant improvement in plan concurrency, with a 25% improvement in individual plans and an overall increase in concurrency of 2.1%.
This research brings together various disciplines, showcasing the multi-disciplinary nature of the concepts explored. It combines principles from AI planning, optimization, and resource utilization to enhance the flexibility and efficiency of planning systems. By incorporating execution concurrency into the already flexible framework of POPs, this work contributes to the field of AI planning by providing new insights and techniques to handle complex planning scenarios.
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