by jsendak | Jul 13, 2024 | DS Articles
Image by Annette from Pixabay One of the common dilemmas in a typical large enterprise is that multiple groups from different geographies and business units are often spending innovation budget to solve similar problems. The left hand typically doesn’t know what the right hand is doing. But even if an innovation initiative’s leaders are diligent,… Read More »Boosting innovation initiatives with knowledge graphs
Enhancing Innovation Initiatives with Knowledge Graphs
Large enterprises often encounter the perplexing issue of managing resources spread across various groups, each spending a large portion of the innovation budget to solve similar issues, unaware of their contemporaries’ efforts. In a bid to rectify this incongruity, a novel solution is being adopted wherein knowledge graphs are utilized to provide a comprehensive understanding of all ongoing innovation projects, thereby aligning resources more effectively.
Long-term Implications
Large organizations can potentially leverage the data provided by knowledge graphs to gain insight into their innovation projects and make substantial changes to their operational processes. This could lead to more resources being implemented effectively, and to resolving the same issues repetitively in different branches of the same organization. Consequently, this could introduce enterprise-wide efficiency, leading to substantial cost savings and a unified approach to innovation.
Future Developments
As the power of data continues to grow, so too does the potential of knowledge graphs. With the development of more sophisticated algorithms and machine learning models, knowledge graphs could provide insights beyond the scope of current capabilities. Looking forward, these advancements could result in the provision of predictive insights, fostering the capability to forecast trends and foresee potential issues before they arise. Such enhancements could provide organizations with a competitive edge and allow them the opportunity to be proactive rather than reactive.
Actionable Advice
- Recognizing Overlaps: Companies should prioritize recognizing overlapping functions and spending within their organizations. Identifying these factors would highlight areas where collaborative efforts and cross-team work could eliminate redundancies and optimize resource use.
- Investing in Knowledge Graphs: Considering the immense value in leveraging knowledge graphs, there is significant benefit in investing in such technology, and the technical skills required to understand and exploit the data they provide.
- Promoting Cross-Team Collaboration: Once overlaps have been identified and knowledge graphs have been understood, promoting enterprise-wide collaboration will foster an environment conducive to innovation and progress.
- Developing Predictive Analysis: As technology continues to advance, companies should make a concerted effort to develop their own predictive capabilities. Such foreknowledge could allow for more strategic decision-making and a more streamlined approach to future planning.
“One of the common dilemmas in a typical large enterprise is that multiple groups from different geographies and business units are often spending innovation budget to solve similar problems.”
In conclusion, while there is ongoing effort dedicated to solving the cited dilemma, fostering a culture of collaboration, and a willingness to adapt technologically, organizations can optimize their resources and better streamline their innovation initiatives. Knowledge graphs offer a unique solution to this problem and should be embraced by large enterprises.
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by jsendak | Jul 11, 2024 | AI
arXiv:2407.07462v1 Announce Type: cross Abstract: Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requires an accurate perception of the environment. To achieve this, machine learning methods rely on large datasets, but to this day, no such datasets are available for autonomous trucks. In this work, we present MAN TruckScenes, the first multimodal dataset for autonomous trucking. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time. It comprises more than 740 scenes of 20 s each within a multitude of different environmental conditions. The sensor set includes 4 cameras, 6 lidar, 6 radar sensors, 2 IMUs, and a high-precision GNSS. The dataset’s 3D bounding boxes were manually annotated and carefully reviewed to achieve a high quality standard. Bounding boxes are available for 27 object classes, 15 attributes, and a range of more than 230 m. The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications. Additionally, MAN TruckScenes is the first dataset to provide 4D radar data with 360{deg} coverage and is thereby the largest radar dataset with annotated 3D bounding boxes. Finally, we provide extensive dataset analysis and baseline results. The dataset, development kit and more are available online.
The article “MAN TruckScenes: A Multimodal Dataset for Autonomous Trucking” introduces the first-ever multimodal dataset specifically designed for autonomous trucking. Autonomous trucking is a technology with immense potential to revolutionize logistics and environmental impact. However, ensuring the safety of autonomous trucks on public roads requires accurate perception of the environment. Machine learning methods rely on large datasets, but until now, no such datasets have been available for autonomous trucks.
In response to this gap, the authors present MAN TruckScenes, a comprehensive dataset that allows the research community to explore the unique challenges faced by autonomous trucks, including trailer occlusions, novel sensor perspectives, and terminal environments. The dataset consists of over 740 scenes, each lasting 20 seconds, captured in various environmental conditions. It includes 4 cameras, 6 lidar sensors, 6 radar sensors, 2 IMUs, and a high-precision GNSS. The dataset features manually annotated 3D bounding boxes for 27 object classes, 15 attributes, and a range of over 230 meters.
What sets MAN TruckScenes apart is its provision of 4D radar data with 360-degree coverage, making it the largest radar dataset with annotated 3D bounding boxes. The dataset also offers extensive analysis and baseline results. By making this dataset available online, the authors aim to facilitate research and development in the field of autonomous trucking, thereby advancing the technology and its potential impact on logistics and the environment.
The Importance of MAN TruckScenes Dataset for Autonomous Trucking
The development of autonomous trucking is a groundbreaking technology that has the potential to revolutionize modern logistics and make a significant impact on the environment. However, ensuring the safety and efficiency of these autonomous trucks on public roads is a crucial concern that requires a deep understanding of the surrounding environment. To achieve this, machine learning methods heavily rely on large datasets, but unfortunately, no such datasets have been available specifically for autonomous trucks, until now.
In a recent breakthrough, researchers have introduced the MAN TruckScenes dataset, marking the first multimodal dataset designed exclusively for autonomous trucking. This dataset opens new possibilities for the research community to explore and tackle various truck-specific challenges that were previously unaddressed, such as trailer occlusions, novel sensor perspectives, and diverse terminal environments.
The MAN TruckScenes dataset consists of more than 740 scenes, each lasting 20 seconds, encompassing a wide range of environmental conditions. The dataset includes a comprehensive sensor set, featuring 4 cameras, 6 lidar sensors, 6 radar sensors, 2 IMUs, and a high-precision GNSS. This diverse sensor setup allows for a holistic and multidimensional perception of the environment, significantly enhancing the accuracy and reliability of the autonomous trucking system.
One notable aspect of the MAN TruckScenes dataset is its meticulously annotated 3D bounding boxes. These bounding boxes have been manually annotated and thoroughly reviewed to uphold a high standard of accuracy and quality. The dataset provides bounding boxes for 27 object classes and 15 attributes within a range of over 230 meters. This level of detail and precision enables precise object detection, tracking, and recognition, facilitating a multitude of applications in the autonomous trucking domain.
Furthermore, MAN TruckScenes introduces a unique feature: 4D radar data with 360° coverage. This is an unprecedented addition to the dataset, making it the most extensive radar dataset available with annotated 3D bounding boxes. The inclusion of 4D radar data enables a more comprehensive understanding of the surrounding environment, enhancing the perception capabilities of autonomous trucks and improving their decision-making processes.
In addition to its rich data, MAN TruckScenes provides researchers with a comprehensive dataset analysis and baseline results. This analysis allows researchers to gain valuable insights into the dataset’s characteristics and performance, enabling them to develop innovative solutions and algorithms specifically tailored for autonomous trucking.
The availability of the MAN TruckScenes dataset marks a significant step forward in advancing the field of autonomous trucking. By providing researchers with a dedicated dataset, including truck-specific challenges and a diverse sensor setup, this dataset empowers the research community to tackle crucial obstacles and develop robust, safe, and efficient autonomous trucking systems.
Researchers and developers interested in exploring the MAN TruckScenes dataset can access it online, along with a development kit and additional resources. The introduction of this dataset holds great promise for the future of autonomous trucking and paves the way for continued improvements in logistics, safety, and environmental impact.
The announcement of the MAN TruckScenes dataset marks a significant milestone in the development of autonomous trucking technology. The availability of a multimodal dataset specifically designed for autonomous trucks is a crucial step towards ensuring their safety on public roads.
One of the key challenges in autonomous trucking is accurately perceiving the environment in which the trucks operate. This is particularly important due to the unique challenges that trucks face, such as trailer occlusions and terminal environments. By providing a dataset that includes these specific challenges, MAN TruckScenes allows researchers and developers to better understand and address these issues.
The dataset itself is impressive in its scale and comprehensiveness. With over 740 scenes, each lasting 20 seconds, and captured in a multitude of different environmental conditions, it provides a rich and diverse set of data for training and testing autonomous truck perception systems. The inclusion of 4 cameras, 6 lidar sensors, 6 radar sensors, 2 IMUs, and a high-precision GNSS ensures that the dataset captures a wide range of sensor perspectives, enabling researchers to develop robust perception algorithms.
One of the standout features of the MAN TruckScenes dataset is the manual annotation of 3D bounding boxes for 27 object classes, 15 attributes, and a range of more than 230 meters. This level of detail and accuracy in annotation is crucial for training machine learning models to accurately detect and track objects in the truck’s environment.
Furthermore, the dataset includes 4D radar data with 360-degree coverage, making it the largest radar dataset with annotated 3D bounding boxes. This is a significant addition as radar data is particularly valuable for object detection and tracking, especially in adverse weather conditions or low-light situations.
The availability of the dataset, along with a development kit and extensive dataset analysis, is a testament to the commitment of the researchers in fostering collaboration and advancement in the field of autonomous trucking. It provides a valuable resource for the research community to develop and benchmark new algorithms and techniques.
Looking ahead, the MAN TruckScenes dataset opens up numerous possibilities for further research and development. It can serve as a benchmark for evaluating the performance of autonomous truck perception systems, allowing researchers to compare and improve upon existing methods. Additionally, the dataset can be used to train and test new algorithms for trailer occlusion handling, novel sensor fusion techniques, and advanced perception algorithms tailored specifically for terminal environments.
Overall, the release of the MAN TruckScenes dataset is a significant contribution to the field of autonomous trucking. It not only provides a valuable resource for researchers and developers but also highlights the importance of specialized datasets in advancing the safety and capabilities of autonomous vehicles.
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by jsendak | Jul 10, 2024 | DS Articles
Mixture of Experts (MoE) architecture is defined by a mix or blend of different “expert” models working together to complete a specific problem.
Analysis of Mixture of Experts: Long-term Implications and Future Developments
The Mixture of Experts (MoE) architecture in the field of machine learning presents fascinating prospects. It essentially refers to a blend of different expert, lite and full-fledged sub-models, each focused on a specific aspect of a problem, working in harmony to solve complex tasks. This innovative approach carries enormous potential as it brings together the strengths of multiple specialists to tackle a vast array of problem domains.
Long-Term Implications
Indeed, this paradigm shift will result in manifold implications. For a start, the MoE architecture can lead to a more comprehensive understanding and processing of complex data systems, as each expert sub-model can focus on its own specialty. Additionally, this model suggests the possibility of adaptive learning, where the main system learns to delegate tasks to the appropriate expert based on their performance and expertise in past problem-solving tasks.
The MoE approach is a move towards decentralized problem-solving, offering a more dynamic, adaptable, and nuanced way of data processing and decision making.
On another note, this architecture also encourages continual learning and could open doors for the inclusion of new expert models as technology and understanding of data evolve. Hence, this model has the potential to be future-proof, adapting and evolving alongside technological advancements.
Future Developments
Given the enormous potential of the MoE approach, it’s quite apparent that we can anticipate several key developments in the future. Machine learning models built on the MoE architecture could become more complex, with an increasing number of experts to handle specific domains of a problem. This could result in an unprecedented sophistication of problem-solving capabilities.
The evolution towards systems that allocate tasks more efficiently to the most qualified expert models is another future development that appears probable. Over time, we may even see a rise in the self-improvement of expert models, driven by continual learning from the larger system’s experience of which experts perform best in what circumstances.
Actionable Advice
- Invest in Education: To harness the potential of the MoE architecture, invest in machine learning education and training. As the sector evolves, it’s crucial to stay at the helm of cutting-edge tendencies.
- Build Adaptable Models: When developing machine learning systems, aim at creating adaptable models that can incorporate new technologies and understandings. This will make the system future-proof and enhance its capacity to resolve increasingly intricate issues.
- Focus on Decentralization: Considering the trend toward decentralization in MoE architecture, strive to develop systems that can delegate tasks effectively based on each expert model’s specialization and past performance.
In conclusion, the Mixture of Experts architecture is set to transform the machine learning landscape in fascinating ways. By embracing this model, we can tap into a more dynamic, adaptable and understanding-driven mode of problem resolution. The road ahead is full of opportunities and challenges and it’s up to us to leverage them wisely.
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by jsendak | Jul 9, 2024 | AI
Self-supervised learning for pre-training (SSP) can help the network learn better low-level features, especially when the size of the training set is small. In contrastive pre-training, the…
Introduction:
In the realm of machine learning, the effectiveness of training neural networks heavily relies on the quality and size of the training dataset. However, when faced with limited data, traditional pre-training methods often struggle to capture accurate low-level features. Fortunately, a promising solution has emerged in the form of self-supervised learning for pre-training (SSP). This innovative approach has demonstrated its ability to enhance the network’s understanding of low-level features, particularly in scenarios with small training sets. In contrast to conventional pre-training methods, SSP leverages the power of contrastive pre-training to unlock the network’s true potential. In this article, we delve into the core themes surrounding SSP and explore how it revolutionizes the field of pre-training by enabling networks to learn more effectively even in data-constrained environments.
Self-supervised learning for pre-training (SSP) and contrastive pre-training are two popular techniques used in the field of machine learning to improve the performance of neural networks. While both approaches have their advantages and have been proven effective in various tasks, they also bring forth some challenges and limitations. In this article, we will explore the underlying themes and concepts of SSP and contrastive pre-training in a new light, proposing innovative solutions and ideas to overcome these challenges.
The Power of Self-Supervised Learning for Pre-Training
Self-supervised learning focuses on leveraging unlabeled data to pre-train the neural network. This technique is particularly useful in scenarios where labeled data is scarce or expensive to obtain. Instead of relying on human annotations, the network learns to generate its own supervision signals from the data itself. This approach allows the network to learn a rich set of low-level features, which can be crucial for downstream tasks like image recognition or natural language processing.
One of the main challenges of SSP is ensuring the quality and diversity of the generated supervision signals. Lack of diversity in the pre-training data can result in biased representations and poor generalization to new data. To address this, we propose the use of data augmentation techniques that introduce controlled perturbations to the unlabeled data. By systematically varying the input and exposing the network to a wide range of transformations, we can encourage the learning of robust features that are invariant to such perturbations.
Contrastive Pre-Training: Unleashing the Power of Positive and Negative Examples
Contrastive pre-training, on the other hand, focuses on learning representations by contrasting positive and negative examples. It operates under the assumption that similar examples should be closer in the embedding space, while dissimilar examples should be farther apart. By training the network to differentiate between positive and negative pairs, it learns to capture meaningful and discriminative features.
While contrastive pre-training has shown impressive results, it suffers from a few limitations. One key challenge is the selection of negative examples. Randomly sampling negatives from the entire dataset can lead to suboptimal representations, as it does not take into account the semantic relationships between examples. To address this, we propose the use of clustering algorithms to group semantically related instances together. By ensuring that the negative examples are truly dissimilar and representative of different classes or categories, we can enhance the discriminative power of the learned embeddings.
Integrating SSP and Contrastive Pre-Training for Enhanced Performance
Both SSP and contrastive pre-training have their own strengths, but they can be even more powerful when combined. By leveraging self-supervised learning to pre-train the network and then fine-tuning using the contrastive loss, we can achieve a two-step learning process that captures both low-level features and high-level semantics.
However, the challenge lies in designing an effective architecture that combines both techniques seamlessly. We propose the use of a dual pathway architecture, where one pathway is responsible for self-supervised learning and low-level feature extraction, while the other pathway focuses on contrastive pre-training and higher-level semantic representation. By allowing the pathways to interact and share information, we can create synergistic effects that enhance the overall performance.
Conclusion: Self-supervised learning for pre-training (SSP) and contrastive pre-training are powerful techniques that can significantly improve the performance of neural networks. By addressing the challenges and limitations of these techniques through innovative solutions such as data augmentation and clustering-based negative selection, we can unlock their full potential. Integrating SSP and contrastive pre-training in a dual pathway architecture offers a promising approach to learn both low-level features and high-level semantics. These advancements pave the way for more robust and effective machine learning models that can tackle real-world challenges.
network is trained to distinguish between similar and dissimilar examples. This approach has shown promising results in various computer vision and natural language processing tasks.
One key advantage of self-supervised learning for pre-training is that it can leverage large amounts of unlabeled data to learn useful representations. By designing pretext tasks that require the model to make predictions about the input data without any external labels, the network can learn to capture meaningful patterns and structure in the data. This is particularly useful when the size of the labeled training set is limited, as it allows the model to generalize better to unseen examples.
In contrastive pre-training, the focus is on training the network to discriminate between similar and dissimilar examples. This is achieved by creating pairs of augmented versions of the same input and contrasting them with pairs of augmented versions of different inputs. The network is then trained to maximize the similarity between the representations of similar inputs and minimize the similarity between the representations of dissimilar inputs.
The advantage of contrastive pre-training is that it encourages the model to learn more discriminative features, which can be beneficial for downstream tasks that require fine-grained distinctions. For example, in object recognition, the model can learn to differentiate between different object classes more effectively.
However, self-supervised learning for pre-training, particularly with tasks like autoencoding or predicting missing parts of an image, can enable the network to learn a richer set of low-level features. These features can capture more detailed and fine-grained information about the input data, which can be valuable for various tasks such as image segmentation, image generation, or even unsupervised anomaly detection.
Moving forward, a possible direction for research is to explore the combination of self-supervised and contrastive pre-training methods. By leveraging the benefits of both approaches, it might be possible to achieve even better performance in various domains. Additionally, investigating the impact of different pretext tasks and designing more effective ones could further enhance the capabilities of self-supervised learning for pre-training. Overall, the field of self-supervised learning is rapidly evolving, and it holds great potential for improving the performance of deep neural networks in a wide range of applications.
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by jsendak | Jul 8, 2024 | AI
arXiv:2407.03340v1 Announce Type: new
Abstract: The addressee estimation (understanding to whom somebody is talking) is a fundamental task for human activity recognition in multi-party conversation scenarios. Specifically, in the field of human-robot interaction, it becomes even more crucial to enable social robots to participate in such interactive contexts. However, it is usually implemented as a binary classification task, restricting the robot’s capability to estimate whether it was addressed and limiting its interactive skills. For a social robot to gain the trust of humans, it is also important to manifest a certain level of transparency and explainability. Explainable artificial intelligence thus plays a significant role in the current machine learning applications and models, to provide explanations for their decisions besides excellent performance. In our work, we a) present an addressee estimation model with improved performance in comparison with the previous SOTA; b) further modify this model to include inherently explainable attention-based segments; c) implement the explainable addressee estimation as part of a modular cognitive architecture for multi-party conversation in an iCub robot; d) propose several ways to incorporate explainability and transparency in the aforementioned architecture; and e) perform a pilot user study to analyze the effect of various explanations on how human participants perceive the robot.
Improving Addressee Estimation in Multi-Party Conversation Scenarios
Understanding to whom somebody is speaking is a fundamental task for human activity recognition in multi-party conversation scenarios. This becomes even more crucial in the field of human-robot interaction, as it enables social robots to actively participate in interactive contexts. However, the traditional approach of treating addressee estimation as a binary classification task limits the robot’s capability to only determine whether it was addressed or not, restricting its interactive skills.
In our work, we propose a novel addressee estimation model that not only outperforms the previous state-of-the-art model in terms of performance but also incorporates explainability as a crucial component. Explainable artificial intelligence (XAI) has gained significant attention in recent years due to its potential to provide explanations for the decisions made by machine learning models. By including explainability in addressee estimation, we aim to enhance the transparency and trustworthiness of social robots in human-robot interaction scenarios.
Inherently Explainable Attention-Based Segments
One of the key innovations in our model is the incorporation of inherently explainable attention-based segments. Attention mechanisms have been widely used in natural language processing tasks to improve the performance of models by focusing on relevant information. By using attention-based segments, we not only improve the performance of addressee estimation but also provide interpretable explanations for the model’s decisions.
These attention-based segments highlight the specific parts of the conversation that the model attends to when inferring the addressee. By visualizing these segments, the robot can provide human users with a transparent explanation of why it made a particular decision. This adds an additional layer of interpretability and can help build trust between humans and robots.
Modular Cognitive Architecture for Multi-Party Conversation
To deploy the explainable addressee estimation model, we integrate it into a modular cognitive architecture designed for multi-party conversation in an iCub robot. The modular architecture allows for the seamless incorporation of explainability and transparency features into the robot’s interactive capabilities.
For example, the architecture includes dedicated modules for generating explanations based on the attention-based segments. These explanations can be presented to human participants in various ways, such as through text, speech, or visualizations. The flexibility of the modular architecture enables us to adapt the explanations to the preferences and understanding of individual users.
Evaluating the Effect of Explanations on Human Perception
As part of our research, we conducted a pilot user study to analyze the impact of different explanations on how human participants perceive the robot. By presenting participants with variations of explanations, ranging from simple textual descriptions to rich visualizations, we aimed to understand the influence of different levels of transparency and explainability on user trust and acceptance of the robot.
Through this study, we gained valuable insights into the effectiveness of different explanation types and their impact on human-robot interaction. This knowledge could inform future design decisions in developing social robots that can effectively communicate their decision-making processes to humans in a transparent and understandable manner.
Conclusion
The combination of improved addressee estimation performance, the inclusion of inherently explainable attention-based segments, and the integration into a modular cognitive architecture lays the foundation for social robots that can actively participate in multi-party conversations with enhanced transparency and explainability. As a multi-disciplinary endeavor, our work bridges the fields of machine learning, human-robot interaction, and explainable artificial intelligence, pushing the boundaries of what social robots can achieve in interactive contexts.
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