by jsendak | Apr 10, 2025 | DS Articles
In this article, we’ll explore what a transformer is, how it originated, why it became so successful that it powered one of the most groundbreaking AI advances, the large language model.
Comprehensive Analysis of Transformers in AI
Transforming the face of Artificial Intelligence (AI), ‘Transformers’ have been heralded as one of the significant advancements powering large language models. Stemming from humble origins, they rose to overwhelming success, heralding a new era for AI applications. This analysis delves into the nuances of transformers in AI, their origins, their journey from inception to recognition, and the consequences of their significant contribution in powering a groundbreaking AI advance – the large language model.
Origins of Transformers
The inception story of transformers traces back to a research paper, “Attention is All You Need”, published by Google Brain in 2017. The paper introduced the transformer model, a novel approach that assisted in solving sequence-to-sequence tasks more efficiently than its predecessors. The innovation proposed in the paper rested on the principle of ‘attention mechanism’, i.e., a method that identifies which parts of the input are vital to the output.
The Rise to Success
Transformers’ success didn’t happen overnight. Offering significant advancements over the previous recurrent neural networks (RNNs), transformers introduced the self-attention mechanism, which allows models to consider different words in a sentence regardless of their positions. It surpassed RNNs by eliminating the need for sequential data processing, thus enabling parallelization and improving efficiency. As a result, transformers have changed the landscape of machine translation and natural language processing (NLP).
Powering Large Language Models
Undeniably, transformers’ most significant feat is fueling the development of large language models, such as GPT-3 developed by OpenAI. These AI models can generate human-like text based on the prompts given, and the credit mainly goes to the transformer architecture. GPT-3 is a testament to the effectiveness of this model, showcasing its potential in various applications such as dialog systems, content generation, and translation among others.
Long-term Implications
The success of transformers in AI has far-reaching implications. From shaping the future of NLP to revolutionizing the workings of machine learning, transformers have revolutionized AI in numerous ways. They have paved the way for a more efficient and nuanced processing of language-based tasks, offering unprecedented accuracy and speed. However, they also present challenges such as increasing computational demands and potential misuse risks in scenarios where generated content can be misinterpreted or misused.
Potential Future Developments
As transformers continue to evolve, we can anticipate several advances. We might see improvements in memory efficiency and computational speed, new variations and adaptations of the transformer model, and applications in a broader range of fields such as healthcare, e-commerce, and entertainment.
Actionable Advice
- Invest in Research: Continued investment in research and development can assist in overcoming the challenges posed by transformers and help harness their potential in AI.
- Pursue Ethical AI: Given the possibility of misuse, it’s crucial to dedicate resources to ethical AI practices, ensuring the safe and beneficial use of such technologies.
- Explore New Applications: Look for opportunities to use transformers in sectors beyond NLP, especially where interpreting and processing complex data is required.
In conclusion, the emergence and success of transformers have dramatically shifted the AI landscape. By fueling advances like large language models, they have made a significant impact. However, their journey is still in progress, and there is vast potential for their application in the future.
Read the original article
by jsendak | Feb 7, 2025 | AI
arXiv:2502.03490v1 Announce Type: new
Abstract: Prior work has found that transformers have an inconsistent ability to learn to answer latent two-hop questions — questions of the form “Who is Bob’s mother’s boss?” We study why this is the case by examining how transformers’ capacity to learn datasets of two-hop questions and answers (two-hop QA) scales with their size, motivated by prior work on transformer knowledge capacity for simple factual memorization. We find that capacity scaling and generalization both support the hypothesis that latent two-hop QA requires transformers to learn each fact twice, while two-hop QA with chain of thought does not. We also show that with appropriate dataset parameters, it is possible to “trap” very small models in a regime where they memorize answers to two-hop questions independently, even though they would perform better if they could learn to answer them with function composition. Our findings show that measurement of capacity scaling can complement existing interpretability methods, though there are challenges in using it for this purpose.
Transformers, a popular deep learning model, have been found to struggle with answering latent two-hop questions like “Who is Bob’s mother’s boss?” In this study, researchers aim to uncover the reason behind this inconsistency by examining how transformers’ capacity to learn two-hop question and answer (QA) datasets scales with their size. This investigation is influenced by previous research on transformer knowledge capacity for simple factual memorization.
The first key finding is that both capacity scaling and generalization support the hypothesis that latent two-hop QA necessitates transformers to learn each fact twice. On the other hand, two-hop QA with a chain of thought does not require this redundancy. This suggests that transformers face unique challenges when it comes to learning and answering two-hop questions.
Additionally, the researchers demonstrate that by manipulating dataset parameters, even very small models can be trapped in a state where they memorize answers to two-hop questions separately. This trapping prevents them from utilizing function composition, which would lead to better performance. This finding underscores the importance of dataset design in promoting effective learning and generalization.
Overall, this study highlights the multidisciplinary nature of the concepts explored. To understand the limitations and potential of transformers in tackling complex QA tasks, it is necessary to consider not only their architectural design and size but also the nature of the datasets they are trained on. These findings also showcase the utility of capacity scaling measurement as a complementary approach to enhance interpretability in transformer models. However, there are challenges associated with utilizing capacity scaling for this purpose, which should be carefully addressed in future research.
Read the original article
by jsendak | Jan 26, 2025 | AI
arXiv:2501.13200v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) demonstrates significant progress in solving cooperative and competitive multi-agent problems in various environments. One of the principal challenges in MARL is the need for explicit prediction of the agents’ behavior to achieve cooperation. To resolve this issue, we propose the Shared Recurrent Memory Transformer (SRMT) which extends memory transformers to multi-agent settings by pooling and globally broadcasting individual working memories, enabling agents to exchange information implicitly and coordinate their actions. We evaluate SRMT on the Partially Observable Multi-Agent Pathfinding problem in a toy Bottleneck navigation task that requires agents to pass through a narrow corridor and on a POGEMA benchmark set of tasks. In the Bottleneck task, SRMT consistently outperforms a variety of reinforcement learning baselines, especially under sparse rewards, and generalizes effectively to longer corridors than those seen during training. On POGEMA maps, including Mazes, Random, and MovingAI, SRMT is competitive with recent MARL, hybrid, and planning-based algorithms. These results suggest that incorporating shared recurrent memory into the transformer-based architectures can enhance coordination in decentralized multi-agent systems. The source code for training and evaluation is available on GitHub: https://github.com/Aloriosa/srmt.
The article “Shared Recurrent Memory Transformer for Multi-Agent Reinforcement Learning” addresses the challenges of achieving cooperation in multi-agent reinforcement learning (MARL) systems. MARL has shown great progress in solving cooperative and competitive problems, but one of the main obstacles is the explicit prediction of agents’ behavior. To overcome this, the authors propose the Shared Recurrent Memory Transformer (SRMT), which extends memory transformers to enable agents to exchange information and coordinate their actions implicitly. The SRMT is evaluated on a Partially Observable Multi-Agent Pathfinding problem and a POGEMA benchmark set of tasks, demonstrating superior performance compared to other reinforcement learning baselines and competitive results on various map scenarios. The incorporation of shared recurrent memory into transformer-based architectures enhances coordination in decentralized multi-agent systems. The source code for training and evaluation is also provided on GitHub.
Enhancing Coordination in Multi-Agent Systems with Shared Recurrent Memory Transformer
Multi-agent reinforcement learning (MARL) has made significant strides in solving complex cooperative and competitive tasks in various environments. However, one of the key challenges in MARL revolves around explicitly predicting agents’ behavior to achieve efficient cooperation. To address this issue, a groundbreaking solution is proposed in the form of the Shared Recurrent Memory Transformer (SRMT). By extending memory transformers to multi-agent settings, SRMT enables agents to implicitly exchange information and coordinate their actions.
Challenges in Multi-Agent Reinforcement Learning
Coordinating the actions of multiple agents in a decentralized environment poses several challenges. Traditional MARL approaches typically require predicting the behavior of other agents explicitly, which can be computationally intensive and restrict the scalability of the system. Moreover, effectively coordinating actions becomes particularly difficult when agents have limited visibility of their environment and receive sparse rewards.
To overcome these challenges, the SRMT framework capitalizes on the power of memory transformers and shared recurrent memory. By pooling and globally broadcasting individual working memories, agents can implicitly exchange information without the need for explicit prediction. This implicit information exchange greatly enhances coordination capabilities in decentralized multi-agent systems.
Evaluation and Performance
The authors evaluate the effectiveness of the SRMT framework in two settings: the Partially Observable Multi-Agent Pathfinding problem and a benchmark set of tasks known as POGEMA. In the Partially Observable Multi-Agent Pathfinding task, agents must navigate through a narrow corridor (referred to as the Bottleneck task). SRMT consistently outperforms various reinforcement learning baselines, especially under sparse rewards. It also demonstrates effective generalization to longer corridors, unseen during training.
When evaluated on the POGEMA maps, including Mazes, Random, and MovingAI, SRMT shows competitiveness with recent state-of-the-art MARL, hybrid, and planning-based algorithms. These results suggest that incorporating shared recurrent memory into transformer-based architectures offers a promising avenue for improving coordination in multi-agent systems.
Conclusion
The Shared Recurrent Memory Transformer (SRMT) presents a novel approach to address the coordination challenges in multi-agent systems. By enabling agents to implicitly exchange information and coordinate their actions, SRMT outperforms existing MARL and planning-based algorithms in various tasks, including navigating narrow corridors and tackling diverse benchmark sets. The results highlight the potential of incorporating shared recurrent memory in transformer-based architectures to enhance coordination and scalability in decentralized multi-agent environments.
For more information and access to the source code for training and evaluation, visit the project’s GitHub repository: https://github.com/Aloriosa/srmt.
The paper titled “Shared Recurrent Memory Transformer for Multi-Agent Reinforcement Learning” introduces a novel approach to address the challenge of achieving cooperation in multi-agent reinforcement learning (MARL) settings. The authors propose the Shared Recurrent Memory Transformer (SRMT), which extends memory transformers to enable agents to exchange information implicitly and coordinate their actions.
Cooperation is a fundamental aspect of MARL, as agents need to coordinate their behaviors to achieve optimal outcomes. Traditionally, explicit prediction of agents’ behavior has been required, which can be computationally expensive and limit scalability. The SRMT approach aims to overcome this limitation by pooling and globally broadcasting individual working memories, allowing agents to share information without explicit predictions.
To evaluate the effectiveness of SRMT, the authors conducted experiments on two different tasks. The first task is the Partially Observable Multi-Agent Pathfinding problem, specifically focusing on a toy Bottleneck navigation task. In this task, agents need to navigate through a narrow corridor. The results show that SRMT consistently outperforms various other reinforcement learning baselines, especially when rewards are sparse. Additionally, SRMT demonstrates effective generalization to longer corridors not seen during training.
The second task involves evaluating SRMT on a benchmark set of tasks known as POGEMA maps. These maps include different scenarios such as Mazes, Random, and MovingAI. The results indicate that SRMT performs competitively with recent MARL, hybrid, and planning-based algorithms on these tasks.
Overall, the findings of this paper suggest that incorporating shared recurrent memory into transformer-based architectures can significantly enhance coordination in decentralized multi-agent systems. The SRMT approach provides a promising solution to the challenge of achieving cooperation in MARL, showcasing improved performance and generalization capabilities.
It is worth noting that the availability of the source code for training and evaluation on GitHub is a valuable contribution to the research community. This allows researchers and practitioners to replicate the experiments and further build upon the proposed approach. Future work in this area could involve applying SRMT to more complex and realistic multi-agent scenarios, as well as exploring potential optimizations or variations of the SRMT architecture.
Read the original article
by jsendak | Jan 20, 2025 | Computer Science
arXiv:2501.09782v1 Announce Type: cross
Abstract: Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods focus on training innovative architectural designs on confined datasets. In this work, we investigate the impact of scaling up EHPS towards a family of generalist foundation models. 1) For data scaling, we perform a systematic investigation on 40 EHPS datasets, encompassing a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. Ultimately, we achieve diminishing returns at 10M training instances from diverse data sources. 2) For model scaling, we take advantage of vision transformers (up to ViT-Huge as the backbone) to study the scaling law of model sizes in EHPS. To exclude the influence of algorithmic design, we base our experiments on two minimalist architectures: SMPLer-X, which consists of an intermediate step for hand and face localization, and SMPLest-X, an even simpler version that reduces the network to its bare essentials and highlights significant advances in the capture of articulated hands. With big data and the large model, the foundation models exhibit strong performance across diverse test benchmarks and excellent transferability to even unseen environments. Moreover, our finetuning strategy turns the generalist into specialist models, allowing them to achieve further performance boosts. Notably, our foundation models consistently deliver state-of-the-art results on seven benchmarks such as AGORA, UBody, EgoBody, and our proposed SynHand dataset for comprehensive hand evaluation. (Code is available at: https://github.com/wqyin/SMPLest-X).
Expressive human pose and shape estimation (EHPS) is a fascinating field that involves capturing the movements and shapes of the human body, hands, and face. This technology has a wide range of applications, from animation and virtual reality to artificial reality and multimedia information systems.
In this article, the authors explore the potential of scaling up EHPS towards the development of generalist foundation models. Currently, state-of-the-art methods in EHPS are focused on training innovative architectural designs on specific datasets. However, this approach has limitations as a model trained on a single dataset may not be able to handle a wide range of scenarios.
To overcome this limitation, the authors perform a systematic investigation on 40 EHPS datasets, covering various scenarios. By analyzing and benchmarking these datasets, they optimize their training scheme and select datasets that lead to significant improvements in EHPS capabilities. The authors find that they achieve diminishing returns at around 10 million training instances, indicating the importance of diverse data sources.
In addition to data scaling, the authors also investigate model scaling using vision transformers as the backbone. By using minimalist architectures, they study the scaling law of model sizes in EHPS, excluding the influence of algorithmic design. They find that with big data and large models, the foundation models exhibit strong performance across diverse test benchmarks and can even transfer their knowledge to unseen environments.
Furthermore, the authors develop a finetuning strategy that turns the generalist foundation models into specialist models, allowing them to achieve further performance boosts. These foundation models consistently deliver state-of-the-art results on multiple benchmarks, including AGORA, UBody, EgoBody, and the authors’ proposed SynHand dataset for comprehensive hand evaluation. This highlights the effectiveness and versatility of the developed EHPS techniques.
The concepts explored in this article highlight the multi-disciplinary nature of EHPS. It involves aspects of computer vision, machine learning, artificial intelligence, animation, and virtual reality. The ability to accurately capture and estimate human pose and shape has tremendous potential in various fields, including entertainment, gaming, healthcare, and even robotics.
In the wider field of multimedia information systems, EHPS plays a crucial role in enhancing the realism and interactivity of digital content. Whether it’s creating lifelike animations, developing immersive virtual reality experiences, or enabling augmented reality applications, EHPS provides the foundation for realistic human representations. By scaling up EHPS and developing generalist foundation models, we can expect even more advanced and realistic multimedia systems in the future.
Read the original article
by jsendak | Dec 18, 2024 | DS Articles
Learn how to enhance RAG models by combining text and visual inputs using Hugging Face Transformers.
Unveiling the Power of Enhancing RAG Models by Combining Text and Visual Inputs Using Hugging Face Transformers
In the revolutionary world of technology, where artificial intelligence (AI) and machine learning (ML) are progressively changing how we perceive and interact with the digital sphere, one can’t overlook the importance and potential of Retriever-Augmented Generation (RAG) models. Combining text and visual inputs using Hugging Face Transformers can tremendously enhance these RAG models.
The Potential Long-Term Implications
The amalgamation of text and visual inputs in RAG models signifies a considerable leap in text-to-text tasks, speech recognition, or any application requiring the understanding and manipulation of human language. This enhancement has several long-term implications.
- Improved User Experience: As the models become more sophisticated and can handle more complex language understanding tasks, the overall user experience improves. Interaction with AI-powered bots can become a lot more human and personalized.
- Advanced Research: Improvements in dealing with multi-modal inputs may open up new frontiers in AI and ML research, moving beyond the limitations of the current models.
- Service Innovation: By making AI more human-like, businesses can innovate their services, like customer support, personalized marketing, and recommendations.
Possible Future Developments
The initiative to improve RAG models by effectively using text and visual inputs sources Iargely from Hugging Face Transformers. This is just the beginning, however, and there are several directions these improvements could lead us.
- Higher Accuracy Models: As the transformers keep evolving, they’ll learn to handle even more types of inputs, consequently improving the accuracy of the models significantly.
- Democratization of AI: The advancements may usher the era of ‘democratization of AI’, making it accessible and understandable for non-experts as well.
- Robustness: Future models may be highly robust to changing data distributions and capable of handling unseen or novel situations.
Actionable Advice
The unfolding advancements in the enhancement of RAG models through the utilization of text and visual inputs suggest the following actionable advice for technology and business stakeholders.
- Invest in AI: Companies should deeply consider investing in AI technology. It’s an inevitability that AI will continue to shape business processes, and having AI integration at the core of your business strategy can yield concrete benefits.
- Focus on Research and Development: It’s important to invest in in-house R&D to stay ahead of the curve and stand out from the competition. Having a dedicated team to understand and implement these advancements can be beneficial.
- Risk Management: Although technology continues to advance at a rapid pace, it should not overshadow the importance of a robust risk management strategy. Issues of cybersecurity, privacy, and ethical considerations should always remain at the forefront.
Read the original article
