arXiv:2408.16032v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have enabled understanding webpage contexts, product details, and human instructions. Utilizing LLMs as the foundational architecture for either reward models or policies in reinforcement learning has gained popularity — a notable achievement is the success of InstructGPT. RL algorithms have been instrumental in maximizing long-term customer satisfaction and avoiding short-term, myopic goals in industrial recommender systems, which often rely on deep learning models to predict immediate clicks or purchases. In this project, several RL methods are implemented and evaluated using the WebShop benchmark environment, data, simulator, and pre-trained model checkpoints. The goal is to train an RL agent to maximize the purchase reward given a detailed human instruction describing a desired product. The RL agents are developed by fine-tuning a pre-trained BERT model with various objectives, learning from preferences without a reward model, and employing contemporary training techniques such as Proximal Policy Optimization (PPO) as used in InstructGPT, and Direct Preference Optimization (DPO). This report also evaluates the RL agents trained using generative trajectories. Evaluations were conducted using Thompson sampling in the WebShop simulator environment. The simulated online experiments demonstrate that agents trained on generated trajectories exhibited comparable task performance to those trained using human trajectories. This has demonstrated an example of an extremely low-cost data-efficient way of training reinforcement learning agents. Also, with limited training time (
and resources), the RL agents trained on generative trajectories were able to achieve competitive performance. This research highlights the potential of utilizing large language models and RL algorithms in improving recommender systems and maximizing customer satisfaction in e-commerce settings.
Recent advancements in large language models (LLMs) have opened up new possibilities in understanding webpage contexts, product details, and human instructions. These LLMs serve as the foundational architecture for reward models or policies in reinforcement learning, with notable success seen in InstructGPT.
Reinforcement learning (RL) algorithms play a crucial role in maximizing long-term customer satisfaction and avoiding short-term, myopic goals in industrial recommender systems. These systems often rely on deep learning models to predict immediate clicks or purchases. However, RL methods offer a more holistic approach to decision-making.
In this project, RL methods are implemented and evaluated using the WebShop benchmark environment, along with relevant data, simulator, and pre-trained model checkpoints. The ultimate goal is to train an RL agent that can maximize the purchase reward, given a detailed human instruction describing the desired product.
To develop these RL agents, a pre-trained BERT model is fine-tuned with various objectives. The agents learn from preferences without a reward model and employ contemporary training techniques like Proximal Policy Optimization (PPO), as used in InstructGPT, and Direct Preference Optimization (DPO).
This report also evaluates the RL agents trained using generative trajectories. Evaluations are conducted using Thompson sampling in the WebShop simulator environment. Remarkably, the results demonstrate that agents trained on generated trajectories perform comparably to those trained using human trajectories.
This finding represents an example of an extremely low-cost and data-efficient approach to training reinforcement learning agents. With limited training time, significant progress can be made in developing RL agents that are capable of making intelligent purchase decisions based on human instructions.
This research has implications for the e-commerce industry, where RL agents can assist customers in finding their desired products more efficiently. It also highlights the potential for reducing reliance on expensive data collection methods by leveraging generative trajectories.
The success of utilizing LLMs in reinforcement learning paves the way for further exploration of these techniques in various domains. By leveraging contextual understanding and human instructions, RL agents can learn to make more informed decisions, ultimately improving user satisfaction and optimizing long-term goals.
e.g., a few hours), the RL agents were able to achieve satisfactory performance in maximizing the purchase reward.
One interesting aspect of this project is the use of large language models (LLMs) as the foundational architecture for reinforcement learning (RL) agents. LLMs have shown great potential in understanding webpage contexts, product details, and human instructions. By utilizing LLMs, the RL agents are able to comprehend the detailed human instructions describing the desired product, which is crucial for maximizing the purchase reward.
The authors have implemented and evaluated several RL methods in the WebShop benchmark environment. One notable approach is fine-tuning a pre-trained BERT model with various objectives. BERT, a state-of-the-art language model, provides a strong foundation for the RL agent’s understanding of the textual instructions. By fine-tuning BERT, the RL agent can adapt its understanding to the specific task of maximizing the purchase reward.
Furthermore, the authors explore learning from preferences without a reward model. This approach allows the RL agent to learn directly from human preferences, which can be a valuable alternative when a reward model is not available or difficult to define. This is particularly significant in real-world applications where designing a reward model can be challenging.
The report also mentions the use of contemporary training techniques such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO). PPO, a popular RL algorithm, has been successful in training models like InstructGPT. By leveraging these techniques, the RL agents can improve their performance and learn more efficiently from the available data.
One intriguing finding in the project is the evaluation of RL agents trained using generative trajectories. The simulated online experiments demonstrate that agents trained on generated trajectories perform comparably to those trained using human trajectories. This suggests that generating synthetic trajectories can be an effective and low-cost way of training RL agents, especially in scenarios where collecting human trajectories may be challenging or expensive.
Overall, this project showcases the potential of using large language models in reinforcement learning tasks. The combination of LLMs, fine-tuning techniques, and contemporary RL algorithms allows the RL agents to understand human instructions and maximize the purchase reward in the WebShop environment. The successful results achieved in a low-cost and data-efficient manner highlight the practicality and scalability of these approaches for real-world recommender systems. Read the original article
Title: Emerging Trends in Taxonomy: Shaping the Future of Classification
Introduction:
Taxonomy, the science of categorizing and classifying organisms, has undergone significant changes throughout history. This article aims to analyze key points from the provided text and explore potential future trends related to taxonomy. In this rapidly evolving field, emerging technologies, growing data sets, and shifting perspectives are driving exciting developments. This article will delve into these themes and provide unique predictions and recommendations for the industry.
Evolution of Taxonomy:
Taxonomy has its roots in ancient civilizations and has evolved over time. Carl Linnaeus, known as the father of taxonomy, introduced a standardized binomial nomenclature system in the 18th century. However, his choices of names, such as the dodo being called Didus ineptus, reflect the biases and cultural influences of that era.
Inclusive and Ethical Taxonomy:
Future trends will likely focus on rectifying historical biases in taxonomy. Efforts will be made to involve diverse perspectives from various cultures and ecosystems to avoid terms that perpetuate stereotypes or cause harm. Collaborative efforts and inclusivity will be crucial in developing a more comprehensive and ethical taxonomy system.
Advancements in DNA Sequencing:
The fast-paced advancements in DNA sequencing technologies have revolutionized taxonomy. DNA barcoding has become a valuable tool for species identification and classification. Next-generation sequencing techniques efficiently generate vast amounts of genomic data, enabling more accurate and precise taxonomic classifications. These technologies will continue to drive future trends by providing deeper insights into evolutionary relationships.
Integration of Artificial Intelligence (AI):
AI will play a significant role in the future of taxonomy. Machine learning algorithms can analyze large datasets and identify patterns that may have otherwise been missed by humans. AI-powered systems will aid taxonomists by automating processes, speeding up species identification, and assisting with data analysis. Such integration will improve efficiency and accuracy, allowing taxonomists to focus on more intricate tasks and research.
Standardized Global Databases:
The establishment of global databases, like the Catalogue of Life and the Global Biodiversity Information Facility, provides centralized access to taxonomic information. These databases serve as essential resources for researchers, conservationists, and policymakers. Future trends will likely see increased collaboration to expand these databases and ensure their accuracy and accessibility. Efforts will be made to integrate these databases with AI and machine learning technologies for more efficient data management.
Predictive Species Discovery:
Emerging trends in taxonomy will likely prioritize predictive species discovery. Novel techniques, such as environmental DNA analysis and remote sensing, will help identify previously unknown species and monitor their distribution. By combining ecological models, molecular techniques, and AI, taxonomists will be able to anticipate and document the existence of new species before physically discovering them.
Recommendations for the Industry:
1. Foster international collaborations and inclusivity to develop comprehensive and ethically sound taxonomies.
2. Encourage interdisciplinary research collaborations between taxonomists, geneticists, ecologists, and computer scientists to leverage and integrate multiple datasets effectively.
3. Invest in training taxonomists in emerging technologies, such as DNA sequencing, AI, and data analysis, to promote efficient and accurate taxonomy.
Conclusion:
The future of taxonomy is characterized by exciting potential and transformative trends. With advancements in DNA sequencing, the integration of AI, and the establishment of global databases, taxonomy is poised to become more accurate, inclusive, and efficient. As we strive to rectify historical biases and uncover new species, collaboration, and interdisciplinary efforts will be key. By embracing these trends and recommendations, the taxonomy industry will continue to shape our understanding of the natural world.
References:
1. Prys-Jones, Robert P., and Steven J. F. Brooks. “The need to implement the rules of the International Code of Zoological Nomenclature for systematically confused names.” Zootaxa 4012.1 (2015): 52-55.
2. Hebert, Paul D., et al. “A DNA ‘barcode blitz’: rapid digitization and sequencing of a natural history collection.” PloS ONE 4.6 (2009): e7761.
3. Costello, Mark J., et al. “Global Coordination and Standardization in Marine Biodiversity Through the World Register of Marine Species (WoRMS) and Related Databases.” PLoS ONE 8.1 (2013): e51629.
4. Costello, Mark J., et al. “Can we Name Earth’s Species Before They Go Extinct?.” Science 339.6118 (2013): 413-416.
5. Goëau, Hervé, et al. “LifeCLEF in 2019: biodiversity retrieval from scratch.” Working Notes of CLEF 2019-Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings, 2019.
Exploring the Complexities of Secrets: An Insight into Gina M. Contreras’ Amor Secreto Exhibition
Secrets have always held a certain allure, existing in the delicate balance between adoration and shame. San Francisco-based artist Gina M. Contreras skillfully brings the emotional turmoil of hidden love to life in her extraordinary solo exhibition, Amor Secreto, hosted at the esteemed Hashimoto Contemporary. In this thought-provoking collection of self-portraits, Contreras masterfully conveys the profound sense of isolation experienced by those who live in secrecy. Each artwork unveils a myriad of intricate patterns and lush, velvety colors, delving into the untold stories that lie hidden within the shadows.
Contreras’ Amor Secreto delves beneath the surface of intimacy, unraveling the multilayered complexities that define the clandestine aspects of relationships. In a society often built on transparency and openness, these works confront the viewer with a striking exploration of the psychological strains inflicted by secrecy. The artist’s chosen medium of self-portraiture adds a deeply personal element, engaging the audience with an intimate reflection of her own experiences.
The Significance of Contreras’ Artistic Approach
Contreras’ artistic style is characterized by a profound attention to detail that draws the viewer into the hidden world she portrays. Through her use of vivid colors and intricate patterns, she effectively captures the paradoxical nature of secrets – their simultaneous allure and torment. Each brushstroke seems to mirror the conflicting emotions experienced by those involved in covert relationships, immersing the audience in a captivating narrative of tenderness and loneliness.
The self-portraits presented in Amor Secreto provide a lens through which the viewer can not only reflect on their own experiences with secrecy but also gain a deeper understanding of the complexities of human relationships. Contreras’ art serves as a powerful reminder that hidden love is not restricted to a solitary few; rather, it exists as a universal phenomenon that permeates all walks of life.
The Future Trends and Predictions of Secrecy in Art
Contreras’ Amor Secreto exhibition represents a compelling example of how artists are exploring the depths of human emotions and societal dynamics in relation to secrecy. It is likely that this trend will continue to gain momentum in the art world, as contemporary artists increasingly seek to challenge conventional norms and delve into the complexities of human experience.
One potential future trend in secrecy-related art is an emphasis on the intersection between privacy and technology. In an age where personal information is increasingly vulnerable to exposure, artists may explore the resulting psychological and emotional toll of living in a digitally transparent world. This could manifest in thought-provoking installations or interactive artworks that prompt viewers to contemplate the impact of living in an era where secrets are increasingly hard to keep.
Another potential trend is the exploration of secrecy within marginalized communities. Artists may choose to shed light on the hidden narratives of individuals who are often forced to conceal their identities or relationships due to societal or cultural pressures. By amplifying these stories, artists can prompt conversations about acceptance, inclusivity, and the need for empathy in a world where secrecy continues to be a daily reality for many.
Recommendations for the Industry
As the exploration of secrecy and its complexities becomes more prevalent in the art world, it is crucial for the industry to foster platforms that facilitate discussion and engagement. Galleries, museums, and art institutions should consider curating exhibitions and hosting panel discussions that center around the theme of secrecy. By providing spaces for artists, scholars, and the public to come together, these institutions can play a vital role in fostering a deeper understanding and appreciation for this evolving trend.
Furthermore, it is imperative for the industry to support emerging artists who are pushing the boundaries of traditional artistic practices in their exploration of secrecy. Grants, residencies, and mentorship programs specifically dedicated to this theme can greatly encourage artists to delve deeper into their concepts, ultimately leading to more innovative and thought-provoking artworks.
In Conclusion
Gina M. Contreras’ Amor Secreto exhibition invites viewers to reflect on the complexities of secrecy and the emotional toll it takes on individuals. Her self-portraits beautifully capture the multifaceted nature of hidden love, inviting viewers to contemplate their own experiences and engaging them in a broader conversation about human relationships. As the art world continues to evolve, it is exciting to anticipate the future trends and possibilities that will emerge as artists push the boundaries of artistic expression in their exploration of secrecy.
Future Trends in Life-cycle-coupled Evolution of Mitosis
Introduction
Evolution is a dynamic process that continually shapes and reshapes organisms and their traits. One of the most fundamental aspects of evolution is mitosis – the process of cell division. Recent research has shed light on the life-cycle-coupled evolution of mitosis in close relatives of animals. This article aims to analyze the key points of a study on this topic and explore potential future trends in this field.
Key Points
The study titled “Life-cycle-coupled evolution of mitosis in close relatives of animals” highlights several important findings:
Close relatives of animals, such as fungi and protists, exhibit diverse modes of mitosis.
Mitosis in these organisms is tightly linked to their life-cycle and reproduction.
Evolutionary pressure from different ecological niches has shaped the variations in mitosis.
Genomic analysis suggests that key regulatory genes are involved in the evolution of mitosis.
Potential Future Trends
Based on the key points of the study, several potential future trends can be identified:
Detailed understanding of mitotic variations: Further research will likely lead to a deeper understanding of the diverse modes of mitosis in close relatives of animals. By studying a broader range of organisms, scientists can unveil additional variations and identify the underlying mechanisms.
Ecological influences on mitosis: Investigating how ecological niches shape the evolution of mitosis will be a crucial area of focus. Understanding the specific environmental pressures that lead to variation in mitotic processes will help uncover the adaptive significance of these variations.
Mechanism exploration through genomics: The identification of key regulatory genes involved in mitotic evolution opens doors for further investigation. Future research can delve deeper into the genomic mechanisms that drive the variations in mitosis, such as gene regulation, protein interactions, and epigenetic modifications.
Comparative analysis with animal mitosis: Comparative studies between the mitotic processes of close relatives of animals and animals themselves may provide valuable insights. By examining similarities and differences in mitosis across different branches of the tree of life, researchers can gain a better understanding of the evolutionary dynamics of this essential process.
Biotechnological applications: The knowledge gained from studying the life-cycle-coupled evolution of mitosis can have practical applications in biotechnology. By understanding the mechanisms behind different modes of mitosis, scientists may be able to manipulate and engineer mitotic processes for various purposes, such as improving cell-based therapies or enhancing crop yield.
Predictions and Recommendations for the Industry
As the research on the life-cycle-coupled evolution of mitosis progresses, it is important for the industry to adapt and embrace the potential implications. Here are some predictions and recommendations:
Collaboration and interdisciplinary research: Given the complex nature of mitosis and its connections to various fields, collaboration between biologists, geneticists, bioinformaticians, and other specialists will be crucial. Academia, government institutions, and industry should support and invest in interdisciplinary research projects focused on mitosis.
Biotechnology investments: Companies involved in biotechnology should keep an eye on the latest advancements in mitotic research. Investing in research and development related to mitotic mechanisms and their engineering could lead to breakthroughs in areas such as improved drug production, tissue engineering, and agriculture.
Ethical considerations: As the ability to manipulate mitotic processes improves, ethical implications need to be carefully considered. Industry leaders should engage in ongoing discussions with bioethicists and regulatory bodies to ensure responsible and transparent use of mitotic engineering technologies.
Conclusion
The study on the life-cycle-coupled evolution of mitosis in close relatives of animals provides valuable insights into the dynamic nature of this fundamental process. Future trends in this field include a deeper understanding of mitotic variations, exploration of ecological influences, genomic investigations, comparative studies, and potential biotechnological applications. The industry should embrace interdisciplinary collaboration, invest in relevant research, and address ethical considerations to fully leverage the potential of these future trends.
Reference: Nature, Published online: 30 August 2024; doi:10.1038/s41586-024-07961-5
