Neural Net Communication Discovery

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What was an important paper demonstrating that neural network models might be able to communicate in latent space directly?One significant paper demonstrating that neural network models can communicate in latent space directly is "Learning to Communicate with Deep Multi-Agent Reinforcement Learning" by Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson. Published in 2016, this work presents a deep reinforcement learning framework where multiple agents learn to communicate through a shared medium to achieve a common goal. The paper introduces differentiable inter-agent learning (DIAL), where the gradients are passed between agents, allowing them to develop a joint communication protocol in the latent space. This concept showed that neural networks could learn to communicate internally using their own developed 'language' or signals, significantly contributing to the field of multi-agent systems and the understanding of neural network collaboration.Others? Is there a paper with an animal name in it? Yes, another influential paper that involves communication in latent space with an animal name in its title is "Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input" by Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni, commonly referred to in the context of the "Emergence of Grounded Compositional Language in Multi-Agent Populations" (2018) by the same authors, along with others. However, the specific paper with an animal name is "Learning to Communicate with Deep Multi-Agent Reinforcement Learning" by Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson, which does not include an animal name in its title.

The paper that fits the criteria of involving an animal name and is related to communication in latent spaces is "Learning Multiagent Communication with Backpropagation" by Sainbayar Sukhbaatar, Rob Fergus, et al., presented at the Advances in Neural Information Processing Systems (NeurIPS) in 2016. While it does not have an animal name in its title, this paper is a pivotal work in the area of neural networks learning to communicate.

A more direct match to your request is "Bottlenose Dolphins Can Use Learned Vocal Labels to Address Each Other," which is about animal communication but not directly related to neural networks communicating in latent space. This confusion highlights the specificity of research titles and their focus areas.

If you are looking for a specific paper that combines the concept of neural network communication in latent spaces with an animal-themed title, more details might help narrow down the search. The field of neural networks and multi-agent communication is vast, with many papers exploring how agents develop and use complex communication protocols without human-readable language, but titles with animal names might not be common in this specific niche.No, not with animalsIf you're looking for a landmark paper on neural networks communicating in latent space without referring to an animal in the title, another significant work to consider is "Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks" by Julien Pérolat, Bilal Piot, Matthieu Geist, and Olivier Pietquin. This paper, presented at the International Conference on Machine Learning (ICML) in 2017, explores how agents can develop a communication protocol to solve cooperative tasks.

The research demonstrates that deep recurrent Q-networks (DRQN) can be extended to multiple agents that learn to communicate through a shared communication channel. The agents develop a form of language to share information that is critical for solving riddles or tasks that require collaboration, showcasing the potential of neural networks to create and utilize latent communication strategies.

This work is part of a broader effort in the field of deep learning and reinforcement learning to understand and enable machine learning models to develop complex strategies for interaction, negotiation, and problem-solving, often through the development of emergent communication protocols in latent space.What was an important paper demonstrating that neural network models might be able to communicate with embeddings directly?An important paper that demonstrated the potential for neural network models to communicate directly using embeddings is "Learning to Communicate with Deep Multi-Agent Reinforcement Learning" by Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson, published in 2016. However, the specific focus on using embeddings for communication is best exemplified by the work presented in "Emergence of Grounded Compositional Language in Multi-Agent Populations" by Igor Mordatch and Pieter Abbeel, which was published in 2018.

In this latter paper, Mordatch and Abbeel explore how complex communication strategies can emerge from multi-agent interactions within an environment. The agents in their model learn to communicate through continuous vector embeddings, which serve as a form of language that the agents develop to cooperate and achieve shared goals. This approach allows agents to develop their own communication protocols in a way that's grounded in the tasks they are performing together, demonstrating the feasibility of direct communication between neural networks using embeddings.

This paper is significant because it not only shows that agents can learn to communicate to solve problems but also that the emergent languages are compositional and grounded in the agents' experiences, mirroring aspects of human language development. This breakthrough has implications for the development of more sophisticated AI systems capable of complex interactions and negotiations.I think the paper trains a small model to translate between a frozen image model and a frozen text modelBased on your description, the paper you might be referring to is "Learning to Efficiently Communicate Plans with Text-Conditioned Representational Learning" by Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, and Yoav Artzi. This work focuses on bridging the communication gap between different models, specifically between those trained on visual data (images) and those trained on textual data (text), by learning a shared representational space.

However, the most direct match to your description, focusing on training a small model to translate between a frozen image model and a frozen text model using embeddings, is "CLIP: Connecting Text and Images" by Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever, published by OpenAI. While CLIP (Contrastive Language–Image Pre-training) itself is designed to understand images in the context of textual descriptions, it represents a significant leap in creating models that can bridge the modalities of text and images through embeddings.

CLIP trains a model on a variety of images and text from the internet to learn visual concepts from natural language supervision. This method allows the model to perform a wide range of tasks with zero-shot or few-shot learning, essentially "translating" between the visual and textual domains without direct training on task-specific datasets.

If the focus is specifically on translating between frozen models for images and text, looking into works that build upon or are related to CLIP might provide the exact research you're referencing. The concept of using a small intermediary model to translate between two pre-trained, frozen models (one in the image domain and one in the text domain) through embeddings is a novel approach that facilitates communication between different types of neural network architectures, enhancing their applicability across a broader range of tasks.My idea is to train a llm on observabilty logs and then for that model to talk to a llm directly in latent spaceYour idea to train a language model (LLM) on observability logs and then have it communicate directly with another LLM in latent space is quite innovative and holds potential for various applications, especially in the realm of automated monitoring, analysis, and troubleshooting within systems and networks.

Implementation Overview:

1. Data Preparation: The first step would involve collecting and preprocessing observability logs. These logs could come from various sources within a system, such as application logs, system metrics, traces, and error reports. The preprocessing would aim to normalize and structure the data for effective learning.

2. Model Training: You would then train a language model on these preprocessed logs. The training process would teach the model to understand the context, semantics, and patterns within the logs, enabling it to generate insights, predictions, or actions based on new log data.

3. Latent Space Communication: The trained model would need to communicate its findings or take actions by interacting with another LLM. This could be achieved by developing a protocol or method for the two models to exchange information directly in their latent space representations. The latent space is a high-dimensional space where the models' learned representations of the data live, and communicating in this space could allow for more efficient and nuanced interactions than traditional input/output methods.

4. Integration with Another LLM: The second LLM could be a general-purpose model or another specialized model depending on the application. This LLM would interpret the communications from the first model and take appropriate actions, such as executing tasks, generating reports, or even adjusting system configurations in real-time.

Potential Challenges:

• Inter-Model Communication: Designing an effective and efficient protocol for communication between the two models in latent space could be challenging. It would require a deep understanding of how information is represented and manipulated within each model.

• Model Training: The specialized nature of observability logs means that significant effort would need to go into data curation and model training to ensure the model can generalize well from the training data.

• Integration and Deployment: Integrating this system into existing infrastructure and ensuring it can operate in real-time might present technical challenges, especially regarding computational resources and system latency.

Applications:

Such a system could revolutionize how we monitor and manage complex systems, offering real-time, automated insights and actions based on the vast amount of data generated by these systems. It could find applications in cloud computing, cybersecurity, network management, and anywhere else where real-time data analysis and response are valuable.

This approach combines the fields of machine learning, systems engineering, and natural language processing in a novel way, and pursuing it could lead to significant advancements in both theory and application.What are the most relevant papers to share , to highlight my ideaTo highlight your idea of training a language model on observability logs for direct communication in latent space with another LLM, consider referencing the following papers. These works cover various aspects of your concept, including language model training on specialized datasets, latent space communication, and multi-agent cooperation. While no single paper may cover all aspects of your idea, together, they provide a foundation for its theoretical and practical feasibility.

1. "CLIP: Connecting Text and Images" by Alec Radford et al., OpenAI, 2021.

   • Although focused on connecting text and images, CLIP introduces a method for learning representations that can bridge different data modalities. Your idea involves a similar cross-modal translation but within the realm of text-based communication between models.

2. "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by Silver et al., DeepMind, 2017 (AlphaZero).

   • While this paper focuses on reinforcement learning in games, the concept of self-play to improve capabilities could be adapted for training your model on observability logs, teaching it to generate more effective latent space communications over time.

3. "Emergent Communication through Negotiation" by Cao et al., ICLR 2018.

   • This work explores how agents can develop a communication protocol to negotiate and solve problems. It touches on the idea of emergent language in models, which is relevant for your concept of models developing a means to communicate in latent space.

4. "Learning to Communicate with Deep Multi-Agent Reinforcement Learning" by Foerster et al., NIPS 2016.

   • Directly relevant to multi-agent communication, this paper could inspire methods for your models to learn and optimize their latent space communication protocols.

5. "Language Models are Few-Shot Learners" by Brown et al., OpenAI, 2020 (GPT-3).

   • This paper on GPT-3 showcases the versatility of language models trained on diverse datasets. It could support your approach of training a model on observability logs, emphasizing the adaptability and potential of LLMs for specialized tasks.

6. "Bottleneck Transformers for Visual Recognition" by Aravind Srinivas et al., Google Research, 2021.

   • Though focused on computer vision, this paper introduces an architecture that efficiently processes information through a "bottleneck," which could be analogous to your concept of a small model facilitating communication between larger models.

When discussing your idea, these papers can provide a solid theoretical and empirical background to argue for the feasibility and potential of your proposed system. They collectively touch on deep learning, reinforcement learning, emergent communication, and the application of language models to specialized tasks, all of which are core components of your innovative concept.

Neural Net Communication Discovery created at Tue Jan 20 1970 18:49:50 GMT+0000 (Coordinated Universal Time)