Welcome to the Agora Paper Club, a collaborative learning community dedicated to understanding multi-modality models and their applications. Our mission is to delve into the depths of mathematics and artificial intelligence, exploring topics such as multi-modality reasoning, generation, and swarm collective intelligence.
- Every night at 10AM eastern NYC time
Our mission is to foster a deep understanding of multi-modality models and their applications. We believe that the future of AI lies in the intersection of various modalities and that understanding these intersections will lead to breakthroughs in AI applications. We aim to create a collaborative environment where learners can explore these topics in depth, share their insights, and contribute to the collective knowledge of the community.
Our discussions revolve around a wide range of topics in mathematics and AI, including but not limited to:
- Multi-modality Reasoning: Understanding how different modalities can be combined to improve reasoning capabilities of AI models.
- Generation: Exploring how multi-modality models can generate new data and insights.
- Swarm Collective Intelligence: Studying how collective behavior of decentralized, self-organized systems can be applied to AI.
We understand that there are many different interesting topics in the field of AI and mathematics, and sticking to a single one for an extended period can be challenging. Therefore, we plan to have sessions on long-running topics like Geometric Deep Learning interspersed with shorter ones.
While we'll generally try to stay close to AI and mathematics, some topics require a basic introduction, so we'll have some of those as well.
- Every night at 10AM eastern NYC time
We welcome contributions from all members of the community. If there are particular topics you'd like us to cover, feel free to let us know or create an issue on this repo, and we'll add them to the list. If you are knowledgeable in some topic, and/or are motivated to present one or more sessions on it, we'd love to have you. Just open an issue on this repo on the topic, or reply to the existing issue for that topic.
In order to prepare sessions on a topic, it's important to have some source material available online that attendees can read. A topic will be considered ready to be covered once it has reading material, people willing to present, and an idea of how many sessions it should take, at which point it will be scheduled.
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Multi-modal Data Fusion: This topic explores the methods and techniques used to combine data from different modalities to improve the performance of AI models.
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Multi-modal Learning Algorithms: This topic delves into the algorithms used for multi-modal learning, including how they are designed and how they function.
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Multi-modal Representation Learning: This topic focuses on how AI models can learn to represent data from multiple modalities in a way that captures the relationships between them.
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Multi-modal Transfer Learning: This topic explores how knowledge gained from one modality can be transferred to another in multi-modal learning.
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Multi-modal Applications: This topic looks at the various applications of multi-modal learning, from healthcare to autonomous vehicles.
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LLM Architectures: This topic covers the different architectures used in language models, such as transformer-based models, recurrent neural networks, and others.
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LLM Training Techniques: This topic explores the various techniques used to train language models, including supervised, unsupervised, and semi-supervised learning.
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LLM Evaluation Metrics: This topic delves into the metrics used to evaluate the performance of language models, such as perplexity, BLEU score, and others.
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LLM Applications: This topic looks at the various applications of language models, from machine translation to text generation.
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LLM Ethics and Fairness: This topic explores the ethical considerations and fairness issues related to the use of language models.
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Swarm Behavior in Nature: This topic explores how swarm behavior manifests in nature, such as in colonies of ants or flocks of birds, and how these behaviors can be modeled in AI.
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Swarm Intelligence Algorithms: This topic delves into the algorithms used in swarm intelligence, such as particle swarm optimization, ant colony optimization, and others.
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Swarm Robotics: This topic looks at the application of swarm intelligence in robotics, exploring how groups of robots can work together to accomplish tasks.
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Swarm Intelligence in Optimization Problems: This topic explores how swarm intelligence can be used to solve complex optimization problems in various fields.
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Swarm Intelligence in Data Mining: This topic looks at how swarm intelligence can be used in data mining to discover patterns and make predictions.
These topics provide a comprehensive overview of the fields of multi-modal learning, language models, and swarm collective intelligence. Each topic is a rich area of study with many subtopics and related areas to explore. By delving into these topics, we can gain a deeper understanding of these fields and contribute to the advancement of AI. Join us in our mission to understand multi-modality models and their applications. Let's learn together and contribute to the future of AI!
- DPC will be recorded and post on YouTube here:
- Agora Discord