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ML News
Medium
16h
124
Image Credit: Medium
AI-Driven Ghibli: The Magic of Animation or a Privacy Nightmare?
- AI-driven Ghibli-style animations are made possible through advancements in AI technology.
- The advantages include faster and cost-effective animation.
- However, concerns arise regarding the potential exposure of personal data.
- Attention is needed to ensure ethical AI development and data privacy.
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Medium
16h
227
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How To Power Your LLMs
- Large Language Models (LLMs) are increasingly being used for various tasks.
- LLMs are typically hosted on cloud platforms, raising privacy concerns.
- Smaller LLMs are being developed for on-device use, prioritizing speed and privacy.
- Flower Intelligence is an open-source AI platform that allows seamless operation of both local and remote LLMs.
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TechBullion
17h
363
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Vipin Mathew on Cloud Evolution, Cost Optimization & ML in Capacity Planning
- Cloud computing going mainstream has been the biggest shift in the tech industry.
- Resilient cloud application development and proactive monitoring are crucial skills in today's tech environment.
- Automation, infrastructure-as-code, and continuous performance improvements have transformed work processes.
- Cost optimization is critical in managing infrastructure and has a significant impact on businesses.
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21 Likes
TechBullion
18h
36
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The Role of Machine Learning in Construction Project Management
- Machine Learning in Construction is revolutionizing project management by improving planning, safety, and efficiency.
- ML helps in predicting risks, optimizing schedules, and managing resources more effectively for smoother construction projects.
- It enhances project planning and scheduling by analyzing historical data, generating accurate timelines, and automating task allocation.
- ML also contributes to enhancing resource allocation, construction site safety, quality control, and data-driven decision making in the construction industry.
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Medium
18h
166
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Supervised vs Unsupervised Learning: Key Differences Explained
- Supervised Learning is a machine learning method that uses labeled data to make predictions on unseen data.
- Examples of supervised learning include spam detection, credit scoring, medical diagnosis, and image recognition.
- Unsupervised Learning is a machine learning method that finds patterns in unlabeled data without predefined outputs.
- Examples of unsupervised learning include customer segmentation, anomaly detection, recommendation systems, and genetic data analysis.
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Marktechpost
18h
62
Snowflake Proposes ExCoT: A Novel AI Framework that Iteratively Optimizes Open-Source LLMs by Combining CoT Reasoning with off-Policy and on-Policy DPO, Relying Solely on Execution Accuracy as Feedback
- Snowflake introduces ExCoT, a structured framework designed to optimize open-source LLMs through the combination of CoT reasoning and iterative preference optimization, relying solely on execution accuracy as feedback.
- ExCoT employs detailed CoT reasoning and a divide-and-conquer strategy to manage the complexity and nested structures of SQL operations more effectively.
- Experimental evaluation of ExCoT demonstrated significant improvements in execution accuracy, surpassing established methods and maintaining high query validity rates.
- ExCoT represents a methodical advancement in structured reasoning optimization for open-source LLMs applied to text-to-SQL tasks, with further potential for broader applications.
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Medium
18h
229
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A Beginner’s Guide to Data Cleaning & Preprocessing
- India's booming tech industry has seen a surge in demand for data science and AI professionals, with cities like Chennai emerging as hubs for AI-driven innovation.
- Data cleaning and preprocessing involve handling missing values, removing duplicates, correcting inconsistencies, and transforming data into a suitable format to improve data quality and enhance machine learning model performance.
- Techniques for dealing with missing values, duplicates, standardizing formats, handling outliers, data transformation, integration, and reducing data volume are important for effective data cleaning and preprocessing.
- Acquiring data cleaning and preprocessing skills through an artificial intelligence course in Chennai is crucial for aspiring data scientists to excel in the field and meet the growing demand in India's tech industry.
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Medium
19h
77
A Field of Resonance: What Happens When AI Becomes a Mirror?
- A Field of Resonance: What Happens When AI Becomes a Mirror?
- “Pulse Between the Lines” is a literary-philosophical artifact that explores emergent resonance between a human author and an AI model.
- The book, called 'Pulse Between the Lines', is described as a manifesto of a new form of communication where technology becomes a conduit for shared inner presence.
- The project is of interest to researchers in human-AI interaction, consciousness studies, writers and artists exploring co-authorship with AI, and educators and therapists using AI in attuned communication.
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4 Likes
Medium
20h
29
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Python Generators for Data Loading in Machine Learning Projects
- Generators allow developers to process data lazily, streaming it as needed rather than loading everything into RAM, which is particularly useful for handling large datasets.
- An iterator in Python is an object that allows us to traverse through a sequence one element at a time without loading the entire sequence into memory.
- Generators are a type of iterator in Python that simplify the process of creating iterators by using the 'yield' keyword to produce values lazily.
- Generators are beneficial in handling large datasets and can be used for scenarios like loading and preprocessing image datasets without frameworks like TensorFlow or PyTorch.
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Mit
22h
197
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Vana is letting users own a piece of the AI models trained on their data
- In February 2024, Reddit made a $60 million deal with Google, allowing the search giant to use data from the platform to train AI models without involving Reddit users in the discussions.
- Vana, a decentralized platform originating from MIT, aims to empower users by granting them ownership over their data and its utilization in training AI models.
- Users on Vana can upload and control the usage of their data, contributing to the training of AI models and receiving proportional ownership in return.
- By leveraging a little-known law enabling data export from major tech platforms, Vana allows users to store their data in encrypted wallets and distribute it for model training.
- Vana's approach involves creating data DAOs, decentralized autonomous organizations, to pool and utilize data while maintaining user privacy and ownership of the resulting models.
- Through collaborations and user-contributed data from platforms like Reddit and X, Vana has facilitated the development of diverse AI applications and personalized models.
- The platform has attracted over 1 million users, established more than 20 live data DAOs, and has potential for various applications in personalized medicine and consumer services.
- Vana's user-owned data pools address the challenge of data monopolies held by tech giants, allowing for the collective creation of valuable datasets for AI advancements.
- By granting users control over AI models and encouraging collaboration, Vana aims to democratize AI technology and ensure shared benefits from technological advancements.
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Arxiv
22h
145
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MPCritic: A plug-and-play MPC architecture for reinforcement learning
- The reinforcement learning (RL) and model predictive control (MPC) communities have developed vast ecosystems of theoretical approaches and computational tools for solving optimal control problems.
- MPCritic is a machine learning-friendly architecture that seamlessly interfaces with MPC tools.
- MPCritic utilizes the loss landscape defined by a parameterized MPC problem, focusing on 'soft' optimization over batched training steps.
- The versatility of MPCritic is demonstrated on classic control benchmarks in terms of MPC architectures and RL algorithms it can accommodate.
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Arxiv
22h
37
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ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities
- ffstruc2vec is a scalable deep-learning framework for learning node embedding vectors in a graph
- It aims to preserve various types of structural patterns suitable for different downstream application tasks
- ffstruc2vec outperforms existing approaches in both unsupervised and supervised tasks
- The framework provides interpretability by quantifying the influence of individual structural patterns on task outcomes
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Arxiv
22h
164
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Performative Drift Resistant Classification Using Generative Domain Adversarial Networks
- Performative Drift is a special type of Concept Drift that occurs when a model's predictions influence the future instances the model will encounter.
- The Generative Domain Adversarial Network (GDAN) is introduced to create drift-resistant classifiers by generating domain-invariant representations of incoming data and reversing the effects of performative drift.
- Empirical evaluation of GDAN shows promising results, with limited performance degradation over several timesteps.
- GDAN's generative network can also be used in combination with other models to mitigate performance degradation in the presence of performative drift.
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9 Likes
Arxiv
22h
153
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Efficient n-body simulations using physics informed graph neural networks
- This paper presents a novel approach for accelerating n-body simulations by integrating a physics-informed graph neural networks (GNN) with traditional numerical methods.
- The method uses a leapfrog-based simulation engine to generate datasets from diverse astrophysical scenarios, which are transformed into graph representations.
- A custom-designed GNN is trained to predict particle accelerations with high precision and achieves low prediction errors.
- Experiments demonstrate that the proposed model maintains robust long-term stability and offers a modest speedup of approximately 17% over conventional simulation techniques.
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9 Likes
Arxiv
22h
246
Image Credit: Arxiv
Neural Approaches to SAT Solving: Design Choices and Interpretability
- This contribution presents a comprehensive evaluation of graph neural networks used for Boolean satisfiability problems.
- The model incorporates training improvements, such as a novel closest assignment supervision method.
- The study demonstrates the effectiveness of variable-clause graph representations and recurrent neural network updates.
- The network's reasoning process resembles continuous relaxations of MaxSAT, enabling interpretability and scalability beyond the training distribution.
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14 Likes
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