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ML News
Medium
2d
27
AI in Everyday Life: You’re Using It More Than You Think
- AI is a part of daily life, determining how we communicate with technology.
- AI personalizes your social media feeds based on your activity.
- AI drives intelligent recommendations on platforms like Netflix and Spotify.
- AI aids in online shopping and makes the experience more convenient and smoother.
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Medium
2d
288
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The Cognitive Computing Revolution: How & Why Application Development has Fundamentally Changed, &…
- The world of software development is transitioning from procedural programming to cognitive computing, representing a significant revolution in how software is conceived and built.
- Large language models like GPT-4 can now write complex code and companies are shifting to AI-augmented approaches rapidly.
- Developers deeply versed in procedural programming face an existential challenge in adapting to cognitive systems.
- Procedural programming is based on explicit instructions, deterministic logic, rigid structure, and static knowledge, which contrast with cognitive computing's non-linear problem-solving, context-aware processing, and adaptive learning.
- The shift towards cognitive computing requires a fundamental recalibration of how developers approach problem-solving and design solutions.
- Understanding the Document Object Model (DOM) serves as a bridge between procedural and cognitive paradigms.
- Cognitive computing systems differ by representing knowledge, reasoning under uncertainty, and continuous learning and adaptation.
- Natural Language Understanding (NLU) and Natural Language Processing (NLP) are transformative applications of cognitive computing.
- Developers transitioning to cognitive computing must adopt probabilistic thinking, focus on knowledge representation, and understand ethical implications.
- The article emphasizes the need for developers to acquire new technical skills in machine learning, natural language processing, and cognitive architectures.
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Medium
2d
221
SRM University of Innovation & Technology
- SRM University collaborates with Tek play to provide interactive, gamified classes for high school students.
- SRM University emphasizes research and global leadership to prepare students for competing in the world.
- Collaboration between educational institutions like SRM offers a variety of learning resources to students.
- Artificial intelligence plays a crucial role in connecting academic institutions and schools, improving accessibility and efficacy in education.
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Medium
2d
158
Just reviewed the State of AI Report 2024, and it’s packed with insights into the rapidly evolving…
- Research is focusing on enhancing AI's ability to plan and reason, with exploration of combinations with reinforcement learning and self-improvement techniques.
- Foundation models are expanding beyond language to process and understand multiple types of data, such as text, images, audio, etc.
- Chinese AI labs are still producing capable AI models despite US sanctions, showcasing the global nature of AI development.
- The enterprise value of AI companies has reached $9 trillion, highlighting the increasing adoption and impact of AI across industries.
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Medium
2d
0
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Random Forests in Machine Learning: A Simple Guide
- Random Forests is a way for the computer to make guesses by using many Decision Trees together.
- Each Decision Tree makes its own guess, and then they all vote to decide the final answer.
- Random Forest builds multiple Decision Trees, each with a random mix of features and data to learn different patterns.
- The Random Forest combines the guesses of the Decision Trees to make a better prediction.
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Medium
2d
134
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Can AI Learn to Ask the “Right” Questions?
- AI that can ask clarifying questions is more helpful, reliable, and less frustrating for users.
- Researchers created QuestBench to test if AI can identify the missing information needed to solve a problem.
- They focused on situations with one missing ingredient to measure if the AI asks the right question.
- Cutting-edge language models like GPT-4, Claude 3.5, and Gemini 1.5/2.0 were tested using QuestBench.
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Medium
2d
15
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Frugal and Powerful: How Fire Flyer Powers DeepSeek
- Deepseek's Fire Flyer paper dives into their AI HPC platform, emphasizing frugality and ingenuity in their approach to optimization.
- Utilizing PCIe architecture over NVIDIA's DGX systems, Deepseek's Fire Flyer showcases efficient communication library and optimizations.
- Memory demands for large models like LLMs necessitate multi-GPU setups, with techniques like Data Parallelism, Tensor Parallelism, and Expert Parallelism playing crucial roles.
- Deepseek's strategy includes Dual Pipe deployment, contributing to high hardware performance and communication efficiency.
- The Fire Flyer architecture is designed for high-throughput training and efficient data access across thousands of GPUs with a focus on efficiency.
- Deepseek's HFReduce outperforms NCCL through a leaner communication approach, achieving higher bandwidth and scalable efficiency.
- HaiScale's Distributed Data Parallelism, tensor parallelism via NVLink Bridge, and pipeline parallelism optimizations further enhance performance in large-scale training.
- The 3FS system and HAI-Platform play vital roles in efficient storage management and system scheduling, contributing to overall robustness.
- Deepseek's approach illustrates a shift towards cost-efficient, innovative AI infrastructure design, potentially impacting NVIDIA's dominance and prompting broader industry trends.
- The Fire Flyer architecture showcases efficiency gains and performance improvements, emphasizing the importance of thoughtful engineering and practical optimization.
- Concerns over VC funding, potential AI innovation bubbles, and the need for non-linear gains from AI highlight broader industry challenges and the implications of AI deployment on economic growth.
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Arxiv
2d
238
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Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
- Automated causal loop diagram (CLD) generation using large language models (LLMs) is introduced.
- LLMs can make inferences and build CLDs from dynamic hypotheses using a digraph structure.
- Four combinations of prompting techniques were evaluated and compared against expert-labeled CLDs.
- Results show that LLMs can generate high-quality CLDs, accelerating the CLD creation process.
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Arxiv
2d
146
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Forecasting Volcanic Radiative Power (VPR) at Fuego Volcano Using Bayesian Regularized Neural Network
- Forecasting volcanic activity is critical for hazard assessment and risk mitigation.
- Volcanic Radiative Power (VPR) serves as an essential indicator of volcanic activity.
- A study employed Bayesian Regularized Neural Networks (BRNN) to predict future VPR values based on historical data from Fuego Volcano.
- BRNN outperforms other models, achieving the lowest mean squared error and the highest R-squared value.
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Arxiv
2d
150
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Comparison of Metadata Representation Models for Knowledge Graph Embeddings
- Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations, enabling the representation of contextual, provenance, and temporal information.
- This study evaluates different Metadata Representation Models (MRMs) and their effects on KG Embedding (KGE) and Link Prediction (LP) models.
- Experimental results show that the Reification (REF) MRM performs well in simple HRKGs, while the Singleton Property (SGP) MRM is less effective.
- Findings contribute to optimal knowledge representation strategies for HRKGs in LP tasks.
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Arxiv
2d
265
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IPGO: Indirect Prompt Gradient Optimization on Text-to-Image Generative Models with High Data Efficiency
- A study introduces a novel framework called Indirect Prompt Gradient Optimization (IPGO) for prompt-level fine-tuning in Text-to-Image Diffusion models.
- IPGO enhances prompt embeddings by injecting continuously differentiable tokens at the beginning and end of the prompt embeddings, allowing for gradient-based optimization.
- The results show that IPGO consistently outperforms cutting-edge benchmarks in terms of image aesthetics, image-text alignment, and human preferences.
- IPGO is effective in enhancing image generation quality while requiring minimal training data and limited computational resources.
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Arxiv
2d
257
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An Efficient Training Algorithm for Models with Block-wise Sparsity
- Large-scale machine learning models with sparse weight matrices are widely used to decrease computation and memory costs.
- Models with block-wise sparse weight matrices fit better with hardware accelerators and can further reduce costs during inference.
- However, existing methods for training block-wise sparse models are inefficient and start with full and dense models.
- The proposed efficient training algorithm decreases both computation and memory costs, while maintaining performance.
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Arxiv
2d
111
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Reward Design for Reinforcement Learning Agents
- Reward functions are central in reinforcement learning (RL), guiding agents towards optimal decision-making.
- Effective reward design aims to provide signals that accelerate the agent's convergence to optimal behavior.
- This thesis investigates different aspects of reward shaping, including teacher-driven, adaptive interpretable reward design, and agent-driven approaches.
- The research explores the impact of reward signals on the agent's behavior and learning dynamics and addresses challenges such as delayed, ambiguous, or intricate rewards.
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Arxiv
2d
337
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NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text
- A novel cross-modal contrastive learning framework called NeuroLIP has been proposed.
- NeuroLIP integrates functional magnetic resonance imaging (fMRI) connectivity data with phenotypic textual descriptors.
- The framework improves interpretability using token-level attention maps, revealing brain region-disease associations.
- NeuroLIP demonstrates superiority in fairness metrics while maintaining overall best standard metric performance.
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Arxiv
2d
51
Image Credit: Arxiv
RocketPPA: Ultra-Fast LLM-Based PPA Estimator at Code-Level Abstraction
- Large language models have transformed hardware design but bridging the gap between code synthesis and PPA estimation remains a challenge.
- A novel framework is introduced to predict power, performance, and area (PPA) metrics from Verilog code.
- The framework utilizes chain-of-thought techniques to clean and curate a dataset of synthesizable Verilog modules.
- Experimental results show significant improvements in power, delay, and area estimation using the proposed framework.
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