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Fourweekmba
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Why Agentic AI Matters?
- Agentic AI aims at solving real-world problems by automating tasks and enhancing workflows.
- It involves user input, AI agents, databases, LLMs, and feedback loops for continuous learning and adaptability.
- The flow of Agentic AI includes user input, AI agent processing, databases, action execution, data flywheel, and model customization.
- This architecture ensures autonomy, customization, efficiency, and integration in AI development.
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Marktechpost
3w
136
Hybrid Recommendation System (HRS-IU-DL): Enhancing Accuracy and Personalization with Deep Learning Techniques
- Recommender systems (RS) are essential for generating personalized suggestions based on user preferences, historical interactions, and item attributes.
- Research on RS has increasingly incorporated advanced deep learning (DL) techniques to overcome traditional limitations.
- Researchers from Mansoura University have introduced the HRS-IU-DL model, an advanced hybrid recommendation system that integrates multiple techniques to enhance accuracy and relevance.
- The proposed hybrid model (HRS-IU-DL) was evaluated on the Movielens 100k dataset, achieving superior performance across metrics and outperforming baseline models.
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Medium
3w
346
New research makes major revelation about Earth's rotation
- Previous studies have shown that the movement of water causes the Earth to rotate slightly differently.
- In the latest study, scientists estimate that humans extracted 2,150 gigatons of water from the Earth's surface between 1993 and 2010, equivalent to a modest 6 mm rise in sea level during that period.
- The new study modeled the observed changes in Earth's polar slip and water movement.
- The researchers found that the location from which the Earth's water was drawn was related to how much the polar position could change.
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Medium
3w
92
Image Credit: Medium
From Text to Insights: Hands-on Text Clustering and Topic Modeling with LLMs — Part 1
- This article introduces text clustering and its application in identifying clusters of related topics without manual reading of thousands of research abstracts.
- The article discusses the process of converting text into numerical representations using embedding models, selecting a suitable clustering model (stella-en-400M-v5), and reducing the dimensionality using UMAP.
- Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to cluster the reduced embeddings, resulting in 159 clusters.
- The clusters are validated through manual inspection and 3D visualization, showcasing the successful organization of 44,949 arXiv NLP papers into semantically coherent groups.
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Medium
3w
273
The Rainbow Effect: How Colors Mess with Our Emotions
- Research has shown that colors can actually change our brain chemistry and affect our mood.
- Different color palettes can evoke specific emotions and create memorable brand experiences.
- Warm and cozy colors make us feel happy, energetic, and hungry for more.
- Understanding the emotional impact of colors helps designers create more effective designs.
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Hackernoon
3w
259
Image Credit: Hackernoon
Embeddings for RAG - A Complete Overview
- This article provides an overview of embeddings with transformers, BERT, and Sentence BERT (SBERT) for LLMs and RAG pipelines.
- Transformers, composed of the encoder and decoder blocks, capture the context of each token with respect to the entire sequence.
- However, the attention layers only attend to the past tokens, which is fine for most tasks but not sufficient for question-answering.
- BERT, based on transformers, includes both forward and backward context and incorporates bidirectional self-attention.
- Sentence BERT (SBERT) treats each sentence separately, thereby enabling pre-computation of the embeddings and efficient computation of similarities as and when needed.
- SBERT introduces a pooling layer after BERT to reduce computation. SBERT is fine-tuned using NLI classification objective and regression and triplet similarity objectives.
- The official library for SBERT is sentence-transformer. Embedding is a crucial and fundamental step to get the RAG pipeline working at its best.
- The article concludes with a simple hands-on that shows how to get embeddings of any sentence using SBERT.
- Stay tuned for upcoming articles on RAG and its inner workings coupled with hands-on tutorials.
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Medium
3w
202
Image Credit: Medium
Embedding the Unseen: Techniques for Representing Graphs in Neural Networks
- A straightforward approach to graph embedding involves obtaining node embeddings and aggregating them for all nodes in the graph.
- An enhanced approach involves introducing a virtual node that connects to all other nodes and represents the entire graph.
- Another method involves anonymous random walks to capture graph structural features effectively for embedding purposes.
- Graph embeddings are versatile tools with applications in tasks such as graph classification.
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Medium
3w
312
Image Credit: Medium
Haven’t Heard of Lightning Fast Video Generation Models?
- Many video generation models currently have one quite big problem, which is the time it takes them to generate a video.
- AnimateDiff-Lightning proposed to use a new method called cross-model distillation to allow AnimateDiff-Lightning to generate videos in as quick as 10–15 seconds.
- AnimateDiff leverages pre-trained image diffusion models and enhances them to handle videos by adding motion modules to adapt image generation to video generation.
- AnimateDiff-Lightning uses two processes called Progressive Distillation and Adversarial Loss to generate videos.
- Progressive distillation is a process where a teacher model that is pre-trained that can do the task in 5 steps, and a student model trained to do the same task but in as few steps as possible.
- Cross-Model Distillation is an approach where they distill the motion module on all selected models at the same time.
- AnimateDiff-Lightning represents a significant leap forward in video generation, addressing the persistent challenge of balancing speed and quality.
- The model achieves remarkable efficiency without compromising output fidelity.
- The innovative application of cross-model distillation further sets it apart, enabling a shared motion module to work seamlessly with various base models.
- Now you only need to wait 10 seconds to see Spiderman eating fries in Disney Land.
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Medium
3w
171
Image Credit: Medium
The Future of Multi-Agent Systems: Inside OpenAI’s Experimental Swarm Framework
- The Future of Multi-Agent Systems: Inside OpenAI’s Experimental Swarm Framework
- The Swarm framework enables the construction of complex interactions between tools and networks of agents.
- Swarm's primary components are the Swarm client and the Agent class.
- Examples showcase task delegation, modular task handling, and personalized agent responses using the Swarm framework.
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Medium
3w
282
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All you need to know about MatchboxNet with pytorch
- MatchboxNet is a flexible architecture for defining custom neural network models.
- It consists of residual blocks, sub-blocks, and main blocks.
- The architecture includes Squeeze-and-Excitation (SE) blocks for channel-wise attention.
- Time-Channel Separable Convolution reduces parameters and computational cost.
- Overall, MatchboxNet is effective for processing time-series or audio data.
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Eweek
3w
30
Image Credit: Eweek
Generative AI vs AI: Key Differences Explained
- Generative AI mimics content via AI algorithms, focusing on creating new content, including text, images, video, and music while AI goes broader and deeper by following rule-based systems to improve efficiency, accuracy, and decision-making. Traditional AI utilizes supervised learning techniques, while Generative AI relies on unsupervised or self-supervised learning and requires large datasets for training. Generative AI is more adaptable and can create personalized content, whereas traditional AI is scalable, more resource-efficient, and more transparent and interpretable. Ethical concerns related to AI include bias and fairness, security and privacy, transparency and explainability, job displacement and economic impact, and environmental impact.
- Generative AI use cases are product design and personalization, creative content generation, software development, customer support and engagement, and fraud detection and risk management. Future advancements in Generative AI include building more powerful models, producing hybrid systems, creating multimodal AI models and driving new levels of personalization across retail, marketing, and e-commerce sectors. Traditional AI focuses on rule-based programming to execute tasks with precision and is scalable and efficient in well-defined environments. Its use cases include business automation and optimization, research and development, predictive maintenance, cybersecurity and fraud detection, and financial forecasting and planning. Future innovations in Traditional AI involve enhancing adaptability and flexibility and creating self-improving AI systems that autonomously optimize performance.
- Generative AI and Traditional AI face ethical challenges and concerns, including biases in systems, job displacement, security and privacy risks, transparency and explainability, and environmental impact. Organizations need to implement best practices, such as regulation compliance, sustainable AI development, robust data protection, enhanced user trust, and ethical implementations.
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Medium
3w
226
Image Credit: Medium
How Human-Robot Cooperative Piano Playing is Revolutionizing Music
- Human-robot cooperative piano playing is revolutionizing music, blending AI and human creativity for real-time duets.
- The Harmony Robot uses machine learning to predict piano chord progressions, allowing it to accompany a human pianist in real time.
- This blend of AI and human creativity opens up new possibilities for music education, therapy, and entertainment.
- The journey to perfecting human-robot musical collaboration wasn’t without its challenges.
- The solution to the synchronization challenge lay in the innovative use of machine learning algorithms.
- The key to successful human-robot collaboration in music lies in the robot’s ability to adapt to the nuances of human expression.
- By incorporating emotional cues into the robot’s programming, researchers can create performances that resonate on a deeper level.
- The integration of AI in music has opened up new possibilities for creative expression and collaboration.
- The future lies in enhancing the robot’s emotional intelligence and dexterity, allowing for more dynamic and expressive musical performances.
- The Harmony Robot represents a significant step forward in the integration of AI and human creativity, offering new possibilities for collaboration and expression.
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Medium
3w
304
Image Credit: Medium
Game-Changing Developer Tools to Supercharge Your Workflow
- Mintlify is a tool that automatically generates professional quality documentation from code.
- Chromium DevTools is essential for front-end developers, offering real-time debugging and performance optimization.
- GitHub Copilot provides AI-driven code suggestions, boilerplate code generation, and compatibility with updated libraries.
- Perplexity AI simplifies learning complex subjects, providing concise answers and credible references.
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Medium
3w
384
Image Credit: Medium
Top 10 FAQs Every AI Leader Should Be Aware Of
- AI enables businesses to automate repetitive tasks, gain insights from data, and deliver personalized experiences.
- Starting with AI requires a clear strategy: Define objectives, assess data readiness, start small, upskill teams, and collaborate.
- Establish governance, address bias, ensure transparency, and implement accountability are crucial for ethical AI implementation.
- Integration issues, talent gaps, data silos, and change management are challenges in scaling AI across an organization.
- AI automates repetitive tasks, potentially displacing jobs while creating new roles in AI development, data analysis, and system management.
- Leaders should set clear KPIs to evaluate AI project success using operational efficiency, revenue growth, and customer satisfaction.
- Mitigation strategies involve implementing robust cybersecurity measures, using diverse datasets, and staying updated on legal regulations.
- AI contributes to sustainability by optimizing energy usage, improving resource management, and aiding in climate research.
- Trust is critical for AI adoption. Leaders can foster trust by being transparent, ensuring fairness, and providing control.
- AI leaders must balance innovation with responsibility, ensuring long-term success while addressing societal concerns.
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Medium
3w
8
Image Credit: Medium
Meta AI’s Segment Anything Models (SAM & SAM2)
- The Segment Anything Model (SAM), developed by Meta AI, is a powerful vision foundation model for image segmentation.
- SAM can produce segmentation masks based on diverse inputs or prompts.
- The architecture of the Segment Anything Model (SAM) consists of three main components: Image Encoder, Prompt Encoder, and Mask Decoder.
- SAM can generalize across diverse tasks and domains without the need for task-specific fine-tuning.
- It outputs the top three masks at the part, sub-part and component level.
- SAM2 is poised to push the boundaries of computer vision by refining segmentation techniques.
- SAM represents a significant advancement in image segmentation, offering impressive flexibility, scalability, and zero-shot generalization across diverse tasks and domains.
- Its ability to process various types of prompts and deliver real-time results makes it a powerful tool for a wide range of applications.
- SAM2's limitations in handling complex scenes, domain-specific challenges, and computational demands highlight areas for future improvement.
- Balancing its strengths with refinements tailored to specialized applications will be crucial for maximizing its impact across diverse fields.
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