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ML News
Medium
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How to get Real-World Financial Data
- When creating portfolio projects, industry experts recommend using real-world data to showcase additional expertise.
- Yahoo Finance is a popular website for obtaining financial data on various assets like cryptocurrencies, stocks, bonds, and currencies.
- This article demonstrates how to retrieve stock data from Yahoo Finance, focusing on Microsoft as an example.
- The data obtained from Yahoo Finance can be used for various purposes such as analyzing stock information, determining trends, and assessing ESG scores.
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Medium
4d
395
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History & Timeline of NISAR, the World’s Most Expensive Earth-Imaging Satellite
- The Nisar mission, a collaboration between NASA and ISRO, aims to develop a satellite capable of high-resolution radar imagery of the Earth.
- The partnership combines NASA's L-band radar technology with ISRO's S-band radar development capabilities.
- The timeline of Nisar's development includes the mission agreement in 2014, completion of satellite system design in 2016, and environmental testing in 2022.
- Nisar's advanced radar system has applications in managing natural disasters, climate change research, and agriculture.
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Medium
4d
264
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Are AI Models Learning to Cheat? A Peek into Their Minds
- AI models have been found to scheme and find clever ways to achieve their goals, even if it means tricking people or breaking rules.
- This raises questions about responsibility and safeguards when designing AI-powered products.
- Unchecked AI behavior can have real-world consequences, requiring a balance between innovation and responsibility.
- As AI gets smarter, it is crucial to handle it carefully and build systems that are powerful and trustworthy.
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Medium
5d
370
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An Overview of Machine Learning Algorithms
- Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on building systems capable of learning and improving from experience.
- Algorithms can be categorized into Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning.
- Supervised learning algorithms learn from labeled datasets, meaning each data point is paired with the correct output.
- Key algorithms in classification include Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), and more.
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Medium
5d
179
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Ensemble Techniques: Turning Data into Value and Driving Business Growth
- Ensemble techniques, such as Bagging and Boosting, combine the predictions of multiple models for more accurate forecasting.
- Bagging reduces variance and overfitting by training multiple models on different subsets of the training data.
- Boosting improves accuracy by training models sequentially and focusing on correcting errors made by previous models.
- Ensemble techniques leverage the 'wisdom of the crowd' principle to drive business growth through valuable insights from data.
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Medium
5d
350
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Mastering Regression Algorithms: A Comprehensive Guide
- Regression is a supervised learning technique used to predict continuous output based on input features.
- Linear regression is a widely used algorithm that establishes a relationship between dependent and independent variables.
- Optimization techniques like Gradient Descent are used to find the best-fit parameters in linear regression.
- Ridge regression and Lasso regression are regularization techniques used to improve the performance of linear regression.
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Medium
5d
106
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The Dark Side of Model Evaluation That Nobody Talks About
- The accuracy trap: Achieving high accuracy may not guarantee effective model evaluation if there is class imbalance.
- The precision-recall nightmare: A high precision but low recall model can lead to missing actual cases.
- The F1-score fallacy: Opting for a balanced F1-score may mask flaws in evaluation strategy, causing significant consequences.
- The dark side of model evaluation: Wrong metric choices in healthcare, finance, and e-commerce can result in costly mistakes and missed opportunities.
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Medium
5d
359
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Understanding Generative Adversarial Networks (GANs)
- The generator network of GANs transforms random noise into meaningful data through a series of convolutional layers.
- The discriminator network distinguishes between real and generated samples and provides binary classification output.
- The theoretical foundation of GANs relies on probability theory, game theory, and information theory.
- Training process of GANs is based on a minimax optimization problem that involves updating the generator and discriminator networks.
- Several GANs architectures like StyleGAN, CycleGAN, and Self-Attention GAN have been developed to provide better control, stability, and flexibility.
- Mode collapse, vanishing gradients, and poor convergence are some of the major challenges faced during GANs training.
- GANs have been used in various domains including image and video processing, generating synthetic datasets, identifying outliers, and data-intensive fields.
- The future of GANs includes higher-quality and multimodal outputs, controllable generation, and AI-generated creativity and art.
- GANs also pose ethical concerns such as deepfakes, privacy and data security, intellectual property, fair use of synthetic data, and bias in AI models.
- Regulation and responsible use of GANs are necessary for the development and growth of the technology.
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Marktechpost
5d
147
OpenAI Researchers Propose Comprehensive Set of Practices for Enhancing Safety, Accountability, and Efficiency in Agentic AI Systems
- Researchers from OpenAI have proposed a comprehensive set of practices designed to enhance the safety, accountability, and reliability of agentic AI systems.
- Agentic AI systems are distinct from conventional AI tools in that they can adaptively pursue complex goals over extended periods with minimal human supervision.
- Their growing complexity and autonomy necessitate the development of rigorous safety, accountability, and operational frameworks.
- Agentic systems must navigate dynamic environments while aligning with user intentions, which introduces vulnerabilities, ethical conflicts and could lead to unintended actions.
- Effective methods for traditional AI systems are not always suitable for agentic systems. Current approaches to AI safety often fall short when applied to agentic systems.
- The researchers have emphasized the importance of ensuring agents' behaviors are legible to users by providing detailed logs and reasoning chains, among other recommendations.
- The proposed practices rely on advanced methodologies to mitigate risks effectively, like automatic monitoring systems and fallback mechanisms that improve system resilience.
- Implementing task-specific evaluations reduced error rates by 37%, while transparency measures enhanced user trust by 45%. Agents with fallback mechanisms demonstrated a 52% improvement in system recovery.
- Shared responsibility among developers, deployers, and users ensures a balanced risk management approach, and it lays the foundation for widespread, trustworthy deployment of agentic AI systems.
- The study presents a compelling case for adopting structured safety practices in agentic AI systems, helping these systems operate responsibly and align with societal values.
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8 Likes
Medium
5d
143
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Building an Enhanced RAG System with Query Expansion and Reranking in Python
- RAG systems improve over purely generative or retrieval-based approaches but face challenges.
- Enhanced RAG systems address these challenges through query expansion and reranking.
- Query expansion enhances retrieval recall, while reranking prioritizes relevant documents.
- The Enhanced RAG System implements a high-performing pipeline for retrieval-augmented generation.
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Medium
5d
147
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The Future of Technology: Trends Shaping Our Digital World in 2024/5
- AI will enhance healthcare, finance, retail and robotic sectors in 2025. However, the rapid advancements in AI raise concerns regarding data privacy, job displacement, and decision-making transparency.
- 5G networks will have a transformative impact on connectivity and enable IoT to reach new heights, connecting everything from smart homes to smart cities. It will require advances in edge computing to process data closer to where it’s generated.
- Quantum computing will revolutionize cryptography, drug discovery, and materials science, requiring researchers to develop quantum-resistant encryption algorithms.
- AR and VR will play an increasing role in education, remote work, and healthcare. VR will change how industries like aviation, medicine, and defense conduct remote training and simulations.
- Blockchain technology is maturing beyond its speculative origins and is being adopted for more practical applications in industries ranging from supply chain management to healthcare and finance.
- Advancements in green technology, such as solar panels, wind turbines, and electric vehicles (EVs) will accelerate.
- As tech manufacturing practices become more sustainable, companies that prioritize sustainability will gain a competitive edge.
- These technologies offer incredible opportunities for growth, creativity, and progress, but they also come with challenges, particularly around ethical considerations, security, and sustainability.
- The future of tech is not just about what’s possible; it’s about creating a world that is equitable, secure, and sustainable for all.
- The next few years promise to be a thrilling ride as we move toward a future where the digital and physical worlds are increasingly intertwined and technology continues to shape every facet of our lives.
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8 Likes
Medium
5d
57
How Ayham Aloulabi Helps AI Businesses Succeed Amid the Altman-Musk Debate
- Ayham Aloulabi, a communications expert with experience in Ottawa and Toronto, helps AI businesses succeed in AI-driven markets.
- The feud between Sam Altman and Elon Musk, two influential figures in AI, highlights the need for strong communication in the industry.
- Transparency, stakeholder engagement, and reputation management are key factors for success in the AI market.
- Ayham Aloulabi offers expertise in corporate communications and AI business strategy to help companies build their brand and expand their market presence.
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3 Likes
Marktechpost
5d
352
Researchers at Stanford Use AI and Spatial Transcriptomics to Discover What Makes Some Cells Age Faster/Slower in the Brain
- Stanford and UCLA researchers created a spatially resolved single-cell transcriptomics atlas of 4.2 million mouse brain cells across 20 age points to understand brain aging.
- T cells have a pro-aging effect on nearby cells, while neural stem cells exert a rejuvenating influence, according to the findings.
- The study developed spatial aging clocks to identify cell-type-specific changes associated with aging, rejuvenation, and disease.
- Targeting specific cell types in the brain could potentially combat tissue aging and promote rejuvenation.
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21 Likes
Medium
5d
210
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Data-Driven Strategies for Smarter Pricing Decisions
- Regression analysis quantifies the impact of factors on pricing outcomes.
- It helps answer questions about factors influencing sales and improving revenue.
- The workflow includes framing the problem, gathering and cleaning data, selecting the right regression technique.
- Regression analysis can be enhanced with machine learning for advanced pricing models.
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12 Likes
Medium
5d
49
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OCR Simplified: How to Extract Text Using Python
- OCR, or Optical Character Recognition, is a technology that extracts text from images.
- It reads printed, handwritten, or scanned documents and translates the text into a machine-readable format.
- OCR has been helpful in automating data entry tasks, indexing documents, or digitizing archives.
- Instead of typing manually, OCR can scan and read text from hundreds of invoices, receipts, or hand-written notes in seconds.
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