Artificial Intelligence

📝 New research on text creativity Scientists have shown: texts created by humans are semantically newer than those generated by AI. 🔎 How it was measured They introduced the metric "semantic novelty" — the cosine distance between adjacent sentences. 🧠 Main findings Human texts consistently show higher novelty across different embedding models (RoBERTa, DistilBERT, MPNet, MiniLM). In the "human-AI storytelling" dataset, the human contribution was semantically more diverse. ✨ But there is a nuance What we call AI "hallucinations" can be useful in collaborative storytelling. They add unexpected twists and help maintain interest in the story. 👉 Conclusion: humans are more innovative, AI is more predictable, but together they enhance each other. 🔗 Details

🔗 Cheat sheets for Machine Learning and Data Science interviews

A developer posted a useful set of cheat sheets for interview preparation. They contain all the most important information on key ML and DS topics.

Convenient to review before the interview or to brush up your basics.

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Key Concepts for Machine Learning Interviews 1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests. 2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE. 3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand. 4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees. 5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE). 6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization. 7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking. 8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data. 9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis. 10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods. 11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients. 12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data. 13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment. 14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound. 15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks

AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning. ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed. Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc. Think of it this way: AI = Science ML = A chapter in the science Deep Learning = A paragraph in that chapter.

🔟 things to know before diving into AI automation An author from Reddit built over 100 workflows and highlighted the most important lessons: 1. Start with simple scenarios — 10 minutes of benefit is better than 10 hours of complexity. 2. Document the process: screenshots and errors are your portfolio. 3. Learn to work with HTTP requests right away — it opens access to almost everything. 4. Don’t call yourself an "expert," say specifically: "I help businesses save time." 5. Know how to say no: sometimes "no" opens the way to more profitable projects. 6. Always think about errors: APIs crash, data breaks. 7. Share failures — they build more trust than perfect cases. 8. Stable income comes not from setup, but from support and improvements. 9. Networking is half the success. Projects come through colleagues. 10. Automate yourself first: the best argument is your own example. 💡 The main thing: businesses don’t need beautiful workflows, but results — for example, "minus 15 hours of routine per week." 🔗 Full post

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⚡️ 25 AI Tools to Boost Your Productivity in 2025! ◽️ Here is a comprehensive list of the most powerful AI tools for various tasks: from audio and video to research and content creation, with hidden links for each tool: 🎙 Audio Field: 🔹 Lovo Tool – for converting text into natural voices. 🔹 Speechify Tool – for turning written texts into audiobooks. 🔹 Murf Tool – to create professional voiceovers. 🔹 Media.io Tool – for easy audio and video editing and conversion. 🌐 Website Field: 🔹 10Web Tool – to create full websites using AI. 🔹 Durable Tool – to build a website in less than 30 seconds. 🔹 AlliAI Tool – for automatic SEO optimization. 🔹 Subpage Tool – to create smart and fast landing pages. 🎥 Video Field: 🔹 Steve Tool – to create videos from texts. 🔹 Pictory Tool – for automatic video editing from text content. 🔹 Deepbrain Tool – to create human videos from texts. 🔹 Heygen Tool – to generate videos with realistic talking faces. 📊 Presentations: 🔹 Beautiful Tool – to design stunning visual presentations. 🔹 Simplified Tool – to easily create designs and marketing presentations. 🔹 Slidesgo Tool – AI-powered ready and customizable presentation templates. 🔬 Scientific Research Field: 🔹 Paperpal Tool – for reviewing and editing academic papers. 🔹 BetaMonic Tool – to suggest recent papers and research by topic. 🔹 Consensus Tool – to get answers supported by reliable research. 🔹 Perplexity Tool – an instant search engine with accurate, sourced answers. 🔹 You Tool – a smart search engine that aggregates results from diverse sources with an interactive experience. ✍️ Content Creation: 🔹 Lovo Tool – to create professional audio content. 🔹 Writesonic Tool – an intelligent writing assistant to generate articles, posts, and ads. — Choose what suits you and start 2025 with higher intelligence and doubled productivity!

🔟 Top Python Libraries for Language AI Models (LLMs) in 2025 🐍🤖 If you work in AI and natural language processing, these libraries are indispensable! 🏆 1. Hugging Face Transformers Library 🔹 Best for: Pretrained language models, training, and inference. 🔹 Why? Provides easy access to load and run the most popular language models, such as GPT and BERT. 💬 2. LangChain Library 🔹 Best for: Building applications based on language models, like chatbots and interactive AI. 🔹 Why? Offers flexible tools to integrate LLMs with databases and APIs. 🧠 3. SpaCy Library 🔹 Best for: Text analysis, Named Entity Recognition (NER), and syntactic parsing. 🔹 Why? Fast and powerful, ideal for enterprise AI projects. 📖 4. NLTK (Natural Language Toolkit) Library 🔹 Best for: Language analysis, text segmentation, and Part-of-Speech (POS) tagging. 🔹 Why? Contains a rich set of linguistic tools for computational linguistics research. 🔎 5. SentenceTransformers Library 🔹 Best for: Semantic search, sentence similarity measurement, and clustering. 🔹 Why? Based on powerful models like BERT and RoBERTa to extract deep meanings from texts. 🔤 6. FastText Library 🔹 Best for: Word embeddings and text classification. 🔹 Why? Developed by Facebook, known for speed and accuracy in multilingual text classification. 📝 7. Gensim Library 🔹 Best for: Topic modeling and text representation (Word2Vec and Doc2Vec). 🔹 Why? Provides efficient algorithms to extract insights from large text corpora. 🏷 8. Stanza Library 🔹 Best for: Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. 🔹 Why? Developed by Stanford University, it is multilingual and highly accurate. 😃 9. TextBlob Library 🔹 Best for: Sentiment analysis, POS tagging, and text processing. 🔹 Why? Easy to use, suitable for beginners in natural language analysis. 🌍 10. Polyglot Library 🔹 Best for: Multilingual text processing, entity recognition, and word representation. 🔹 Why? Supports over 130 languages, making it ideal for global projects. 🚀 Whether you are a beginner developer or an AI expert, these libraries will help you build the most powerful applications based on language models!

🚀 A fantastic resource for everyone who wants to understand how Qwen3 models work: Qwen3 From Scratch This is a detailed step-by-step guide to running and analyzing Qwen3 models — from 0.6B to 32B — from scratch, directly in PyTorch. 📌 What's inside: — How to load the Qwen3‑0.6B model and pretrained weights — Setting up the tokenizer and generating text — Support for the reasoning version of the model — Tricks to speed up inference: compilation, KV cache, batching 📊 The author also compares Qwen3 with Llama 3: ✔️ Model depth vs width ✔️ Performance on different hardware ✔️ How the 0.6B, 1.7B, 4B, 8B, 32B models behave ⚡️ Perfect if you want to understand how inference, tokenization, and the Qwen3 architecture work — without magic or black boxes. 🖥 Github

✔️ Higgs Audio: an open platform for training and experimenting with audio LLMs Higgs Audio by boson-ai is a repository for researchers and developers who want to quickly assemble, train, and test audio models: speech recognition, audio question-answering, multimodal voice agents, and custom experiments with embeddings. Key ideas • Unified framework: the project structure simplifies working with datasets, preprocessing, and training launch. • Flexible configs: switch models, batch sizes, augmentations, and optimization strategies via customizable YAML/JSON parameters. • Modular blocks: encoders, decoders, prompt adapters, and task heads can be combined without rewriting the core. • Quick start: ready-made scripts for data preparation and training on one or multiple GPU nodes. • Experimental playground: conveniently try fine-tuning for your domain acoustics (podcasts, calls, streams, noisy datasets). Typical use cases 1. Train a small speech recognition model on your own corpus. 2. Create a voice bot: audio input → text → LLM → audio response. 3. Fine-tune an embedding model for sound search (similar signals, music fragments, events). 4. Research zero-shot / few-shot adaptation of audio models to new languages or accents. https://github.com/boson-ai/higgs-audio

📂 Full description AI is one of the biggest topics in cybersecurity right now, and unsurprisingly, that includes the area of governance, risk, and compliance. In this course, cybersecurity manager Terra Cooke explores risk management, legal considerations, and other ways AI can integrate into a GRC environment. Terra covers different aspects of GRC work that could benefit from AI–while keeping in mind that it is not a fix-all solution for all problems. She analyzes roles, responsibilities, and the pros and cons to consider before moving forward with AI solutions. Plus, hear about the factors you should look into before agreeing to the terms and conditions of an AI program.

🔅 Leveraging AI for Governance, Risk, and Compliance 🌐 Author: Terra Cooke 🔰 Level: Intermediate ⏰ Duration: 17m

🌀 Learn how you can integrate artificial intelligence tools into your GRP environment.

📗 Topics: AI Governance, Governance, Risk Management, and Compliance 📤 Join Artificial Intelligence and Machine Learning for more courses

Productivity tools

📈 METR: AI is starting its own "Moore's Law" When will AI be able to independently complete long projects? Researchers from METR found a pattern: the time horizon of tasks that AI agents can handle doubles every ~7 months. Now they tested this on 9 new benchmarks: MATH, OSWorld, LiveCodeBench, Mock AIME, GPQA Diamond, Tesla FSD, Video-MME, RLBench, and SWE-Bench Verified. Results: 🧠 Similar growth rates in science, math, robotics, programming, and even autopilot. ⚡️ New models like o3 grow faster than predicted — median doubling now ~4 months. 🕐 On reasoning tasks, agents last 1+ hour. 🖱 But in OS and browser — still about ~2 minutes, due to weak tools. > "Moore's Law for AI": not about chips — about the ability to think and work longer. Faster. Independently. AI agents grow not by days, but by benchmarks.

Key Concepts for Machine Learning Interviews 1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests. 2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE. 3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand. 4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees. 5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE). 6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization. 7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking. 8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data. 9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis. 10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods. 11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients. 12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data. 13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment. 14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound. 15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.

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