Artificial Intelligence

✔️ 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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AI is getting out of hand 😂 Baby Joe and Baby Theo Von