Before training a model, you have to preprocess data. This is necessary to transform raw data into clean data suitable for analysis. In this guide, we will cover essential steps to preprocess data using Python. These include splitting the dataset into training and validation sets, handling missing values, managing categorical features, and normalizing the dataset. Why do you need to preprocess data? Data preprocessing is important for several reasons: Improves data quality. Data preprocessing techniques such as handling […]

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Crypto contract verification is the definitive proof of identity in the DeFi ecosystem, transforming opaque bytecode into trusted logic. However, the process is often misunderstood, leading to frustration when the “Deterministic Black Box” of the compiler produces mismatching fingerprints. This article demystifies verification by visualizing it as a “Mirror Mechanism,” where local compilation environments must precisely replicate the deployment conditions. We move beyond manual web uploads to establish a robust, automated workflow using CLI tools and the “Standard […]

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arXiv:2601.04221v1 Announce Type: new Abstract: This paper presents a new approach to algorithmic composition, called predictive controlled music (PCM), which combines model predictive control (MPC) with music generation. PCM uses dynamic models to predict and optimize the music generation process, where musical notes are computed in a manner similar to an MPC problem by optimizing a performance measure. A feedforward neural network-based assessment function is used to evaluate the generated musical score, which serves as the objective function […]

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arXiv:2601.03268v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has shifted language model evaluation toward reasoning and problem-solving tasks as measures of general intelligence. Small Language Models (SLMs) — defined here as models under 10B parameters — typically score 3-4 times lower than LLMs on these metrics. However, we demonstrate that these evaluations fail to capture SLMs’ effectiveness in common industrial applications, such as tone modification tasks (e.g., funny, serious, professional). We propose an evaluation […]

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arXiv:2501.00755v3 Announce Type: replace Abstract: Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and outcome. This individualized posterior representation yields estimates of the individual treatment […]

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The podcast explores the different types of artificial intelligence (AI) and their capabilities. It distinguishes between narrow AI (weak AI), which performs specific tasks, general AI (strong AI) with broad human-like cognitive abilities, and superintelligent AI (ASI), which surpasses human intelligence in all aspects. It also explores the AI categories based on functionality (e.g., reactive machines, limited memory, theory of mind), learning models (supervised, unsupervised, reinforcement, and deep learning), It exemplifies specific branches of AI like machine learning, […]

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