What machine learning technology does IN-V-BAT-AI use?

IN‑V‑BAT‑AI is powered by advanced heuristic AI and machine learning, built on a foundation of natural language processing and a full‑text inverted database architecture — giving you fast, accurate, and reliable learning support every time.

Heuristic AI:

Uses rules of thumb or equation or approximation to solve problems quickly and efficiently.

Relies on predefined rules and expert knowledge to make decisions.

Often used in search algorithms, optimization, and decision-making models.

Machine Learning:

Machine Learning helps IN‑V‑BAT‑AI learn patterns and relationships in data so it can make predictions, inferences, and recommendations. This capability is actively improving as we continue to develop the system.

✅ Relies on algorithms and statistical models to find relationships between input and output variables.

✅ Often used in tasks like classification, regression, clustering, and recommendation systems.

Machine learning enables computer systems to learn from data, discovering relationships between input variables (x) and predicting target outputs (y). Essentially, machine learning derives a generalized equation. While our human minds can visualize up to three dimensions (e.g., x, y, and z) in latent space, ⤵ 👈 machines can process thousands of dimensions to uncover patterns beyond our visualization capabilities. Finding generalized equations and discovering pattern clusters are crucial for tasks such as prediction, information retrieval, search, classification, recommendation, anomaly detection, pattern recognition, facial recognition, and fraud detection.

An important lesson to keep in mind when dealing with machine learning is that the training data used to discover relationships or equations or good enough approximation must be constantly monitored for validity and accuracy. If your machine learned the generalized equation using consistent and clean batch data, be aware of its limitations when using it to make predictions with streaming data as input. Conversely, if your machine learned the generalized equation using streaming data, be aware of its limitations when using it to make predictions with batch data as input.

A machine learning model or discovered generalized pattern is effective in a controlled research environment but often falls short in production environments with unclean data and new features not captured during training. In computer science, this phenomenon is known as concept drift, where the learned relationship between input variables (x) and target outcomes (y) changes from the original model. Therefore, subject matter experts must periodically validate the predicted outcomes. If prediction accuracy drifts significantly, the next step is to retrain the model with new input data causing the drift. Decision-makers using machine learning predictions must understand its limitations. A well-maintained machine learning process can achieve feats beyond human capabilities. For instance, solving the problems of forgetting and computational thinking is now within reach.

🔗 Regression is not useful for finding equations with billions of parameters. 🔗 experts developed automated feature extraction techniques used in deep learning 🔗 In neural networks, the learning algorithm supervises weight adjustments. 🔗 Categories of Machine Learning Algorithms
How do you process batch data and streaming data for machine learning? You can use Lambda Architecture, ⤵ 👈 where batch data is the default source of truth. Streaming data is transformed into clean batch data to synchronize the relationship discovery between streaming and batch data.

Alternatively, you can use Kappa Architecture, ⤵ 👈 where streaming data is the default source of truth. Batch data is transformed to synchronize the relationship discovery between streaming and batch data.