MATLAB EXPO 2024 is back! Attend this online event that brings together engineers, scientists, and researchers from around the world to explore the latest advancements in AI, algorithm... read more >> ..read more

In my last post, I talked about new AI features introduced with MATLAB R2024b - the latest release, which is now available to you. In this blog post, I am going to present a few new and exciting... read more >> ..read more

MATLAB R2024b is the latest release and available for you to try. I am here to talk specifically about new AI features in the latest release, and if you're interested in other features, check out... read more >> ..read more

Embedded AI, that is the integration of artificial intelligence and embedded systems, enables devices to process data and make decisions locally. It enhances efficiency, reduces latency, and... read more >> ..read more

This blog post is from Maggie Oltarzewski, Product Marketing Engineer at MathWorks. In R2024a, four new blocks for co-executing deep learning models in Simulink were added to Deep Learning... read more >> ..read more

The following post is from Sagar Hukkire, application engineer for AI, and Conrado Ramirez Garcia, application engineer for design automation and code generation. Visual inspection is the... read more >> ..read more

Local large language models (LLMs), such as llama, phi3, and mistral, are now available in the Large Language Models (LLMs) with MATLAB repository through Ollama! This is such exciting news that I can’t think of a better introduction than to share with you this amazing development. Even if you don’t read any further (but I hope you do), because you are too eager to try out local LLMs with MATLAB, know that you can access the repository via these two options: File Exchange - Large Language (LLMs) with MATLAB GitHub - Large Language Models (LLMs) with MATLAB I am glad you decided to keep read ..read more

In this blog post, I am going to show you the most interactive way to create a Simulink model that includes a machine learning model by using the Classification Learner app. Simulating and testing machine learning models is becoming increasingly popular, but how do you integrate machine learning models into Simulink models? The workflow shown here is very similar if you are using the Regression Learner app. The export functionality was introduced in MATLAB R2024a and you can learn more in the documentation topics Export Classification Model to Make Predictions in Simulink and Export Regression ..read more

In R2024a, Computer Vision Toolbox introduced the d-rise function. D-RISE is an explainability tool that helps you visualize and understand  which parts are important for object detection. If you need a refresher on what explainable AI is and why it’s important, watch this short video. D-RISE is a model-agnostic method that doesn’t require knowledge of the inner workings of the object detection model, as proposed in this paper. It produces a saliency map (image with highlighted areas that most affect the prediction) given a specific image and object detector. Because it’s a general and mo ..read more

Constrained deep learning is an advanced approach to training robust deep neural networks by incorporating constraints into the learning process. These constraints can be based on physical laws, logical rules, or any other domain-specific knowledge. For example, if you are designing a deep learning model for a mobile phone’s battery state of charge (SoC), it makes sense that the SoC will be monotonically increasing when the phone is plugged in and charging, and monotonically decreasing when the phone is unplugged and being used. Imposing constraints in the learning process guarantees that cert ..read more