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The AI Chat Playground at MATLAB Central has two new upgrades: OpenAI GPT-4o mini and MATLAB R2024b!
GPT-4o mini is a new language model from OpenAI and brings general knowledge up to October 2023. GPT-4o mini surpasses GPT-3.5 Turbo and other small models on academic benchmarks across both textual intelligence and reasoning. Our goal is to keep improving the output of the AI Chat Playground. This upgrade is available now: https://www.mathworks.com/matlabcentral/playground/
One more thing... we also updated the system to the latest release of MATLAB. This is R2024b and comes with hundreds of updates and new plot types to explore.Check out Mike Croucher's blog post about the latest version of MATLAB: https://blogs.mathworks.com/matlab/2024/09/13/the-latest-version-of-matlab-r2024b-has-just-been-released/
We are looking forward to your feedback on the updates to the AI Chat Playground. Let us know what you think and how you use this community app.
Spring is here in Natick and the tulips are blooming! While tulips appear only briefly here in Massachusetts, they provide a lot of bright and diverse colors and shapes. To celebrate this cheerful flower, here's some code to create your own tulip!
I found this plot of words said by different characters on the US version of The Office sitcom. There's a sparkline for each character from pilot to finale episode.
This is Stella while waiting to see if the code works...
I am often talking to new MATLAB users. I have put together one script. If you know how this script works, why, and what each line means, you will be well on your way on your MATLAB learning journey.
% Clear existing variables and close figures
clear;
close all;
% Print to the Command Window
disp('Hello, welcome to MATLAB!');
% Create a simple vector and matrix
vector = [1, 2, 3, 4, 5];
matrix = [1, 2, 3; 4, 5, 6; 7, 8, 9];
% Display the created vector and matrix
disp('Created vector:');
disp(vector);
disp('Created matrix:');
disp(matrix);
% Perform element-wise multiplication
result = vector .* 2;
% Display the result of the operation
disp('Result of element-wise multiplication of the vector by 2:');
disp(result);
% Create plot
x = 0:0.1:2*pi; % Generate values from 0 to 2*pi
y = sin(x); % Calculate the sine of these values
% Plotting
figure; % Create a new figure window
plot(x, y); % Plot x vs. y
title('Simple Plot of sin(x)'); % Give the plot a title
xlabel('x'); % Label the x-axis
ylabel('sin(x)'); % Label the y-axis
grid on; % Turn on the grid
disp('This is the end of the script. Explore MATLAB further to learn more!');
More than 500,000 people have subscribed to the MATLAB channel. MathWorks would like to thank everyone who has taken the time to watch one of our videos, leave us a comment, or share our videos with others. Together we’re accelerating the pace of engineering and science.
Most of my projects are the tension between what exists and what I must create to make the project work. The more I leverage what exists, the faster I can complete my project in a shorter amount of time. I often find out later in the process that a MATLAB function already exists and has existed for 10 releases. The faster that I discover built-in functions, the faster I can get to the new stuff that I have to invent.
I asked the AI Chat Playground why to use MATLAB's builtin functions and I got some interesting ideas. What are your thoughts?
AI Chat Playground:
- Efficiency and Performance: MATLAB's built-in functions are highly optimized for performance. They are written in optimized and compiled languages like C or C++, which means they run faster than equivalent functions written directly in MATLAB language. When you use these functions, your code can execute more efficiently, which is especially beneficial for handling large datasets or complex mathematical computations.
- Reliability and Stability: Built-in functions have been rigorously tested and debugged by the developers at MathWorks (the company behind MATLAB). They are less likely to contain errors compared to custom-written code, which means you can rely on them to produce accurate and consistent results.
- Ease of Use: Built-in functions are designed to be user-friendly, with clear documentation and support materials available. This can make it easier to perform complex operations without needing a deep understanding of the underlying algorithms. It also reduces the amount of code you need to write and debug, saving time and reducing the potential for errors.
- Compatibility: Built-in functions are updated alongside MATLAB itself, ensuring compatibility with new features and versions of the software. Using built-in functions can help ensure that your code continues to work correctly as MATLAB evolves.
- Advanced Features: MATLAB’s built-in functions often include advanced features and options that are not readily available or would be difficult to implement on your own. For example, MATLAB’s mathematical functions can handle a wide range of special cases and data types, providing more flexibility than custom implementations.
- Community and Support: Using built-in functions means you're working with standard tools recognized by the MATLAB community. This can make it easier to get help from other users or from MathWorks support, as more people will be familiar with the functionality and potential issues of these functions.
I found this list on Book Authority about the top MATLAB books: https://bookauthority.org/books/best-matlab-books
My favorite book is Accelerating MATLAB Performance - 1001 tips to speed up MATLAB programs. I always pick something up from the book that helps me out.
spy
Over at Reddit, a MATLAB user asked about when to use a script vs. a live script. How would you answer this?
I would tell myself to understand vectorization. MATLAB is designed for operating on whole arrays and matrices at once. This is often more efficient than using loops.
The MATLAB AI Chat Playground is now open to the whole community! Answer questions, write first draft MATLAB code, and generate examples of common functions with natural language.
The playground features a chat panel next to a lightweight MATLAB code editor. Use the chat panel to enter natural language prompts to return explanations and code. You can keep chatting with the AI to refine the results or make changes to the output.
Give it a try, provide feedback on the output, and check back often as we make improvements to the model and overall experience.
Chen, Rena, and I are at a community management event. It's great to be with others talking about relationships, trust, and co-creation.
Here's a screenshot from 22 years ago. Thanks for building one of the best engineering and science communities together.
AI, Robotics, Autonomous Systems: MATLAB EXPO is coming up soon and it is time to register. It's free and open to everyone. You will have the opportunity to connect with engineers, scientists, educators, and researchers, and new ideas.
disp('It is time to register for MATLAB EXPO 2023!');
Featured Sessions:
- Project-Based Learning and Design with Simulation - Professor Claire Lucas, King’s College London
- How Siemens Energy Enables the Global Energy Transition - Jens Dietrich, Siemens Energy AG
Features Topics:
- AI
- Autonomous Systems
- Electrification
- Robotics
With the release of MATLAB R2023a, we also are giving you early access to the new MATLAB Desktop. This includes Dark Mode! Download via Add-on Explorer in MATLAB or find here on File Exchange.
These are some of the many enhancements and new features of the new desktop:
- Dark mode / theme support - Change the colors of the desktop by selecting a dark theme.
- Updated layout - Quickly access tools and change your layout using the sidebar.
- Expanded search capabilities - Easily find actions, settings, and resources using the new universal search box.
- Figure toolstrip - Use the new figure toolstrip to easily modify annotations, text, and line styles, and view generated code.
- Keyboard and screen reader accessibility support - Use a screen reader to interact with the Command Window, Editor and desktop tools.
- Enhanced MATLAB Projects workflows - Collaborate using an improved user interface, manage multiple repositories using the Branch Manager, and investigate project hierarchies using the new Dependency Analyzer view.
Have fun trying it out and let us know what you think!
We welcome Brian Buechel and Nikola Trica as this week's guests. They will analyze experimental data to quantify the ride quality of a car suspension in MATLAB. They'll work with signals recorded from a car suspension, analyze the data, and visualize the results. At the end, we'll have a shareable report containing code and formatted text that clearly communicates our findings.
Watch the event live or the recording on YouTube. We welcome your questions during the event and let us know what other topics that you are interested in seeing.
MATLAB EXPO is open to everyone:
- It's free.
- It's online.
- Registration is now open.
Industry Tracks:
- AI in Engineering
- Modeling and Simulation
- Implementation and DevOps
- Algorithm Development and Deployment
- 5G, Wireless, and Radar
- Autonomous Systems and Robotics
- Electrification, Motor Control, and Power Systems
- Preparing Future Engineers
Special Event: Save the Earth: Accelerate Climate Science and Electrify Everything
The climate crisis is here. Engineers and scientists are engaged to help. Engineers innovate rapidly to decarbonize energy production, electrify everything, and design sustainable products. Scientists accelerate their research to inform climate adaptation and enhance understanding through advances in cloud computing and artificial intelligence. And educators train the next generation to take these advances even further. In this talk by Dr. Tanya Morton, you will learn how scientists and engineers use MATLAB® and Simulink® to tackle this great challenge—to save the earth and build a clean, electrified future!
Climate Data Toolbox was developed by Chad Greene, a postdoctoral research fellow at NASA Jet Propulsion Laboratory, and Kelly Kearney, a research scientist at University of Washington. The Climate Data Toolbox is freely downloadable from File Exchange and has been downloaded over 5,000 times since 2019.
The toolbox was inspired by one big idea: There are a common set of tasks related to data processing, analysis and visualization that Geoscience researchers and students working with climate data typically perform. Greene and coauthors make the case in their paper published in Geochemistry, Geophysics, Geosystems that having everyone who is tackling climate analysis separately recoding these same tasks is not a good use of time, for the individual or the collective, as it takes away from other more innovative climate work. Better to have a set of reusable, publicly shared functions for those repetitive tasks.
Recently, Lisa Kempler published an example of how to look at the change in temperature of the Pacific Ocean over time using MATLAB and the Climate Data Toolbox.
Try the example here by loading up MATLAB, installing the Climate Data Toolbox, and following along the tutorial.
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