477: One Thousand New Instructions

Kwabena Agyeman joined Chris and Elecia to talk about optimization, cameras, machine learning, and vision systems. 

Kwabena is the head of OpenMV (openmv.io), an open source and open hardware system that runs machine learning algorithms on vision data. It uses MicroPython as a development environment so getting started is easy. 

Their github repositories are under github.com/openmv. You can find some of the SIMD details we talked about on the show:

Kwabena has been creating a spreadsheet of different algorithms in camera frames per second (FPS) for Arm processors: Performance Benchmarks - Google Sheets. As time moves on, it will grow. Note: this is a link on the OpenMV website under About. When M55 stuff hits the market expect 4-8x speed gains.

The OpenMV YouTube channel is also a good place to get more information about the system (and vision algorithms).

Kwabena spoke with us about (the beginnings of) OpenMV on Embedded 212: You Are in Seaworld.

Transcript

Elecia is giving a free talk for O'Reilly to advertise her Making Embedded Systems, 2nd Edition book. The talk will be an introduction to embedded systems, geared towards software engineers who are suddenly holding a device and want to program it. The talk is May 23, 2024 at 9:00 AM PDT. Sign up here. A video will be available afterward for folks who sign up. 



437: Chirping With the Experts

Daniel Situnayake joined us to talk about AI, embedded systems, his new book on the previously mentioned topics, and writing technical books. 

Daniel’s book is AI at the Edge: Solving Real-World Problems with Embedded Machine Learning from O’Reilly Media.

He is also the Head of Machine Learning at Edge Impulse, which makes machine learning on embedded devices simpler. They have a Responsible AI License which aims to keep our robot overlords from being too evil.

We mentioned AI Dungeon as an amusing D&D style adventure with an AI. We also talked about ChatGPT.

Daniel was previously on the show, Episode 327: A Little Bit of Human Knowledge, shortly after his first book came out: TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Transcript

DALL·E 2022-12-08 15.37.51 - artificially intelligent robotic cricket planning the singularity


408: Room In Your Heart for Your Robot

Machine learning engineer and science fiction author S. B. Divya joined us to talk about artificial intelligence, robotics, and humanity.

Divya’s first full-length book is Machinehood which has been nominated for a Nebula (as was her novella Runtime).

You can find more about Divya on her website (sbdivya.com) or on her Wikipedia page.

Divya also co-hosted EscapePod, a podcast of science fiction stories. 

Transcript

275: Don’t Do What the Computer Tells You (Repeat)

Janelle Shane (@JanelleCShane) shared truly weird responses from AIs. Her website is AIWeirdness.com where you can find machine-learning-generated ideas for paint colors, ice cream, and cocktails (and many other things). We never said they were good ideas.

Janelle’s FAQ will help you get started trying out RNNs yourself. We recommend the Embedded show titles.

We talked about BigGAN which generates pictures based on input images.

Wikipedia list of animals by number of neurons

Janelle’s book is You Look Like a Thing and I Love You. Sign up for her newsletter to get the PG-13 versions of her hilarious AI outputs.


327: A Little Bit of Human Knowledge

Daniel Situnayake (@dansitu) spoke with us about machine learning on microcontrollers.

Dan is the author of TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers. You can read the first several chapters at tinymlbook.com. 

TinyML is a part of TensorFlow Lite. See the microcontroller getting started guide.

Dan works for Edge Impulse (@EdgeImpulse) which is making tools for easier machine learning integration at the edge. Their tools are free and they also have a getting started guide.

Dan recently posted on the Edge Impulse blog about training a TinyML model to capture lion roars.

For TinyML meetups and a forum, check out tinyml.org

Lacuna Space: low cost sensors transmitting to space

The Rainforest Connection is using small sensors to monitor for chainsaw sounds


326: Wrong in the Right Way

Erin Talvitie of Harvey Mudd College spoke with us about machine learning, hallucinating data, and making good decisions based on imperfect predictions.

Paper we discussed: Self-Correcting Models for Model-Based Reinforcement Learning

Erin’s grant: Using Imperfect Predictions to Make Good Decisions

For a reinforcement learning book, Erin suggests Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto or the lecture series by David Silver.

For a machine learning book, Elecia likes Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

306: What Is in the Magic Box?

Dr. Loretta Cheeks (@loretta_cheeks) spoke with us about implicit bias in text, machine learning, getting a PhD, and STEAM outreach via Strong Ties (strongtiesaz.org).

Also see:

Thank you to our Embedded Patreon supporters for Loretta’s mic, particularly to our corporate patron, InterWorking Labs (iwl.com).

283: Flippendo Is Kind of a Swirly

Jennifer Wang (@jenbuilds) spoke with us about machine learning, magic wands, and getting into hardware.

For more detail about her magic wand build, you can see Jen’s Hackaday SuperCon talk or her !!ConWest talk. The github repo is well documented with pointers to slides from her SuperCon talk and an HTML version of her Jupyter notebook.

Check out this good introduction to machine learning from scikit-learn. It was their choosing the right estimator infographic we were looking at. (Elecia has bookmarked this list of machine learning cheat sheets.)

Jennifer’s personal sites are jenbuilds.com and jewang.net. She recommends the Recurse Center and wrote a blog post on her experience there.

275: Don’t Do What the Computer Tells You

Janelle Shane (@JanelleCShane) shared truly weird responses from AIs. Her website is AIWeirdness.com where you can find machine-learning-generated ideas for paint colors, ice cream, and cocktails (and many other things). We never said they were good ideas.

Janelle’s FAQ will help you get started trying out RNNs yourself. We recommend the Embedded show titles.

We talked about BigGAN which generates pictures based on input images.

Wikipedia list of animals by number of neurons

Janelle’s upcoming book is You Look Like a Thing and I Love You. Sign up for her newsletter to be the first to order it (as well as getting the PG-13 versions of her hilarious AI outputs).


252: A Good Heuristic for Pain Tolerance

Katie Malone (@multiarmbandit) works in data science, has podcast about machine learning, and has a Phd in Physics. We mostly talked about machine learning, ways to kill people, mathematics, and impostor syndrome.

Katie is the host of the Linear Digressions podcast (@LinDigressions). She recommended the Linear Digressions interview with Matt Might as something Embedded listeners might enjoy. Katie and Ben also recently did a show about git.

Katie taught Udacity’s Intro to Machine Learning course (free!). She also recommends the Andrew Ng Machine Learning Coursera course.

Neural nets can be fooled in hilarious ways: Muffins vs dogs, Labradoodles vs chicken, and more. Intentional, adversarial attacks are also possible.

Impostor syndrome is totally a thing. We’ve talked about it before. You might recognize the discussion methodology from Embedded #24: I’m a Total Fraud.

Katie works at Civis Analytics and they are hiring.

234: The Good Word About AI

Dustin Franklin of NVIDIA (@NVIDIAEmbedded) spoke with us about the Jetson TX2, a board designed to bring AI into embedded systems.

Dusty wrote Two Days to a Demo, both the original supervised learning version and the newer reinforcement learning version. In general, check out Dusty’s github repo to see what’s new. Also, The Redtail project is an autonomous navigation system for drones and land vehicles based on the TX2.

The NVIDIA GPU Technology Conference is in San Jose, CA, March 26-29, 2018. Your coupon for 25% off: NVCYATO

The Jetson TX1/TX2 ChallengeRocket contest ends February 18th.

You can find Dusty on on the NVIDIA forums.

208: What If You Had a Machine Do It

Elecia gave a talk about machine learning and robotics at the Hackaday July Meetup at SupplyFrame DesignLab (video!) and LA CrashSpace. She gives it again in the podcast while Chris narrates the demos. 

Embedded Patreon

Embedded show #187: Self Driving Arm is the interview with Professor Patrick Pilarski about machine learning and robotics applied to prosthetic limbs.

I have also written more about my machine learning + robot arm on this blog. My code is in github (TyPEpyt).

My machine learning board is Nvidia’s Jetson TX2. The Two Days to a Demo is a good starting point. However, if you are new to machine learning, a better and more thorough introduction is the Andrew Ng’s Machine Learning course on Coursera. To try out machine learning, look at Weka Data Mining Software in Java for getting to know your data and OpenIA Gym for understanding reinforcement learning algorithms

I use the MeArm for my robot arm. For July 2017, the MeArm kit is on sale at the Hackaday store with the 30% off coupon given at the meetup (or in Embedded #207).

Inverse kinematics is a common robotics problem, it took both Wiki and this blog post to give me some understanding.

I wasn't sure about the Law of Cosines before starting to play with this so I made a drawing to imprint it into my brain.

Robot Operating System (ROS) is the publisher-subscriber architecture and simulation system. (I wrote about ROS on this blog.) To learn about ROS, I read O’Reilly’s Programming Robots with ROS and spent a fair about of time looking at the robots on the ROS wiki page.

I am using OpenCV in Python to track the laser. Their official tutorials are an excellent starting point. I recommend Adafruit’s PCA9685 I2C PWM/Servo controller for interfacing the Jetson (or RPi) to the MeArm.

Finally, my talk notes and the Hackaday Poster!

174: It's Not Weird

We spoke to Evan Shapiro, CTO and cofounder of Knit Health (@KnitHealth), about baby monitors, IoT security, neural nets, and professional poker.

The Knit Health Kickstarter ends November 17, 2016.

Evan recommended Google Tensor Flow and Python's Theano for an introduction to machine learning. (If those sound familiar it is because Kat Scott mentioned them as well.) Evan also suggested that if you'd like to know more about the history of neural nets, check out this post by Audrey Korenkov. 

If you'd like a gentle introduction, check out a Narwhal's Guide to Bayes' Rule.

Evan mentioned some videos he did about poker, they are on Card Runners (NOTE: it is a paid site with free tastes).

Final quote was from Neil Gaiman's excellent Graveyard Book.