I’ve picked the Mastodon instance sigmoid.social, an AI-related instance that is only 3 months old but already has close to 7000 users.
Machines talking to each other
Each Mastodon instance has a public API so it’s straightforward to fetch some basic statistics even without any authentication. I wrote some simple Python scripts to fetch basic info about my home instance.
I wondered: Who are the other users on sigmoid.social? To gain an overview, I fetched the profiles of all user accounts that are discoverable (which at the time of writing means 1300 accounts out of 6700).
Most profiles have a personal description text, typically this is a short bio. I plotted these as an old-fashioned word cloud.
The insight isn’t that surprising: The place is swarming with ML researchers and research scientists, both from universities and commercial research labs.
A stroll through the neighborhood
You don’t want to have an account surrounded by AI folk? No problem, there are more than 12,000 instances to choose from (according to a recent number I found). And they can all talk to each other.
I wanted to see how connected the instance sigmoid.social is and plotted its neighborhood.
This is the method I used to generate the neighborhood graph:
Fetch the 1000 most recent posts present on the instance (which can originate from any other Mastodon instance).
Identify all instances that occur among these posts, and fetch their respective recent posts.
With all these posts of a few hundred instances, create a graph: Each instance becomes a node. Two nodes are connected by an edge if at least five of the recent posts connect the two instances.
My method is naive, but it works sufficiently well to create a simple undirected graph.
The graph yields another unsurprising insight: All roads lead to mastodon.social, the largest and most well-known instance (as far as I know).
Ever since I’ve picked up my first set of bean bags as a kid, juggling has become a hobby that has stayed with me over the years. In my later teens and during my time at university, one of my part-time jobs was being a juggling teacher. I worked at a local youth club, at events and fairs, and had the chance to teach juggling to many people — starting from just 4 or 5 years old to seniors in their late 70s.
Fast forward to today. In the last few years, I have been working in the field of AI, working with my team to build computer vision systems that understand human motion and assist people in learning how to move correctly (i.e. with fitness exercises in our latest product).
Doesn’t this sound like something I should combine with my long-time hobby? While every person learns differently and at their own pace, I think juggling is a great skill to learn yourself while being assisted by an AI. When it comes to juggling, I’ve observed most people struggle in a similar manner to overcome common obstacles as they progress — a perfect example to put into an application.
Here’s the idea: You pick up a set of juggling balls and position yourself in front of your webcam. Step by step, you progress through basic juggling moves as software analyses the live video and provides feedback: Is your juggling pattern stable? Should you throw higher or lower? Are your hands positioned correctly? Is your rhythm fine?
With this in mind, I sat down one weekend this winter to build an AI-powered juggling teacher. In this post, I’ll show you how I did it.
Understanding what’s happening inside a video
To analyze the video of a person learning how to juggle, we’ll train a neural network (also “neural net” or “model”). If you are not familiar with neural networks, don’t be intimidated: It’s a concept that sounds fancy and comes from the field of Artificial Intelligence, but ultimately you can imagine it as a function, or a simple black box: We input a video clip and it returns as the output some information about that video.
We’ll set up our neural network to be able to classify a given video clip: Given a video, what visual class does the video belong to. A class in our case is the name of an action that is happening in the video – like “throwing 1 ball and dropping it”. In our application, we’ll use that visual class in order to give appropriate feedback to the user.
How to train the neural network
But how does the neural network know what to do? How does it know the difference between correctly tossing a ball versus dropping a ball? Well, it has to learn it first, which means that we need to train it.
Training a neural network means presenting it with example video clips of all the visual classes it should be able to recognize. Initially, the neural net doesn’t know much. It simply guesses what’s inside the video. If a guess is incorrect, we can adjust the internal parameters of the function (= of the neural network) so that the network is improved based on the error it just made. We’ll do this over and over again with all videos we’ve prepared for training until the network doesn’t get any better. At that point, we stop training and move on to build the application around it. But first, we need to prepare some video data for the training process.
To train the neural net, we need a training dataset — that is a collection of video clips, each belonging to one distinct visual class we want the net to be able to recognize later. For the juggling use-case, I wanted the network to recognize the following:
How many juggling balls is the person using (1, 2, 3, or zero)?
Common mistakes people make when learning how to juggle: Throwing too high or too low, not standing still, having the hands too close or too high in the air, and a few others.
It is also good to add a few background and contrastive classes — examples of other things that can happen in the video but aren’t exactly part of the juggling activity. I’ve recorded videos of an empty video frame, a person entering or leaving, reaching towards the webcam when controlling the computer, and more.
All in all, this class catalog contains 27 different classes. I’ve recorded 545 video clips, each 3 seconds long. This took me around 1 hour. 70 videos went into a hold-out validation set so that I ended up using 475 videos to train the network. Is this enough data? We’ll discuss this in a bit. First, let’s have a look at the actual neural network.
The neural network
Neural networks come in all kinds of flavors. For the juggling project, we want a network that can process a video stream, digest its visual characteristics to produce a classification output, and be compact enough to run in real-time.
I got all of this out-of-the-box by using the SDK we are developing and currently open-sourcing at Twenty Billion Neurons: SenseKit, an open-source project (work in progress) that makes it easy to train a video classifier without needing millions of videos.
The neural network architecture is a MobileNet-style neural network. Models of this architecture are popular for computer vision applications because they are designed for visual data while being compact enough to run in real-time on many devices, even smartphones. 3D convolutions instead of 2D convolutions allow powerful feature extractors on videos that include motion.
These “deep” neural networks (= many layers of feature extractors) require a lot of data to be able to learn useful features. One trick to get away with less data is called transfer learning: We don’t train the network from scratch. Instead, let’s take an already trained version and only slightly re-train it for our specific juggling task. In fact, the SenseKit version of the network comes with a pre-trained model. This means that my handful of juggling videos are enough to teach the network about juggling and the different kind of juggling mistakes we want the application to react to.
Typically, training a video classification network requires thousands, if not millions of videos. With that in mind, it’s quite impressive that I could teach the network a completely new set of activities with just a few hundred videos. In addition, not training from scratch gives us a huge speedup. Training the juggling net took less than 10 minutes on a GPU machine (NVIDIA Geforce 1080 Ti). As a comparison, these big networks can often take days to train from start to finish.
The juggling trainer in action
Having trained the network, I built a small juggling trainer application in Python that takes care of the following:
Neural network input. The application reads the live video stream from the webcam and feeds all frames to the neural network. Internally, multiple frames together are just like one video clip to the network. This is the same behavior that we mimicked during the training process, only then we were reading the frames from the video clips in our training data.
Neural network output. Every time we pass new data to the neural network, it produces an output: The visual class that the network determined from the video input.
Extract juggling information. As we’ve picked our class catalog to encode different information (number of objects, the action performed, quality of action performed), we can extract the different pieces from the recognized class name. For example, any prediction of a class name that starts with 2b_... will be interpreted as “2 balls” being present in the video.
User interface. UI is fancy for saying that the application opens a window to show the webcam stream and overlay it with the juggling information we’ve extracted.
Based on the juggling information I can extract from the recognized class name, the interface displays the following information:
Object count: How many balls is the person juggling?
Trick performed: If the user performs a trick correctly (3 ball shower in the video), they receive positive feedback.
Quality of juggling pattern: If the juggling pattern is stable, give positive feedback; if it’s unstable, give negative feedback.
This is what it looks like in action:
No data diversity. There is exactly 1 person in the training data, plus the demo video was recorded with the same person (yours truly). From other experiments at work I know that the pre-trained network transfers very well to other people, but to move this juggling case forward, I’d need some data recorded by multiple people in different settings.
Some classes are unreliable. I did play around with more nuanced feedback: Are you throwing too high or too low, are you not throwing at a steady pace, and similar. For these more subtle differences, the predictions aren’t stable enough yet. Looking at the training data, I found that I didn’t record those “mistakes” in a consistent fashion. I think cleaning the training data a little and adding some clearer recordings could help.
A demo, not a juggling trainer yet. Right now, there is no application logic aside from the debug display shown in the video. What I envision is a step-by-step guide to walk the user from 1 ball tosses all the way to a stable 3 ball pattern and maybe their first trick.
Not shareable. I’ve trained the neural network based on an early internal version of the SDK, so the license currently doesn’t allow me to share the network freely on the internet. There’s a research version of the model coming, so I may port my juggling code to that one. In addition, it would be cool to package the juggling demo up in an accessible format, like a mobile app or an in-browser demo. Let’s see.
A glance at the past
The idea to combine juggling and computer vision isn’t new, of course. Not to the world (check YouTube), but also not to me. Back at university (think 2014), two friends and I used the Kinect depth sensor to look at juggling patterns. It took us a few weeks and some failed attempts to produce a demo, held together by some carefully tuned thresholds. It was fun and we were able to produce some entertaining visualizations, but the demo was prone to misclassifications. To actually react to a person’s juggling pattern wasn’t feasible with our solution back then.
Conclusion: A lot is possible in one afternoon
Throwing together a few videos and fine-tuning a neural network: It’s amazing to see and experience how much is possible with the tooling that’s available in 2021. Yes, I’ve only built a prototype of a demo so far — but the goal of building a real juggling trainer powered by computer vision isn’t out of reach. Looking back at my early attempts with the Kinect six years ago and comparing it to my recent attempt, it’s almost unreal to see that the same can be achieved in just one afternoon of work. I don’t know if I’ll push the project further than this, but it sure was a lot of fun.
If you have an idea for a similar computer vision project, I recommend you follow the progress of SenseKit. It comes with some built-in demos and provides everything you need to train your own video classification network similar to my juggling project.
Earlier this year, afewfriends and I have started a remote Data Science study group. Since then, we’ve met once a week to talk about Data Science, Machine Learning, and Python. Our aim is to get better, together. In this article, I want to share how we’ve set up the group and what has been working for us so far.
There are a plethora of reasons why running a remote study group for any topic is a good idea. Here’s what motivated me.
Reach personal goals. Improving and practicing my Data Science and Machine Learning skills outside of work has been part of the goals I’ve set for myself at the beginning of 2020.
Healthy peer pressure. Social pressure works, at least for me: I know that I would have a hard time sticking to a weekly cadence of studying on my own, but if a peer group holds me accountable to at least show up, I would always try to have something to show for it.
Share knowledge. If you find a group of motivated people, they will bring different experiences and questions to the round. This leads to healthy discussions and skill sharing.
Study from home. Remote study groups are very compatible with a pandemic lifestyle.
Find a group and make it easy to commit for everyone
To get started and get others on board, I made two choices to reduce the initial friction of getting things running:
Set the initial topic. “I am going to read the following book on Data Science in the next few weeks. It’s available as a free PDF. Do you want to join me?”
Reasonable commitment. We’ll meet once a week for a video call of 1 hour. It won’t ever take longer.
This was easy to say “Yes” to and three friends immediately joined me.
Start simple: Read a book together
The first few weeks, we’ve read a book together. The goal was to start broad with a high-level overview. Our first book was Steven Skiena’s The Data Science Design Manual, which is available for free from Springer.
The book lends itself well for this purpose because it goes over central Data Science topics at a conceptual level. In some chapters, Skiena dives into algorithms, but not too deep. As an overview to get our group started, it was a good choice. Moving forward, most of us agreed to pick a book that has more in-depth explanations and code examples to encourage trying things out.
We’ve read 1-2 chapters per week, depending on their length and complexity. In our weekly discussion, we went through our notes and shared in turn, asking: “What’s one thing you’ve learned from this chapter?” These discussions easily filled 60 minutes and I think it never got boring.
Intensified Learning: Code together
It’s hard to argue that trying things out yourself will lead to deeper understanding, so we’ve tried from the start to incorporate that. While reading, we would experiment with one of the mentioned algorithms or look at a dataset linked from one of the chapters. Having finished the book, we continued that practice: Everyone picked a personal data project to work on, and we updated each other once a week. These data projects were all motivated by challenges available on Kaggle, and we had good fun toying around with them.
Learning with Pandas
We’ve recently moved on to a new book: Wes McKinney’s Python for Data Analysis. As the Pandas library is the de-facto standard for data handling in Python, a book by the author of Pandas seemed like the right choice. In the group, we have different levels of experience with Pandas, but revisiting the foundation and strengthening the practical skills were favored by all of us. As this book is heavy on code examples, we hope to get a good balance of reading and coding in as we move along.
Reading a book, working on mini-projects, starting with the second book. It almost feels like we’ve entered “Season 3” of our little Data Science journey now. So far, I’ve learned a lot as an individual, and I think as a group we are motivated to keep going, probably experimenting with the format in the future.
The experience of launching a remote study group has been great so far. If you have a topic you want to explore more thoroughly, take this as an encouragement: In the age of video calls and free online resources on every topic imaginable, collaborative learning has become as easy as never before.
2020 is the year of doing things remotely. It was therefore my home home office and a healthy internet connection that provided the space to participate in the AI for Good hackathon last weekend, organized by Deep Berlin. The task description was broad, but it pointed the teams to work on something related to climate change, specifically the occurrence of wildfires.
As a team of four, we spend the weekend looking at the relation of human activity and wildfires. We focused on data about touristic activity in Northern Spain, an area that has seen intense wildfire seasons in the past.
Final presentation video
(Excuse the nervous beginning, anyone who has attended a hackathon before will be familiar with the last minute push, in this case submitting a final video to the hackathon organizers on time.)
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A few thoughts on what we did and what I took away from the weekeend.
Pandas and scikit-learn
In my day job, I mostly work with Python, and am familiar with deep learning libraries like PyTorch and Tensorflow/Keras. The hackathon was a welcome opportunity to do some hands-on Data Science work again, and I enjoyed using Pandas and scikit-learn for quick data analysis and plotting. What a nice ecosystem.
Free location data
Open street map is an amazing community project providing labeled location data from all around the globe. Open Street Map location data is provided in the osm format. To read these files in Python, we used the osmium package. Reading the file and filtering the nodes for our usecase was straight forward, but loading from that format can take surprisingly long.
Free geo data
Once you start looking, you discover some interesting datasets out there which are freely available. We used the MOD14A1 dataset, which provides satellite data of very recent recordings (up to a few days from today), with access to multiple levels of abstraction in the data format.
Pretty maps in folium
Our team member Markus spend some time creating pleasing visualisations of maps in folium.
What I valued during that weekend was being in my default work environment. Our team quickly developed a working rhythm where we would have a video call for 30 minutes and then disconnect and spend some focused 2-3 hours by our own. I’ve never experienced such a focused working environment at an on-site hackathon.
Obviously, the main shortcoming of being remote was not having the chance to talk to people outside your team, or just bump into someone. Also, there was no way of passively observing what everyone is up to. From what gathered on Slack, many teams actually didn’t constitute properly, and then some lost participants tried to get into other teams, but it wasn’t as easy for them, as it might have been in person.
Would I join a remote hackathon again? Yes, to really get something done on 2 days. To actively socialize, it isn’t the right thing for me, though.