How HTM Studio Came to Life

Numenta • Marion Le Borgne and Taylor Wirfs

It’s been a few months since we first launched HTM Studio and
things have calmed down from a development perspective. I sat down with Marion
Le Borgne, project manager and lead engineer for HTM Studio, to get her thoughts
on the process and efforts it took to build the application.

Before we get into the nitty-gritty of software development, can you tell us a little more about yourself and role at Numenta?

I am a Senior Software Engineer at Numenta and Project Manager for HTM Studio. I
started my career as a Business Analyst at Partech Ventures in Europe and then
joined CloudWeaver (acquired by F5 networks) as a Data Scientist. After
attending a Hackathon organized by the
Numenta OS community in 2014, I was hooked by the mission
of the company. I started working at Numenta in 2015. In the same year, I
co-founded NeuroTechX, an international non-profit for
neurotechnology.

Why did you build HTM Studio?

HTM Studio was built in response to the many inquiries about our technology from
users who wanted to use HTM algorithms, but lacked the technical skill needed to
experiment with our open source code. We decided to build an app that would
accomplish exactly that: allow users to try HTM algorithms on their own data in
minutes, without prior coding experience or a knowledge of HTM algorithms.

What was the development process like?

Research and Product Design

My initial research helped me develop the first set of features in HTM Studio. I
started researching existing tools to understand how people packaged other
streaming analytics offerings. I also reviewed past proof of concepts to learn
what had and had not worked for Numenta. During my research, it became clear
that HTM Studio needed to be a desktop app to address data privacy concerns.
After that, I created a first round of wireframes for the general flow of the
app. A couple of feedback sessions later, the wireframes started looking like
today’s HTM Studio.

Architecting the Application

Next, we began to greenfield the app and lay out the architecture. This leads me
to the unique technical stack in HTM Studio. We wanted to build a desktop app
that leveraged modern web technologies, so that HTM Studio was responsive and
beautiful. We chose Github Electron, a framework that lets you write
cross-platform desktop applications using JavaScript, HTML and CSS. We packaged
Python with NuPIC, so it could run within HTM Studio. This allowed users with no
programming knowledge to use NuPIC without having to install it separately.

Testing the Machine Learning Algorithm

We developed a method to simplify the process of finding the best HTM parameters
called “param finder”. Historically we used a complex method called swarming,
but the new param finder skips this step and quickly finds the optimal
parameters. Then, we spent about a month on the accuracy of the algorithm
results. It was important that the app (and especially the new param finder)
provided accurate results on various datasets. We added compatibility tests with
NAB to test the results.

Testing the UX

Once we had these three elements (Electron, portable NuPIC and param finder) in
place, we started UX testing. The goal was to gather feedback from a variety of
users with different datasets and use cases, to fine tune the app and ensure HTM
Studio was easy to use. We launched the private beta in May, and went through a
lot of QA testing and bug hunting. A few weeks later, we were finally able to
launch the public release.

Was there anything that surprised you during development?

We had a couple setbacks that were mainly on the technical side. First,
packaging NuPIC (the Python bindings and C libraries) along with a portable
Python distribution was a project in itself. We wanted to ensure that users who
did not have NuPIC or Python could run HTM Studio. Second, we wanted to
incorporate dynamically updating charts to demonstrate the continuous learning
that occurs in the HTM algorithms. It took us a while to reach a point where
chart navigation was smooth and responsive.

How was the launch experience?

The launch went well. I had numerous 1:1 sessions with private beta users to get
feedback on the app before the final launch, so there weren’t any last minute
surprises. The feedback from these sessions was very valuable. We learned about
various use cases, data formats and how users intended to use HTM Studio. These
1:1 sessions confirmed two things: the target audience for HTM Studio and which
features should be added or simplified.

What’s your favorite feature and why do you think it’s useful?

The param finder is a great feature. It allows you to get up and running with
HTM technology in a couple of minutes, without having to worry about setting any
parameters. I especially like that this feature is coupled with the “advanced
settings” feature. That way, if you are familiar with HTM theory, you can have
more control over the machine learning algorithm.

HTM Studio Screenshot

HTM Studio automatically determines the best parameters for your dataset.

HTM Studio Screenshot 2

Users familiar with HTM can tweak their parameters in advanced settings.

If you had an infinite amount of resources available, what would you include in a future version?

Predictions. I think this would be a really great addition to HTM Studio, which
is currently geared towards anomaly detection.

Any last thoughts?

Shaping and building this app was a really collaborative process, with a lot of
feedback from the Numenta team and private beta testers. Collaboration was
required between research, engineering and product teams, and I enjoyed this
aspect of the project the most. HTM Studio is built on years of research that
made the HTM algorithms what they are today. It’s great to see that HTM
algorithms are now accessible to anyone.

Numenta • Marion Le Borgne and Taylor Wirfs

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