scikit-learn & data science you own

Welcome. Thanks for having us.

Probable is a company that is typically known as a spin off from a research center in France called INRIA. And INRIA is the place where this technology, Psychiclearning, has been developed over the past ten, fifteen years. Not many people know that, and the project has been somewhat protected and sort of incubated within that research center.

And after all that time, as you know, Psychicarn has been adopted or even probably participated in creating the field of data science because it is applied math and essentially has created a sort of paradigm for how data scientists approach data science, typically through two functions, fit and predict. And the French government has a national strategy for AI like many many countries. And the government decided to double down on Psychic Learn.

And, they came up with a budget. They entrusted the research center with that budget. But then they also asked for the project to be breakeven at some point.

And the team said, okay, breakeven is fine, but we don't do that in the research center. We don't breakeven. So why don't we call an entrepreneur to try and help us figure it out?

And they called me. So I have a track record as a software engineer and an entrepreneur in tech for the past twenty five plus years, but I'm not a data scientist. So I did my due diligence, and I sort of, you know, dug deep to find out what this project was all about under the hood.

Is it any good? Is the community any good? And of course, PsychicLearn is this quite amazing jewel of the technology that every data scientist on the planet uses that discovered that it was downloaded 1,500,000,000 times, cumulatively 80,000,000 times a month, 22% in The US, only 3% in France.

So this is a project that is used all over the world and Probable is essentially the spin off that takes all of the team, including Guillaume here, from the research center and turns it into an open source company that inherited the mission that was initially given to the research center. And the mission is to build a suite of open source technologies, including Psychic Learn, but above and beyond Psychic Learn as well, for data science. So the scope is large.

The mission is noble. And this is what we're building, essentially. So Probable is a one year old company that has already started doing many, many things.

Psychicarn is a technology that is, again, applied math. It's not rocket science, but it's applied math, and it's quite intricate. The thing is, the scientific community uses it, day in day out, and everyone depends on this.

So typically, when I when I discovered the scope of the project and the mission that was entrusted to the research center, I realized that this project is bigger than me, number one. Number two, the mission is to actually create more open source. In other words, you know, in 2024, it's even more acute.

Typically, big tech keeps on amassing so much power concentration, and we could argue that they do not distribute as much as they should. So that's not a judgment, but it is a fact. And Psychic Learn is precisely the contrary.

It actually enables so many companies to to do data science. So with that in mind, before creating the company, we decided to craft sort of architecture for the company that would respect that. And so, you know, before we created the company, before Guillaume joined as a cofounder, before even incorporating the company, we had a template that actually created the the governance, the shareholding structure, and also leveraging a new law in France that allows us to do a sort of b corp.

So a company with a mission where the mission is clearly stated in the bylaws, and that mission is to create open source for data science. So in a way, we've created a sort of constrained environment that is unlike many companies because it's by design. This company by design has created guardrails so that the governance cannot take this company too far on the right, let's say, proprietary technology or even changing the license.

That's not in the cards. And we've created a sort of mechanism where, you know, if we do not uphold to the mission, then we can actually lose some of the assets such as the brand. We are the official brand operator, but the brand belongs to the research institute.

Right? Still. So there are many mechanisms, trigger mechanisms that force us, including shareholders that we would would bring in, to actually bind with the mission long term.

Gotcha.

in that field. So maybe, Jan, you have something more to add. Perhaps also at the macro level is to say, you know, scikit does a lot of things, including deep learning.

But in to be to be frank, when you wanna do deep learning, typically, you'd go to PyTorch or TensorFlow. But for everything else, you know, scikit learn. In other words, in the great AI family of algorithms, there is machine learning.

And within machine learning, you have deep learning. Within deep learning, you have other categories of algorithms such as, you know, transformer based models that lead to LLMs. So it's basically, you know, Russian dolls of sorts, and psychic learning is the biggest provider of algorithms in the machine learning space.

And in fact, if you look at the downloads, typically, Psychic Learn is downloaded as many times as PyTorch and TensorFlow combined, which is crazy because now everyone is talking about, you know, LLMs, of course, but also deep learning because deep learning is currently in a spring state, not quite a winter yet. So, of course, deep learning and Gen AI is a wonderful breakthrough. That being said, you know, I like to simplify sometimes the eighty twenty Pareto distribution.

So I had the intuition that 80% of the use cases out there use Psychic Learn when it comes to machine learning. And people actually tell me, Yan, you're wrong. It's more like 95%.

Right? Because in terms of, you know, technology that is robust, that is tried and true, that is used to actually, you know, turn a profit or return on investment. Banks and insurance companies, right, Guillaume was mentioning fraud detection.

Fraud detection typically uses psychic man, and that actually saves money. That actually you know banks would be losing money without that. So it is actually quite essential but again it's applied math right so scikit learn is only a facilitator to this category of problems.

Psychicarn typically is this one technology that is patrimonial. In other words, it belongs to everybody. In fact, there is another stat when you look at, you know, the the the figures that are public, by the way, the number of dependencies.

So Cycoturn is is actually used by nearly 900,000 projects on GitHub. So there's nearly a million projects that depend on Cycoturn. And there's a new law that I discovered recently, someone mentioned that, Linde's effect, which means that something that's been used long enough will remain important for long enough.

So not saying that Cyclerean will go the way of COBOL, but Cyclerean is here to stay, and we are with the community, the guardians of that. So we're gonna make sure that Cyclerean remains there forever for companies that actually need it in a stable version. And, of course, Guillaume and the team are building up new features as we go.

Right? So there's a a dedicated effort, and and I should say that we have carved out nearly, you know, 10 people in the team are doing only that, contributing to Psychicarn and the other associated libraries. Now your question, Daniel, is is, you know, whether Psychicarn will be obsolete in, say, a number of years because general purpose technology has made it, you know, irrelevant in some ways.

Number one, Cyclean is extremely frugal. It actually works on CPUs, and it is well controlled, well understood. It's actually quite predictable in some ways, whereas deep learning is usually known as a black box where it's really, really hard to introspect.

And so Cyclone does produce for certain categories of problems things that are actually working quite well, more so than large language models for sure today and and more so than any sort of deep learning based technology that we understand today. Now it is possible that with additional data, additional training and techniques, and even evolutions on on the transformer based model, we could improve and probably render obsolete cyclotron. But to us, and then Guillaume and I, we talk about that and with the team, we also experiment with other lens.

And we are also trying to figure out how we can use these new technologies to actually help our first persona. And that is with this data scientist. So we are a technology provider to help data scientists, and increasingly so, the data scientists in enterprises, because we will be creating value adding services and solutions so that we can generate revenue to sustain our mission.

So the goal for us is to actually project ourselves while contributing to open source, but also create a sort of business value proposition not dissimilar to Red Hat, because that is the closest type of company that we identify with in terms of spirit.

the company is built with multiple business units, if you wish. That's that's a big word for a start up. Right?

But we have multiple, revenue lines and multiple activities even within the open source team, which is dedicated. So Guillaume perhaps can elaborate on some of the other libraries that we support that complement Cyclone. So, you know, that's one way to answer the question.

But also, we are building a new product, which I call reversible SATs. So we are building a product that will provide additional value to data scientists, and the goal is to create a sort of I don't want to use the the term copilot because that is too close to LLMs, but it is the spirit. We are building a companion to augment the work of data scientists all the way to teams.

So that is an additional product on top of SecuKern because SecuKern just works. And so we don't wanna change that. And contrary to a company that would build a SaaS solution with a proprietary approach, we wanna say, okay, whatever you guys use is fine.

We need to find a way to add new value, and some of it will be open source, certainly modular. But for those companies that have more money than time, that need more service than beyond their own, we'll have a solution for you and will make your life easier. And, data scientists are a new breed.

It's a new type of job. It's not been around for very long. And in a way, when I talk to people so I've been in code forever, and you know this.

Right? The the developers, when they get hired, they are turnkey in some some ways. Right?

They have their Git environment, and they know how to pure code, and that's all pretty standard. But when you talk about data scientists, it's actually quite artisanal. It's an art and a science at the same time.

And you're you're manipulating two objects, actual code, but data scientists are not coders, and you're manipulating actual data. It's not code. It's patterns.

And so data scientists are have a difficult task, which is to combine these two things and create value for the enterprise. And then they talk to business units, and they're like, what do I do with this model? How do I put it in production?

Right? So there's a huge conundrum to solve, and that's what we're gonna do additionally to building open source that are modules that people can use. Maybe Guillaume, you can elaborate on some of the other libraries that are key, to actually help.

Yeah.

means that by design, we decided to not affect the license of SecuClient. We're not branching it out. We're gonna care for it.

And so the governance of scikit learn being so sane already means you don't touch it. If it ain't broke, don't fix it. So the the governance is unchanged.

So the the center of gravity was at INRIA, the research center, but also involving people all over the world. I I don't know. Guillaume, many contributors?

Maybe 200?

And then by design, the open source team that is full time on Segiturn and other open source libraries means it's a cost center to the company. So that cost center is by design, and we know that's a cost we have to cover. So we we will cover it through different types of activities.

So for instance and this was something that was done in the past where brands were sponsors. So either they hire someone that becomes a core developer, and they're naturally sponsoring someone to build up this technology, or they were giving money as a donation to the research center. But now that the team is with us, we are translating this to into a contractual sponsorship framework.

And so, you know, brands who wanna contribute to Psychic Learn and help us compensate for salaries will get something in return, exposure. And and and if they actually put more money into it, then we'll have a conversations around the road map. Find a way to make it converge in a win win kind of way.

Because Guillaume, for instance, can say, you know, this brand wants us to do something, but it makes no sense for the community, then we won't take their money for the sponsorship type of business. Right? However, if companies want to pay us to do a certain type of paid for software, we'll do we'll look at it.

But that's a different branch of the company. So we've we've really clearly separated. And by design, we know there's a there's a cost to it.

And that cost is actually, if we are doing well, it's compensated by the fact that we have done good by the brand. In other words, hopefully, the the community will actually resonate with what we're doing. And so they'll pay us back by actually appreciating what we're doing, which will carry the message further.

So we think that there is a self fulfilling prophecy if we actually keep adding value to the whole scheme as opposed to removing value, and I will not name certain projects that have chosen a different way. But on the other hand, going back to the governance of the company, when a company flips and becomes VC funded or only VC funded, VCs require a sort of return on investment that is too radical. And so that sort of forces a change of posture with vis a vis the community and the licensing scheme.

In our case, we've actually created a structure that is balanced in terms of shareholding groups and so we will ultimately have or that's the goal of the structure of the architecture is to have as much money from public support than from private support. So it's again sort of balanced.

the books, pursue books and go like this. But that's one anecdote, let's say. That's good.

I might be the CEO, but I do have the imposter syndrome because psychic learning is so impressive. It's day in, day out. I mean, that team and Guillaume is very humble and very discreet, but the amount of knowledge and the amount of technicality that is trapped inside this library is mind blowing.

And you haven't met the other members of the team. It's pretty much very, very hard to compete in terms of the amount of CPU cycles that go in there. So scikit learn is the gift that keeps on giving in some ways.

And the team is just out of this world and nice, and it's just a pleasure to work with that team all the time. Now the more I discover Psychic Learn and the more I find it amazing because of what the brand means to people. And so last week and and today, actually, we just released, and if you allow us to actually put the the link in the notes Of course.

Absolutely. We released the very first official Psychicarn certification program. And what's amazing is that we so this is the first time.

So we're doing it step by step, and the system works. People can register. They can pass or fail the test.

But without advertising, we had, like, within a couple of days, 600 registrations all over the world. A lot from India, actually, because people in India, they do work also remotely for other clients across the world. And so they do need a stamp of approval to showcase their ability to provide a service.

So very interesting that this brand almost instantly can promote a sort of service that is value adding. So that's the one thing. But then on the more technical level, I fell in love with one new feature that came out with 1.

5 of Psychic Learn developed by another co founder and and core developer, Jeremy. And that is the callback feature. Why?

Because Psychic Learn, in fact, is a platform. It is a platform. And the callback feature allows us to to provide extensions, if you wish, where people can hook into their inner workings of as they are building new models.

And in fact, I find that to be essential because we are entering an age of liability with regards to AI. Companies need to be able to introspect. They need to actually find out why the model is producing such and such results.

And so introspection is is critical. And as I said earlier, deep learning is sort of a black box type of approach, which I love, by the way. Again, in 1992, I was building deep learning models in the middle of the winter of AI at the time.

But psychic learning is actually quite introspective, quite transparent, frugal, as I said. And so callbacks are yet another feature that provides actual introspection into how we build models. Because talk about insurance companies, fraud detections, you've got human beings at the end of the of the spectrum being handled by algorithms.

And so that is critical. And I think we we fulfill a very important need with these features.

The mission is bigger than me, bigger than us. And so that's why the governance creates a self sustaining model. So, of course, you know, it's not trivial.

So there's a lot of work to achieve the mission long term, but that mission ends up with an IPO. In other words, this company is not meant to be sold or wrapped up. The goal is to do an IPO so that this company can carry on with the mission and allowing people to invest and be part of that story.

And that's why earlier, Daniel had asked a question about, you know, investors and all that. So we do have 70 individual investors, including people who were contributors or are contributors to Psychic Learn who don't have the chance yet to be employees full time of the company. So the goal is to create this sort of dynamic vehicle.

And if we look at the North Star, there is no such company today that is the provider of open source machine learning technology. That company does not exist, and we aim to be that because we need that in an age where there is too much concentration within just a handful of players. That's not okay.

It's not okay for the global South. It's not okay for Europe, which is lagging behind. But it's not even okay for The US.

The US may have big tech, but that's not okay as a single model. We need people to own their data science. That's why that is our tagline.

That was good. Guillaume, what are your thoughts?