Okay, all right, so thanks everyone, I'm Stefano Stefano Fully and I'm just going to give you a very quick rundown of the work that we've done at the open source initiative for the past couple of years that allow us to reach through the conclusion at one conclusion of what the open source means in the ice space.
And we started when we noticed that we were having issues with automatic decision-making systems that were making decisions and they were wrong.
Like this was one of the cases a couple of years ago, the father was nothing a picture of his naked child to send it to his doctor to say, hey, what's going on with my kid and the system caught that image and recognize it and flagged it correctly to some extent as a child nudity.
But also immediately triggered the child abuse process.
And that was obviously, it was the case of one automatic tool making the wrong decision.
Frankly, very visible for us in the technology space.
We just stand in that corner because we can't run.
Yes, let me move the laptop here.
Okay, so I can see the slides actually.
And so this is clearly a bug, right?
It's we recognize as a bug, but the next question immediately was, how do you fix a bug in a system like this one?
See, the machine learning systems that we were talking about that we are talking about.
There are so popular in the past few years.
They're not programmed. They're not written by humans. They're not deterministic. They don't.
They don't have source code in the same sense that we are used to.
And in fact, they learn automatically. They look like black boxes to some extent.
So we didn't have a question, an answer to the question like, how do you fix a bug you want?
How do you go back and what do you ask for?
Because in the software space, especially in the early years of advocacy of free software inside the European Union,
when we talk to public administrators, we're always saying, give us a source code.
Ask for the source code. What is it that we need to do?
We need to take a step back and we need to very quickly.
Because there were three major forces pushing for a sense of urgency on us to coordinate actions to come up with a definition of how to fix this issue.
First, the technology was different.
It's different from what we are used to.
Then there is a lot of pressure from market actors.
They are trying to use the term open source because we have been so good that make it successful and valuable,
so that everyone wants to use it.
Even if it's applied to a new domain and there is violating some of the fundamental principles,
at least from the face value that we recognize a few years ago,
and then regulators were coming very quickly asking for clarifications and writing laws without having the answers or at least authority demands.
So a year ago, we had a very clear idea of what an open source AI looks like.
It's not really that difficult to expect that one needs to be able to use study,
modify and share whatever they have to see.
Whatever system that they have in their hands.
But there is a piece missing.
In familiar with the concept of the three software definition,
they will have recognized that next to the freedoms to use and to study and modify,
there is a short sentence saying you need to have access to source code.
This is a precondition for that.
And remember, we don't know what source code really means in the context of machine learning.
The open source definition point number two also has reference to source code as a mandatory component of a program in source code.
In that point number two of the OST says you must be the,
the must be the preferred form used by a programmer to modify the program.
So we can not be obfuscated.
So we needed to understand how what is the preferred form used by builders of AI to modify the program.
So with the system of the AI.
So we put together a large collaboration.
Over a hundred people have collaborated from 27 nations.
33% from the global south.
Over 50 volunteers have followed the co-design process.
That started by defining what is that we were talking about, which is an AI system.
Using the OECD definition, the AI system is something to make it short.
It's anything that gives in first and output based on input in various degrees of independent.
So we analyzed systems.
First we started with five.
We asked them to, we asked this volunteers, all experts, builders of AI,
ethicists, philosophers, data scientists, etc.
To answer the question, what are the components of the systems that you need in order to use study modifying share.
They evaluated, they gave us an answer.
We drafted a longer, complete definition of what that preferred form of making a modification is.
We expanded in a validation phase.
We analyzed 13 systems at this point.
Then try to see which ones were matching the definition which ones were not.
So we were trying to find whether we would catch some irregularities.
Clearly, something like Lama that is not respecting freedom.
That is not releasing information about the training, etc.
What was going on?
In fact, that we did not pass the validation phase.
Then we would release a fine-tuned detect and finally at the end of October last year we released version one.
The version one of the definition is not surprising.
We should not be surprising any of you.
The four freedoms have to be conveyed.
That means that you must require access to the weights parameters.
So there is also the training.
Everything that went into that training.
So the code that used for the training algorithms, etc.
The machines and all the code used to build the training data set.
Complete so that you can rebuild the data set.
And finally, all the data used to train the system unless there is a footnote when legally impossible.
I want you to focus on this.
It is requiring the model weights, the code, complete that built the machine and the data unless it legally impossible.
This is what is written in the definition.
Do you have everything that you require to rebuild something that works that way without any restrictions?
Now, there are some actors in the market that are really pushing for saying, well, AI is different.
So we need to accept the fact that you will have to live with conditions.
Every time these machines are so powerful, they are dangerous, they can kill us all and there is this rhetoric going around.
They want us to accept that there are restrictions.
Some restrictions are even inevitable.
And that's frankly, that's a trap.
This is not something we want to think about.
And we're not against the fact that once a system that is powerful and capable of breaking things, that should be regulated on the deployment.
But we cannot really expect that we turn developers into police or other actors going around and checking or evaluating a little where.
And the deployment is what needs to be regulated, not the development.
And they're also insisting on this idea that there are degrees of freedom.
And that's like telling someone who's imprisoned, that they have freedom because they're not shackled to a wall or they can take an hour playing in the yard.
Like, there's no freedom until you're out of the gate.
And so there are degrees of restrictions for sure.
There is an open source gate.
Once you pass that gate, you have an explosion of freedom.
None, not before that.
So we have validated, that's another thing that we have done that I want you to focus on.
The open source definition of AI version one that we have released comes with a list of systems that are then evaluated during the process by the volunteers in the 100 plus code designers.
And the ones that pass are only the ones that are developed by research institutions, nonprofit groups and organizations.
And the ones that don't usually are developed by corporations with commercial interests.
And they sometimes release some pieces, some of these clothes, some of the materials, but not all of that's required.
Now, you may have heard some of the criticisms, like, why is the sensational source, why are you calling it open source?
You have why is the open source initiative stretching into machine learning since clearly it's not software.
So why are you even going there? Why did you do this process this way?
I can speak or I didn't know about it.
These are all fair criticisms.
I have addressed some of them at the very beginning, but the main thing here is if not us, if not now, if not in this process, like who, how, when should have that happened.
And the other comment that you have read is why is that footnote?
And I get it. Why is there a footnote that says, unless you're legally impossible? Well, it's because why will we want to force something that is not legal or put ourselves into a corner where only open data like open data is really a smaller, open data the way we think about it is only a smaller fraction of the possible available data.
And other organizations like three software foundations of the freedom of conservancy that have been criticizing us and commenting on this, they haven't come up with a solution either.
So we have noticed that the possibilities here are either going with a meta that is running TV ads, telling us that Lama is open source and open source AI, or we want to go with a definition that has had support.
A wide collaboration happening over a longer period of time and that is open also for evolution as the technology and the legislation go.
Now, there is another criticism that we have received and that it's too restrictive, like you may have noticed only the, it's a very, it puts a very high bar into the requirements.
Some of the corporations have been arguing that it's not really clear whether it's not really clear or they they argue against the fact that the during the process during the evaluation process and the validation process we have engaged mostly with academics and the corporations did not have, although they were following the process that did not feel like they had the time to intervene without.
And giving authoritative comments from the corporation's perspective. So this is another area where we want to, we want to think about collect the feedback and think about the evolution of the definition towards version two in in the future.
Today what you can do is to go on open source org slash AI we have published everything that is related to the process how we got to the conclusion there is a very detailed report that was sent to the board about who contributed how it's very long document there is a very long paper research paper on data.
Specifically that will it's over 30 pages of research material that you can that you can refer to to understand what the issues are when we talk about petabytes of data content that goes into training.
And the other thing that we ask you to do today is to help us correct Lama or medias authorities like Mark Zuckerberg and Lekun to correct them when they say that Lama is open source because clearly it's not it doesn't even go into the debate whether it's an open source AI it's just not an open source anything that they give us.
And that's creating really problems for the whole ecosystem. And that's it.
Do we have questions or.
Oh, sorry.
Yeah.
Check the check the rest of it and check the code requirements the whole thing right and you need to read the whole thing in one go and and internalize the fact that that's what is.
That's what is requiring.
In practice once you go and you get into the code requirements says you need to be able first of all it needs to be able to rebuild the data set it says that data information and the code says it needs to be complete so that you can rebuild the data set.
Yes, yes, we ran out of time in October we needed to release version one.