Next up, we are going to stream a video from a speaker, speaker, that couldn't make it
because of visa consideration, what's the person from Africa, the continent, I will
discuss the specifics, but we intended the diffusion of the video as some sort of maybe
that's better not recording. I was going to say that we intended the diffusion of her video
more or less against the first-time policies as an act of resistance because it was
tough hearing that she didn't get her visa. We will have her video for 20 minutes and then
we are going to have a live Q&A session with her on the screen, if it works. We did test it beforehand,
but we are going to have a live Q&A session, we are going to have a live Q&A session with her on the screen,
that's true, go ahead and then, that's it.
Can you hear anything?
Oh really?
Okay, maybe we should test that instead of...
Oh, you mean a centred?
Yeah, but actually if you...
Or is that just...
You might be the first light?
Yeah, it's just the first light.
That's good.
Okay, perfect.
Tell her, tell her, we are launching the video.
I'm just gonna write after her.
I guess we should be in advance.
So we have time for switching from the video to the queue.
So let's do this.
Hi everybody.
I hope that you're doing well and you are enjoying it.
I'm a first-end.
He's telling me that I am not able to leave here physically.
Everybody bought something for technology.
And the amazing first-end thing for making space for me.
And maybe we need to send you a recording of my talk.
I'm also thankful that you have decided to give me the next few minutes.
Please, let's do what I have to say.
Now, without further ado, let's get into it.
So, I'm going to talk to you in a reproducivalier practices.
And then I will be focusing on L.M.I.C.S.
Good morning.
My name is pressure seniority.
I am an open source manager from Disaster Science Program Policy Project.
Glyst-Ostronal team.
I am a software source and ability in 2006.
Hello?
Yeah.
And I sometimes just memorize the question, I have a question, I have a question,
I do it on a podcast adaptation, I'm speaking, I love to read and I absolutely love to believe.
From my talk today, I have a few things that I really like it to take from it.
Because this is a foundation and what I have to say.
So, to definition of the key words, reproducibility AI.
And then open as a blanket statement for what opens up and opens times right?
I would also be explaining why which is ability and open is important.
I will be talking about the challenges with non-feeting for which is ability and openness.
I would also be talking about how to build for which is ability and openness in mind.
And sharing some case studies from a global context.
Then I would also be talking about how to build for which is ability and openness in mind for and in my sees.
Now, to definitions, we can't talk about which is ability, without talking about research.
So what exactly is your religious ability?
Your religious ability research is work that can be independently created from the same data from the same code that the original scene used.
So if anybody decides that they want to make use of that in material and they get the code of data and the code,
it should give them the same results.
This definition is that that pair from the turnery and amazing community and resource that typically tries to make which is a bit reset to easy not to do.
Now, explaining why which is ability is essentially almost the same thing.
But now like which is ability now when we start to work in practice and independently created from the same data and same code at the original scene used.
Right, as you can see, this same definition is also that pair from the turnery.
So there are some words that are usually using the same context and sometimes can be in touch games.
They don't mean the same thing and this image here tries to explain that.
So if the analysis is the same and data is the same, then it should be which is support.
If the analysis is different or the data is same, then it is robust.
If analysis is the same, but the data being used is different and the research work is applicable.
And then if the data is different and the analysis is also different and the research work is generalizable.
This image shows the whole process of building of like a research work.
So data collection and data processing.
And analysis, data publishing and access, data visualization and data reuse.
And then research ideas can come up again and become the whole process.
We said data planning and design collection.
Like it's a whole cycle.
Now, we come to explain what open source is.
This is generally an open source compressive.
It might be the assumption that everybody knows what it is.
But for every fresher, I will just explain what it is.
The open source.com describes open source as something people can modify and share because it design is probably accessible.
Something is quite important because as we've seen, the concept of open source is now up to the extent that beyond the context of original
Chinese, which was software, right?
So now other forms of cultural, creative outputs, such as hardware, education, resources, and even now sciences, right?
And then refer into something that's open source, generating means that it's available under an open license.
This definition, this general definition has got to come to an egosry.
And then explaining what an AI system is because that's what we're talking about here.
And AI system is in machine-based system.
It will exist or implicit objectives within the fairs from the impetus we see is how to generate output such as predictions, content, recommendations, or decisions are coming from physical or virtual environments.
Different AI systems vary in their levels of autonomy and adaptiveness, are part of the program.
This is the OECD definition.
And it's quite important because it's the one that the open source initiative uses to explain what an open source AI is.
That's where the adapts the definition from.
And now we explain what an open AI system means according to the open source initiative.
So an open source AI is an AI system that is made available on that terms.
And in a way that grant freedom to study, modify, and share.
It has to have these four freedoms to be considered open source AI.
This freedom is available to fully functional system and to display elements of these systems.
So put in models, we wait to train in data.
And then if the condition to exercise in these freedoms is to have access to the preferred form to make modifications to the system.
It's very easy when we make mention of open our reproducibility to sometimes think that they mean the same thing.
But they do not, right?
It work can be reproducible and open, but doesn't have to claim that all reproducible works are open, right?
So reproducible doesn't always mean open, but the central intersection, where they meet is the answer to the question,
can others reproduce understand and then build on this work without insight on knowledge?
Can I see material and just work with what has been created there?
What has been provided there?
With having to know the actor researchers or having to find out whoever it is, right?
If I can't do that, it means it work has been considered for open and which is all.
What sticks to this intersection for this presentation?
As I'll be speaking about AI systems and creating them within this context.
So what is the potential open our reproducibility?
It helps make results very viable and trustworthy.
When a material is created and publicly available, it's easy for people to trust what they've seen.
If it's made, if it's reproducible, if you can't find the data or the analysis, that was made from it.
As opposed to a research, I just making claims and everybody can't check if it works or if it makes any sense,
I would have to read those what it has been said to do, right?
It also enables adaptation across different contexts because if person B in the global north is able to apply this to the demographic,
passing C in the global south, you'll be able to reproduce that same result by what has been provided.
So it can work in any part of the world that's applied or any situation depending.
And then it enables the support of a responsible and SQL AI.
Because of how AI is very prevalent right now, and everybody's making use of it, and there's a lot of probing to how they use this.
This is an important tool, but we also need to make that this shift and also respect basic human rights.
There is a lot of questions into how is the data being sourced and the ethics of it.
So when the data is created and open as a reproducibility is covered in, it supports the use of it to be responsible.
And it also enables research as a user to be able to trace where it's come from, and everyone was actively sourced and also probing to make ensure that.
And if you nice using feature is you, or it's got some activity.
It also makes the research as a cognitive because you know that somebody's going to look into your work and they're going to question where this has come from.
Right.
So it also reduces waste and the vacation because when you see that something has happened or has been done before, it's easy for you to build on it as opposed to.
The research from someone else doing the same work, knowing that somebody else has worked on it because that's material was not made publicly.
I really wanted to probably found out when they had to submit it to a journal.
Publication and also reduces the prevents the concentration of power.
Most of us or some of us might have seen the current news of how much our AS systems are taking.
The way it's affecting certain environments when we are able to spread the use or the knowledge and study of AS systems.
It enables power to not be concentrated at one place because now different environments and different researches in particular about what are trying out the same thing.
So it makes the use of energy to not be one place.
It also enables the strengthening of global research capacity of mention how it work can be adapted in different contexts or in different environments or different demographics.
By doing that and creating a material or a research article that is.
Global, so that's reproducible, given from different parts of the world can try it and because now they have something to do don't, it makes work.
It makes innovation very easy to work with or to work on and now it's like widespread and you have people from different parts of the world are trying out the same thing.
So it's very great that the strengthening of global research capacity.
So now that is the importance. What are the challenges?
Interestingly, the inverse of the importance of all these things has challenges right now.
So we see that results cannot be trusted.
There are models out there in the world that people are making use of or that researchers are making claims of and saying, this is open or this is just a problem.
Up to practice or in the up to theory of it, which is not, which is why there are.
Which is why there is open source air definition to be able to help use that in the bio key.
If someone says this is open source, this is what you use as a checkmark to see.
Okay, this works. So this is not a good answer to the definition.
And there is also the difficulty for we using or adapt an existing research material.
It is what's exist and what is publicly available that people can use and build on and can foster innovation.
When something is not publicly available, when it is not accessible, it is harder for researchers to be able to build on the right.
And then you now find out there is also the consistency on in the end twist, which I will come to.
Another challenge that we see is our resources have been concentrated because material is not being shared or publicly available and kind of be produced.
So you find out that it is most likely the same set of maybe people, demographic or organizations that make.
Or work on these systems because the most likely are ones who have access to the first set of resources.
We find that another challenge is power resources being concentrated because, again, this materials are not publicly available or they are not able to be produced.
And so it's just the same set of people that keep on turning out materials or findings.
And people with low resources or lower access to these tools are not able to work on something that is groundbreaking because.
The resources are not available for them.
It's cool because also increase because now nobody can probe into how something is being built.
There's no way to see, okay, let me try this test results that you said brought out this and to be able to check.
It's correct or it's right or it's not right. So when.
It's not easy for people to look into the work that's been done.
Then the question of ethics comes up and there's less happening to question it.
And then we see that time energy on funding our police said, I can mention previously when we put the difficulty in using or adoption and existing research material.
And you can also happen that people now work on the same team and you cannot tell until you've probably submitted it to be approved.
And then you can also go over participation strings which is really sad because when work cannot be reproduced.
It's hard for people who are not able to do like first set of research or innovation to now be able to do don't plan and that's to their own context.
Building open our business meeting mind, we wanted first of all start with transferring training. So log and ten more than a detector, lost functions optimizers.
I have a permit as I want them seeds and separate data, pre-processing training and money.
You want to report the results across multiple rounds to capture variability.
This is important because research has once we would see different methods that we tried and different results that came out from them.
If you want to document data pipelines, try to describe all the resources, filtering the very non-sleeves and book these relevant statistics.
Then in the case when data cannot be shared, it's just like if you're working on something sensitive, which you must like do you have some points if in that's the case then.
Just let the researchers know that we made use of the data but it's currently sensing what we cannot be shared.
We have the documents, computational and environmental requirements.
We report hardware, memory, training time, batch sizes and energy or cost requirements.
Again, it seems we could use what we've done.
We need to have the same analysis and then same code or the data set.
So share with you the variables between libraries and class setup instructions.
We want to model behavior as possible.
We also want to design for other patients.
They need to reproduce the whole evaluation by achieving them and doing other patients.
So AI can be meaningless across different contexts, including resource constraints, constraint and LMIC environments.
These are some resources and commences that exist to enable education and common practice to use openness and reproducibility.
There's a two-in-way which has made an image on to make reproducibility as to either not to do.
We should try it and go check it out.
It's like whole found called resources.
There's open data and then there's get from the data or the images to collect data anywhere.
The next point is now with building an open array which is being made for LMICs.
So the previous points that we've considered, which are transparent training, document head data pipelines,
of heat and environment clarity.
One of the behavior is constantly released and designed by the patient.
Considering them and an active active concentration for lower research settings,
we have a design for reproducibility from the start.
It's not something that should happen so many things we do.
Because again, this is something that should be applied, but regardless of the context.
But again, if it's applied from the beginning, it helps lower research like context.
So define who can reproduce your work on the level of reproducibility.
The exact results, similar performance or conclusions, is also going to be where you now specify tools that were used.
So whoever is going to reproduce the work, nobody needs to have a very good view to do that.
Right, we'll talk about the constitution for technology to somewhere in Lita.
But that's for point one.
You want to call it a use local data.
A availability of data sets within the African context is constantly improving.
Training AI models on data sets from here improves the chances of creating AI systems with local contact.
Here means Africa.
In general, like other LMICs.
Citizen client, you want to design models to run a limited hardware and encourage local communities and contributions.
This is important because first of all, when you're doing a research work, trying to implement or reproduce some of these.
Reset copy.
Sorry.
Let me start that again.
Citizen science, design models on limited hardware and encourage local community collaboration.
This is an important because from the beginning, when you decipher which is basically from the start,
you want to factor in using tools that people from lower resource settings will have access to.
Obviously, that happens when you cannot help it.
You have to use something very, very high end.
When the case is where you can, it is encouraged to try out into the actual property accessible.
And then also encouraging local community contributions means that individuals, normal citizens,
I mean, not be really strategized by involved in the process.
That way, it also gives you a great larger data sets.
And it shares more knowledge about what you're building.
And this is just create interesting to what is being built.
Enable adaptation on local reuse, provide guidance for high end journey and reproduce and result on smaller or local data sets.
So let's whatever resource that is created to guide documentation.
Documentations should be clear and easy to use for future readers.
And sometimes also it's so really important because even for the team that has done the work,
you want to be able to come back to two, three weeks data or one year, two years data.
You say you want to be done that work, are you done before?
They want to understand what you, what you were previously.
I have listed some existing communities as the teachers and tools that exist to help our open area,
which is ability to focus on the analyses as DHIS to,
then as the African reproducible network community,
then as DHIS has done, what are the projects which is focused on making up the data,
accountable, accessible and interpretable.
And then open science, Africa, every system, as a list of what are open science communities or projects or funders,
just basically policy makers that focus on building the open area which is easy in mind.
So to end my talk, I've mostly done this as a way to share knowledge for books,
core researches, not so core researches, like the technical and non-technical,
mostly as general knowledge.
And at least understand what this means and how to apply it and what our work is to study the current.
The open area is how we ensure communities and the main cities and not just these other way,
or contributors and it's showing me how it's built and applied.
And it means science that truly knows what that is.
And that is one of the key tenets that we focus on,
such as the ability to transparency, collaboration, and sharing.
Thank you so much for your time.
So this is a some research that I can tell you,
how I have understood from tools that helped me in making this presentation.
Yeah.
I've shared some resources that you found in the class to get a better idea.
So this is an exhaustive, so I just listed up a bunch of them.
If you like to find out more of it, please connect me and I'll do that.
Yeah.
If you kind of really was actually back, because I like to learn what you learn
or you think I can improve on, because I know and I look forward to it.
And I'm going to continue on some time and we'll see you in some months.
Thanks, sir.
So this works really glad we could host that talk even through video.
I especially liked the part where precious said enabling science that truly knows no border,
because that's really the problem here.
We would like to try to have a live Q&A session.
It looks like it works, and I hope it will.
Can you hear us, precious?
Yes, I can. Can you hear me?
Yes, we can.
Okay.
So how do we do that? Do we have questions?
I may repeat them myself.
Yeah, so we have one right here.
Okay.
I want to know.
Maybe, maybe you can take the mic.
I remember I participate on project from the Wikmedia Foundation called Global Development.
And there was a documentary called People are Knowledge focused on people from India and South Africa
where the oral citations or the main source of information.
So I'm wondering if you are thinking about training some of the LLMs in the African context.
Now that's easier to make the transcription from voice to train this model and improve the African context.
Just I don't know if you know something about it.
So precious, did you hear the question?
No.
That's terrible.
Sorry.
It was a great question.
And you, do you hear me?
Yes, I hear you well.
I heard some part about like contributing to projects Wikmedia.
And then the question about not how we can easily chant and chant text to speech.
But in 19 here, after I've got the question.
Okay, so you got most of the important part of the question.
The question was, do you plan or anyone around you plan on training LLMs on transcribed oral data?
On data that's mainly oral and then because it's getting easier to transcribe,
having specifically oral purposes, corporas of data for training models.
Did you hear me?
Yes, I did.
Thank you.
So I do know some projects that have stats in that.
Honestly, I don't know about it in the science context.
That's why I do know of a musla and funded projects where people from the Hebrew region have encouraged to send video records.
Sorry.
What do the recordings of the language transcribed in some of the things that have written in text for longevity purposes?
So the hope is that since we have these things beginning,
then a lot of other sectors can also apply it and then if it's applied in one part,
which is a vision of language, then it can be applied to other sectors.
So it's a great way to start.
So I do think that in the future we can see more things like that.
Great. Great.
Any other questions?
Okay.
Shout it out and then I repeat it.
Great question.
So precious the question was,
can you give us an idea of the constraints and restrictions for training and using AI and LLMs in LMIC's context?
Okay.
So this is considered a general ability of data, right?
So obviously there's more improvement,
conversations like this are happening,
and you have institutions that are making it their goals,
and we have more data available.
Because that's in from there.
We have less data than it should be available currently out there.
So one is not particularly proper according to data.
You get things happen and then,
but time you want to check,
can we find where this happened?
What is the data saying?
It's hard to see that they were documented.
Sometimes that it's not digitized,
and then you find out that probably from some hospitals,
where I need to have direct codes,
taking by pen and paper,
and then if maybe a large fire happens,
or that's a thing,
then the information is lost, right?
So one of the things that projects like DSWB's
and DSWB is to first of all train people
to become more digitally trained, right?
So by doing that,
then we have more data that's digitized,
and that's like one constraint.
Another is sometimes for internet and power,
because you obviously need like a little power
to use a run system.
So some places don't have as good access to the internet,
as there is the water as it should be,
and then now it's like less availability of power.
And sometimes it's also like computational resources,
because it takes a lot of memory and RAM to be able
to run some of the LLMs.
And from the first part of the world,
it's generally a little bit more expensive
to procure these devices.
So, yeah.
These are thoughts or conversations that have been had,
but some of the constraints like listening them out
would be an ability to see a data,
lack of digital literacy to large extent,
but that's just been with respects to storing data
and then level power and internet constraints.
And so sometimes lack of education too,
but thankfully more and more people are talking about this,
and then we have more organizations
that are coming up to educate members of the public,
and as many people as I will need to listen.
Cool.
Thanks.
Questions?
It's good.
We are on time.
We have.
Yeah.
It's good.
So.
Well, there's no questions.
It's fine.
Yeah?
We can wrap up.
Brush is you want to add anything.
If you hear me.
I want to say that this is part of my SSI.
This is my SSI topic.
So, next year, I'm going to be working intensively
on trying to actually make the practices easier to reproduce
and educate people and how to do that.
So, hopefully, I lend better.
But next time, I'm probably speaking on something like this.
I would have like a larger resource.
I sent out like my links in profiles.
If you have maybe more information on something,
you think that might be what they can need to please reach out to me.
And let me know.
And thank you so much for attending my talk.
I'm for the full of them.
I'm giving me space.
I'm making a possible for this to happen.
Thank you so much.
Thank you.
Thanks.
See you soon.