So, hello everyone, this is also going to be a short presentation.
Together with Mathias and Christina, we will present you a picoscolor, which has the aim
to advance open research with an open source AI platform.
Currently, we are an open source proof of concept, this is our stage, and we are trying to leverage
domain adapt to the AI, and real-time collaboration to create this tool.
And our goal in the future is to gather the community of developers and researchers
around the tool in such a way that we can advance systematic reviews.
We are a group of master and computer science students from Georgia Tech, our story started
with, initially, a team of four software engineers, then we moved to five software engineers
and we participated to two hackathons, and also we collaborated with Robert Gordon University
research researchers, and with the help of DevPost, we actually managed to win a hackathon.
We got second prize in the Red Hat and AI hackathon, which actually motivated us to continue
with this project and try to move it from a proof of concept to an actual tool that
people can use and be useful.
The reason why we want to do that is because we believe that currently there is an existing
challenge, a user's face certain issues, and existing tools in the field of systematic reviews
struggle with increasing volume and complexity of technical literature, specialized terminology,
transparent selection workflows, and also there is a limited ability to integrate new
publications into ongoing or past literature reviews.
In terms of the users that we believe that might find this tool useful, first of all, the
academic researchers, we believe that it's challenging for them to navigate through vast amounts
of literature and new knowledge is hard to integrate and publish literature reviews.
So conference organizers, imagine that you have to organize like a conference with thousands
of publications, and you have to scheme through all the articles that are being submitted
to the conference, and this tool is actually helping to avoid manually reviewing submissions,
and also avoid a situation that might lead to time-consuming and potentially bias selection
processes, and also students and educators.
They currently lack access to comprehensive resources and structured guidance for conducting
effective literature reviews.
In terms of how we solve this problem, this profile concept currently leverages AI powered search
and ranking, which helps with enhancing literature reviews, literature relevance.
So basically, when we search, it's not just like a typical search tool, it uses AI to search
through all the papers in such a way that the search becomes more relevant.
It has a scalable architecture, uses microservices for flexibility, and this in the future
will help us adapt and use different, for example, plug-and-play models.
So we are thinking about collaboration tools, and the tool is designed for shared work spaces
and role-based access, and in terms of the systematic review flow that we have, this includes
metadata extraction and selection criteria, and currently it's inspired from the biomedicine
methodologies, actually peak of stance from population intervention, comparison, and outcome.
And yeah, if you didn't understand how the tool works so far, I will do a little demo for you.
This is one of the main functionalities, this is the search tool.
So you can see here the user inserting the search.
You can see that there's an advanced search option with also the possibility to have scientific
notation translation, and also we have an AI query optimizer that helps users optimize their
question for the search tool. After inserting the question, basically we get to this funnel section
that has an AI summary on top with all the papers that are being displayed on the first page,
and the user can select the paper and move it throughout the funnel and give it different
other statuses. For example, in this case, it has moved from identified to screened and also
the user can archive that selection and also bring it back, of course.
And also it has the feature of showing only the items that has PDFs. For example, if we get
in a search, we get like 2000 results, for example, but not all of them has half PDFs.
This will just retrieve only the items with PDFs, and we have the possibility of chatting
with a PDF AI assistant about that PDF in specific, and you can see a display here.
Now let me tell you how to explain you more about the rest.
Okay, hopefully that works all right. Hi, everyone. Thanks for your attention.
So yes, so now we covered the problem with the space as well as some of the key pain points
and you'll watch the tool in action. So now I want to show you how because
color works behind the tools. So we behind the scenes and also what we see for the future
because the work where are proof of concept, right, but we want to push this into like MPP,
where it becomes functional for the users. So here's a high level view of a because color and
the solution. Essentially, users have the ability to upload their own documents and this triggers
a data cleaning pipeline. After that pipeline, we have a fine-tune AI models that will perform
metadata extraction. So in the case of biomeric and we extracted the peak elements, but
the idea is to tell all these specific domains with fine-tune adapters. So once we have this,
we embed everything so the documents as well as the metadata and store it on a vector store
that is able to perform semantic and similarity search. So yeah, in essence, like we're ranking
these stories. Finally, we have a rack chatbot. So this can plug into like your favorite LLM
and that can provide some rich summaries and insights. Additionally, if you see all of those
like feedback loops, that's the automatic tension where every annotation or user decision
fits back into the model in that making this whole process smarter and more accurate over time.
In terms of our current stack, we use fast API and Python and also like power our AI features
with fine-tune cyber and LLM index. We have re-acting the in the front end and we have
containerized all of these applications. Well, we mentioned before, we participated in Red Hat
and Intel Hackathon. So you know, we actually the hosties on the Red Hat OpenShift environment
and we take a advantage of Intel OpenBeno model formats for faster inference.
And this data, the moment sits on TIDB for scalable vector search. So I think that you have
a free tier that we were able to use, but in the future we would like to host or
provide the path to host the solution and full like open source infrastructure.
We have microservices and we have AI adapters.
So in terms of the potential and how we see this tool is like surrounding like some key aspects,
right? So it's all about like this principle of collaboration and transparency within the systematic
review and how you know let's say a professor can interact with their TAs and form their own projects.
But as well, how can that move like a cross project, right? And how can we perform like
archive of different annotations that could be trained for specific domains.
For this human AI collaboration is quite important for us, as well as we aim to integrate
with other tools such as automatic annotation tools or reference annotation managers.
We want to create an intuitive user experience, hopefully you got that from having a look at the
demo. So dashboard and data driven insights. So we are here on the POC and we aim to go into
a self-host solution. We have some personas which I'll talk in the next slide, but essentially
we're taking one little like thin slice, produce a valuable product there and then we plan to
expand for a more complicated use case like, you know, full-on system attribute.
So in terms of our first persona that we aim to achieve is the conference organizer.
And we picked this because it's quite like a close-knit, bring your own documents type of a scenario.
Currently, like in terms of this visual, you can see that we are nearly the end of the phase one,
which is all about the back end and the core features. These are like the growth operations for
management documents, the AI driven, driven as core in, so on and so forth. And on the next phase,
we want to focus more on, you know, how to make this collaboration and more user management.
And yeah, as well as increasing the efficiency of our ranking processes.
Lastly, provide the PI2 on top of all of that. So this is just a little bit more of, you know,
once we nailed that first scenario, our focus areas are just improving the system, improving the
automation and connecting to more data stores, right? So at the moment on our demo, we only have, like,
you know, 30,000 documents, but we want to link more to databases like both methods or archive.
Turns off, you know, if you want to contribute, we welcome contributors, especially,
like some back end developers, right? So if you like go, for instance, we're applying to to build
a back end based on that, as well as you, if you make very intuitive interfaces, come and talk to us.
Ultimately, you know, I think we're interested on developing a lot of the AI models, and we have
some help from Intel and some of the processors to the hackathon. So if you're interested on those
technologies come talk to us, or if you're a user, right? If you want to use this proof of concept,
now for your current project or, you know, maybe on the future projects or to change these two
towers, you know, something that might help you, we're happy to hear your ideas. In terms of how to
join this community, so we have our GitHub repo, currently we're meeting weekly, so usually on
two states, and yeah, we're happy to engage online as well. So think that's the end of the presentation.
Sure, so the question was, what do we use for the extraction pipeline? I assume you
mean the metadata extraction. Yeah, so that's how like the initial stage of this two, right? So
it was based on one paper from Robert Gordon University on training adapters, specifically for
biomedicine, and there are other open source, also data basis for extracting people. So we use
that about like a thousand records, and I'm fine killed a bird model, a cyborg model, and that will
become our metadata extractor. So at that point, you know, it knows how to extract these four key elements
put it into our tool. And we do some forther cleaning on top of that, right? But the idea is like if you
bring in other domain with the core elements for your systematic review of your selection criteria,
you can decide those four, five key elements, bring in a thousand annotations, and then you can't
explore the literature at the scale.