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The emerging field of neurosymbolic AI — combining LLMs and knowledge graphs in hybrid AI architectures — is new to a lot of people.
For Yann Le Franc, it’s the story of his career — from his academic days as a computational neuroscientist to his current work creating enterprise systems that ground LLMs in ontology-backed knowledge graphs.
Over his extensive research and consulting career, Yann has honed his ontology design process, which he’s now sharing in a platform that guides others in applying those time-tested ontology practices.
We talked about:
- his work on the European Open Science Cloud, the LUMEN and GRAPHIA projects, and his KG and AI consultancy
- his academic evolution from cellular biology to neuroscience and then to computational neuroscience, neural network research, and neuroinformatics — and how that led to his discovery of the power of ontologies
- his current work to connect LLMs and ontologies
- a presentation on ontology-backed AI that he delivered at the Cultive Ta Data (Cultivate Your Data) conference earlier this year
- the refreshing emergence of the word ontology as a buzzword (as opposed to its “bad word” status only a couple of years ago)
- how he applies his research and innovation work to improve enterprise information systems
- his work to advance the practices of ontology design and knowledge graph building
- the inspiration he has taken from Robert Stevens to approach ontology development in a more aligned (non-siloed) way
- his ensuing adoption of the Linked Open Term methodology, an agile approach to ontology, and his broader work to reconnect “semantic silos”
- his journey into proper philosophically grounded ontology practice, and the notable absence of a canonical source of best practices
- the work at the LUMEN to develop a collaborative platform that guides practitioners to the tools available to each step in the ontology process
- how an enterprise ontology is the “skeleton of your whole information system, a representation of your company” — and how it accelerates BI and discovery and retrieval
- how much he’s enjoying the return to neuroscience that comes with his work on neurosymbolic AI systems
Yann’s bio
Yann Le Franc, PhD is the CEO and Scientific Director of e‐Science Data Factory S.A.S.U. Created in 2014, e-Science Data Factory is a French Innovation Center, aiming at proposing innovative solutions to transform data as key assets for industry and public organizations making their use of AI more efficient. The company leverages its participation into European Research Infrastructure projects to transfer knowledge and innovation to industry and public organizations.
Yann Le Franc has a PhD in Neurosciences and Pharmacology in 2004. After a postdoctoral experience in the US, he worked on data management projects for Neurosciences in the context of the International Neuroinformatics Coordinating Facility (INCF) where he developed a strong expertise in ontology design and semantic web technologies. He then contributed to several Research Infrastructure projects aiming at building EOSC, the European Open Science Cloud (EUDAT, EOSC-Hub,…) as an expert on Semantic Web, ontology design and FAIR Principles.
He is co‐chairman of the Research Data Alliance Vocabulary and Semantic Service Interest Group and the FAIR Mappings Working Group. He is the former co-chair of the FAIR Digital Object Forum Semantic Group and actively contributed to the EOSC Semantic Interoperability Task Force. He has been spearheading the FAIRification and standardization of semantic artefacts (i.e. ontologies, controlled vocabularies, …) and mappings/crosswalks in the context of FAIRsFAIR, OntoCommons and FAIR Impact projects. Through e-Science Data Factory, he is one of the co-founders of the Knowledge Graph Alliance. For the last 2 years and a half, Yann has been the Head of the EUDAT Secretariat, a pan-European e-Infrastructure, providing data management services, storage and computing services to European researchers. He is leading the creation of the EUDAT Node, one of the first nodes of the EOSC Federation.
Connect with Yann online
- email: ylefranc at esciencefactory dot com
Resources mentioned in this interview
Video
Here’s the video version of our conversation:
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 54. One of the most interesting developments in AI architectures is the emergence of hybrid systems that integrate neural-network based LLMs with symbolic AI like knowledge graphs. For Yann Le Franc, this is very familiar terrain. He began his career in the neural network world as a computational neuroscientist but then quickly discovered the power of ontologies during a post-doc. He’s delighted to see his interests re-converge in the current hybrid-AI era.
Interview transcript
Larry:
Hi everyone. Welcome to episode number 54 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yann Le Franc. Yann is, as you can tell from his name, he’s based in France. He’s in Montpellier. He’s the CEO and founder at e‐Science Data Factory. He also heads the EUDAT Secretariat. He’s the co-chair of the Vocabulary and Semantic Serviced Interest Group at the Research Data Alliance Association and he co-chairs the Fair Mapping Project. I have no idea how he found the time to talk with me with all that going on, but welcome, Yann. Tell the folks a little bit more about what you’re up to these days.
Yann:
Yeah, thank you, Larry, for having me on the podcast. Well, as you’ve said, a lot of things are going on. We are working a lot on European research project to help build the European Open Science Cloud. And of course, we specialize on semantic web services, knowledge graphs, and the connection with AI. And in some of these projects, so the two projects that are currently going on are called LUMEN, and the other one is GRAPHIA. In LUMEN, we are developing a platform for industrializing ontology development and in GRAPHIA we’re building a federation of knowledge graph for social science and humanities.
Larry:
Yeah, that’s actually how we met. There was a conference in Zagreb last fall and put together by, I guess OPERAS, the overarching org, I guess that-
Yann:
The coordinator, yes.
Larry:
Yeah. And one of the first things I want to talk about is that I think it was there we talked about your journey from, you started as a neuroscientist and here you are this world-class ontologist. How did that transition happen?
Yann:
Yes, indeed. I started as a cellular biologist to be correct that specialized in neuroscience. And then I get my finger hooked into computational neuroscience. I was fascinated by the idea that you can simulate brain cells in computers. So move forward and keep going on working on, how do you say, simulating small neural networks, realistic biologically grounded neural networks. And then while during that time, I generated my own data and I had to deal with the data from my colleagues and I discovered a hell of working with data generated by others and even sometimes your own data.
Yann:
And I ended up after my postdoc in the US to actually join a team in Belgium in Antwerp and work for the international neuroinformatics coordinating facility based in Sweden. And there, my job was to actually develop an ontology for computational neuroscience models. And that’s how I discovered the world of ontology and realized the power of it in terms of helping you structuring your data, sharing with others with the included meaning, which means the other understands what’s in the data more easily. And of course, the connection to knowledge graph, where in the end you can start interlinking all your data together in a more simpler way and a more flexible way.
Larry:
Nice. We had a close call there. We could easily have lost you to the whole neural network community with that kind of background, but I love the story of how the need to understand and work with your data drove this. That’s great. But that was a little while ago. So you’ve seen a lot of this. You’ve seen how neural network stuff is informing this, the benefits of ontologies and knowledge graphs in managing science data and understanding it better. And now we’re all of a sudden, fast-forward to this year and we’re three and a half years into this crazy new generative AI era. What’s your kind of take on the current landscape? How has it changed I guess your interests and your work and your…
Yann:
We kind of see that coming with the development of very major AI. I don’t remember the name (AlphaGo), but the one that actually won against the Go champion and all these big advances. And then we had the LLMs coming up, which is interesting. It’s an implementation of neural network, gigantic neural networks. It’s like trying to simulate the full brain. Unfortunately, it’s not a brain definitely because it’s just a statistical model. So it’s interesting to see the democratization of the usage of the AI, at least the LLMs. It’s a bit scary because I think the naming is wrong if we should keep calling them LLMs and not AI because there’s nothing intelligent in these systems. It’s just statistical models that gives you some interesting outputs that can emulate intelligence, but they don’t understand the meaning. And that’s also why I’m happy to go back to neuroscience and neural networks because now we are working a lot also on connecting knowledge graphs and ontology to LLMs to make them smarter because they are fantastic models for language.
Yann:
I’m using them also for helping me writing text in English because I’m French, so I’m writing in Frenchlish. So it’s nice to have an LLM that helps me correct some of my writing but still you realize that sometimes the answers are completely dumb and it keeps answering me something, which is wrong. And we realized that we had some tests internally where we were playing on an internal project connecting LLMs and graph and we realized that, well, if you contextualize the data properly for the LLM, then it starts giving you proper answers with very little or smaller numbers of errors than you would do with a basic LLM.
Larry:
That’s the presentation we were talking about that you did recently, you presented that work some place?
Yann:
Yeah.
Larry:
Yeah.
Yann:
So basically what we started is the company works extensively on European projects and therefore research projects and therefore we have to do a lot of reporting. So we started thinking about why don’t we use the technology we are implementing at our customers or with our colleagues in science for us. And so we started building a sort of information system internally with ontology for the company so that we can retrieve data for the reporting that we have to do, financial reporting and connect that to LLM so that we can just ask questions in natural language and the system answers you back. And so last year we had a first presentation in an event here in Montpellier called Cultive Ta Data. So Cultivate Your Data, which is sort of evidence that let’s say bring companies and data experts to discuss about the data problem in company in different aspects.
Yann:
So basically business intelligence and AI usage in industry. And we had a presentation last year and this year, and this year, people were very, very, very interested because well, we showed some results that were convincing, I guess, that shows that, well, if you contextualize the LLM, you can get answers that are specific to your company without having to go through retraining or refining an AI, which is quite nice.
Larry:
I love that this is an emerging architecture around that, that you have the general intelligence of the LLMs and then your specific stuff. But hey, was that the presentation? You said that some place you presented recently that you had used two years ago, you used the word ontology and people almost left the room and then this year you used it and everybody’s perking up and leaning forward. Was that the talk? Was that the place?
Yann:
Yeah, that was the place. That’s interesting because ontology was a bad word for many years I think because mostly people were thinking that ontology lives in the realm of academia. It’s just scientists and philosopher having fun modeling the world, except that now with the emergence of LLM, the usage of AI, somehow the current strong trend on GraphRAG approaches that actually connects the LLM to a knowledge graph, which is a graph structured with an ontology. They start realizing that, well, ontology is a key. And now you can see, I’m checking on LinkedIn and you can see on LinkedIn ontology becomes a buzzword almost because all the big players, they started to say, “Last time I saw SAP,” so all the big players are starting to integrate ontology in their own systems while they completely neglected that for the past decades while ontology exists for a while now.
Larry:
Yeah, no, I remember at Sapphire, the big SAP conference, they announced they’re putting knowledge graphs underneath their new Joule agent framework. So yeah, it’s very cool. It’s everywhere. But one of the things, we’re an awesome industry, but there has this been this kind of reputation among ontologists as being kind of building their own silos within the practice and having sort of not optimal approaches to always to ontology design and knowledge graph building. You kind of talk about your company both as you do a lot of consulting and innovation work, but you talk about your company specifically as an innovation center and part of that is around the practice of this as well. Can you talk a little bit about that? As this interest in ontology is arising, you’re like, “Oh, I think we have to do it better.” Is that kind of the idea?
Yann:
Yeah. So for many years I have been working extensively in research. So my company was doing a bit of consultancy on the side, but mostly we were doing research. And part of the research is developing proof of concepts and tools and services that can be useful for, of course, the scientific communities, because our core, let’s say, set of customers are our colleagues from the different scientific domains like biodiversity, climate change, earth science and so on. But one of the things we saw is that they use investment in research from the European Commission that is barely making through to industry for the usefulness of industry. And you have tons of very good experts working on these problems and hard problems that actually their solution is just not even seen or considered by companies, not because they don’t want, just they don’t know it’s a matter of knowledge.
Yann:
And so basically we realized that part of our job should be to actually, of course, doing the research and thinking about innovation and innovative architectures, and that’s what we’re doing in project. But then to take out these results and all the results from different projects we are aware of to see how we can integrate them to actually improve the information systems in companies and industry. So basically doing that for our clients.
Larry:
Nice.
Yann:
For me, innovation is more… Yeah, of course research is part of innovation, but the whole innovation pipeline goes from the idea to a product that’s useful for people. And so that’s why we changed there.
Larry:
So you have a specific kind of framework, it sounds like almost for… And just it sounds like… And it was sort of pragmatic too in that, God, there’s all this amazing EU research and nobody knows about it and can’t apply it. Let’s figure out how to do that better. Another one of the things we’ve talked about is your desire not to just take the data per se and all these awesome discoveries but to do the process of ontology design and knowledge graph building and everything better. Can you talk a bit about how you’re tackling that?
Yann:
So how do we tackle the fact that we need to build ontology better? So I’ve been building or trained to build ontology. Don’t forget that I’m a stupid biologist, so I’m not an IT guy for many years, more than a decade now. And I’ve been struggling because I’m not an IT guy, so I can do beautiful spaghetti code in Python but not proper software. And the tooling is nice. Some of the tools are very nice for building ontologists, but they are all disconnected. And so basically if you really want to work on a pipeline and a workflow, well, it’s a pain. It’s really a pain. And throughout discussions and also meeting with fantastic people, I realized, and in particular one person that inspired me a lot, it’s Professor Robert Stevens from Manchester. He was the keynote speaker in an event we organized in 2014. And basically in substance, what he said is that ontologies are part of information system basically working with software.
Yann:
So maybe we should also consider ontology development as the development of software. In software, we have now continuous integration, continuous developments. There are proper methodologies in place to actually guarantee that you can write proper code, but we don’t have that in ontology building. And usually what happens throughout my experience, of course it’s only my experience, but I’ve seen many colleagues and everybody works in its own corner. There are few community good practices. I’m thinking about the OBO Foundry coming from the biomedical domain, that’s my reference point and they have done an amazing job. However, these good practices, basically they don’t make it through to other scientific domains, for instance, right?
Yann:
And so what we have been doing in the company is advocating for these good practices because within that, I mean, I think that this is missing. It’s like you see so much diversity in the way ontologies are developed that basically if you really want to start working with all these ontologists together, it’s just becoming very painful. And so one of the thing we’re doing in the context of the LUMEN project is to say, okay, let’s break this and try to bring tools together into a coherent workspace with a proper methodology. And so we’re using methodology developed by my colleagues, Maria Poveda and Daniel Garijo from the University of Madrid that’s called the LOT methodology for Linked Open Term methodology that was developed in the context of OntoCommons project, which aimed at helping industry to catch up with ontologies and use ontologies for their own system because it’s sort of a summary of all the different methodologies that have been proposed into a coherent and agile workflow for designing ontologies that goes from, let’s say the conceptualization, which is why do you want an ontology, what for, what’s your use case?
Yann:
Going through competency questions, then the development, and then the publication and the maintenance. And we want to sort of democratize these tools and help people to integrate these methodology and good practices into their usual day or everyday practice in ontology building. Again, as a biologist, when I started with ontology, I realized that you get a technology that’s there to break out data silos, right, because you connect points together and you can do that on top of existing systems, which is just amazing. In the meantime, when you look at practice, due to the lack of common methodology, due to the lack of common good practices, you end up having semantic silos with systems that cannot talk to each other, which for me just feels the point of the technology as well. And so that’s what we are trying to change a little bit in our ways by providing some tools, some recommendation, pushing for existing things, raising awareness on good practices.
Yann:
So for instance, now you also have the industry ontology foundry recommendations that is very similar to the OBO Foundry recommendations that comes in as well as a good set of good practices. No, these kind of things are getting there, but the awareness of people building ontology on these things is not necessarily always there. So I think it’s also good too.
Larry:
Well, yeah, I guess because you have both the extreme nerdiness that comes with being a real proper ontologist and being deep. It’s like the PhD thing, you’re so narrowly focused, but then you’re also in subject matter silos as well like science or finance or these others as well. And so how well do you think you’ve accounted for the various practices? Because you’ve done a lot of different client work in your consultancy I know. So you’ve had exposure to a lot of different industries. Do you feel like you’re getting a fairly comprehensive look at maybe not best practices, but the most common practices out there?
Yann:
I’m not sure I cover everything. You cannot know everything, especially in this world where there’s tons of things that appears every day. What we’ve seen is that in many cases, when we work with some of our customers, I’ve seen ontologies that were just not ontologies I say it because people do not know what it means. It’s a little bit like when I got thrown into the ontology bus when I was doing my second postdoc, “Your job is to build an ontology for computational neuroscience model” and you’re like, “Yeah, duh, what’s an ontology?” And then you start looking for definitions and you end up finding out that it’s a concept from philosophy and you’re like, “I’m supposed to develop an information system, not do philosophy. I’m definitively not a philosophy major, although I’m interested, it’s not my job.” And then you start digging in.
Yann:
And then the first journey for someone that doesn’t know whether ontology is to actually go and get and build an ontology is very difficult and there are tons of contents, some of them are contradicting themselves. And again, there is not, let’s say, a central place that actually lists the good practices, right?
Larry:
Mm-hmm.
Yann:
Now for me, most of the time people are doing this ad hoc without necessarily looking outside, which leads to, well, strong divergence in the way of ontology are being built. Now, of course, I cannot be exhaustive.
Larry:
And I guess part of that too is you don’t have to know every single vertical in the world to identify the best practices. You can probably at least make a startup like a POC of an understanding of ontology development. And then you’ve been working on sort of a platform to facilitate this as well, right, to not only account for the best practices but to embody them in some kind of a system. How’s that going?
Yann:
Yes. Well, thanks for mentioning that, Larry. It’s basically a work we’re doing in the LUMEN project. The idea that came to our mind is to say we have tons of tools that helps at different stages of the development of an ontology. The most widely known tool for editing ontology is Protégé, for instance. We have other tools, again, developed by my colleagues from the University of Madrid like OOPS! that does wrong design pattern detection. You have FOOPS! that evaluates if an ontology is FAIR or not. So just for the people listening, FAIR stands for findable, accessible, interoperable, and reusable. These are sets of principles that are driving the development of the, well, European and international data management practices on science but not only on science, also industry are trying to implement that. And so the idea in these FAIR principles is that you use ontology for documenting your data and this ontology themselves must be easily findable. They must be accessible, they must be interoperable and reusable. Yeah.
Larry:
Yeah, no, I love that. It’s like that in the startup world or business world, they talk about eating your own dog food and it’s like, okay, if we’re designing ontologies to help with FAIR data practices, let’s make our ontology FAIR. That’s awesome.
Yann:
Yes. Well, it’s one of the key principles for interoperability. And so basically in the LUMEN project, we conceptualized the idea that… Well, again, I said the talk of Robert Stevens was inspiring. So we’re building a platform that actually connects the different tools that exist or that can be used for building ontologies. Of course, not all the tools because we have a limited budget and it’s just for three years. But the idea is to build a platform that interconnects these tools to structure the workflow of building an ontology, following up the LOT methodology. So basically the platform is an implementation of the LOT methodology, including some good practices for making more ontologies FAIR. And the idea is that, well, you have to know so much to build an ontology that if you integrate this into a platform, all these good practices and enforce that, that makes things easier.
Yann:
You don’t need to remember that, “Oh, we need to add this metadata filled in the ontology in order to be FAIR because then you get it automatically with your ontology.” And again, this workflow is very useful in the sense that you know where you stand in the development process of ontologies, because again, you’re using tools from different places. Of course, most of the people use systems like Git for version tracking and things like that, but you don’t necessarily have a clear view sometimes on where you stand in the development process. So basically the platform interconnects these open source tools that are actually widely used. So the idea is not to disrupt the workflow of people by adding them new tools, but it’s just having a overarching systems that allow you to interconnect that and support to somehow following the process of designing ontology based on LOT and integrating some of the good practices and the recommendations for making ontologies FAIR.
Yann:
So we are working now on minimum variable products. Basically, the idea is that we go from competency questions implementation with Protégé. So the platform is integrated with Protégé through a plugin. And then we also have a small visualization tool on this web-based platform and then you can publish that onto an ontology repository based on OntoPortal platform.
Larry:
Cool. Yeah. And how many of these have you done with the… Because I assume you’re just sort of aggregating your practices and how you approach it and putting them into the system. So you’ve done this a few times, but have you actually had the chance to see… Has it accelerated your own work, I guess, as you’ve been developing it to have that sort of… I’m picturing almost like checklists as you go and just…
Yann:
It’s exactly that. That’s the checklist, right? And the idea is that the platform is also collaborative. So basically you have a workspace that you create, you can invite people. As an ontologist, I structure the knowledge, but I don’t have the knowledge. I work with domain experts and I need to actually collaborate with these people to actually get the concept, their meanings, and discuss with them the different interactions and interrelation with the models and also look at the kind of data so you can actually shape the ontology properly. Because again, an ontology, at least in my view, is not supposed to be a beautiful model of the world, right? It needs to be a practical elements. And that’s one of my frustration. I build ontologies that never been used and that’s… What a waste of time. I had a lot of fun building them, right, but it’s supposed to be useful.
Larry:
Yeah, that’s interesting. It’s almost like you want to have a little shed out in the backyard where you can build your beautiful ontologies and then you go to work and you build the one that actually fulfills a business purpose. Yeah, no, I love that.
Yann:
So that’s why we build the platform. It’s like, well, again, eat your own dog food. We are building ontologies for customers, different kinds of ontologies. We need systems that help us keep track on the process, sort of implementing a CI/CD pipeline for ontology so that we internally in the company, we do have these tools and we can work with these tools more efficiently for our customers.
Larry:
And I love that the focus is on collaboration because that’s just… Oh, I don’t know if you were at Connected Data London or she did it at both Connected Data London and Knowledge Web Conference last year. Tara Raafat from Bloomberg did this presentation on what she calls the star team, this five-pointed star that has ontologists, the business experts, subject matter experts and data engineers and a product owner kind of overseeing the whole thing. Does that list of stakeholder or business participants sound about right?
Yann:
It sounds about right. Yes. These are the kind of expertise that you need to have at hands when you build an ontology. I mean, the ontology is the skeleton of your whole information system. It’s a representation of your company and different things. And basically you need someone from the business because they are the guys that will ask the questions to the system. Again, the system is built not just for having a beautiful system that says, “We have a beautiful system.” It needs to be there for doing business intelligence, collecting information quickly, retrieving information quickly and getting answers to questions that would take months to get an answer because, well, you have to go through all your data that is stored in your nice data lake, but you have to mine it, clean it, organize it and so on. So that takes forever. And the idea of these systems is to accelerate this.
Yann:
And especially now if you connect that with AI, you don’t even need to know Sparkle, but you don’t even have to ask to do the query. The AI is doing the query for you, but still you have to build that. So you need the business perspective, you need the domain experts because, well, I know finance, but I’m not an expert in finance. So if I have to build a finance ontology, I need someone that explains me how the things are connected together so that we can represent that. You need a product owner because someone has to make sure that the process is well respected and the product is delivered with the quality required. And the data engineer, because one of the thing that is very important, and I think it’s one of the key things in the LOT methodology is you have what we call these competency questions. Basically, these are natural language questions you would like to ask to your ontology-based system.
Yann:
And basically these questions, at some point they need to be tested. And in a way, that’s what we’re also trying to implement in the LUMIS platform in the LUMEN project is that you have a way to actually do the competency questions, build, extract some of the information from these questions because in the, let’s say, natural language questions, you would have the business concepts, you can already identify some relations on the questions, you build your ontology, but then at some point you need to evaluate whether or not that ontology or that modeling is good enough. So you need to test whether your ontology can answer these competency questions or not. So you can do that on the ontology itself, that’s nice. But I think the best practice is to actually look at the link with your data because the ontology at some point needs to contain the data, right?
Yann:
And so that’s what we are trying to do is to have this pipeline where you can actually map your data with the ontology and then start testing the system by seeing if the answers to the queries that you get is the right one, right? So it’s somehow a quality check, and for that, you need the data engineer because he knows the structure of the data, where to find the data and of course he will contribute to the mappings and the reshaping of the data. One interesting things is like when you work in the knowledge graph world, many people thinks that you need to get all your data into the graph, which is not necessarily the best approach, right, because you end up having a gigantic graph, which is very hard to maintain. And also costly, if you want to have a good performance, now you get systems that actually connects the knowledge graph to existing SQL database, right, where basically the data remains in the database, but you just link this data with the graph so that you can get access to this content.
Larry:
Yeah, I love that these architectures are sorting out and that’s a common story, but hey, I can’t believe it, Yann, we’re coming up close to time. But before we wrap up, is there anything you would like to revisit from the conversation or that you just want to make sure we share before we close?
Yann:
Well, I don’t think I want to revisit anything. So we just say that, well, being in the ontology world and the knowledge graph has been so much fun that I’m stuck here and I’m staying here, so I’m going to keep working on this very nice infrastructure and architecture. The good thing is I’m going back to AI now and back to my original, let’s say, love neuroscience and it’s supposed to be in intelligence. So let’s say the place for intelligence is in our brain that we don’t understand much. So there’s still a lot of progress and I think we can start investigating new or real AI if we had more biologically realistic things into our dumbed down neural networks. However, it’s a huge task and I’m interested to see the emergence. We see the LLM with neural nets.
Yann:
Now in AI, there are two different types of AI. There was the symbolic AI, which is the Turing way and there’s the neural network, which is the realistic way. And I think, well, the good thing is we see with the connection with semantics and ontology and graph that somehow we are going back to the symbolic AI because you need to deal with logic, you need to need to deal with the meaning. So it’s interesting to see this emergence now of the neurosymbolic AI or reemergence of where I think there would be a lot of very interesting research project and innovations coming up in the upcoming years.
Larry:
Yeah, I agree. It’s so fun to watch that. And I also love the way your whole career is kind of circling both the neuroscience and now we’re back to neurosymbolic AI. Hey, one very last thing, Yann, if folks want to connect with you or connect online, what’s the best place to find you?
Yann:
Sure. So you can find me on LinkedIn already, so you can always contact me on LinkedIn, although I’m very slow in answering LinkedIn because there are tons of solicitations, or you can contact me by email. My email is Y-L-E-F-R-A-N-C @esciencefactory.com.
Larry:
Excellent. I’ll put that in the show-
Yann:
Yeah, I guess you’ll put it in then.
Larry:
I’ll put it in the show notes as well. But well, thank you so much, Yann. It’s always fun to talk, but I really enjoyed this conversation.
Yann:
It was fun. Me too. Thanks, Larry. It was a fun time.
