Exploring Productivity in Python Programming

Jans Aasman Hybrid AI architectures get more complex every day. For Jans Aasman, large language models and generative AI are just the newest additions to his toolkit. Jans has been building advanced hybrid AI systems for more than 15 years, using knowledge graphs, symbolic logic, and machine learning - and now LLMs and gen AI - to build advanced AI systems for Fortune 500 companies. We talked about: his knowledge graph and neuro-symbolic work as the CEO of Franz the crucial role of a visionary knowledge graph champion in KG adoption in enterprises the two types of KG champions he has encountered: the magic-seeking, forward-looking technologist and the more pragmatic IT leader trying to better organize their operation the AI architectural patterns and themes he has seen emerge over the past 25 years: logic, reasoning, event-based KGs, machine learning, and of course gen AI and LLMs how gen AI lets him do things he couldn't have imagined five years ago the enduring importance of enterprise taxonomies, especially in RAG architectures which business entities need to be understood to answer complex business questions his approach to neuro-symbolic AI, seeing it as a "fluid interplay between a knowledge graph, symbolic logic, machine learning, and generative AI" the power of "magic predicates" a common combination of AI technologies and human interactions that can improve medical diagnosis and care decisions his strong belief in keeping humans in the loop in AI systems his observation that technology and business leaders seeing the need for "a symbolic approach next to generative AI" his take on the development of reasoning capabilities of LLMs how the code-generation capabilities of LLMs are more beneficial to senior programmers and may even impede the work of less experiences coders Jans' bio Jans Aasman is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of Knowledge Graph Solutions based on AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as government entities worldwide. Connect with Jans online LinkedIn email: ja at franz dot com Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=SZBZxC8S1Uk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 20. The mix of technologies in hybrid artificial intelligence systems just keeps getting more interesting. This might seem like a new phenomenon, but long before our LinkedIn feeds were clogged with posts about retrieval augmented generation and neuro-symbolic architectures, Jans Aasman was building AI systems that combined knowledge graphs, symbolic logic, and machine learning. Large language models and generative AI are just the newest technologies in his AI toolkit. Interview transcript Larry: Hi, everyone. Welcome to episode number 20 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Jans Aasmann. Jans is, he originally started out as a psychologist and he got into cognitive science. For the past 20 years, he's run a company called Franz, where he's the CEO doing neuro-symbolic AI, so welcome, Jans. Tell the folks a little bit more about what you're doing these days. Jans: We help companies build knowledge graphs, but with the special angle that we now offer neuro-symbolic AI so that we, in a very fluid way, mix traditional symbolic logic and the traditional machine learning with the new generative AI. We do this in every possible combination that you could think of. Larry: Who? Jans: These applications might be in healthcare or in call centers or in publishing. It's many, many, many different domains it supplies. Larry: Is it mostly large enterprises or is there a certain scale at which this stuff works better? Jans: Our customers are always Fortune 500, Fortune 100 companies. It's all the companies that are trying to do innovation. Most big enterprises now believe that knowledge graphs is in their future. They're experimenting with it. They do experiments and we help them build their first knowledge graphs in most cases. Once they get that going, they can do it on their own. Larry: Interesting. It's often their first knowledge graph. Where in the organizations is this typically pioneered? As you come into an organization, is it IT or is it data science? Where are you typically entering the organization? Jans: That's actually the wrong question. Larry: Okay. Jans: The thing is, all the places where we're successful, there's a champion, a person that's really looking into the future and has this vision that it's possible to not build a new silo for every new problem, but there should be a way to integrate all the knowledge in the organization into something incredibly useful with that. You can't leave it to a single programmer or a single architect. It's usually someone with some business experience and also some architectural role that believes in this approach. If you leave it up to an IT department, it's just not another database, but it's not the philosophy of a knowledge graph of integrating knowledge. It's just, okay, this is a problem I can solve with the graph. Let's do it that way. Jans: You need a person that says, "Hey, I've got so many different sources of information in my organization. I know we're not combining it in the right way, and it's too complex to put in a relational database. I know we have to solve it with a thing that they now call knowledge graphs, but it's even more than that. I know partly it's data science. It's machine learning. Partly it's rule-based. It's the logic, symbolic logic. I also know that I need the new generative AI in this, but how do I do this all? This is incredibly complex. I can see the future, but how do I do it?" That's where we come in, help build them knowledge graphs, but with a symbolic angle. Larry: I love that, and that knowledge graphs are a key element in the architecture of the future and of the present, it sounds like. That champion who comes to you, it sounds like they're somebody who's been aware of the hazards and consequences of siloed information and data. Is that typically what they're coming in for, of how can we better integrate and understand all of this? Jans: Again, I would say there's two types of champions. One of them is just, they want magic. They see all the articles about generative AI. They see the Gartner articles, Forrester articles about knowledge graphs, and they think, "I have the feeling that something can be done with knowledge graphs and neuro-symbolic AI." Those people are not very super technical. They usually have a technical background but then went into business, but they know something can be done. Jans: Then you have the second type of champion, of people that are literally always over 35, that have spent their active life building application after application. Every time when they created a beautiful application where their bosses were really happy about, the sad thing is, they build a new silo and they made their whole enterprise even more complicated. These are the people that say, "You know what? There has to be a better way of integrating my knowledge." Those are the people that get interested in semantic technology and they say, "There has to be a way that we don't build new silos every time." It's this thing that we call data-centric computing. Data comes first and applications need to go on top, but we don't need to change the data all the time, rewrite and copy the data all the time. That's the disappointed IT person that says, "There has to be a better way." Does it make sense? Larry: Yes, that makes sense. Jans: One is really looking at the future like, "Wow, my company needs magic to make more revenues. The other one is, "Hey, we need to reorganize our IT house, because this is madness the way we do it now." Larry: Yeah. They don't want to do that, repeating the same mistake over and over again. Jans: No, let me try. How do I turn my sound off? Okay. Yeah. Larry: Cool. Yes. You said they're always older, because these folks have been around the block a few times who come in with that. I'm going to guess. Are they generally? It sounds like both. It sounds like maybe in the first type, the magic seekers, you're probably doing a lot of education, but with the second type, you're maybe just more finessing the implementation. Is that? Jans: No, it's a huge difference. The magic seeker will give you freedom to think together, "Okay, how are we going to do this? What is the first baby step we can do to show the rest of the company that this works?" You try to find the low-hanging fruit where you can show how neuro-symbolic AI and knowledge graphs can help and do things they couldn't do before, whereas the second type of champion, the disappointed IT person that says, "We have to find a new way," then it's way more IT-oriented. Let's find three databases where we want to do something extra with. Let's build a semantic data catalog. Let's build an ontology of the objects that we really care about in our business. Then let's see how we can replace an existing system by something that is 10 times more simple, 10 times more easy to understand, and 10 times easier to do data science with. Does that make sense? That second champion is very more IT-oriented and wants to make the flow within the company better, and the first one just wants more revenue by magic. Larry: Right. Jans: Huge difference. Huge difference in the two champions. Larry: Is it pretty much an even mix between those, or do you seek out one or the other more? Jans: No,