Intro

Yaakov Belch Yaakov Belch is an AI researcher with strong ideas about the role of humans in AI systems. Instead of "human in the loop," he argues, we should put "humans in control." Yaakov's research looks at business contracts and how knowledge graphs and AI systems can both capture their meaning more accurately and help managers make better business decisions. We talked about: his assertion that we need humans in control, not just in the loop his research on applying AI technology to business contracts, in particular the issue of resolving inconsistencies in language model results reasons to put human concerns ahead of any particular technology the importance of having humans in control when interpreting ambiguous business decisions the importance of both accounting for business intent and asking the right questions of your data and how the loop between the two tightens over time the responsibility of human users to understand how LLMs work and to prompt and otherwise interact with them accordingly why he doesn't use the term "hallucination" when talking about LLM outputs the role and implications of applying different kinds of logic in the use of knowledge graphs an analogy that shows how the concept of a Git fork can help knowledge graph engineers account in their models for different versions of reality the real-world applications of his research, especially how the practices he is exploring can create new business value the importance of building any model off of real data and always thinking about which human needs to be in control Yaakov's bio As a mathematician and data scientist, Yaakov Belch brings a unique perspective to the world of AI and knowledge graphs. With a strong background in mathematics, including participation in prestigious International Mathematical Olympiads, Yaakov went on to earn a Ph.D. in pure mathematics from the University of Cambridge. Yaakov's career has spanned both research and industry roles. He has worked as an Algorithm Programmer, collaborating with researchers in bioinformatics and economics, co-authoring academic papers. Yaakov also served as a Senior Data Scientist at Israeli e-commerce startups, where he tackled challenges in symbolic and semantic search from different angles. Currently, Yaakov is on a sabbatical, working as an independent Data Scientist to develop his method of reliable business reasoning, precise contract understanding, and humans-in-control.ai. He sees an interesting connection between the problems from the International Mathematics Olympiads and taming the inconsistencies of large language models: "At one hand, the problems are hard and just don't open up to the known, standard methods of the field. But you know that there is a beautiful solution. You need to find the right perspective to appreciate the problem and to see that beautiful solution." Connect with Yaakov online humans-in-control.ai LinkedIn Video Here’s the video version of our conversation: https://youtu.be/ZS9r0bfkGQc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 17. Machine learning architects often talk about the "human in the loop." Yaakov Belch thinks that when it comes to language models the right approach is to put "humans in control." Yaakov's research looks at how knowledge graphs and large language models can help put humans in control of business contracts, capturing the actual intent that underlies them and facilitating better business decision-making based on the discoveries that they enable. Interview transcript Larry: Hi everyone. Welcome to episode number 17 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yaakov Belch. Yaakov is an independent senior data scientist and he’s made this really provocative statement about … There’s all this talk in the AI world in general about humans in the loop. And Yaakov says, “No. We need humans in control.” So Yaakov, I would love to talk about that today. Yaakov: We don’t need humans in the loop. We need humans in control. This is a paradigm which crystallized over time in the research which I’m doing, about how to apply language models for contract understanding in the business context. Let’s unpack that and see where the human in control comes in. A business contract is a memorization, a written-down expression of agreement between two or more parties; with the intention to fulfil that agreement in the future. It contains provisions for what the parties promise to do, the rights and obligations they have. And also descriptions what you do when things go wrong. After you make an agreement, you actually want to execute it, you want to fulfill it. Yaakov: When you make a large contract, some people negotiate the contract and other people will be doing the work. There are situations where the people who actually do the work are not really aware about what exactly has been promised in the contract. The huge contract is written in a way as contracts need to be written. But it can’t be understood on the spot when the person needs to decide: Is this promised or not? There can be a disconnect between the expectations of the customer and the provider based on just not being aware of what’s in the contract. In a more advanced setting, you may want to understand the risks which are in the contract in hypothetical situations. When you draft a contract you want to make sure that you’re not setting up yourself for problems when things go in a specific way. Yet more advanced: When you purchase a property, you may acquire contracts which go with them, like liens, leases and bills. In your due diligence work, you want to check that every liability has been properly addressed, so that you don’t acquire additional risks with the property. And if there are problems, you want to resolve them. Yaakov: I want to understand contracts from a business perspective. I want to use language models to understand their language. It’s not a surprise anymore that language models can help with that. You put in part of a contract. You can ask a question. You can get an answer. What is problematic is that if you don’t do it right, these answers will not be consistent. You may ask the same question twice and get two different answers. Or you get an answer that is justifiable, but it’s not consistent with the intention of your question. It is against your business goals. Some answers may be completely disconnected from reality. The essence of this research project is how do we deal with this inconsistency? How do we create a reliable system where language models are just a part of it? We have knowledge graphs, we have logic, and we have humans in control … So that businesses can rely on it for their business needs. How do we detect and how do we deal with all the mistakes which happen with language models? But also in your data you have mistakes. And how do we detect them? How do we deal with them? That’s the research. Yaakov: In this research, I find that you really need to be careful to use the right paradigms of what you’re doing. If you just apply a playbook from machine learning or from expert systems and just try to do hard work, you may break yourself against language models being different. It’s a new technology with new characteristics. You need to adjust your goals, your paradigms of work, and the structure of your work to the capabilities and to the limitations of the problems of language models. One key point is to understand the right role that humans must play in the whole scheme. It’s not technology alone, it’s technology and people. We don’t need people in the loop. We need people in control. Larry: Okay. We actually had a long conversation last week to set this up and I was beginning to really get it, but that was an excellent summary of your research and the business insight and the technical challenge that comes into this. I’d love to really focus on how knowledge graphs figure into this. I’m inferring that there’s a business ontology and stuff that drives… But can you talk about the specific role of knowledge graph in how you see it helping address these new paradigms that we need? Yaakov: For sure. This is an excellent question, especially in the audience of Knowledge Graph Conference and knowledge graph podcast. I suggest, however, that before we talk about the knowledge graph, which is basically a technology which supports this work, to first understand: What does this really mean, human control? How does it work? What do we want to achieve? What is the goal? Then, let's understand the mistakes of language models. Their answers may be inconsistent with what you want. But they are not just making random mistakes. Where does this come from? And then we’ll see how knowledge graphs and logic play an important role. So maybe we go into three steps and get the knowledge graphs at the end. Larry: That sounds perfect, because that makes knowledge graph the culmination, which is what we’re looking for. It’s a knowledge graph podcast. But, no. So the first thing you said is, what does that really mean, humans in control? Yeah. I’d love to hear those three steps you just mentioned. Yaakov: Okay. Humans in control versus Humans in the loop. When you have a machine learning system, you know you need training data. When you get incorrect results, you want to double-check and correct them with a human in the loop. As a benefit, you create training data that will hopefully make your model more precise. But this usually doesn’t work. It works only when you do it right: When you do this with one human in control in addition to many humans in the loop. Without that, the people in the loop either don’t know the right answers, because they’re not experts. Or they are experts and don’t want to answer your question: you overload them with repetitive work,