Podcast: Conversational AI with Openstream CPO Magnus Revang by Mark FefferTrue conversations, collaboration, agents, and building a solution with data that’s never been written down.Read on Substack

My guest is Magnus Revang, the chief product officer of Openstream.ai. Their engine, which they call Eva, allows users to interact with technology naturally, using plain language but avoiding AI issues like back-end complexity or hallucinations. We’re going to talk about true conversational AI, human collaboration, agents, and how you build a solution with data that’s never been written down. All, on this edition of PeopleTech.

Upgrade Your SubscriptionTranscriptWelcome to PeopleTech, the podcast of WorkforceAI.News. I'm Mark Feffer. My guest today is Magnus Revang, the chief product officer of Openstring.AI. Their engine, which they call Eva, allows users to interact with technology naturally using plain language, but avoiding AI issues like back end, complexity of the informations we're going to talk about. True conversation, the AI human collaboration agents and how you build the solution with data. That's never been written down all on this edition of people tech.

Hey, Magnus, welcome. You folks are very active in building conversational AI, and I wondered if we could talk about that a bit. Can can you tell me what sort of led you into wanting to get into conversational AI and what you think it's potential is?

Magnus Revang

Of course. Yeah. So conversational AI is the ability for a machine or an AI to hold the conversation with a human and natural language conversation. UM, and really, it's the most natural way to communicate, right? If you look at the science fiction movies going back, you know, back to the 1950s that people imagine sort of the future operating system, the future way to communicate with machines, they they've always ended up. Pointing out that it's going to be a conversation. A natural English conversation.

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Magnus Revang

And conversational AI has always been a part of computing. Uh, way back. But really in it was, I think in around 2014, 2015 uh, where this concept of chat bots started getting a rise. In the market, what people didn't tell you at that time was that conversational AI was. Very little AI at the time, so basically it was the ability to take what somebody said and do two things with it. Basically it it tried to to find out by saying this what is the intent. Of the user, what's the intention and what keywords were mention? That uh fills out the the the slots of the request. So basically that just mapped to a part uh to a decision tree that were were part of of a dialogue tree, so to speak. Right.

And from there it. It was very little AI. And and that's how conversationally I started out. Now my interests have always been unscripted. Conversational AI, conversational AI that can deal with things where where the the scripts it becomes too. Complex for a. Script and when when Bert first came in 2018 and later on the GT versions and you know 2022. With shaft, GPT and instruction following L alms, things started to become very. Now one thing that you have to think about in conversational AI is that if you're doing something for consumers, you you can. You don't need to have full control, right?

Just like metal. Then you don't have full control what it's going to say, but if you're an enterprise where you're doing things like. Opening account cancelling credit cards you know, and doing, doing customer service things you can't really live with something that. Is unscripted and low control. You need to have unscripted and high control right and that is where you know, I play around with conversational AI where you have full control over what the machine can say, but also the ability of the machine to deal with things that. A scripted approach. Cat, uh. So what would would kind of like a thing that scripted approaches can't do is is simple things where there is some sort of ambiguity. For example, if I go a simple thing of what's your name? Right. Well, for a lot of women, for example, they don't necessarily remember. If.

Ohh, did I get this account before I was married or after? Uh, is it easy? Am I registered with a maiden name or my real name? Right. And being able to to say both. Is is actually hard when it's a scripted approach because the scripted approach. Assumes that you're going down to 1 and you're not splitting and doing both the two in parallel. To check for example. So that's a simple example of of where you can trip up any, you know scripted chat bot. Is by just giving two names or or maybe I use my middle name three names right and you start to add add to it, another is in scheduling right where where?

The natural way to to schedule is that I give you a set of constraints. So if you ask me then can we do? The podcast next week, right? I will go. Hey any? Monday or Tuesday after 3 o'clock. Right. And and and suddenly I have constraints on it and you go maybe. Yeah, 6:00 on Tuesday. Is that OK? And that's a little bit too late, right? Maybe maybe 5. So so it becomes a negotiation between us in a way and being able to deal with that in scripted.

Is also, you know, almost impossible because the number of branches you would need to the script would be too many to create anything, so you'd need to add alternate approaches to it. Our lamps can come in. And do some of it, but with RLMS you don't have the full control. You have don't have the guardrails to say don't go outside of these things. So often you need a more symbolic AI system to determine what to say. And then you can use LLNS to. Determine how to say it right and and you can combine the technologies in in various ways and that's how you you can you can get to this this form of conversational AI with high control and with with the kind of the the strengths of LLM. These systems and having unscripted approaches. And, but conversational AI is kind of expanding as well. So so for us, I mean we do things like avatars and emotion and personality detection, being able to render and personality and being able to render. Motion into both in paraphrasing of what's being said, but also in tone of voice if it's going to be rendered for a text to speech right. So all of those things come in as well and and it's becoming quite complex.