Super Dooper Intelligence Is Probably Next

Bren: Hello and welcome back to This Is Not a Data Podcast. My name is Bren, and as ever, I'll be joined by my co-host Leno, who's joining us from the foothills of Germany whilst he's on holiday. Lucky chap. Someone in the White House has decided we should change what AI is called, which got us wondering: when you change the words, what happens to the meaning? Leno is a great example about SAP, and I've got one of my own around seven different names for a report. So the question is When does shared language help? And when does it just cause more problems than it's worth? Let's get into it. Hello and welcome back to This Is Not Another Data podcast. As ever, and this is tremendous to say after a week away, I've got my co-host, Leno. Good morning.

Lenno: Good morning, Brian. Hey, how are you doing?

Bren: I'm well, I'm well. I'm British, I love to talk about the weather and the sun is actually shining on the morning. So I'm very happy. how,

Lenno: You do?

Bren: I'm gonna be really boring. How was the weather with you?

Lenno: Well the weather at at my end and I'm in in the Eiffel in in Germany at the moment. there's a bit of rain,

Bren: Very nice.

Lenno: there's an overcloud, and the last couple of days have been amazing. clear blue skies, twenty-nine to thirty one degrees, driving a motorcycle through the the Eiffel hill area with my missus, which has been an amazing period of

Bren: Really.

Lenno: Today we're taking it slightly easy. There would probably be a hike in the forey forest. Yeah.

Bren: sounds. So you're going to put the backpack on, put the shoes on and disappear into the mountains.

Lenno: Exactly. Yeah, that's the idea. That's the idea. But come on, first things first, we need to to get this post podcast also out there. Yeah.

Bren: This is it. and listen, I know you've, we've talked about this sort of behind the scenes. I want to talk about super intelligence. I just...

Lenno: So today so today we're going to talk about you then.

Bren: Well, then a flattery will get you everywhere at the end of the day and for anybody listening that right that rigs true for all of you as well

Lenno: Sorry.

Bren: So the the whole super intelligence thing if for anybody listening you might have missed it and There's there's somebody based over in the White House in the US who's decided that we should stop calling it artificial and call it super Now, I mean, I do like the word super and I think it's quite a funny thing to try and mandate across the board. But the questions that kind of sprung out of it and it'd be interesting to get your thoughts, Lenoir. In terms of terminology and certainly within the data space, are we guilty of making up or using new terms to try and articulate something and in actual fact, all we do is make it more confusing in the end.

Lenno: It's a bit of a leading and loaded loaded question, I would argue. And I've I've when this news came out, or when the outing was made in the UN last week that we're stepping away from artificial because that sounds fake, and we therefore are going to call it super. In my head, I made reference to quote unquote governance.

Bren: Okay.

Lenno: I think the moment that we are talking about governance, then we we're making it the whole subject loaded to begin with. There's a whole lot of well governance around it, and therefore translated that freely into complexity. Complexity, confusing, and I think from within the data space of course. Setting up governance is is very well intended because it it gives us the nomenclature. we then all have the same understanding of what that element or what that phrase particularly means and how we can all use the same terminology. Also in this space, artificial intelligence versus superintelligence, there seems to be A a common ground established already what is going to be called superintelligence and what is going to be called at this moment a moment in time artificial intelligence. That that didn't come up from within maybe the data space in itself, but it it did come up from Silicon Valley where the whole developments have occurred. Superintelligence is supposed to be something completely different. Nevertheless. my take on this is a bit like we seem to be in an in in in in a space maybe a vacuum by which we can we we can drop or or parachute IDs left front and centre and then we call it reality.

Bren: Yes.

Lenno: And at the one hand side I find it funny. At the other hand side, I also find it a bit worrying. trying to make a a a sort of a bridge and it it may be a stretch. Please stop me if it is turning into much more confusion or not. I think there's a a piece of ERP software. That is used across a whole lot of bigger companies. this is SAP. And

Bren: in.

Lenno: SAP from the old days has put a lot of time and energy into standardizing its its models, standardizing its terminology, and everybody knows what that particular table and what that particular field means and how that is being used in a business process. That's the standard nomenclature. That is so standard that you can you can build additional applications on top of it, you can put integrations on it, you can basically write scripts to set up connectivity with with that application as well. Of course, there's complexity in it, so oversimplifying perhaps. What I mean is that because of that standard we can sort of rely upon it to work in that manner. The moment that we start to change the language, then of course also the understanding is gone.

Bren: Interesting.

Lenno: And I and I'm I'm also pulling that from my current my my my current working environment. So we also have an SAP backbone in in the mix. And the region that is that is using the SAP structure has made or is sharing the SAP data also with the data platform. And in the data platform all of the the data from different ERP systems, CRMs, etc. is coming together. So that we can run analytics on top of it, integrations and digital use cases, etc. what the team has done is while inserting that data into the data platform, they've changed the nomenclature. So instead of the the the the the table and field names, they've used quote unquote business terms which makes sense if you if even where you're working with people that do not have the background of that technology. I get it, I understand why. However, in the further use of that data and the ease of running additional integrations or running additional models on top of an SAP foundation, all of a sudden that is complicating things. Because it's no longer standard.

Bren: Yeah.

Lenno: Now coming back to your your original question, and sorry for the listeners for this lengthy explanation

Bren: Yeah.

Lenno: of it. changing from artificial intelligence to super intelligence, okay, what does it mean? Why would we do that? And do we now as a complete industry also need to shift and rebrand everything?

Bren: I guess I mean, I love your thought process because one of the benefits and beauties about the podcast is that we get to share kind of thoughts and processes that we or approaches that neither of us have thought of and I hadn't actually put it together as you've described. But what you're saying is very astute because obviously it's all about meaning and if we think about the gentleman who's decided that he wants to change it from artificial to super it's all It's all impression. It's all perception. It's all your bravado. It's all an image. Whereas I think in the data space and as you've articulated in the kind of the changing the term and it means something different to the business, we struggle in the data industry to articulate

Lenno: Mm-hmm.

Bren: the value of a term and activity and output generally. And if they are quite data centric technical terms anyway, and by changing a meaning to something that people will understand, then all of a sudden you've got like a total cross net of ideas and understandings. And so, yes, somebody might understand it, but all of a sudden we're now meaning many things to many people. And I don't think that that helps because we are trying to deliver value. in a language that people understand so that they can help us because we rely on influence. And if we're now moving and changing terminology, then all of a sudden, neither of us really know what we're trying to achieve. But we've started using cool language that sounds better than before.

Lenno: But related to that, and and while you're talking, d also the thinking process continues there. How have the the the hyper automation models been trained? They've been trained upon information data or nomenclature that already existed. So and in the example I just gave you, when we're changing the terminology, can I now deploy an AI to run analytics in my organization? or will the AI be conflicted as well because it will be looking for a table and field reference, but that has been renamed. I'm I'm I'm I'm wondering, is is it is it now artificial or is it so super that it can actually make the connections already?

Bren: Well, does that then mean that when it gets even more clever we have to call it super duper intelligence? I mean, where does it end?

Lenno: I would I would not be surprised if this fine gentleman will come up with with with with that addition when it really becomes smart. Yeah.

Bren: I do understand a little bit of the logic in terms of the perception that artificial gives off and for somebody, and I'm going to pick my parents because the older generations and they look at the technology that's coming along and it all feels a bit baffling and chat GPT back in the day felt like a black box that just spat out some magic and it was very cool. I get that. The artificial term might be a bit of a disservice and we've spoken about the, is it hyper? or advanced automation versus true intelligence. But the fact that, and I think this is where you're coming from as well, we can't talk about the same terms and get to where we want to come or get to where we want to as it sits, let alone changing these things. And

Lenno: Yeah. Yeah.

Bren: I'm definitely not a governance fan and this is probably coming out on some of the podcasts, so certainly not going too heavy on that. But in a previous place, we had seven... versions of what we used to call a report or a name. So we'd have the project name and then we'd have the long reporting name, then we'd have the legal name, then we'd have the internal

Lenno: Yeah.

Bren: name, we'd have the short reporting name, we'd have the what it was also known as sort of off the record and these were all captured in different systems and used by different people and every time we'd talk to somebody they'd be like you mean the whatever it was and that and and i don't know whether artificial intelligence, super intelligence or super duper intelligence would actually be able to help solve that at the moment. Because if we as people are not able to put it in, A, come up with a standard or a common definition and B, use that consistently, how is technology going to help us on that?

Lenno: Yeah, so what what I have seen is that technology will also look for commonality and how that commonality

Bren: Okay.

Lenno: is being established. So like like in your example, you get seven versions of synonyms and it is therefore then also be categorized as synonyms to a a specific term. So what the the models do nowadays, they will crawl through All of the information that is available. And then it will be presenting that back to you with an already pre combined set of terms. And it will present it back to you, asking you to validate and confirm that indeed, hey, we need to club these together. These are all synonyms. So the moment that you are talking about report one, two, three, that's actually the same as daily sales rate. instance or anything else. It is being combined in the background, and I suspect that then the technology will also put in a database anchor, which is a meaningless

Bren: Yeah.

Lenno: number or a meaningless text string or a token or whatever that it may be, but it will club it together so that everything is then tied back to Now I've also heard you say I'm not the biggest fan of governance, which I get. Nevertheless, the moment that we want to truly leverage hyper automation, we also need to give that piece of automation a frame of reference, context. And context typically is being found in a catalogue. A catalogue by which Indeed, as a business, you are documenting that's my report, this is the KPI set, these are the metrics that is in that in that KPI, these are the fields that are are clubbed together, these fields are all coming from these twenty-five different source systems, but equally have the same meaning, etc. And that is driving that commonality, that is driving that consistency across an organization, so that instead of seven versions of truth. You end up with one.

Bren: You're really annoying because you've put across exactly why governance is important and I can't disagree with you. I think just to clarify, think the bit that really aggravates me is where people get kind of sort of way over sort of high and mighty about governance is to be all and end all and you know, I'm an ex external auditor, an ex accountant. So I understand the need for good governance. I think it's where the governance starts driving everything then almost detracts away from the problem you're trying to solve and how the people implement and all that sort of stuff. So I agree with you and you're absolutely right. But without it, it does make it much harder to be able to get to where we want to be.

Lenno: No, so yeah, if if w where we are trying to to to bridge this back to people again, I think this is where the data community loses the the business organization around it. Because

Bren: Okay.

Lenno: a data community or a data expert in itself most likely will lose him or herself in driving forward on documenting, driving forward in standardizing, driving forward in establishing additional metrics to avoid disruption to avoid dirt coming into the landscape to begin with. Which conceptually and it it it starts in the in in the right place, but the phrasing of it, you'll lose the people around you. Because it's it's one, it's it's not so very interesting. And two, is it really, really necessary to document and standardize everything to to to To that maximum. It's not a goal in itself. And that's where I fully agree with you. also, we yeah, I am convinced that we do need a certain level of standardization and documentation as such, so that we all understand what it is that we are talking about. Yeah.

Bren: That is very fair. And I would agree with you on that. guess it's a bit like if you're speaking Dutch and I'm speaking English, we need to know that the term revenue would be, I'm saying revenue, you understand what we're saying. That

Lenno: Omzet, yeah. Yeah, exactly.

Bren: common understanding is definitely needed, I guess. And tell me what you think is,

Lenno: Mm-hmm.

Bren: do we lead with that or do we lead with actually what the meaning is? and then try and come up with the right term based on what the business is after because I think where I'm coming from is ultimately we as a data community have tried to enforce what we think onto the business and the business generally rejects it going I don't really care about the technical stuff will it help me solve my problem or give me what I want

Lenno: Mm-hmm. Yeah.

Bren: and how do we strike that balance where it really needs to be a partnership doesn't it in terms of being able to say How do we both get to we want to be rather than a parent-child relationship? How do we kind of cross that chasm in terms of saying we need their help and their input, but also we do need to have just a bit of rigor so that we're not just putting any old thing into the CRM or into SAP or et cetera.

Lenno: To me it sounds like what what what it needs is further curiosity and

Bren: Hmm.

Lenno: establishing or or in all of the conversations that people are having, keeping that curiosity and and basis that that that people are feeling confident and comfortable to raise a question, a clarification question. Like I I don't necessarily understand. The word or the language that you're using. You explain to me what you truly mean by that. Or I've understood this. Is this indeed correct in what you meant to convey to me? The moment that that baselining is is happening, that's actually a form of governance. Now, a step further could be. can be or I I don't know should be is then to document it. And the moment that you document it it can also be reproduced. And by that you are actually establishing that how do you call it? In in in Dutch we call it the warden book. That's the

Bren: But

Lenno: book by by by by by which translations is happening so English to Spanish Dutch to English. It's the translation document by which okay, this is what I mean. That's my language, my context, and this is what you meant. Your language, your context, but effectively we're meaning the same.

Bren: Yes.

Lenno: Yeah, it's establishing that that that commonality. And I think governance. When that word was actually invented. But establishing commonality, that that's out there for many, many years.

Bren: Yeah.

Lenno: And maybe that even goes back to what is it, the biblical times and where where the languages were coming up. You know

Bren: Well, just using the power of the interweb, apparently governance is not a singular invention, but a concept that evolved over thousands of years with its linguistic roots tracing back to ancient Greece. So you're not far off.

Lenno: Shit, there must be some level of intelligence in it. I don't

Bren: You

Lenno: know if it's artificial, which which which is close to fake or super. I don't know. I don't know. It's no.

Bren: So not losing your point for a moment. And I don't want to lead the witness, but do you think in the data community we've been guilty of not listening enough and not listening well enough? Almost with a, do you think we're in danger of having a pre-canned idea and then enforcing it onto the business?

Lenno: Honestly, I think that's a two-way street.

Bren: Okay.

Lenno: I think that that that is is more originated in the fact that we're we're we're working as individuals and a lot of the individuals that that are coming together will all have individual ideas. And by that also an individual conviction that that idea is actually a great idea.

Bren: Hmm.

Lenno: At the moment that I come prepared with this is this this is a great idea, I'm going to deliver that ID then quite honestly I think that I I then also have a sort of a listening filter. I'll be biased. And the person with whom I'm speaking may also have his or her ideas. And having a a likewise filter to what you are receptive on. And The data community, a data expert, will will have his or her field of expertise, and and within that field of expertise, and also because that is that is part of the role that you have, you'll be much more focused on safeguarding quality. Now, how are you going to define quality? Yeah, that means I need to standardize a field, I need to standardize a metric, da da da. So cause and effect. will lead towards the drive, yes, I'll be governing and therefore I will try to to push that upon you as well. Because hey, come on, that's that's the nature of the beast.

Bren: Yeah.

Lenno: Yeah, that's the nature of the role as well. Whereas and and I I I maybe yeah pick picking on a a a sales representative a sales representative will be selling Yeah, wants to establish deals with a customer. You know, I I don't wanna get into the administrative part of it. I want to make my sale. Because that's what in the core I'm there to do. so don't bother me too much with the standardisation, the definitions, the metrics, the whatever I I need to get the Euros in. Come on. Yeah, if I'm not getting the euros in, you're not getting your salary. So there's there's a whole different perspective of which basis that that people are working together.

Bren: The, I guess the dichotomy is that you're as a chief executive or a C-suite member, you want the outcome that you want and actually how it comes to life, maybe you're less interested in. But actually the guys and girls on the ground that we interact with from the data industry or community is, it needs to be that two-way street. And I think you're absolutely right. You remind me of a, I had a line manager and we'd have the weekly, you know, we'd have the weekly catch-ups and it would be.

Lenno: Yeah.

Bren: I used to call them one at once because it wasn't a one to one. It was a one at one. She would just talk at me basically solidly for about 35 minutes and then be like, cool, is there anything you want to talk about? I was like, obviously not. And then we would

Lenno: Great talk.

Bren: move on. And I think sometimes that kind of slightly flippant example is a little bit how we want to say, you're talking about your revenue for us means this or a customer means this and this and this and that's what fits in the domain and the quality is going to drive this and the framework is going to do that. And the person that opposite you is just saying, well, when I say customer, I client. When I mean client, I mean Dave. It's like, okay, let's be clear on that. And I think you've articulated it in a wonderful way.

Lenno: And that and that exactly is is is establishing yeah, sorry for using the word, establishing governance, establishing clarity to that level of detail. And annoyingly, I think also the data community is is is absolutely right to go into the fine grain and build this up from the detailed ground. The more that detail is expressed As it needs to be. You are indeed, as in effect, generating clarity, confidence, solid foundation. over breakfast I had a conversation with with Yvonne and I also said, you know, everybody wants to to see the house coming together. literally, I want to live in it, I want to experience the the warmth of the house. I want to experience the what is it the paintings, I want the nice decorations, etc. That's that is all visual. But the the duration, the end no not the duration, the endurance of the house is established by the the the amount of time and effort I've actually put into laying the foundation and ensuring that the piping that is that is behind the walls for the electricity, for the water, for the the heating is is is well established and is is endurance. Is assured for the for the next 25, 50, 75, or 100 years. You know, but the only thing that we we we tend to see is the housing itself. And the color that is on the wall.

Bren: Do you do you know why I'm laughing because so for anybody listening my wife and I we bought our our first house together two years ago, and I'd never bought a house before and So just bear with me because it will all become clear in a moment from what you just said and so not only Did it take us a long time to find one that we that we that we liked and they always say a house chooses you and I didn't believe that but I've seen it happen now When you go in, and this is a foible of the UK system, I don't know how it works in

Lenno: Mm-hmm.

Bren: Holland, you walk in, you basically have 15 minutes because there's probably a fair number of people looking at it, you wander around, you go, this looks nice. And then you make the biggest decision you ever have, and then you hand over all the money you've ever earned to basically buy this house. So that's what happened with us. And then you walk in and we discovered this, that basically the house is held together with duct tape and plasters, but it looks great. This is literally how it is. So the people that it before, the lady's father was a builder and he seems to just put everything together with sort of a slightly botched job and screws and all sorts. But on the exterior, it is beautiful. But you peel back, you open the door, you take the, you peel the shelf away and all of a sudden you're like, this is not gonna work.

Lenno: Is it not okay? Yeah.

Bren: And maybe that's a slight metaphor for how we operate often in the data space. It's like, as long as it looks cool, does anybody really matter? But then the slightly less flippant answer or point to this, think is quite key is we as data leaders and executives in that space, it's our responsibility to make sure that when the business does ask, i.e. pulls back the curtain, we can stand behind and deliver what we need to without getting cross that they've pulled the curtain back and said, what's going on?

Lenno: Yeah. No, exactly. And and I think that that is indeed what I'm well personally striving for is the ability to reproduce the same outcome over and over again because I trust what is underneath it. I need to be able to sustain it, I need to be able to expand on that on on on that frame framework as well.

Bren: Yeah.

Lenno: It did in my current company as well. It did mean that I basically needed to perform one open heart surgery on well the house that I bought, indeed. I walked in, I said, ooh, this looked nice. And then you start to peel away or or open up closets, and there's a whole bunch of skeletons that is coming out of it. So you then need to to to make a bigger a bigger cut in it, rebuild it from the ground up, and then you know that. There's a solid foundation which is annoying to a lot of people around me because it takes longer

Bren: Hmm.

Lenno: to establish the nice things that we actually wanted to have. The nice exterior, the way that it it it presents itself.

Bren: Do you not think though, just quickly, how do you weigh up and I'm conscious I'm mixing the metaphor with the

Lenno: Yeah, no works.

Bren: data industry in the house. We put in the foundations and we fix our house now. Ultimately, we are not going to get the long-term benefit of that because we probably won't stay here for, let's say we stay here for five years. We

Lenno: Mm-hmm.

Bren: fix it and we do it well. People in 30 years time are going to get the benefit. And in the data space,

Lenno: Yeah. Yeah.

Bren: I think, do we lose sight of that as well? that we fix it now and the benefit will be felt way in the future. Is that fair?

Lenno: I think the

Bren: Is it worth it, I guess?

Lenno: No, I think the cycle in in organizations, especially now, is shortening.

Bren: Hmm.

Lenno: so getting the ability to establish the bolt-on programs, that that cycle time is quicker.

Bren: Yeah.

Lenno: if if we rewind to that clock many many years, then indeed there was cycles like Let's put in a strategic plan for the coming five years. And that would would hold through for indeed that period of time. If you were now to create a strategy for the coming five years, then that would go probably obsolete in six months. Because the world changes. The ability that you have at your disposal changes as well. So the cycle time is shorter. Now in a house, I'm I'm right there with you. that's the physical object, which indeed the revenue of that is and and by extending the endurance of the physical house, that's different. In a company, I think the the turnaround time is much much faster nowadays, and especially with the implementation and the adoption of insights, digital use cases, the artificial intelligence or superintelligence. Hyper automation. Let's stick to stick to that one.

Bren: Yeah, I think, I mean, again, I've got to the half an hour and my mind is actually going like the clappers. I think I've got more questions than I started with. Although it would be the best way to sum this up is it's been a super conversation. How about that?

Lenno: Yeah, I like it. Yeah.

Bren: Well, I think it's probably about the time where we ask for the listener's input. mean, Leno needs to... carry on his holiday and get out into the sunshine and get some fresh air. So for everybody listening, this is when we need your help. Please do pass the pod. I'm stealing that from the Peter Crouch podcast that was happened a long, time ago, but pass the pod. Let others listen to our conversations, but please do leave us a review, like and subscribe. And as Lando keeps telling me off about, I want the five stars. That's all I'm after. I want a five star review. But yeah, to everybody listening, thank you very much. You've made it to the end of another, This is Not a Data podcast episode. Any closing thoughts, Leno? I mean, you're looking very relaxed still. mean, this is wonderful to see.

Lenno: I it's this this this this this conversations with you work therapeutically. so this is this is this is all part of holidays getting the minds off. this has been very, very enjoyable again. we'll probably have not seen the end of the different phrases that are going to be used for the same terms. And we just need to to continue to to raise the questions from curative. Curiosity to re baseline our common thinking. So thanks for leading the conversation, Brent.

Bren: It's always a pleasure and it takes two to tango. So for anybody listening, maybe keep your eyes and ears open for this is not another day to chin wag and we'll start using different terms for podcasts, but that's a whole different

Lenno: If yeah.

Bren: conversation. But to everybody listening, thanks very much. We'll see you next time.