Doug Engelbart's 1968 computer demo was hugely influential for the tech industry, so much so that it has since been dubbed "The Mother of All Demos." But it took some time for his vision to be accepted by his colleagues. In fact, some of his ideas are just now being implemented.
Why does it sometimes take so long for the future to take hold? And why is it that some "get it" much earlier than others?
And what does that mean for the banking industry? We tackle all those topic in this week's podcast.
Jim Young: Hi and welcome to the purposeful Banker, the podcast Pike brought to you by PrecisionLender where we discussed the big topics on the minds of today's best bankers. I'm your host Jim Young, director of communications at PrecisionLender.
\I'm joined again today by PrecisionLender CEO, Carl Ryden. Today we're going to talk about a moment in tech history that's been branded the mother of all demos and why that moment actually has some important lessons for commercial bankers.
Carl, when you gave a talk at Bank on Purpose back in May, you covered a lot of futuristic topics about banking. You talked about coaching networks. AI was in there, automation, etc but you started off that talk with an anecdote about an engineer from a lab at Stanford let's put it that name Doug Engelbart and a computer demo he gave way back in 1968. Why did you start there?
Carl Ryden: I actually started as I was looking into trying to find existing language for how we think about AI and machine learning here at PrecisionLender with Andi and really thinking about it as augment intelligence or intelligence augmentation as a field that talked about the intelligence augmentation, or IA, is really AI's kind of first cousin.
AI's the one that gets all the hype cycle, where it had the first AI winter where everybody thought it was going to be the greatest thing and then it kind of went dormant. Then the second coming of AI which is where we are now it gets lots of hype and lots of things around Terminators and taking over the world and Sky net and all this other stuff because the news media really likes that particular cousin.
The slower simpler cousin that has been plotting along the entire time is intelligence augmentation. AI is about replacing humans, it's about taking over task from humans. IA is about augmenting humans and how do we supplement their intelligence.
I talked about it. I gave a similar talk over at business of software in Europe and the title of talk was Built Iron Man Suits Not Terminators and I think that's a really good metaphorical way of thinking about intelligence augmentation as Iron man suits. Tony Stark wears it, it enhances the things. Does everything the machine can do so that he can do what humans need to do and makes him better. The Terminator, it replaces the human entirely and actually tries to destroy most of humanity that's where it came out. As I was going through researching that for the talk, I stumbled upon article on intelligence augmentation that followed the thread all the way back through history and it's one of those things where you start going down a particular avenue on the Internet and it takes you places and you go, “Wow, I never knew this existed.” I found my way and I got into Dough Engelbart.
Doug was a engineer who worked at Stanford Research Institute and he gave a demo in I think December 8th 1968, almost fifty years ago which is amazing if you look back to where the world was in 1968.
In that demo, he went to into a room of 2000 engineers, had a screen up behind him with a projection unit that he had borrowed from NASA because these things didn't exist in the world at that time. He walked on stage with a headset, a keyboard, this new thing called a mouse and another thing for coded input like you're playing a guitar, first doing operations with a computer and proceeds to pull up a graphical user interface video conference with his lab mates twenty miles away at Stanford, shows a windowed operating system that no one had ever seen, he hows the ability to click on something. He is funny because in the demo if you look at Mother of All Demos on YouTube where you put a link to it in the notes for the show, you can watch this thing he'll click on something and say, “Hey look, I'm going to delete it in here, but I've got it in memory and then I'm going to go click and put it over here.” And you scream at the screen and you go, “You're copying and pasting, you're copying and pasting.”
But he was doing this for the first time ever so there's no language to describe it, but he was real-time co-editing a document over a video conference to a networked colleague thirty miles away. He did a windowing operating system. He had the first hypertext language that was the forerunner of the World Wide Web and in the whole thing he said, “And next year, maybe I'll do this with my colleagues at Harvard using this new thing we're inventing called the ARPANET,” which was the forerunner of the Internet.
This was 50 years ago. This guy showed them the future and what happened? The 2000 students in the room most of them got up and kind of walked away and went back to using their computers as calculators. What Doug saw was that a computer should be more than that, could be more than that, that it would be a means of raising our collective intelligence of solving the world's greatest problems but he saw it as a means of augmenting human capabilities of an intelligence augmentation device.
Actually the place he ran at Stanford at the SRI Stanford Research Institute was the augmentation Research Center where he studied how computers could augment in capabilities and so that's that the two paths. One way of viewing computers as things that replaced the human computers if you've watched the what was the movie? Hidden figures about the ladies who worked, literally they were human beings called computers who would actually do the calculations, just do calculations by hand. They saw the machine as replacing that we call a computer as a means of replacing those folks.
Doug Engelbart saw it as much more. He said, “This can actually be a collaboration tool, a communication tool, it would augment human capabilities allow us new forms of creativity.” And if you look over the past 50 years, everything done has shown us as almost everything has come to fruition. It was really fascinating so I ended up going deeper on studying Doug Engelbart. One footnote on it if you go on Wiki page on Mother of all Demos, there's a statement on there that says prior to doing the mother of all demos, Doug Engelbart was widely regarded as a crackpot.
Wikipedia has a little footnote so to put something on the Wikipedia you have to reference a credible source on the internet too and what is the credible source for determining the world thinks you're a crackpot? So you had to follow that link. I followed that link and it was actually an article. A great article written I think by a friend of yours as we found out about Doug Engelbart and it was quoting one of his own lab people who said, “There were days we didn't understand what he was talking about and we thought he was we thought he was a crackpot,” until it all came together and all of a sudden now they celebrate the anniversary of The Mother of All Demos. Alan Kay who's one of the fathers of Silicon Valley who was in the demo that day and he said it was life changing, was quoted as saying, “I don't know what Silicon Valley will do when they run out of Doug's ideas."
So for me when I start researching back to intelligence augmentation and kind of what's the arc of history that took us to this point? I found my way to Doug and it was a really fascinating story.
Jim Young: It is a fascinating story but there's parts of it that's a little depressing, a little frustrating because like you said that's 50 years ago and I don't recall having a lot of those tools in my hand 25 years ago with that. And it brings me to another quote that I don't know if you used it in your talk which by the way, will have a link to Carl's bank on purpose presentation in our show links along with the article in the wiki page on The Mother of All Demos. But this quote that I know you've used since plenty is from the author and futurist William Gibson, “the future is already here, it's just not evenly distributed.” How does that quote apply to what happened with Engelbart's demo post 1968?
Carl Ryden: lt is true so even slowly you know the future was already here, he showed us the future, he demoed it live on stage, but it still took quite a lot of time to get there. A journalist went back to interview the people in the room and they said, “Why didn't you see this and why didn't you do it?” Back when I described as watching the Youtube video of the demo and now we can look at it like its copy and paste. We have the metaphors and the language to describe things. Back then he didn't have the language didn't exist and so what happens is the humans weren't ready for what he was showing them and the language and the other things built around humans. Doug calls this ... He actually studied this later in life ... And calls this bootstrapping the technology allows humans to do things they haven't done before and then the humans catch up with that and then move past it and then they bring the technology up and it's like lifting yourself by the bootstraps.
When they interviewed the people in the room they said it was just too big of a leap for them, they couldn't make it. Some could, Alan Kay was one. Alan Kay went on to found a Xerox PARC Palo Alto Research Center which by the way ended up hiring away a lot of Doug's folks who work for Doug who works at Augmentation Research Center. Xerox PARC is the one that actually really made the mouse and the graphical user interface a thing because Xerox was allowed to purchase 100,000 dollars of Apple's stock in exchange for allowing Steve Jobs to Tours Xerox PARC twice where he saw the graphical user interface and he saw the mouse and he then created the Apple Macintosh from that and really ushered in the systems that we use today so it's fascinating. You start to see these things creep in and we're still not done on that but it's useful to go back when you see this kind of fountain of knowledge about the future and you start seeing those things come in true.
Two things I wanted to study was well what else did he say and the second thing is what was his view that allowed him to see the future before everyone else? Right now we're seeing things filter from the consumer side of technology, the Amazon's and the Googles and the other places into the enterprise side of technology and stuff that is day regards like every day stuff that Amazon does seems magical inside a bank. What we do with Andi is, Andi's job is to figure out for a customer like this, with a deal like this, with a relationship like this, at a bank like this, here's the things that they probably want and they probably need right? Well, that problem has been solved. We're not the first ones to solve it, Amazon solves it every day. When you go to shop on Amazon and click on a 60 inch Sony flat screen television and then look at a 59-inch Samsung.
I may be messing up the inches of these things but you look at the different screens looking back you say, you're probably going to buy the 62 inch LG and when you do it you're going to need these cables to go with it. That problem of bundling things together and figuring out front running what the human is going to buy and making shopping experience easier is the same thing that we do with an Andi, in fact it's easier with an Andi. So we try to draw the parallels and show folks that, but you see this right now within a lot of banks I see some banks who are doing really great things with AM machine learning because they're looking at the right way. Other banks if I tell them that, that's possible, they'll look at me like I'm Doug Engelbart 50 years ago. And so the future is already here it's just not evenly distributed and being able to see what your peers are doing and compare that, one it gives you a language to understand and communicate these things which is necessary.
The second thing, it allows you to get a sense of what's truly possible and the future is here, it's just not evenly distributed.
Jim Young: Got it. Before I go to the next question I should note for you, if you happen to be tuning into this podcast for the first time and you wonder who is Andi, what would be your 25 words on Andi description?
Carl Ryden: Andi is an intelligent virtual assistant that lives within PrecisionLender. She's every RMs dream pricing analyst. She sees every deal at the bank, what's winning, what's, every relationship how it's evolving and then coaches the Rms to be better. We built in, I tell the story in the presentation of how we came to building Andi and it was a little bit of just following our nose and listening, truly listening to the customers who use our product and what the problem they were trying to solve and they didn't want a calculator, they wanted something to help them have a better conversation with the customer. And they wanted something not to replace them and tell them how to do, they wanted something to help them do it better. They wanted Iron man suit, they wanted intelligence augmentation and that kind of story of us building Andi is what led me to find Doug Engelbart because it was a direct parallel. Not that we predicted the future or anything like he did, just the view of intelligence augmentation being the pathway.
Most of the ... And I'll use air quotes around this AI that folks use today is really IA and I would say the company that is really ... IA being intelligence augmentation ... And the company that is probably operationalized more of this without you knowing it in the light of its customers is probably Apple because right now if you pick up your phone and go home and get in your car, as soon as you get in your car it will tell you, you're five minutes away from home when the traffic is good. It knows this time of day you get in your car and you're probably going home. It just automatically picks and if you notice when you swipe down or up, I can't remember, but to get access your most on app, it knows what time of day it is and what type of app you typically use. So the five apps it shows you are different depending on the time of the day and based on what you've done in the past and all it's doing is augmenting the experience that you're having making it better.
But they do it I think Apple has the idea. You see a lot of companies who are big PR machines about AI and all the stuff they're doing, Apple takes it. it's at its best when it's invisible when all of a sudden it just gives you a smart suggestion or a helpful thing and you just take it and use it. You don't even know why, you don't even notice it and I think that idea of augmenting the human experience is that cousin has been steadily plodding along in making progress for 50 years since Doug Engelbart lit that flame. And it's flashy cousin, shows up with big broad promises dashed hopes and big broad promises, but meanwhile the intelligence augmentation train just keeps making progress slowly and steadily.
Jim Young: You talked a little bit just about and in banking you mentioned sort of thing some people are open to it, other people look at you like you have two heads, if you are to describe, are we at sort of an Engelbart moment in banking?.... Is there a whether it's a technology or a way of doing things that is a breakthrough that may have already occurred that some people in banking just aren't seeing or is that AI or just something else you would describe as ...
Carl Ryden: The word AI means everything and nothing is a bull---- word. I don't know if I can say that on podcast? What I mean by that is if you literally look at the definition of a AI, it basically says, a paraphrase but it's roughly this is whatever machines can't do until they can. And so it's almost but literally AI is machines doing things that are human like that machines can't do, couldn't do before. So what happens is it's almost guaranteed to be not real because once machines can do it, it's not AI anymore. AI is the thing they can't yet do so there's a lot of things within banks that are machine learning that are statistical inference models on credit and defaults and other things that have been around for a long time that right now could easily be recast as AI, but what they are is mechanized ways of learning. How do you process more data, turn that data into insights and then translate those insights into actions and kind of get around that loop which we talked about in the power presentation, the learning loop which AJ Aguas calls The Anatomy of a task, Gordon Ritter calls the coaching network feedback loop.
But you can take that all the way back by the way to W Edwards Deming, the father of all quality who basically created most of industrial Japan after the war with his quality movement. Deming had the PDCA loop plan dou check act and that was a loop of continuous improvement. What we're getting to now is where banks and other institutions are seeing their data as a source of competitive advantage by feeding it into these continuous improvement loops, these learning loops, these coaching loops and you need the entire loop to make it work. We call it applied banking insights. I cover that in the presentation but AI right now there is a reflection point. A friend of mine once said this about technology but I think he was paraphrasing Gandhi. He says, all technology goes through three phases. First is dismissed as foolishness and ridiculed. That's a silly idea.
Second one is opposed often violently, “You were risking your career doing this, I don't know if you want to do this.” Then the third stage is excepted as having been self evident all along, of course we all knew this is going to be a thing. You can look through this, take like Facebook, “Really you're going to build a business where folks can post the details of their life every day all day on a service? Why would anybody do that? That's just stupid?” I heard those words said back when Facebook first was created. Then the second phase is, “Look this is a really bad thing.”I don't know if you want to do this one. And then the third was of course everybody knew that and I meet so many people they say, "I had the idea for Facebook I could have done that." And it is now obvious all along.
I think we're getting to the point where so many banks and so many industries are making real progress with AI or will contend that most of the stuff that is progress is the flashy cousin AI taking credit for things that are being done by IA. It's the smart suggestions. I get an email from Delta Airlines with my travel itinerary and I'm traveling to different countries in different time zones. It automatically ... It says, “Hey, I'll just put this on your calendar for you.” It figured out that that was the travel itinerary, put it on my calendar for me in all the different time zones so you know how painful that was before to have to transpose that over myself, but it just did it. In my car my phone says you're probably going to eat at this Mexican restaurant. I eat there too much, like it knows me too well. But these little things that people are seeing and starting to experience and what that does is it really demystify it.
And folks, they have a language now, they have an expectation of an experience that can be created with AI and now those things are creeping into everything. I joke a lot about bankers in some ways that fear schedules their day. They move from quickly from the fear of being first and being wrong to fear of being the last to be right and right now I think AI with quotes around it, machine learning, all the components of it and is at that tipping point of where folks are on the fear of being the last one to be right because there really are some things that banks are doing not just with intelligent virtual systems like Andi, not just on the retail side facing outward to customers with virtual assistants and those things chat box. I hate the word chat box I said that in a different podcast, but also kind of really good substance of work where I was talking to a large bank, you won't believe this a use case, but they actually use AI to go back and read all of their legal loan documents to extract.
Because it's actually a pretty easy natural language problem to extract all of the terms and the exact things as they are in the legal documents which is the ground truth and invalidate the data they have in their other systems. Even things they didn't capture in their digital systems that were in the loan docs before like what was the prepayment penalty and how does it work in those things so they can understand the economics and sensitivities, but also things like, “Okay, if we go away from larboard to something else, how many of those are tied to leeboard without a provision for something else?” Normally they have to hire analysts to go back and read all that stuff. The other one I see is a with the tax change laws. Go read all the documents to see for the tax exempt loans, what adjustments do we need to make and highlight those and hand them to a human. And it really is about using AI and machines.
We've talked about since the beginning at PrecisionLenders. The guiding principle is we want machines to do everything that machines do well so that humans can do what humans do well. And that's this idea of the symbiosis that humans are really good at some things, computers are really good at a different set of things, you put them together and they're really quite powerful. I did cover that in the presentation too which was about the Centauri chess story which was a pretty good one as well so we'll leave that for folks to watch on their own.
Jim Young: Just one other thing and you talked about a little bit of that whole where we are in this tipping point. If a banker comes to you and says, Well, Carl you're the CEO of a tech company that has software that sells to banks so of course you're going to tell me that there is an urgent need right now to upgrade and innovate and adapt the technology, but we are still clicking along doing just fine. How do you make that argument that you're not being a chicken little for them that this is there's danger at this point?
Carl Ryden: To be honest, I don't.
Jim Young: Okay. Fair enough.
Carl Ryden: This is one of these things where you know the timing is right when I don't because I don't have to. There's enough banks out there where they've realized this. There are some quotes from Jamie dominant and J.P. Morgan and there's some others where we're past the point of ridicule. My friend who works in a newsroom, ridicule dismissed as foolishness you ring a bell, you feel moved to next phase, you're at opposed sometimes violently where sometimes you say, “This is a dumb idea, why am I even doing this?" Of course you're going tell me, we're past that now. We are at the point of it's been self-evident all along. Some folks say it doesn't apply to us because of whatever reason, I hope you're right. Maybe you are, maybe you're not. But for us, I would say I don't spend a lot of time arguing about it, it's not worth.
Jim Young: You don't have to prove you're not a crackpot basically. Am I right?
Carl Ryden: No. Ultimately because there are so many things and right now if you complete that flywheel, where you can train, you can gather the right data, analyze that data and translate it into action. If you can get around it with a few simple used cases, it's actually pretty easy to prove out and so it's one of these things were I could spend three days arguing with you or spend half a day just showing it to you. I can take you to another customer who's using it and talk to them, see it. It's not a theoretical discussion because it's in practice and now I don't have to think about the pain that Engelbart had describing what today we all know as copy and paste. If I try to convince you the importance of clicking here, it goes into memory clicking over here and hitting another key and it puts it on the screen there back then, people didn't see it.
There's a funny story in that article that's linked on the crackpot that your friend wrote and the story was four days after the Mother of All Demos, he was out east and he gave a presentation I think in MIT. And one of the guys in the room was a guy whose last name is Van Dame. Van Dame is a giant in the computer science world. You may not know this but a giant in computer world. He told the story of seeing Doug Engelbart's demo and there was another person in there. Another MIT professor I think, he was a giant in the computer, really well respected who after it was all over raised his hand at Doug Engelbart and says, “I don't get what you're saying. I can do all this today with my teletype.” And technically he was right, you could do all of it with a teletype. You didn't need this mouse, you didn't need a graph, you didn't need all these things. Technically he was right, but Van Dame was like, "Oh my God, if this guy who's a leader in the industry can't see what Doug sees, we're in trouble.”
This is the hill you've been pushing this ball up, or the world has been pushing this ball up this hill for all this time and it reminds me of I know I tell stories. When I was at IBM we had this guy that I worked with so that when I was at IBM built first noble computer that worked with the folks who built the first PC's who were literally there sitting in an office next to Bill Gates putting in place the operating system for the first PC's ever to go out. And they didn't know if it was going to work, they thought it was crazy when they did it. His name is Hugh Mcneal, he's a wonderful guy he tells all these wonderful stories. But he told me a story once about at IBM. IBM made in the banks are really free of these green screens for accessing the main frames. The guys in Greenock Scotland where they made these display terminals created a color display terminal and they thought it was the greatest thing in the world.
They went to the higher up execs of IBM and says we've got this new thing, it's a color terminal and we think the world's going to love it. And the higher execs' os IBM said, “It seems like folks like it, I like it, but I don't know what's the ROI on color?” And they put their best in MBA's and the best business folks they had because they're a national business machine. We've got to sell ROI. How do we make an ROI case for color? They came back and they couldn't do it because anything you can do with color, you can do with a green screen. I can cross hatch the bar charts and technically I can do it. At the end of the day, somebody there had the good sense to say screw it. People like color, the world is in color. This better matches the world that we have learned to live in over these many thousands of years, people like color.
They released the color margins the 3270 monitors or something like that. Within a year it was the number one selling most profitable thing that IBM had and within a couple of years they were really no longer making green screens and purely green screens you could actually fix upon the color ones and have operating green screen which is crazy. But they were no longer selling those and this is one of these things where what's the ROY on color? You couldn't prove it but you could feel it and that was a big thing.
Jim Young: You don't have to make a very big leap to just find similar sort of stories and with banking in terms of making changes you're making to customer experience and empowering relationship managers and things that seem warm and fuzzy, but ....
Carl Ryden: Which also has another side piece to it is, it was a more human experience and so when you do the ROY on a more human experience is hard to prove, but it's there and sometimes you have to take that blind leap of faith.
Jim Young: You have to recognize that the future is already here to bring it all back to Doug Engelbart. That will do it for this week's show. Again, I highly encourage you to check out Carl's presentation. It can be found at the bank On Purpose website which is bankonpurpose.com. Click on previous button on top of the home page, you'll have access to tons of content from the conference not just Carl's talk, but a lot of our other thought leaders and people that really have unique ways or powerful ways of looking at the way we do business and the way that we bank. Speaking of accessing more content reminder if you want to listen to more podcast check out more of our content. You can visit our resource page at PrecisionLender.com or you can just head over to our home page to learn more about the company behind that content.
Finally, if you like what you've been hearing, make sure to subscribe to the feed in iTunes, Sound Cloud, Google Play, or Stitcher. We'd love to get ratings and feedback on any of those platforms. Until next time, this has been Jim Young for Carl Ryden, and you've been listening to The Purposeful Banker.
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