Episode 192:

Business Logic As Code: A New LLM-Powered Operating System for Business Automation with Binny Gill of Kognitos

June 5, 2024

This week on The Data Stack Show, Eric and John chat with Binny Gill, Founder and CEO of Kognitos. During the episode, Binny shares his journey from programming on a Casio calculator to founding Kognitos. He reflects on the evolution of learning to code, from his own self-taught beginnings to his son’s coding project during the pandemic. Binny discusses the inception of Kognitos, aiming to democratize programming by making it more accessible and intuitive. The conversation touches on the need for a new operating system that simplifies business logic, the role of AI as a problem-solving tool, the importance of human control over AI systems, and more. 


Highlights from this week’s conversation include:

  • The history of computer science and AI inflection point (1:23)
  • Binny’s early programming experiences and the constraints of technology (2:14)
  • Getting interested in computer programming (5:02)
  • The experiment that impacted the starting of Kognitos (8:23)
  • Challenges in traditional computer science (16:04)
  • Reimagining programming and debugging through natural language (19:08)
  • The operating system for applications (20:19)
  • Changing the paradigm of programming (21:25)
  • Complexity in software compilation (22:05)
  • Challenges in automating business processes (24:50)
  • Solving business process problems with Kognitos (27:39)
  • AI as a tool in business solutions (34:05)
  • The future of AI and specialized intelligence (37:08)
  • Using LLMs for Context Generation (40:43)
  • Biases and Data Set Source Transparency (41:48)
  • Next Innovation in Data (44:34)
  • Final Thoughts and Takeaways (47:06)


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Eric Dodds 00:15
Welcome to The Data Stack Show. We’re here with Binny Gill of Kognitos. Has, Vinnie, thank you so much for joining us on the show today,

Binny Gill 00:33
Eric, thanks for having me. On the show.

Eric Dodds 00:37
Alright. Well give us tons to dive into but give us just a brief background. Where do you come from? And just a little bit about Kognitos? Yeah,

Binny Gill 00:46
so I’m a software engineer by profession. I’ve been writing code for 30 years. I started cognitives, about four years ago. And prior to that I was CTO at a company called Nutanix. For about eight years it grew from zero to an IPO and beyond great experience there learned a lot. My experience prior to that is an IBM Research, mostly in the storage background, bringing cash technologies to the enterprise masters. And from you i UC and bachelors in IIT Kanpur computer science grew up in India. Right.

John Wessel 01:22
So Binny, one of the topics I’m excited about talking about is the history of computer science, how that’s evolved, and then this AI inflection point that we’re at now, and how things are changing. And then some really unique ways that your company’s trying to solve those problems. Yeah,

Binny Gill 01:38
That’s a topic really close to my heart. When I was a teenager, I was programming, and I had a handheld Casio graphing calculator, which happened to also support basic programming. And I had a large total of four kilobytes of memory where I used to fill in my basic programs. And I had to be really, really careful about how much code I’m writing. One of the first cool programs I ever wrote was the Tic Tac Toe game. And it was easy. It was like lines and X’s and O’s. So it was doable. And I showed it off to my friends. And they were like, What is this? I said, Oh, this is a computer. And this is not an equation? No, this is basic language. Those are the days there was no Internet, there was no YouTube, I just had a manual. And I loved the power of making a machine operate in a way that was custom to what I wanted. That was the power that, you know, got me hooked until they you know, that’s what keeps me happy with what I’m doing. Yeah,

John Wessel 02:41
That’s exciting, I’m excited to dive into that. All right.

Eric Dodds 02:44
Well, let’s dig in. Binny, I love this story that you were telling us when we were prepping for the show. Your father recently gave you a notebook from your childhood. And on one of the pages, you wrote 975 kilobytes. That’s right, that yeah, so. So you wrote that at the top of the page. So give us the story. Like give us the story of the notebook? What was the notebook for? And your father, you know, dug it up after so many years? Yes,

Binny Gill 03:27
this physical Lord book, and I had almost forgotten about it, but my dad preserved it. That was a notebook of all the programs in basic that I had written in this handheld Casio calculator slash computer that I had. And, and I forgot why I had written it. Normally, people don’t write computer programs in a notebook. And I saw the number on the top and it said 975 characters, and characters this, I mean, because it’s one page, so you can actually count the number of characters, right? So the reason I was counting the characters, and I was also writing it out, because back in those days, there was no Internet, there was no connectivity between machines. The only way I could create room in my computer was actually to delete stuff, but I didn’t want to delete programs that I wrote with, you know, a lot of effort. So I would actually jot down the program in my notebook. But then I also would write the number of characters it frees because now I can write another program, but it has to fit into that. So that

John Wessel 04:36
was one of the first air gapped backups I think yeah.

Binny Gill 04:43
You are still backed up. Every bit is intact after 30 years.

John Wessel 04:47
That’s a solid track record. Even some foreshadowing for what you did as a career, too. Yeah.

Eric Dodds 04:56
Where did you get the calculator and how did you figure out that you could write programs on it?

Binny Gill 05:02
So the story is that my dad was a mechanical engineer by profession, and he would do engineering drawings. And back in those days, computers were a thing, right? So the best job you could have is you’re designing the machines that will be built in a factory in our factories, where molten iron is being poured, and all sorts of things. And you’re sitting in a nice clean room and you’re designing stuff and optimizing things. And one day, my dad comes and says, Binney, you should not grow up into what I’m doing. So what happened? This is such a cool thing, right? I like to design stuff, build stuff, and engineering is sort of what I liked. is a no, I saw AutoCAD today in the office. What is that? Oh, it’s a computer. And I saw that, in two minutes, somebody could build a drawing and print it out with the big plotter. And what somebody like my dad does in a whole day, you could do it in five minutes. That is the future. And so you should do computer programming, or whatever it is. And there were no computers out there. I had never seen one, my dad saw one in the factory and like was blown away. And after a few weeks, he comes home with this Casio personal calculator slash computer, he says this is a calculator, but it also can understand some computer language called Basic. So here’s a man who will go figure it out. That’s how it started.

John Wessel 06:33
Wow, that’s cool.

Eric Dodds 06:34
I think it’s really in John, interested in your thoughts on this. I think, you know, as a parent, I have so much appreciation for your dad, not reacting to what he saw and fear, but seeing future opportunities for his kids, you know, yeah. That’s just such a, that’s so encouraging. And your dad sounds like a great man.

Binny Gill 06:55
And you know, and I was, I didn’t know what to do. Like, what could I do? Nobody knew, like, I started out drawing lines, because I had this idea that AutoCAD draws lines. And I got to draw lines. And I said, but what can be nice in and then I realized tic tac toe is nothing but lie in so let me just make a game. A tic tac toe and show it off to my friends like, whoa, right now, video games. Were becoming popular, heavy, very expensive pieces of equipment to buy. And, yeah, but here, I could make my own game. And that was, you know, that got me to computer science. I learned a whole bunch of languages in the three decades after that. Yeah, I’m trying to follow that as well.

Eric Dodds 07:42
Well, so the second part of the story, which I love, is that your son also built a tic tac toe for 30 years. You know, from when you had that first experience of, you know, following the manual for a Casio, you know, calculator that could also do programming. And the revelation was that he wasn’t any faster than you. He had YouTube and you know, sort of Khan Academy and all these amazing resources at his disposal.

Binny Gill 08:23
Yeah, that was the moment where I decided to start my own company and start building Kognitos. So what happened is, it was the pandemic and the schools, public schools were closed and kids were like, you don’t know what to do. The schools hadn’t figured out how to do curriculum remotely. My son, 12 years old, I was getting bored at home. And I said, You know what, you should learn programming, learn Python. And you didn’t say anything, but after a couple of days, comes back and shows off tic tac toe to me. I’m like, This is awesome. And I played it. It was working. I say, how did he do that? Say, oh, Google, YouTube figured it out. I was very proud as a dad, right? Yeah, sure. And I slept. And when I woke up in the morning, that’s when I remembered, hey, I had made the same game 30 years. 30 years ago. Yeah. Computing. I was the same age and there was no internet. How long did it take? I was super excited back then. And I also did it in two days. And I went back my son and said, You know what? I remembered I admit the same game without the internet in two days. They say oh, now you’re saying, you know, I’m not as good, man. No. And then it struck me that something is wrong. Because you know what my dad did. He gave me a tool that allowed me to do stuff that he would do in a day in five minutes. And he gets me asking my son to do the same thing I did 30 years ago and it takes the same amount of time. There’s something wrong. I didn’t believe it. I said, No. Why did it take you so long? Let’s go and write a program together. Alright. They said, I wanted to do something quickly there. Do you know how to figure out what A prime number is? You say, Yeah, of course, you divide by factors. And if it can be divided, it’s not a privacy. Great. We’ll write a Python program in two hours or one hour, right? And just and then I want to go for lunch. Let’s just do it. We sat down. And I gave him a Python book said no internet, because nobody had it back then. Here is a book. I admit, it’s much thicker than my book. My book was only 100 pages long. Yeah. Now it’s this massive. I said, Maybe you just need the first few chapters. Don’t worry about the other stuff. All right, let’s write totally blank. What should I do? And I said, First, think about a plan. Right? And I taught him pseudocode is okay. Use this right. In English. What do you want to do with computers? Not gonna understand it, but it’s for you to, you know, mentally prepare for that thing.

Eric Dodds 10:58
Okay. Organizational. Yeah.

Binny Gill 11:02
He’s good at math. So he said, Okay, let me write it down. So if it is one, it’s not a prime, that’s the convention. If it’s divisible by any number from two to the square root of that number, then it is not prime. Otherwise, it’s prime. That’s what you wrote. Yeah. So that’s, that’s correct. were nice. Now let’s translate into Python. So first line, if it’s not, if it’s one, it’s not prime, fairly easy. After five minutes of digging into the book. The next one, you know, do we divide by factors from two to the square root and involve the loop in the opposite way, or in concept? And even though you can say that, yeah, you do it for each of these factors. But stuff changes. Now I had to introduce variables I had to all sorts of things, and it is getting more and more complicated. And then I was pushing hard because I was running out of time, like, if it’s going to take too long, something is wrong. So anyway, we ended up in a fight. And he said, No, this is bad. I don’t want to do it. And I said, No, but let me show you the basics. So I dug a basic and a program in basic for prime numbers. And I said, “Do you understand what it says?” Okay, shape now what to do?

Eric Dodds 12:17
If I just you’re way past lunch, yeah, Everybody’s

Binny Gill 12:20
hungry. I’m like, this is not going to work out. I said, You know what? I’m going to build a programming language that will be easy for you to understand and all that. And he’s like, inquisitively looks at me and says, But why is it a well meaning word, he says, Alexa can already do this. He was pointing to the pseudocode. So Alexa can run this? And I said, No, Alexa cannot run this. And he didn’t believe me. And he said, No. In the kitchen, we talk about all sorts of things to Alexa, and it’s working. So why are you telling me that I need to learn a weird language? This English word work? So that’s, you

Eric Dodds 13:03
imposed constraints where his practical experience? Didn’t see a constraint? Yeah.

Binny Gill 13:12
And I’m like, maybe he’s right. Right. And I told him, okay, don’t learn Python. Let me try to figure this out. Because, you know, if Alexa wasn’t there, then I would have said, No, you know, all my computer science training in school has said, this is not possible. But here is saying one line is possible in the kitchen. If you can make one line two lines in computer science two becomes 10. And 10 becomes a million very, very quickly, we have seen that all sorts of things in computer science, zero to one had already happened in one to two needed to happen, right? And say, Okay, I’m going to take this English that we wrote, and I’m gonna make this work. That’s what I did. The next three months, I wrote a compiler interpreter for just those three lines to work. Obviously, it was a bunch of hacks, I just wanted to see what comes in my way. Not much came in my way. And, you know, surprisingly, because deep learning was in a good spot compilers, you know, parsers, I understand. And I showed it to him after three months. And he said, Okay, good. He said, Now, can I make a game with this? I said, Oh, you know, a game is hard. But then I said, Okay, I’m gonna work on it. Give me some more time. And I really, in Earnest, started building a proper framework for understanding natural language. And then I had so many insights until date. It’s like, what if I have to summarize one thing is basically, it’s all about unlearning computer science, all of it. And only then you will figure out really how the human brain works. And that’s what we need to mimic in machine And obviously we’re sitting in a world of MLMs. And people understand that now back then there was LLM, Cisco. Yeah.

Eric Dodds 15:05
I love it well, so two follow ups, one of them observation that maybe more people should look into having their child be their product manager. He sounds like your son, you know, really? Product. Yeah, exactly. But to tell us about Kognitos snow. And so what an incredible story for you to have that experience where your son saw past limitations of traditional computer science, as expressed in, you know, code languages, and the way that we create logic for computers to read, he saw beyond that you went and solved this problem, and you started Kognitos. So what is ketosis? And what core problem do you solve? Or what’s the core of the technology and the solution that it provides? Yeah,

Binny Gill 16:04
you know, when I started thinking about it, I was getting worried that this is going to be continued to be a dark art, like we’re living in even today, we are living in the dark ages of computer science, meaning, just point 5% of the world’s population can actually read and write computer programs, computer language, wow,

Eric Dodds 16:25
smarten up, it

Binny Gill 16:28
really is polymer. Yet, all of the world depends on computers. Right? Right. That’s the classic definition of the dark age, where a few people wield the power of controlling the world. Right? And that’s why, you know, I’m sitting here as a software developer, and I, you know, in Silicon Valley, and last company IPO, and all of that did well, why it’s not because I’m smarter than other people who don’t know this language, it’s just that the language is the key to a lot of power. I was thinking, can we? I mean, we have to change that. Now, why has it not happened in the last 70 years? We’ve been figuring out how to make computer languages easier and easier. I spent a full month of February, reading up on the history of computer languages, just trying to understand if there is a trajectory where it’s actually getting better. And at some point, it’ll actually become democratic, everybody understands it. And my realization was, now we are actually we went from punch cards to assembly. And then the symbolic language is the big jump, C Plus Plus Fortran COBOL. But then now we are in circles, the most recent languages go Lang, and rust, and all of that looked closer to C language than actually. So we are going in circles, and I’m like, Oh, this is not going to change anytime soon. Now, the whole world depends on computers. Very few people have the interest or the capability of actually dealing with computers in that language. Something has to change. Alexa is changing it. Can we jump into this other level? That is what I wanted to go and solve. And cognitive is a way of bringing it to the market in a way that makes financial sense and makes it real. I realized that it isn’t just the language, though. Natural language, obviously everybody understands that. But that’s only 20% of the puzzle. And this I realized after building a prototype and playing with it, and then I realized, you know what, the biggest problem with programming is debugging, and maintaining and fixing issues. If you think about Oracle database written decades ago, right, you would think by now, you should not need any engineer on it. Because you know, it’s all set. No, there are more engineers now than there were in the first year. And the code of the database has been bloating and bloating. That’s the fundamental problem when I was trying to grapple with it. Like I had to forget computer science to figure out what the true solution would be. And one day, you know, it came to me that grandma’s recipe for apple pie is a program, right? It’s a step by step instruction and outcomes that the program has withstood the test of time. Nobody has filed bugs, it did not have to be you know, complexity did not grow. If it was a software program. Then for the first time the oven did work. You see, I think you guys only thought it was a software program. By now it would be a million Inns of Court. Yeah.

Eric Dodds 20:03
Right. And it would be a seven course.

Binny Gill 20:07
And then there would be a section on how to use the fire extinguisher, there’ll be a section on how to go to a grocery store and get sugar. If you’re missing that, there will be a section for everything. That is computer science. Now, language wouldn’t help there. So if my recipe was in English, but all these other things were also in English, it would be a million lines of English, hard to maintain. And then there would be sections that don’t match with each other and contradict each other. So the light bulb moment was the platform on which the application runs has to be differently built. So the operating system is where applications run. Traditionally, in computer science, operating systems do not handle all the edge case scenarios, that’s the part the application is responsible for. Whereas in the human brain being the operating system, if my oven is not working, grandma doesn’t have to write it in the recipe, my operating system here will figure out, oh, maybe electricity is not there, or gas is turned off, or whatever. And I’ll go and fix it and come back to the program and run. So fundamentally, there was a need for an operating system that keeps the application code simpler by being smarter about the world. And nobody had built that in computer science. And I started building that. And that’s why it’s called cognitive assets, an OS for cognition. The idea is to build a platform, which Yes, can run programs written in English, or more importantly, keeps that English simple, because the platform is getting smarter and smarter over time. And that’s where AI comes in. business logic, as we call it. In computer science, we say this is business logic, not translated into something. No business logic is code. And that is the dream. So that’s precisely what we’ve been doing, trying to change the paradigm of how business apps are written. And eventually, it’ll change the paradigm of how we program computers.

John Wessel 22:05
So you’ve got me thinking about this? Were like, what if a software compiler aired on complexity? Right? Like, what if they were built in things to the process of like, now, like, this technically works, but it’s too complex? This technically works, but it’s unmaintainable. Right? It’s fascinating.

Binny Gill 22:25
That would be so nice, because isn’t that what humans would do. So for example, before computers existed, businesses would do programming anyway, they would have a partner onboarding program, they would have a program for, you know, organizing the end of quarter activities. Now, people would write programs or standard operating procedures in English. Or maybe employee handbooks would have all sorts of programs in them. This is how you apply for vacations. These are all programs. Now, some human would read it and say, hey, you know what, this is too complex, make it simple. There is always that thing. Now, I envision a future where that standard operating procedure, that employee handbook is the final program, you’re not translating this into Python anymore. Anything. This runs natively on a platform that understands natural language. But what’s more important, standard operating procedures like grandma’s recipe don’t get polluted with all sorts of edge cases. So the platform needs to be smarter, so it can handle the edge cases separately. And then that’s how humans operate. So this is, the future is all about creating a paradigm where you can program in a more natural human way. And obviously, there is a role AI has to play in there where you need to use AI, and yet not give up on the benefit of computers that computers have. John,

Eric Dodds 23:50
I have a question for you. And, Binny, I want you to tell us how accurate you are in thinking about how Kognitos could help a business but John, you ran. So as CTO, I mean, you actually like you were CTO. So you manage all the data infrastructure. But marketing also rolled up to you, which is really interesting. You had a ton of input from the sales side of the organization, we were just talking about sort of managing or business processes to your point, right. I mean, you probably oversaw like, whatever, you know, 2050 depending on the organization’s 100 standard operating procedures. Sure, sure. Yeah. So just hearing what Binny said like, what would you do if you could essentially like, operationalize those standard operating procedures that were probably like Confluence docks, or whatever you guys use, like, what problem would you solve first, if you could essentially turn that into a computer program? So

John Wessel 24:50
it’s interesting so we because we didn’t you know, have anything like this available? We were doing kind of the opposite of we out Like, for example, we had a sales manager that actually learned to program, right? really learned a little bit of Python and learned some SQL and started writing his own reports. He had financial analysts that learned SQL. So we kind of went the opposite way, which is much more difficult, right?

Eric Dodds 25:17
Oh, sure. Oh, yes, arguably, like not adding points, not the best use of that person’s time. Right, right, especially the

John Wessel 25:24
certain level of complexity. And then try to whenever we made purchases, like, usability was always the number one thing like, it’s easy, like, especially as the CTO to prioritize features or other things. But we always basically left it up to the business users for the final decision. Like you’d have kind of a vetted, like, here, the option that wasn’t limitless options, but really leaned on them to pick and to own, as much as was possible with the solutions. Most of these were SAS solutions at this point. But yeah, I mean, to answer your question, there, there are several solutions that we looked into, like, Oh, that’d be so great if we could have this. No, this business logic app or workflow or this, that or the other. And the complexity was way too high for non technical users. And, quite frankly, sometimes for technical users to get value out of it. And you just ended up with practical, like you said, I mean, just like a knowledge base, with how articles are really what you end up with. And when you do that, you do get the advantage, though, of that, you know, older school way of like, when you bring people on, and you train them, you get a really unique advantage of making the process better than when something’s fully automated, you don’t get that advantage. Because if it’s fully automated, people are like, you know, this thing runs, and it spits out this result, and we use it, and then they and then people will go for so long with workarounds in that state, because well, it’s automated and like it is busy, and we don’t want to, you know, we don’t want to bother them. So we’re going to do workarounds. And they do it for so long, and then eventually get to a breaking point. And then often you have to completely you’re so far away from original intent, and maybe different people are even there now. Then you move to this state of like, okay, we’re basically in the scrap that and rewrite it.

Eric Dodds 27:24
Yeah, many help us understand, like, okay, so Kognitos comes into this world, can you help us understand, like, on a very practical level for someone like, John, where does Kognitos fit in? And how does it help him solve that problem?

Binny Gill 27:38
Yes, it would, John said, is precisely what’s happening everywhere. Right? So before automation is done, people on the business side know the process, because they do it manually, right? Now they do it, you know, in an ad hoc manner. It’s not really recorded in a proper way and all of that, but still, they know it, okay, then comes an automation tool. They say, hey, we could do it, but it’s quite technical. Either train your own people who understand the business logic, or just write down the business logic for me, or maybe we have a meeting. And then there’ll be a developer listening to that developer who doesn’t understand business logic as much. But the developer will take that and translate it into dark art. Okay. And it goes into a black box,

Eric Dodds 28:25
which is most often Apex

John Wessel 28:31
words, yeah, whatever. Yeah.

Binny Gill 28:32
Now what has just happened, we have disenfranchised the business decision makers from actually making changes in how the business works, because you can hit and translate it into something that’s a black box, fine. Now, it works good for a few months, because that’s doing what I had just explained to you. Now, to be honest , I want to change something, but it is busy. Yeah. And after some time, it forgets what it was truly meant to do. And that’s the challenge of having you use a language that is not common between the machine and the business user, right? Imagine a new world where cognitives come in and say, Look, you write a standard operating procedure in your own language, and nobody’s going to translate it into something else, that is the program. So anytime you come and you can see what the program is, the machine is also trying to read and understand it. If the machine has a question, reach out to you, Hey, I was trying to do this. In this particular case, I could not see the discount chord you had mentioned, it’s in this table in the database, what do I do? You come and say, Oh, in this case, just use 10%. And the machine says, is that just for this case, or all times when I don’t see this as the default? He says, Oh, this is before now the machine has become smarter. You didn’t program and your standard process still remains the same. It’s always readable for the business side. It never becomes a black box. That is the new world that is emerging. That’s the correct place to be in. I’m trying to bring the world to a place where computers are, you know, and it is sort of not visible anymore. Right? So instead of before computers existed, John would have gone to an intern say, do this, right? The standard operating procedure would be in English, you just hand it over to them. Anytime you want to change your behavior, hey, in turn, show me standard operating procedures, scratch, write a new normal, boom, you are programming the human. And that’s what machines need to allow people. And suddenly, everybody who understands business becomes a programmer, even though they don’t call themselves programmers, I think they are the true programmers. If you think about it, IT developers are not true programmers. It’s actually the business people who say, I want this to happen. And if this happens, then I want that to happen. The product managers or the programmers, the actual programmers are today, our translators, we don’t need translation anymore. That’s the point.

John Wessel 31:11
Yeah, so this is a huge vision. Where are you starting with this vision? What problems are you first starting to solve? With this new paradigm? Yeah.

Binny Gill 31:21
So we are going after financial processes, like invoice processing, or purchase orders coming in reconciliation of payments. Anything that’s document heavy, even if it’s shipments, billing bills of ladings, packing slips all that in the place? You’re smiling. Yeah, every business has this.

John Wessel 31:47
Yeah. Lots of time in the distribution and third party logistics space in the past. Yeah.

Binny Gill 31:54
Right. So a lot. We’re working with large companies, fortune 500 companies who have this problem at monumental scale. And because machines have not yet been able to solve this problem, because it’s never cut and dry. There are so many variations that only humans can handle. Now, with ai, ai can handle variations. But you still need a deterministic documented process that is visible and auditable by the business side, where it’s not being translated into something crazy. That business doesn’t understand. So that’s what we are solving right now. We already have. We’ve been doing business in production for more than a year now. Binay,

Eric Dodds 32:37
I have a question for you. And I want to dig in a little bit to the AI side of things, because you mentioned AI and MLMs. And I want to start this with what we talked about when we were prepping for the show. And there’s a quote that came to mind. We didn’t talk about this. But there’s a great Mark Twain quote that came to mind. And Mark Twain said, God made man in His own image. And man, being a gentleman returned the favor, which I think is a really great quote. And kind of encapsulates what you pointed out, I think, very, which is a very salient point in that we’re essentially treating AI that way. So I’d love for you to sort of first talk about, you know, maybe react to the Mark Twain quote, in terms of the way that we’re treating AI, and then help us understand how AI fits into Kognitos. Because one thing that I think is really compelling to me, and I think will be to our listeners is that we haven’t really talked about AI this entire conversation, and we talked about natural language. We’ve talked about programming, but you paint it in terms of an operating system, where it seems like AI is an input. So we’d love to break that down. John would love your thoughts, but start with, you know, start with, you know, man creating God in His own image, per Mark.

Binny Gill 34:04
Yeah, the reason? You know, I think AI is the tool, and we are in the business of helping people solve their problems and remove their pain points. AI is everywhere in what we do, except we don’t. It’s not about the hype of AI. It’s like, what are you doing? So we are in production, in financial processes. Things are going on, customers know that the system won’t have Alice nation and biases and all of that. So talking about AI is not really the goal. It’s like electricity when electricity comes in. I don’t go and say hey, I have electricity. No, I say I have a microwave. Oh, I have a light bulb. Right? That’s what Kognitos says about Yeah, AI is obviously a given. How are you going to use it in your business so that your business doesn’t catch fire? Where is the fuse box? Where is the insulation around the wires? That’s what we are the electricians of AI if you think about Hmm, bringing it to the world. Now, the current hype around AI, and Mark Twain, obviously, is a very smart person. And then the way he put it is also politically correct and kind of, but here’s what happens. My observation has been humans. Anytime we have something fuzzy, and we think it is going to be powerful. We just think it will be like a human. Right. And I was a kid in the mechanical world, it was a giant robot. Right? Okay, it will be a giant robot that I could control and it will stomp on us, you know, city and all of that. That’s how I dream about stuff or mechanical stuff. Now we are talking. Now we are talking about AI. Oh, AI will be like a human. Right, AGI will be just like humans, but much more powerful. It will have emotions like GPT four. Oh, now I was also emotional, right? We were mimicking humans. But now look at reality. In the industrial age, we didn’t build robots, giant robots. If I look out the window here or look at my home or my office, there is nothing that mimics even the fingers of a human’s legs, a few humans, nothing of that sort. But we have machines all over the place. A car does not have legs. It has wheels, why? Men’s wheels are better. You can’t just build a machine that runs on legs and plus 100 miles an hour. Right?

Eric Dodds 36:27
Yeah, Flintstones.

Binny Gill 36:31
Right. So why do we want to limit the power of the machines we create by mimicking human biological constraints, right? Learn that in the industrial age, a bullet train is like a million times more powerful than a human. Okay, but I don’t have legs and cannot even twist and turn. Who cares? Right? An elevator does not have arms to climb up ropes. I mean, it works differently, but it’s far more powerful than a human. Now, think about AI. AGI is like, Okay, I want to mimic and create AI in my own image. Just like Mark Twain said, No, let’s create artificial specialized intelligence, something that works beautifully for my finance department, for my legal department, all of that. Now, build a system where the human is in charge, human first AI future where just like I get into an elevator, I press a button and elevator obeys me. So I’ll use an AI system that does finance whatever, but it obeys me. And it can only do finance. It cannot have it won’t blush, when I say something I mean, it doesn’t matter. For that future to work, you need to have a platform where you can say I have a plethora of LLM or a plethora of AI models that I can use. And now as a human, I’m stitching these things together just like on a daily basis, we get into a car, we get into an elevator, we are leveraging different machines to accelerate what we do. We will be leveraging different types of AI and we will melt mentally knowing which one is safe to use. Which one isn’t saved? Like when you get into an elevator, you don’t see where it’s going. When you get into a car you see where it’s going. So mentally, we need to understand which AI model is going to behave in a more pragmatic future. And I think that will happen anyway. Humans know exactly what they want. You see self-driving cars, the ones that don’t put humans first get recalled, the ones that put the human with the steering wheel still running. So that’s what’s going to happen with the I believe and pretty excited about it as thinking about this. There is what I call the GPS theorem for AI is like between generality power and safety. You can only pick two. So if you’re building a general AI, or even a general mechanical robot doesn’t matter. Then between power and safety, you can only pick 111 More is remaining. So if it is general and powerful, then normally it’s a weapon. Whether it’s a mechanical system or an AI system doesn’t matter. Wow. If it’s general and safe, then yeah, you need to limit the power. And that’s what my request is for any people working on AGI to build it, but you cannot make it more powerful. It’s like in my home I will not sleep well if there’s a robot that has full freedom to go around anywhere anytime in the house. And then this knife on the kitchen,

Eric Dodds 39:45
right? Yeah, yeah, but at the

Binny Gill 39:49
same time if there is a Mickey Mouse like hand that comes out of the hood in my kitchen and does my cooking, but I know it is constrained. It’s not general, it will only do that thing that can’t reach my bedroom. It’s okay, that arm can be far more powerful than my arm. And I’m okay with that. Yeah, yeah, yeah.

Eric Dodds 40:06
I love it. I think, John, I was gonna ask you, you know, John, and I actually have worked on a number of LLM flavor projects together. And you’ve gotten much deeper into the guts of it. But Binny, one thing that you said that I actually hadn’t really thought about. But when we’re using the generic, you know, sort of, you know, like a, GBT, or you know, sort of basic, like prompt based, most of the time is spent trying to infuse the system with context. And so it’s pretty compelling to think about an operating system that has that built in. Just interested, John and your reaction to that based on your experience, because I mean, you’re using LLM to generate prompts that give it sort of an inception level, like using MLMs. And multiple loops in order to generate context, because it’s so it’s actually very hard to imbue that, right, which,

John Wessel 41:02
which is basically accounting for that it is general and I want it to be specific. So exactly what you just said, Binny, like you spend all this all this time just to get, you know, a summary of the document or whatever you’re working on, right? Like, I want this to be specific and technical and not flowery, and you know, x, y, z. And then the end result is like, well, I don’t want it to be general. I mean, I do want it to be general, but not really like my end result that needs to be specific. The only reason I like it is the same reason that you liked it. The Casio, you know, where you would like, I can code it to do what I want to do. I still want that. But this specific, is what you end up needing to do anything practical.

Binny Gill 41:47
Yeah. And that’s, I believe, that the LLM vendors need to publish, what are the biases? What is the data set source for these MLMs, it needs to be open book. And that will do two things. One, we will know what to worry about what not to, you know, that’s one the other thing, it will create a future where there is a demand for a lot more variation in MLMs. Right. So for example, if you have to hire a human, you don’t hire a general human, and then train them to be a good content writer, you interview people and say, Okay, this person’s content, actually, I like already. So I don’t need to do prompting, right, it’s already there. Now, where is the resume of these LLMs? Because you first look at the resume, okay, this is the education that this person has had, they’ve gone to Harvard, or they’ve gone to this. So that gives you some idea of that data set. And then you come and say, and then in the interview, you make this decision which one you like, more importantly, humans are consistent. So if they have certain kinds of bias, they will be consistent. So you know, if I’m a Democratic politician, I want somebody with Democratic bias. I don’t want an LLM that could be either or depending on the prompt and whether I was right or wrong. So fundamentally, the future is going to be a future with a large number of MLMs, to pick from a lot of them specialized to the tasks. Our job as humans is to just like to interview other humans, interview the LLM, pick the ones we like for our business, and then fire them if they’re not good. Get the other one. Right. And I think not much is going to change in how we do work. We just need to go back to a world where computers didn’t exist. And then some humans are sort of like computers. I mean, it’s like some humans are like Spock or data from Star Trek. They are very logical, but they can also understand what you’re saying. That kind of Olive is what we want to bring to businesses.

Eric Dodds 43:58
A lot of it. Alright, Binny will work close to the buzzer here, as we say, interested to know, what are you so outside of the world of LLM is an AI, when it starts to come to the world of data. What’s maybe another technology that’s been really interesting to you over the last couple of years? I mean, you have, you’ve done so much work and when we didn’t even get into your work with, you know, sort of hard drives and computing speed. But outside of LLM is what excites you that you’ve seen. See,

Binny Gill 44:33
I’ve spent 20 years in the storage space, making sure not one bit is flipped from your data. So all the data scientists have lived on technology that I’ve been working on. And we have gone through a world where we are leveraging data to generate insights. Google is the same thing, a lot of data you’re generating insights for people. And I think the time has come for The next jump, and this is where I am interested right now. Once you have insight, what do you do? You act on it right now. AI can act on it, a programmer can act on it. How do you deal with that situation? Right? So what we give is data that will lead to insights. The real innovation right now is, AI can come up with a plan of action based on the insight. But that plan of action has to be reviewable by a human. And therefore the language has to be non programming non API. That’s what we are trying to solve. And that will really accelerate actions based on data. Right? Imagine a world where, yeah, the AI is going to come up with a plan, but the plan is in Python, I mean, that’s open AI is already doing that. But who’s going to trust that thing the first time, that is a bad action, you will say, Oh, I need to hire a Python developer to look at everything that AI is going to do. And there you go.

John Wessel 46:02
Yeah. And you can’t dismiss human rights. Like in the past, it’s like, oh, that developer no longer works here. Like that makes acting

Binny Gill 46:09
really weird. And then you have to fire the LLM. But unfortunately, even that’s not happening. We’re going after sort of LLM washing of the world like okay, one LLM for everybody, you can’t even fire that. There needs to be a Darwinian evolution of ideas inside LLM says, Well, that’s the power of the human race. You know, 30 years ago, what was considered okay is not no longer considered Okay. Today, that is going to happen with MLMs. And the only way to happen is right now there are 8 billion physical, biological LLMD humans out there, and they constantly fight to see who’s going to win the, you know, the Twitter wall or whatever, right. Nothing of that sort is happening between LLMs like I have generated a model spec. Let’s align on humans, all humans on that model spec. And that is going to be the future. I highly doubt it.

John Wessel 47:04
Yeah, I agree.

Eric Dodds 47:06
Binny, this has been such a fun conversation, I really can’t believe we’ve been talking first. This has been great. We would love to have you back on to dig even deeper into, you know, sort of LLM theory and how we handle that, you know, as a society, in the way we build our technology. But this has been absolutely wonderful. And thank you so much for giving us some of your time. Yeah. Thank you for coming on.

Binny Gill 47:32
Thank you. Thank you. Great talking to you.

Eric Dodds 47:36
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