961: Distributed Artificial Superintelligence, with Dr. Vijoy Pandey

28:11So agents and humans operating at the same fabric, same level, not in hierarchies, but at the same level. And so that is a goal that we're going after. If that were the case, then the lowest common denominator is natural language. And so we have to solve for the natural language problem, because there are going to be humans and agents in the mix for a long, long, long time to come. So that's the lowest common denominator. Then you go one step further up and say, okay, let's say, and let's think about the ASI definition that we started with, which is, can agents solve for either discovery or economically viable work 100 % of the time without human intervention?

28:59And so in that case, there is no human in the loop by definition. And so what does that look like? I would say even in that case, the lowest common denominator is still language because you will get agents and you will get components that are not from the same vendor, they're not from the same framework, and so on and so forth. So there, the lowest common denominator does become language. And I'll put an asterisk there and come to that in a second. And then the third level is, let's say we all did Kumbaya and we agreed, you know, this thing is really inefficient. And it's like putting neural links in our brains.

29:41And that's the fastest way to communicate with each other. And that's your scary nightmare, John. But let's say we go there, then absolutely, yes, the vector space is the way to communicate because the amount of information that you would have in vector space is actually reduced dramatically, significantly, when you convert that and you tokenize that and actually make it go towards language. So I would say if everything was standardized and there was no capitalism in mind and people all agreed to things, then I would say vector space is the way to communicate. But in reality, I would say we would fall back to number two or number three because that is where how things will evolve over time.

30:32And so that's the answer I would give. And I would go back to that asterisk for a second and say that we're talking about language here, but language models are not the only models that we're going to tackle in the future. So they're going to be world models. And one of the things that actually we've just read recently that came out of the FAIR labs and a bunch of other places is when you think about world models, tokenization is actually a big detriment. So vector space is the only way to communicate. So I would say that is something that we need to solve for anyway. but for now we are going after tokenization and language and that's what we're trying to solve for in distributed superintessence.

31:18Jon Krohn:I'm so glad you dug into that asterisk there at the end because it's amazing how this whole conversation, the entire more than 30 minutes that we've been recording so far this episode, any example that you've been talking about, I imagine with language tokens as the inputs and outputs. and it is so interesting how that is such a small set of the kind of meaning that can be conveyed the kinds of problems that can be solved the kinds of understandings of the world that we would need to solve all the kinds of problems that a human intelligence human intelligence human intelligence can solve and so yeah it's a really interesting point in history where it seems where we've been able to do things like take all of the language on the internet.

32:07And that's been a relatively easy thing to feed through large language models that are

32:14Jon Krohn:increasingly massive and therefore have all of these unexpected properties emerge out of them, intellectual abilities. But there's still years and years and years, probably decades of work ahead of us to even be capturing the data that we need for world models, 3D navigation of the world. And yeah, it's a completely different kind of information storage. And yeah, I love how at the end of your asterisk there, you touched on how, because if you take a multimodal system, a multimodal AI system, it has to have, if it does both machine vision and natural language processing, it has to have embeddings, a vector space where that meaning converges.

33:03And that's not something that you can't just look at that and get a snapshot

33:10Jon Krohn:and understand it as a human looking at it. But we might not be far off from a machine that can explain that to us. And so maybe bringing this all back to shared intent and semantic protocols, It sounds to me like the semantic protocols that the distributed artificial superintelligence framework that you've developed at OutShift, it allows us to, regardless of what is being transferred, what specific information is being transferred between agents or humans in this multi-agent human collaborative system, whether it's natural language tokens or pixels or vector embeddings, whatever it is, The semantic protocols are a way of ensuring that the proper meaning is understood between multiple players in the system.

34:01The way I would phrase it is that we are starting with the language side of the equation. And so what we're starting with is the tokenized output. We're starting with natural language because that's where the world is today. And the other thing I would say is it's not apples to apples, but you can represent, like anything that you see or experience, you can represent through language, whether it's natural language like English or whether it's a mathematical language or the language of physics and equations. But most of the things that you see around you, you can represent in some form of language.

34:41It's an approximation. It's not apples to apples, but you can get pretty far along by just broadening your view of what a language is beyond natural language to the languages of science and physics and mathematics and so on. So where we are starting out with is a language, because that's also the world where Cisco plays in, where we are the glue in enterprises between heterogeneous systems in brownfield environments. And that's where most of the world is going to be. We are not going to appear in a world where it's a very purest one model just from one vendor. I mean, that's not where we play.

35:23So for us to tackle this heterogeneous distributed brownfield environment is where the bigger problems are in enterprises.

35:32Jon Krohn:Awesome. I love that answer. Thank you for clarifying everything for me. I think I now have a great picture of this first pillar of the distributed artificial superintelligence framework. Before I get on to the second pillar, I have a really quick random social side question for you, Vijay. Have you come across or maybe even watched a new TV series called Pluribus? Please do not give that away. I've been waiting to watch it. It's in my queue. It's in my queue. And I've been like, everybody has been telling me you've got to watch it. I was waiting for all the episodes to drop and now they've dropped.

36:03So I'm probably going to binge watch it right after this episode.

36:07Jon Krohn:Nice. Yeah. I don't want to give anything away because I really enjoyed having the story unfold for me without knowing what was going to happen at all. So I won't spoil anything for you at all, but I think you're really going to enjoy it. And I can't wait to record a third episode with you so that can be focused exclusively on Pluribus. And because there's ways that I'm not going to go into it all because I don't want to spoil anything for you. But there's things about our conversation that we've just been having, semantic protocols, these kinds of things that I think you'll, yeah, you might pull out of, you might see what you'll, you might understand what I'm saying now Now you're making a really difficult job.

36:45Jon Krohn:All right, so onward to the second pillar of your distributed artificial superintelligence framework. So you call this cognitive memory fabric. And this pillar is all around sharing knowledge. Like 20 minutes ago, I interrupted you as you were about to start talking about sharing knowledge to distinguish it against sharing intent. So now the floor is yours. Go for it. Yeah. So intent, I mean, intent can be shared through protocols and semantic protocols and so on. We just talked about that where you need to figure out grounding. you need to figure out missing information, the evaluation, and so on and so forth.

37:27But that is for the task, the objective that as a multi-agent human society you want to achieve, you need to share intent. But there is knowledge that is being built over time as well. So remember when we started back and compared this cognitive, agentic cognitive evolution to what happened with humans. The big thing there was, can we build up a knowledge base, the corpus of human knowledge, as we like to call it, the standing on the shoulder of giants, as we like to call it, what does that look like for heterogeneity? So what does that look like for many agents, for many vendors, inclusive of what humans contribute in that framework?

38:14And so the way we're thinking about that is to build out what we're calling a cognitive memory fabric. And the reason to pick those terms is, so it is cognitive in nature. So it's not data, it's not information, it's actually higher order. So that's number one. Number two is it's memory because it's persistent. So you are keeping it around for a while. You're summarizing things and you're you're extracting knowledge out of it. So that's number two. And number three is it's a fabric. So it's distributed in nature because by the very fact that we're looking at heterogeneous environments, there are things that we want to share and place in this fabric.

39:03And then there are things that we want to keep private. So as an example, I mean, you and I are having a conversation. This is a bad example, but this is getting recorded and this is going to get published on the internet, but let's say we were sitting in a coffee house somewhere and we're having a conversation. That conversation is private. So everything that we talked about in terms of shared intent applies because we are grounding ourselves, we are exchanging, we're discovering things, we're resolving conflict and so on and so forth. But there is no shared knowledge. I mean, the shared knowledge is only, shared.

39:40I mean, you've imparted knowledge to me and I have imparted knowledge to you. So we've become better individual geniuses in some ways. We've scaled ourselves vertically, but we have not published anything that anybody else can benefit from, or including us. I mean, 10 years later, we might forget a lot about that conversation. So we've solved the shared intent part. We've solved the semantic communication part, But we have not solved the shared knowledge part so that the bigger agent human society can actually go and do something with it. And so to us, the way to think about shared knowledge is so that we want to move the ball forward when it comes to the knowledge base of this shared multi-agent human society.

40:31And the way we think about building that out is we're looking at all kinds of memory. So we're looking at working memory, we're looking at knowledge, we're looking at ontologies, we're looking at knowledge graphs. And it's going to be a combination of all of those things shared between multiple entities with strict privacy and access control mechanisms layered on top of that.

40:57Jon Krohn:Nice. I like it. This makes a lot of sense to me. It kind of reminds me how in our previous conversation, when you were in episode 941 with me, you talked about how having different kinds of systems like knowledge graphs paired with probabilistic predictions. So this kind of this blend of very firm, very rigid knowledge with probabilistic language that those two kinds of systems paired together are what can make the most powerful AI systems of today. And so it sounds like perhaps with this now new term, new pillar, cognitive memory fabric that we're talking about in this episode, it sounds like it captures that idea from the previous episode, right?

41:45That's exactly where this came from. So going back to, again, the example that we've been running with, the cognitive example, which is you have two agents and they're trying to build the network for SwissCog. And this idea behind the cognitive memory fabric actually came because of the work that we did with Swisscom, where we realized that probabilistic engines like agents need to work hand in hand with deterministic software and a good, solid, formal representation of the world that exists. And so in this case, again, in the case of Swisscom, this was the network digital twin. And so what we realized was if you just throw agents into the mix and say, go and build out Swisscom's next network, that's a recipe for disaster.

42:41You can go the other way and say, let's just build digital twins and write deterministic software. and then you're not exploring the entire massive space that generative AI and agentic software does allow you to do. So you're writing deterministic software, which is very narrow in scope and space. So the combination of the two, which says, let's build a formal model of the world around us, in this case, a network digital twin, and then layer on these agents that can go and explore and just go at it, right? and be creative and do that shared innovation that we talked about. That combination is super powerful.

43:21And so we had some learnings from there. There were like three or four use cases that we went after and we realized we need to generalize this layer. This layer of representing formally what a world looks like so that other agents can act on it over time and contribute to it is what was missing. And so that's what the cognitive memory fabric is, which allows you to not just use it as a working memory of sorts, but synthesize it, summarize it, evolve it, redact, add to it. And that is what we are building out in a generalized manner. And that's what, yeah, it exactly came from this use case that we talked about.

44:05Jon Krohn:Cool. I'm kind of surprised that I nailed it so much on the head because, you know, lots of other points in this conversation. I guess any conversation I'm in, I'm like, ah, I think I have this really relevant point you brought up before. And you're like, ah, well, actually, so I'm delighted to have got one there. So we've now talked about the first two of three pillars for distributed artificial superintelligence. So the first one was semantic protocols for sharing intent. The second one, which we just were talking about was cognitive memory fabric for sharing knowledge. The third pillar is sharing innovation.

44:40Jon Krohn:And I understand you have something called cognitive engines as the formal name for your solution as part of this distributed artificial superintelligence framework. This brings up a quote from Raj Reddy, who's a Turing Award scientist. He came up with this quote, and I heard it recently being shared by Satya Nadella on one podcast. I think it was Lex or Dvakesh, I can't remember which one it was, but he brought this back, which is artificial intelligence in general, AI, can either be guardian angel or a cognitive accelerator. And that quote resonated with me so strongly because we were thinking about cognitive engines and then Satya brings up this quote from Raj Reddy and I'm like, yep, that is exactly what this cognitive engine layer is trying to do, which is we've done shared intent through semantic protocols.

45:41We've done shared knowledge through a cognitive memory fabric. What do we do with it? The whole aim is now going back and thinking about how do we do distributed superintelligence? How do we do economically viable work or new scientific discovery 100 % through agents, through a horizontal as well as a vertical scaling mechanism. And so that is a problem that we want to go after. And so while we get towards, let's say, solving for something brand new in this, again, going with a running example of let's build a net new network, which nobody has seen before. How can we build a net new network? Let's say it's a low-earth satellite network that they've never built before.

46:33That is new innovation. That is net new that none of them have been trained about. But they need to get towards building that through shared intent, shared knowledge, and shared innovation. So this is where the shared innovation piece comes through. None of them have seen it. They want to invent something new. The way you invent that is through making sure that you have cognitive engines that are helping you along the And there are two types, like Raj Reddy said. There are accelerators and then there are guardrails. And so the accelerators allow you to do things faster. So I want to lean on, the simplest thing could be, like we said, it could be a calculator.

47:17I want to lean on something that does certain tasks faster than any human or agent in this case can do. So we are building out engines that can accelerate certain aspects of a workflow. So that's an accelerator. Guard-race is like making sure that it's not going haywire. Because you're doing something new, you're inventing something new, you might not be within the boundaries of what is allowed. So how do you ensure that it's, again, going back to security or compliance, or it does not violate the laws of robotics. I'm just making that up, but there are guardrails that allow you to work in spaces that don't disrupt the laws of physics.

48:04So it could be as simple as, you know what, this is a world model, and I know what jello feels like, and that's encoded. I know what a wall feels like, and that's encoded. And this acid test that all robotics people go through, can you sort blueberries, right? So I know what a blueberry feels like, And that is encoded. And so these are guardrails that need to exist so that the models don't go beyond those guardrails. And so I think those are the two classes of engines that we want to build and then let these two agents of ours go and innovate. So you have the knowledge base, you have a way to communicate intent and coordinate.

48:47And now you have these two engines that you can rely upon, sets of engines, to accelerate work innovation. and be guarded so you're not doing something stupid. And across all of this, now you have shared innovation. Now just go ahead and have fun and give me the solution to the problem I'm looking for. Sorry. Yes, the solution to the problem I'm looking for.

49:10Jon Krohn:Yeah, yeah. This is so fascinating. And there's kind of even this energy in the way that you're describing it. I feel like I now need to kind of try to roll forward with your brain and kind of get your vision of how having something like this distributed artificial superintelligence can change the trajectory of what machines can be doing. We're kind of starting to talk about it there with maybe one agent has encoded what a blueberry feels like. And so it's able to be able to delicately pull blueberries. And sharing that information with other agents that have other expertises could allow for innovation to happen.

49:55Jon Krohn:So maybe there's another agent that knows how to use solar power to move a vehicle around a field. And so those two together can say, oh, wow, we should be making a solar powered blueberry picking device. And so that kind of gets me thinking about the same kind of vision for the future that you talked about a little bit in the previous episode. So in episode 941, you talked about how drug discovery could potentially be accelerated by agents working together. So you could have LLMs planning the research. You could have protein folding models exploring shapes, nucleotide configurations that lead to new kinds of proteins, robots running wet lab experiments, compliance agents monitoring everything.

50:45Jon Krohn:So with decentralized artificial superintelligence, with that framework, how does that kind of pipeline that you were describing in your previous appearance on the show evolve? Right. So that's a super, super awesome example there because it brings in capabilities that cross vertical boundaries. So let's think about direct discovery where you have maybe a large language model that allows you to first plan for what you're trying to achieve. So this is the goal building exercise and iterating exercise. So you want to build or create a drug that does X. So when you build a plan for that, you actually want to define the problem, the goal, really carefully and iterate with a large language model to then break down that goal into discrete tasks.

51:36So that's step number one. And you figure out which parts of the task goes to which kinds of agents moving forward. So that's the first step. And then from that, you might go into an agent that is based on an alpha fold-like scientific foundation model that is doing the protein folding exercise and exploring that space in a pretty comprehensive manner. From that, AlphaFold is going to give you a set of possible outcomes that look interesting. And you want to test that against these compliance agents, these effectiveness agents, these manufacturability agents, and so on. And so that's what you do next.

52:19And that's an iterative curve itself that you'll have to go through for a long time. Till you narrow the space further down and get to, yeah, maybe these are the 10 options that I want to take and do some wet lab experiments, actual rubber meets the road kind of situation here. And then you get into physical AI or embodied AI, and you have a bunch of these robotic labs doing these wet lab experiments to make sure that it passes muster in the physical world before you get into maybe human trials or even animal trials for that nature. And so this entire pipeline is going across multiple kinds of models from various vendors who are experts in each one of these things.

53:05And they need to get connected and talk to each other. And that's where the world is today. So just being able to pass messages is where the world is today. And that's what we're doing with agency and A2A and MCP and all the work that's happening today. But then all of these entities need to then share common intent, which is let's build or create a drug for this specific purpose with this price point in mind, with this population in mind, with these risk characteristics in mind. So that is the goal that needs to be defined. And that is the shared intent. And so you need a set of semantic protocols to make sure that you iterate on that, make sure you discover information, resolve conflict, negotiate, coordinate, and get to a common goal.

53:59So that needs to happen. Then we need shared knowledge because as you're doing these things, as you're iterating between the compliance agent, the manufacturability agent, and the protein folding agent, they need to share knowledge and look at what's worked, what's not. The compliance agent even needs to pull from the past and say, you know what, even if this looks like it's going to work, you're going to get stopped when you're looking for approval from FDA. So there needs to be shared knowledge and you need to add to it, redact, modify it. And then finally, you need these guardrails and cognitive engines that are doing guardrails and acceleration so that you can do these experiments faster, but within the boundaries of what's possible.

54:50And so that's the shared innovation that needs to happen above the shared intent and the shared knowledge layer. So that is how I see the drug discovery of the future that's going to happen, that's going to make me a healthier person and make me live for 200 years and let me play the guitars that I still have to pick up and I still don't know how to play. You don't know how to play the guitars behind you? I mean, this is always, it's like super intelligence, John. It's like you will always be working towards that goal.

55:23Jon Krohn:I see. I see. I got you. Well, they look, they're occluded a little bit, but they still look beautiful. I'm sure they'll sound nice once you have the time to play them when artificial super intelligence is handling everything from drug discovery, climate change issues, healthcare, education. It's unreal the number of areas that these artificially super intelligent systems, when they're distributed, when they can share intent, knowledge, innovation effectively, what they're going to be able to do for us in the coming decades. It's staggering and I'm really excited for it. So thank you for playing such a big part in this.

56:03Jon Krohn:Something that we talked about in the preceding episode that we haven't in this one is that you're doing this from the position of leading OutShift, which is an internal incubator at Cisco. Probably most of our listeners have heard of the company Cisco. For listeners who might not remember from your preceding episode on the show, can you remind us of OutShift's mission and where does distributed artificial superintelligence fit into the broader work you're doing there, including really mind-blowing things like quantum networking that you mentioned in the preceding episode? So OutShift is Cisco's internal incubation engine.

56:39So we are a group of people that are looking at horizon three, horizon two problems in spaces such as artificial superintelligence, as well as quantum computing, and spotting the next trends and paradigm shifts that are going to happen in these spaces, and making sure that Cisco is well positioned in those spaces to build businesses with those paradigm shifts. And the way we're thinking about those things is we put the Cisco lens on these problem spaces. And you mentioned this in the guitar playing narrative that John, but to me, these two problem statements of super intelligence and quantum computing are problems that we as an industry have worked on for decades in the past, and I believe we'll be working on them for decades to come, because these are shifting goals, and these are hard problems.

57:35And so we'll be working on both of these problems for a long time to come. But at Cisco, we have a pretty specific take on both of these swim blades. And the take is that we are a networking and security company, primarily, and then an observability company. And so we are in the business of enabling distributed systems. So the way we look at any problem statement is, whereas the world is trying to build these massively vertical, big, bad systems, or like we like to call it lone wolves, we are here to empower the pack. So while you are building the lone wolf, we are here to build our distributed scale, horizontal scale, scale out systems, and empower the pack.

58:23So when we look at superintelligence, and we've talked about this in this episode, we are looking at distributed superintelligence. And the same thing that we're doing in quantum computing, where as people build out these quantum machines, which are bigger and more effective and can solve larger problems, and have more and more qubits, we are here to build the quantum network that connects all of those quantum nodes together in a distributed quantum computing system so that you can solve problems, larger problems faster. So that's our entire gig when it comes to both superintelligence and quantum.

59:00And it's our job to spot the next paradigm shift and enable it to Cisco.

59:05Jon Krohn:Really cool, Vijay. For listeners out there who are inspired by the vision that you've been talking about today, which is about as inspiring as it gets, what can they be doing? I understand, for example, that you have a white paper that's just come out. It's called Scaling Superintelligence, a Cognitive Architecture for Distributed Artificial Superintelligence. Maybe tell us about that white paper where people could find it. I mean, we'll have it in the show notes, but maybe there's kind of the white paper. What are other things that people can be doing to engage with outshift agency or be involved in distributed artificial superintelligence?

59:42Right, so that white paper actually talks through a lot of the conversation that we just had today in this episode. And it talks through the parallels to human intelligence and how that has evolved as well. It talks about the details behind the three layers we just mentioned. So reading that white paper would be step one. Definitely go and check that out. I would say if you are a data scientist, a developer, or just plain not happy with the state of things when it comes to agentic AI and wished that agents could do something better and bigger and bolder and not be in this state where they are today, come and join that vision and come and join that mission because we are all in the same boat.

1:00:31We are trying to take agents and make them solve really hard and sometimes really boring problems. And the reason they're boring is that we've talked about them time and again, and we have not had the tools or the capabilities to solve them in new and different ways. And I think we are at the precipice of having those tools at our disposal. So let's come together and build those use cases out and bring us those use cases. Let's solve those boring, hard problems through these new tools that we have and look at that paper and help us solve this in a holistic way. Because yes, we are trying to solve the identity problem through agency.

1:01:09We're trying to solve the communication problem and messaging problem through A2A and MCP, the observability problem and the discovery problem. Those are all great problems, but we are looking at the weeds here. We need to take a step back. Let's say tomorrow we solve for all of those problems. We will not be solving for this vision that we have. We will not get to a place where agents can come together and solve for this definition of superintelligence because the bigger problems are the semantic problems. And we need to start with those problems right now. And the way to start with those is read the paper, but bring us the use cases and join us in this journey.

1:01:53Jon Krohn:Awesome. That sounds great. And because the agency is a open source Linux foundation project, People can also be getting involved through that route as well, right? That is a great way to get involved. You can go to agency.org. That's A-G-N-T-C-Y.org. Agency is a Linux Foundation project. We are also core members and launch members of the A2A project from Google, which is also a Linux Foundation project. And then just this November, we became a launch partner for the Agentic AI Foundation, where MCP exists. and that's also a Linux Foundation project. So there's a theme here, but we are launch partners for all of those three because we believe all those three projects are complementary to each other.

1:02:40They bring things to the table. They need to get glued together to solve for that communication layer problem that we just talked about. And then, yes, above this, we are building out the semantic layer and the knowledge layer to make everything work better. So, John, yeah, one thing I just wanted to state there is that I think I misspoke that, uh, the agentic AI foundation where we are a launch partner, as well as where MCP currently resides. Uh, it was formed in December and not in November. So I just wanted to clarify.

1:03:13Jon Krohn:Awesome. So many things for us to dig our teeth into after the episode, we already have your book recommendation from earlier in the episode. It's sapiens by Yuval Noah Harari. so we can skip right to my final question that I ask all my guests, which is Vijay, you are brilliant. You are such a joy to listen to. I loved our previous episode. I loved this episode with you. I can't wait to do another one. I'm sure our audience can't as well. But in the meantime, how can they get your thoughts? Where can they follow you on social media? I'm the most active on LinkedIn, which is linkedin.in-vjoy. So we'll put the link in there.

1:03:52So I'm the most active there. I do post on X, but it's actually getting very noisy. So it's hard to extract signal from the noise. But you can also go to vjarpanday.com, which is a personal website. But the best place to get all of this information is actually to head towards outshift.com, which is where it's not just me, because I'm just the spokesperson, but it's the team that is actually innovating, the team that is doing work, the team behind all of the things that we've talked about, they are providing more details and more flavor to what's happening. So go to Outdoor.com because that's where you'll find a lot of the stuff that we've just touched about and a lot more to come.

1:04:34Jon Krohn:I love it. Congrats also on getting the LinkedIn URL that's just your first name. That is quite a catch. Nicely done. All right. It's been so awesome having you on the show. And as I just said, I can't wait for the next time. Thank you so much for taking the time with us from your busy schedule. This was amazing, John. As always, a fun conversation. Thank you for having me. Always great to have Vijoy Pandey on the show. In this mind-blowing episode, he covered how distributed artificial superintelligence is the idea that artificial superintelligence will emerge not from building ever-larger individual models, but from enabling AI agents to collaborate, much like humans, scaled intelligence through language 70 ,000 years ago.

1:05:17Jon Krohn:He talked about how Now that cognitive revolution gave humans three capabilities, shared intent, shared knowledge, and shared innovation, the same three pillars Visualize team is building for AI agents. On that note, his team at Outshift, Cisco's internal incubation arm, is building this framework while contributing to open source projects like Agency, A2A, and MCP, so you can get involved too. As always, you can also get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Vijoy's social media profiles, as well as my own at superdatascience.com slash 961.

1:05:52Jon Krohn:And now it's my turn to give thanks to everyone on the Super Data Science podcast team, our podcast manager, Sonja Breivich, media editor, Mario Pombo, partnerships manager, Natalie Zajski, researcher, Serge Masise, writer, Dr. Zarikar Shea, and our founder, Kirill Aramenko. Thanks to all of them for producing another amazing episode for us today. For enabling that super team to create this free podcast for you, we are deeply grateful to our sponsors, you can support the show by checking out our sponsors links, which are in the show notes. And if you yourself would ever like to sponsor the episode, sponsor an episode, you can get the details on how by making your way to johnkrone.com slash podcast.

1:06:29Jon Krohn:Otherwise, share the episode with somebody who'd like to be blown away by distributed artificial super intelligence, review the podcast on your whatever podcasting platform you use to listen, or on YouTube, subscribe obviously if you're not already a subscriber but most importantly we just hope you'll keep on listening i'm so grateful to have you listening and i hope i can continue to make episodes you love for years and years to come till next time keep on rocking it out there and i'm looking forward to enjoying another round of the super data science podcast with you very soon