SingularityNET - A Decentralized, Open Market and Network for AIs (Whitepaper Explained)
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#ai #research #blockchain

Big Tech is currently dominating the pursuit of ever more capable AI. This happens behind closed doors and results in a monopoly of power. SingularityNET is an open, decentralized network where anyone can offer and consume AI services, and where AI agents can interlink with each other to provide ever more sophisticated AI, with the goal to create a singularity that's beneficial for humanity. This video takes a look at the basics behind SingularityNET and some of its core components.

OUTLINE:
0:00 - Intro & Overview
2:55 - Document Summarization Example Workflow
5:50 - Why AI needs a Marketplace?
9:20 - A network of APIs
12:30 - AI Evaluators & Matchmakers
15:00 - My criticisms of the Marketplace
17:45 - What is on the Blockchain?
20:45 - AI Marketplace Demo
22:00 - The AGI Token & Inflation
26:30 - Reputation System & other features
30:00 - Democratic Governance
33:00 - Benefit Tasks
36:15 - My general thoughts on the application examples
38:05 - Measuring Intelligence on SingularityNET
45:15 - OfferNet Economy
50:00 - Summary & Comments

Whitepaper: https://public.singularitynet.io/whitepaper.pdf
Website: https://singularitynet.io/
AI Marketplace: https://beta.singularitynet.io/aimarketplace

References:
https://www.hansonrobotics.com/wp-content/uploads/2018/12/Using-Tononi-Phi-to-Measure-Consciousness-of-a-Cognitive-System-While-Reading-and-Conversing.pdf
https://arxiv.org/pdf/1601.02626.pdf
https://blog.singularitynet.io/singularitynet-the-past-the-present-and-the-future-7bacb2b8e7f0
https://blog.singularitynet.io/singularitynet-supervisory-council-e7c513fd3ea6
https://blog.singularitynet.io/singularitynet-phase-two-massive-token-utilization-toward-decentralized-beneficial-agi-6e3ac5a5b44a

ADDENDUM:
I forgot to mention one important example for the utility of dynamic matchmaking: If I have a German text to summarize, and there is a German summarizer, but there is also a better English one, a clever AI could figure out for me whether to use the German one or whether to use a translator to English, then the English summarizer, then a backtranslator. And it could even do so depending on the input text.

Abstract:
[...] Most AI research today is controlled by a handful of corporations—those with
the resources to fund development. Independent developers of AI tools have no
readily available way to monetize their creations. Usually, their most lucrative
option is to sell their tool to one of the big tech companies, leading to control of
the technology becoming even more concentrated. SingularityNET’s open-source
protocol and collection of smart contracts are designed to address these problems.
Developers can launch their AI tools on the network, where they can interoperate
with other AIs and with paying users.
Not only does the SingularityNET platform give developers a commercial
launchpad (much like app stores give mobile app developers an easy path to
market), it also allows the AIs to interoperate, creating a more synergistic, broadly
capable intelligence. For example, if a text-to-speech AI and an Italian-to-English
translation AI were both on the network, then the network as a whole would be
capable of using Italian text to produce English speech.
Within this framework, AI transforms from a corporate asset to a global
commons; anyone can access AI tech or become a stakeholder in its development.
Also, anyone can add an AI/machine learning service to SingularityNET for use
by the network and receive network payment tokens in exchange. [...]


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 Hi there. Today we'll look at SingularityNet, the global AI marketplace, as it is advertised on their website. Specifically, we're going to look at the SingularityNet white paper 2.0, as it appeared in 2019. So it's version two, version one, I think appeared in 2017. So SingularityNet is a, as it says, a global AI marketplace, but it is also kind of an effort. It is a foundation, it has blockchain in it, it has AI in it, it has symbolic computation, it has graphs, it has all the things, all the buzzwords you could possibly want. So the high level summary of this system is that it is a marketplace for API is basically on blockchain, where either humans or API's can call other API's and pay them for that service. And the goal is to sort of get a network going of API's that call API's that call API's and sort of have that build into a global AI, not only marketplace, but like as itself a global AI. This is backed by the SingularityNet foundation. And they do a whole bunch of development of the platform, but also research on the platform. And we'll look at all of this today. So it is a white paper, which is not a research paper, as we usually look at. That means a bunch of things. First of all, as you can see, it's quite long, and we're going to skip most of it, actually. But also, I have maybe it's just it's just because it's a white paper, and that's usual. But this, all of this is, it's sort of marketing, and it's, it's, it's sort of never fixates on one level of analysis, like it goes into this, and then a bunch of buzzwords, and then super detail. And then it talks about, you know, what kind of cache do we need for the database, but then it goes back and it just references a bunch of stuff without explaining it to just kind of beef it up for investors, I guess. I don't know. In any case, we're going to go through it, we're going to go through what the marketplace looks like, how it works, what it's good for, or some of my criticisms. The central components, as I said, are the API's, but also a rating system. And it is also decent really governed. So the goal is to have the community govern the network. And lastly, the goal is to have all of this be beneficial for humanity. So we're going to see how this all ties together. So what's the the current the current situation and what the singularity net want to do. So let's say you are this external software, you're a person, okay. And what you want to do is you want to summarize a document. The view that this system has is that you could give this to a document summarizer. The document summarizer, however, looks at this and sees, oh, what are you giving me, you're giving me. And in this case, it might be, you know, an article of the New York Times that has both text and video, okay, so you give it you see an article has like a title, it has a bunch of text. And here it has like a little video to go along with it. And you simply say summarize this to me. So this document summarizer, all it does is it looks at the document and it sees up there is a bunch of text. And there is a video here. And I'm going to. So in order to summarize the document, I need to summarize the text and I need to summarize the video. So it will take the text and it will send it to a note that's dedicated only to text summarization. And then it will send the video to a note that's only dedicated to video summarization. The video summarizes summarizer in turn could do stuff like call face recognizers and call some databases in order to sort of get who is in the video or what's in the video, you could call object detection and so on. The text summarizer, in turn, it could call some word sense disambiguators, it could call entity extractors to also realize what is in the document. And then these nodes will send sort of so every node can call other nodes in the network. And at the bottom, you'll have these sort of AI primitives, like face identification, entity extraction, and so on. And they are not to be meant to be called by you directly, they're meant to be called by higher level nodes that sort of aggregate them. Okay. And this, if you look at this, and if you are a software developer, you, you think of libraries, like you think, of course, you know, this is this here, this stuff here is maybe that's hogging face. And this stuff here, probably in spacey that exists, right? If you are a software developer, you know, if you have to do subtasks, someone probably already solved that subtasks, I can just call a library. Now, the view of singularity net is that no, maybe you don't want to call a library. Maybe you don't know yet what's the best. So their view is a marketplace. And why is a marketplace better for AI than for regular programs? Because, you know, for regular programs, we don't need a marketplace, we simply call a library. Why is that not good for AI? I'm, you know, I'm trying to, I'm trying to sort of make sense of this right here. I am not convinced by this system either, but I'm sort of trying to make the best case for it that I can. So if you are this, let's go back to that graphic. If you are this text summarizer, and you need to do, you need to do entity extraction, right, you might have a lot of a lot of choice. So there might be, you know, entity, entity extractor, a, there might be entity extractor, b, and so on, there might be many of these entity extractors, and then a new paper comes out, right. And then entity extractor, f is somewhere on GitHub, you know, but so what you need to do every time a new entity extractor comes out is released, you know, someone makes a paper, maybe put some code, the code doesn't really work, you have to go fetch that code, you have to look, you have to plug this into your system, right, you have to test against your data sets, and you have to decide, is this better than what I had before? Or is it worse? Is it worth including and so on? So it is in the in the classic software world, if you have a library that does something, it does that thing, right, it cannot necessarily do it better or worse. However, in the machine learning world, it can definitely be you know, that this thing here is like 90% accurate, which is already good, but then something comes out with 95% accurate, and that's better, and you would like to sort of switch to the better thing, or the thing that meets your needs more, the thing that works on your test data set, and so on. So that's sort of the case to be made for an AI marketplace. Now, this singularity nets vision is that let's say, I'm a researcher, I come up with a new entity extractor, right? I have my so I have my paper here, I have it written, I have maybe a bit of code somewhere. What I can do is I can plug this into singularity net, right, and then I am say, here, here, I am entity extractor x, and you can advertise yourself to this network. And then all the other nodes like this text summarizer node, but you know, many other nodes could then come and sort of in an automated fashion, test some sort of test data set that they have against you, right, they tested against your system. And they can evaluate you and then they will switch to you to using your code. If you are better than the competition for them, or maybe if you're cheaper, right. And for that, if you're a researcher and do all that, for that you would get money, because every time a node calls you, they're giving you some money for analyzing their data. So that is the that is the sorry, that is the the core idea behind the AI marketplace right here. So the AI marketplace as a whole looks something like this. And there's a lot of stuff here. But we'll go through it sort of one by one. Okay, so it is so this, this here, it mixes kind of conceptual and technical and so on. But ultimately, you have is there a way I can draw this more easily? Yeah, maybe. Okay, so you have consumers, okay, and consumers can be people, or can be robots. And you have a whole network of them, right. And the robots, if it's a robot, the robot exposes an API, as we said, the robot exposes an API that says exactly what inputs it takes and what outputs it provides. And it can also do tags. So here are my inputs, here are my outputs, and it can it can have some tags, it can, for example, say, Hey, I am an entity extractor. My, you know, I do it, I do entity extraction in English, and, and so on, though, maybe the English would actually go into the into the input definition. So we could do entity extraction. So the input definition says I need a string that's called text. And that string needs to be language English. And for that, I can produce a set of a list of entities, and to T, something like this, okay, it is very much like you would specify an interface in regular programming, except that in singularity net, these types here, so the string with the language parameter, and like the definition of what an entity is, they are set, I don't want to say centrally, because it's on a it's on a blockchain. But in essence, they are on the blockchain centrally deposited, you can add your own, but you can also implement the the ones that other people have already defined. And what would be the good thing about not defining your own? Well, if if this is the kind of commonly agreed upon standard for entity, or entity recognition, did I say augmentation extraction entity extraction, I said, I put an A all the time, sorry about that. If this is the common definition for entity extraction, and you implement the same right, you have your new algorithm over here, and you implement the same API, you know, you have this this green API, and you implement the same types, then anyone who uses this API, can, if they want switch without any work to your API. And if you are better, then, you know, you get probably their business because they want to call the better one. The idea of singularity net actually goes further, because this is not only callable by humans, this is also callable by other robots. So here I have a other robot. And this is a special robot, because this robot is like an evaluator robot. So this robot can go around, and it has a little data set inside of it. And it will just do nothing else but scan for new AI's on the network that implement a certain API, it will recognize it will say, ah, this is the this is the API for entity recognition, or entity extraction, I will simply run my test data set against it. And I will run my test data set against this and so on. And I will report. So my API will be, I simply output, I simply so input would be a task name. So task would be a string or something like this. And the output would be a list of model and performance like model a model m 90% model x 95%. Okay, so there couldn't there can be robots that test other robots, and then publish sort of ranking lists, and then I as a, like, I as a human or the robot, you know, the the higher order robots, they can go read this robot, and then decide to which of the of the all the listed and things they want to go. So at central core to the system is this kind of shared type system. If you share the types, if you share the API's, your API's become replaceable with one another. And therefore you can enable sort of automatic competition and automatic matchmaking. So these robots, the there are evaluator robots, and there are matchmaker robots, where you can tell a robot, I would like to extract some entities, please find me the best node in the network that does it. Okay, and the marketplace makes sense because it's AI and it constantly shifts which one is good and which one's appropriate. That's the best case I can make for it. Like, I have my doubts that this is actually the case, like, but we'll get to we'll actually know let's make the case against it. So my case against the AI marketplace as it is listed here is twofold. So first, first point against it. Everything we know right now is end to end. The direction of research is clearly going into less structured data and more end to end. That means if I want to do a text summer or a document summarizer, I am right now much better off just training a giant model that does it end to end, rather than using many, many small models. Because if I call an entity extractor, right, and I simply only rely on that information, I lose the rest of the text and the nuances in the text, I simply get the output of that model. Now, I could combine that, of course, but this this idea of modularizing AI, I'm right now, research is pointing into a different direction. And second of all, I still believe, like, if I make a product, if I build a product towards a user, I want to know what's in it. Like even if I have to go with myself and test the stupid API, I would never use like a matchmaking agent that dynamically goes and finds me someone who can implement this API. Because implementing an API only goes so far implementing, you know, like I require image and I output value, that's an API. But that can be many. And then you know, maybe these tags here, maybe these tags could do something. But it is not like I think the system, even though it's, you know, thought out well with the types and the API is and so on. I don't think that's enough. I think that works for a very, very small subset of AI tasks. I don't think that works for most of the AI tasks that we have right now, because simply API definitions just don't convey what the models so wait API. So API does not convey what the model does function. In my mind, so I would ask yourself if you would if you were there to use a matchmaking agent, and then you know, sell that product to a customer. It's it's but I guess the goal here is that in the future, these matchmaking agents will be much more intelligent and so on. Yeah. So here's how it works on a more sort of technical level. So there is two components here, there's off chain and on chain. So if I'm assuming you know, what a blockchain is, if you don't know what a blockchain is, a blockchain is basically a distributed database, and in some forms, also a computation engine. So it's kind of a distributed computer that you can't fake. So you can't cheat, no one has authority over it, everything is visible. And so that's secure. The drawback is you cannot do hardcore computation on blockchain. So this is not AI on blockchain, the blockchain is simply there to first of all, register the AI's so register the types. So this this API is here, and register what AI's are available in the network. And second of all, to facilitate the payments to the AI. So how does that work? It goes via this sort of multi party escrow escrow contract right here. So there's a registry, by the way, that's where AI's register and put their types. So that's one function of the blockchain. The other function is to escrow money. And this, if you know, lightning network is very similar to this. So what you would do if I don't know, Alice wants to call Bob, Alice would sort of put a bunch of money like a big bunch of money. How do I do that? Alice would send money to this escrow account like this much money. And then that establishes a channel between Alex, Alice, sorry, and Bob. So there is a channel channel is opened, and it's tied to this money. And now Alice can sort of send incremental amounts of that money to Bob. And every time you know, one of these, like a little bit of that money is used up. And the way the reason you do it in escrow form and not so all of these could be transactions on the blockchain, right. But that's first of all, it's slow. And second of all, it's expensive. And if you do it like this, you actually only need at, you know, you need one transaction in best case. So if Alice spends this much money to Bob, there needs to be only one transaction to putting all of it to Bob at the same time rather than all these small transactions. So that's kind of the the channel principle. I think yeah, it's very similar to lightning network. And it's still secure. So there, it's still secure. The way it is done. I don't want to go into channel economics and security right here. But suffice to say, you can make this secure and fast to a certain degree. Okay, so that's how it works. Every time you call an API, you just send it some money in order to call it. So how does this look? This looks something like this. Sorry. Here is this AI marketplace, they've actually built it. And they have a bunch of services on there. As you can see, it's, it's, it's kind of they take some standard AI tasks, and they put them on here. And if you click on one, you can either, you know, pay a GI tokens. That's a thing we're going to get to in a second. Or you I think you have like 10 free calls a day if you make an account. So I've tried it out, you know, it works. But it's important to realize that the computation does not happen on the blockchain, you send money on the blockchain, and the AI service, it runs off chain. So this is off chain. Okay. So it is not a secure AI, you still need to trust the thing you're calling, um, it's not about privacy that much, but you, you can't verify the outputs, you can't verify the computation as you could if if it were happening on chain. Now there are methods to sort of do heavy computation on chain, but these, I guess wouldn't be that efficient. So just take that in mind. Now, the other thing is, I always sent say, you send around money. But what you actually send around is a token. So a token is a very special concept. If you if you don't know what a token is, it's like money on top of money. So it's like if you go to a fair, and the fair has like its own internal money system, and at the beginning, you pay like 20 bucks, and you get 100 fair coins, and you can use the fair coins inside the fair. And that just enables the fair to sort of have its own monetary policy. And it's usually done with these projects to at the very beginning, you sort of sell those coins to a lot of people and the people buy it not because they can use it right there, but they estimate they can use it later. And it's a way to found a project that's called an it's called an initial coin offering usually or initial token offering the coin that singularity that uses is aptly called a GI. And there is 1 billion. And you can see here, it's still active. So it's still being traded. You can see this is an hour ago, 20, 15 minutes ago, and so on. If you look at here is analysis. If you look at the activity on the network, it had a lot of activity at the beginning, it dropped and now it picked up a little bit again. I don't know exactly what that's related to. But so it is still alive. If you look at the price, however, this sharply dropped and is now actually below the price of the initial coin offering. And what you hope when you you know, buy the initial coin is not only that you can use it later, but you know that since there is only limited amount of tokens that that will be more valuable in the future. Because people want to buy it off you because they want to use the network here, it sort of looks like that's not exactly happening. And we'll get to what they're doing against it. Right in a second, the answer is inflation. So in a new blog post, actually, as I was preparing for this video, this new blog post came out yesterday. And here, they're announcing sort of the path forward Singularity Net phase two. And essentially, what they're doing is they're switching blockchains from Ethereum to Cardano. And I have my doubts isn't like I don't know much about this whole the whole crypto space, but isn't Cardano where massive amounts of the of the coins are like in some I think there are massive amounts that are just never moved and so on. And it's quite scary. But you know, they probably know what they're doing. And with that, they are doubling the amount of tokens like they could do it without increasing the token tokens. But with that, they're issuing another billion tokens, I think 50 or 25% will go to themselves. So that's usually you do that in initial coin offering, right, you keep some of the tokens to yourself, because as people buy it, it's valuable. And that's how you fund the operation. So here, they need to fund it some more. So they just inflate the currency with the new with the new token. And they project, you know, they project that the network is used is going to be used a lot more than double now. So I guess if you buy the new tokens here, phase two plan five years from now, there will be 2 billion instead of 1 billion tokens, my strong assessment is that in this case, the overall value of the network in 2025 is going to be far more than twice what it would be if we didn't release the new token. So they need money. They inflate the currency. It's you know, it's government. I guess it's valid, but just just to be aware. Okay, that's the network. There are a few crucial components that I have left out now. But that's essentially how it works. So one crucial component, so you the registry is where you register. One crucial component is the reputation system. And this is something that's quite difficult. So the reputation system is important, because if you want to sort of find agents that that perform well, you can also sort of rely on reputation. So if a lot of people have bought services from a particular node in the past, and they rated high, then you can sort of trust that node more than if if a node is lower rated or has dissatisfied customers. So they spent quite a bit here talking about reputation systems and how you could do them. And that is an open area of research is really hard problem to make a good reputation system that can't be gamed and so on. Yeah, there are various ways like, for example, a stake deposited by a consumer service owner to be forfeited should its rating in some dimension fall below a given threshold. So you can like put some money and say, Well, I I if my rating falls below a three, then that money is gone, I will like it's burned, it's automatically burned and that gives people more trust in you because you're now forced to uphold that rating. But it also allows some kind of mafia games like you could go to that, you know, service owner be like, well, it would be a shame if you had a bunch of one star ratings coming in, then you can sort of blackmail them in given circumstances. It's not easy, right? It's not easy. But that's built into into it. By the way, because this is on chain, anyone can participate in the market permission less, which is a really good thing. However, they maintain kind of a a DAP a centralized platform where they that they control. So you you sort of have this decentralized thing wherever you can participate, but only some people are listed on the central on the main hub, let's say, but you can technically build your own hub, like you can build you can build your own Android app store and so on. So think of it like, it's a marketplace for apps, but only the ones that are, you know, KYC compliant will be in the in the Google App Store, but you can build your own alternative app store. They also want to provide AI infrastructure as a service. And that I feel it's really irrelevant, like they say, okay, we want to provide this, but it really doesn't matter for the singularity net. So they, they, here is where they go into, oh, you could do this, you can do that with it, and so on, you can deploy it on embedded devices. So their idea is really that the whole world will be connected to this network. And whenever you require any sort of functionality, you just call the network, and the network solves your problem. As I said, I'm kind of doubtful, I still think it's probably going to be people just build the functionality either into a custom, you know, uni service, or they, they just build it on device. So the last component here is democratic governance. So they are, they are invested in, in sort of making this a community effort. And one thing is this governance, right? How do you govern decentralized organization? And that is also an unsolved problem. They do it in multiple stages. So they stay say, okay, in years one and two of network operation, basically the foundations, the foundation says everything in according to any any major changes the foundation decides. So the foundations are the maker of the network. In years three and four, they transition. So major changes, agreement of the foundation plus a majority AGI holder votes. Minor changes don't actually even require the foundation. And then there's also this introduction of benefit tasks. Yeah, so years three and four, and from year five on forward, this the the foundation is gone. And only there it's only done by votes by AGI token holder votes, which are logarithmic such that rich people don't have too much power. Yeah, so this was launched in 2017 at the end. So technically, we are in this phase right here. And I have searched for like an announcement that yeah, we're going to transition from this mode to this mode. But I haven't found it on their blog instead of what I found are like announcements that they're going to they're going to launch this supervisory council, which are like elected members that check the foundation. And also in this roadmap of part two that we've just looked at, they also saying, oh, progressive decentralization, making it real. They also talk about this supervisory council, and they now pay them and they release financial reports. But nowhere does it say that you see here, it's 3.5 years in so they should be in that second phase. Maybe they are, but I would guess they'd make an announcement if that's the case. Maybe I've just missed it. And they're actually doing this. But I have my feeling that if you you know, launch such a system, and you have power to do stuff, and especially this if the system doesn't grow as much as you expect, and so on, you're not going to give that power away. So that's, that is my my doubt here is that if you have the power, it's of course, it's always better for you if you say, well, I'm just gonna hold on to it a little bit longer. Eventually, you know, when everything goes well, but it's never that everything goes well. Like, yeah, alo communism. Okay, so enough rant. The benefits tasks. So they also have in mind, you see, there's a lot of stuff in this network, right? They also have in mind that this this network should benefit sort of humanity as a whole, which is, you know, a laudable task. But they have a system where it's some tasks are classified as benefits tasks. And the these benefit tasks, they are suggested by by a GIs by actors in the network that has so each agent gets a certain number of benefit votes, right? to cast each month based on its benefit rating. So the rating system is multi dimensional. One aspect is the benefit rating, someone can rate you beneficial if you like, do if you're a GI cures cancer or something like this. And, and then you nominate you vote. And then some of the some money goes to these benefit vote winners. Once a qualified benefit decided nominates a certain task, yada, yada, yada, yada, yada, if 25% votes are cast in the affirmative, then the task becomes a benefit task. Once a task is a benefit task, any agent capable of performing it and possessing a sufficiently high rating, and benefit rating will receive benefit payment for doing it. Okay, so the idea is the community nominates beneficial tasks, and these tasks will get benefit payment. Like, the only question is, where does this come from? Where does that money come from the benefit payment? So I guess it has to come from other people. So you have to have like some sort of a benefit tax or something like this, that you have other transactions that you give to the benefit tasks. And then this is like, you the whole system work, there's nothing about this that makes it benefit specific, you can switch out the word benefit by evil, like some you have an evil reputation, and then some tasks are evil, and get evil votes. And if you are especially evil, you get evil payments. This whole notion rests on the fact that people somehow recognize what's beneficial, which is a highly, highly controversial, right. And it's basically politics, right? Every politician advertises themselves as beneficial, every, every, you know, organic food is beneficial. But then you just do the bare minimum, you like cut, you take 99% of tomatoes, and you put a little bit of dirt on top of them, and boom, they're organic, like they're now labeled as organic. It's, it's, I this is, to me, this just seems like a thing that's going to be gained so hard, it's going to become irrelevant. It's basically a political game at this point. Because you cannot define benefit other than through human voting, and human voting is subject to money. And yeah, that's how politics starts. Okay, so they have, they have a lot of examples. So here you see sort of this network idea, they have a lot of examples, what can be done with this, I don't want to go into into these, because this video is already quite long. But it's, it's a lot of talk. I just want to say that it's a lot of talk. And, you know, they're basically putting up everything they have done so far, and they're doing on the network, what they can do with the network, which is all cool, right? It, but it's it's sort of advertising, what kind of research they do on it. And yeah, the last point. The last point. Yes, it's very long. So these people, for some reason, they actually, they're like two things they love or three, there's graphs, domain specific languages for some reason, they love graphs and domain specific languages. So their idea of AI, it all revolves around kind of classic notion of AI. So there is knowledge bases, and then there is graphs that and you can see this reflection in singularity net, right? This idea that lots of things by themselves network together can make up a bigger AI and so on that it is exact reflection and exactly goes counter to like the deep learning idea of let's do everything end to end. So the singularity net here is very much a reflection of what these people think. And yeah, for some reason, they love inventing DSL for new problems. Like why? What like, I've never understood DSL aficionados, but I guess if you are, you're having fun. Okay, so here they say, measuring modeling and extending singularity net. Okay, so this is sort of their research on singularity net itself, which is, you know, quite a, a important thing if you build a system like this. But what I want to, I wanted to do so, I've read through all of this kind of research suggestions and what they're doing, and they just make it seem great, but it's also very washy, in my opinion, and I was wondering, is it just because it's a white paper? And I you know, there's actual good research and for most things, I can definitely guess you know, they're, you know, they're also the people behind this Sophia robot. I don't know if you you know, like this Sophia robot and so on. They so they have a lot of success. So precision medicine and so on. There's a lot of research, but some things just sounded also just washy. So here that this is something that made me particularly just kind of stop. So they want to measure with this phi quantity for measuring integrated information in complex cognitive networks. So this phi, this number phi by this researcher tontoni is sort of a measure fundamental measure of the level of consciousness. And they themselves say, you know, maybe it's net, it's not, you know, the measure, but it's certainly an interesting measure, and so on. And they say we have experimented with measuring phi across time series generated by open call, by the way, open cog is from the same person that's one of the co founders, Ben Garth, so of singularity net, open cogs attention, allocation module, yada, yada, yada. While the while the system parsed and semantically analyzed a series of short documents, we have also calculated phi values while the open cogs system controlled the Sophia humanoid robot, as she led a person through a structured meditation system. So they like the extent of them describing the research is simply we have experimented with it. And we have measured it across time. And so I was wondering, like, what's behind this? So I went and I read the paper that's linked there. That's this using tontoni phi to measure the consciousness of a cognitive system while reading and conversing. And so this is a paper, it's quite short, but they let it read like texts from about different things. And they measure this phi quantity. And when you go and look first, what's this phi quantity, this is kind of a one of these papers, it's, it's very mathematical, actually. And there's a lot of information theory in there. So it has something to do with mutual information, there's a lot of ways you can calculate it, as you can see here on the left, and there's a lot of ways you can approximate it. So this is like a serious quantity. But measuring it is like super hard. And here, they let this open cogs system read short texts with, with respect to, as you can see here, poison and insects. And they look where the sort of, I guess the attention, the attentional focus of the system rests on which of these concepts, right? And then they measure the phi over time. And their claim here is I was okay, we also calculated five based upon the concept nodes. No, wait up here. As the system ingests each sentence, word nodes corresponding to each word are simulated as stimulated with this system, thus triggering attentional focus dynamics correlated with the reading process. One goal of the study was to observe whether after reading documents regarding insects, then poisons attention would spread to the concept related to insect to insecticide. This phenomenon did occur. So they say, okay, when you read, when you read insect and poison, after that, you got to put a focus on insecticide. And you can see so insect is blue, poison is orange, and you can see maybe the insecticide, you know, bumping a little bit after while you read poison. But honest, like this could also just be because it's associated with poison. This is, you know, I don't know that this is a bit interpreted a bit too much into that graph. And then what's even more astounding, we also calculated five values based on the concept node insect, poison and insecticide as figure three shows, there was an interesting jump in the five value when insecticide first became important, suggesting that the five increase was correlated with an increased complexity of attentional spreading within the atom space. So the atom space and so on, that's that's sort of this classic AI concept of knowledge bases and atoms. But here, so the claim is that the fire on the right somehow, somehow correlates with the insecticide attention on the left or with anything interesting. And that to me is a stretch. In fact, I have, I've put the I've put these things above one another. So in the gray background here, you can see the five value, and I've matched up the the time steps right here. And so the claim is that here, insecticide marginally bumps up, and then sort of this five spike is here. But if you look anywhere else, like here, insecticide bumps up, okay, but much delayed spike, and here, it doesn't bump up at all. But there's a spike still. And it's, it just seems, it just like that is just not a inference you can make right here. Like, I'm not sure. Let me let me know what you think. But if you know, you can't just nah, nah, sorry. This one, you know, this one, it was the one that that was kind of the most strange to me. But also, yeah, don't, don't, don't tell me that this does anything. But in any case, they, this is the type of research that they do. And so they measure these measure the intelligence of the system, and so on. Yeah. The last thing is these, what they want to do is this offered net economy. And you know, in researching this paper, I have also watched a bunch of talks from from Ben, and it seems like sprawling with ideas. And the talk about these offer nets is, is also so the idea behind it is that offer net is sort of an economy without money. The offer nets domain model, the other where is it? So huh, I don't I don't remember where it said, but offer nets is like an economy without money. So the idea behind it is okay, person A, person B, person C, or machines, they are sort of in an economy. And person A wants something that person B has, but B doesn't want something that A has. But instead, B wants something that C has, and C wants something that A has. And the logic here is couldn't you, you know, a cannot, a cannot trade with B, B cannot trade with C, C cannot trade with a but they can trade in a circle, right. And this offer nets, they do make this possible. And so that the idea is sort of everyone puts out there what they want. And the offer nets, they will sort of figure out, they will figure out who needs to trade with whom. And thereby, you could make an economy without money, right, without Yeah, you can make a money free economy. And is this the right paragraph? Because there was a fun sentence, there was a fun sentence that I've I've seen right here. So this is another another thing where I think that just like that the ideas they go a bit, they go a bit too far. offer nets analyzing the data, yada, yada, yada, open ender process. Okay, I don't I don't know where it was. But they say something like, yeah, offer nets could mediate this process. And I'm, and how do they mediate this process, you know, such that everyone actually gets their worth of stuff that they put out, they mediate this process by means of the offer coin. Okay, so the offer coin is now transferred from B to A, or sorry, or from A to B, let's say because a wants something that B has, and the offer coin is transferred from B to C, and then from C to A. So the offer coin makes all of this happen in an economic sense. And like, huh, are you saying there is an asset going along with a certain service, and the asset is sort of agnostic such that you can, if B gets the asset from A, B can then give the asset to C in order to obtain services from C. And that, you know, asset actually is what makes the whole economy work, even though no one directly wants to trade with each other. And you're doing all of that without money. That's crazy. So yeah, in any case, I think, oh, ah, there we go. Offer nets. A decentralized economy providing an alternative to purely currency based exchanges. This economy features a complex network of interactions that optimizes reciprocal changes of goods and services by finding agents with compatible and complementary preferences and coordinating their interactions dot dot dot by means of a coin, which is money. That's this is exactly what money does. Like that. That's what money is for. In any case, I'm like this. These people are very smart, and I'm probably too dumb to see what the exact difference is right here. So I just found it funny. If you know, if I'm completely wrong, then let it be stated that you know, that's what a semi only semi smart person would conclude from reading these things. All right, this was lengthy. But I hope you sort of got the idea. The base system is an a an API marketplace. Now the API marketplace in itself doesn't have anything to do with AI necessarily. But I've made the case that the API marketplace only makes sense in the in the world of AI, because if it was regular software, you would just hard code either the API calls or you would actually include the library. So the marketplace makes sense in the realm of AI. Okay, it's doubtable whether that's actually the case. It very much goes against the end to end principle, it bets on a form of AI that works on discrete graphs, it works on sub components divided into sub components, it works on networks, networks built together to achieve higher order functions, it could definitely be that the future of AI lies in this direction. It's just that the current direction is pointing away from that. The whole marketplace runs in on the blockchain, and only the marketplace so the AI processing is off chain. So it is not a on blockchain AI. And yeah, they've built it and they are in money problems. Currently, they're inflating the currency. But they're switching blockchains, because they think the new blockchain will be better and faster. And they project high growth and the token is actually active. So it's you know, it's not a dead project. And they are in the news quite a bit, especially with this this Sophia robot, I think that is that is a very it's a kind of a PR magnet. Alright, that was what I had to say. I hope you enjoyed it. If you did share it out. Let me know what you think in the comments. Let me know what I did wrong. And bye bye.
 Hi there. Today we'll look at SingularityNet, the global AI marketplace, as it is advertised
 on their website. Specifically, we're going to look at the SingularityNet white paper
 2.0, as it appeared in 2019. So it's version two, version one, I think appeared in 2017.
 So SingularityNet is a, as it says, a global AI marketplace, but it is also kind of an
 effort. It is a foundation, it has blockchain in it, it has AI in it, it has symbolic computation,
 it has graphs, it has all the things, all the buzzwords you could possibly want. So
 the high level summary of this system is that it is a marketplace for API is basically on
 blockchain, where either humans or API's can call other API's and pay them for that service.
 And the goal is to sort of get a network going of API's that call API's that call API's and
 sort of have that build into a global AI, not only marketplace, but like as itself a
 global AI. This is backed by the SingularityNet foundation. And they do a whole bunch of development
 of the platform, but also research on the platform. And we'll look at all of this today.
 So it is a white paper, which is not a research paper, as we usually look at. That means a
 bunch of things. First of all, as you can see, it's quite long, and we're going to skip
 most of it, actually. But also, I have maybe it's just it's just because it's a white paper,
 and that's usual. But this, all of this is, it's sort of marketing, and it's, it's, it's
 sort of never fixates on one level of analysis, like it goes into this, and then a bunch of
 buzzwords, and then super detail. And then it talks about, you know, what kind of cache
 do we need for the database, but then it goes back and it just references a bunch of stuff
 without explaining it to just kind of beef it up for investors, I guess. I don't know.
 In any case, we're going to go through it, we're going to go through what the marketplace
 looks like, how it works, what it's good for, or some of my criticisms. The central components,
 as I said, are the API's, but also a rating system. And it is also decent really governed.
 So the goal is to have the community govern the network. And lastly, the goal is to have
 all of this be beneficial for humanity. So we're going to see how this all ties together.
 So what's the the current the current situation and what the singularity net want to do. So
 let's say you are this external software, you're a person, okay. And what you want to
 do is you want to summarize a document. The view that this system has is that you could
 give this to a document summarizer. The document summarizer, however, looks at this and sees,
 oh, what are you giving me, you're giving me. And in this case, it might be, you know,
 an article of the New York Times that has both text and video, okay, so you give it
 you see an article has like a title, it has a bunch of text. And here it has like a little
 video to go along with it. And you simply say summarize this to me. So this document
 summarizer, all it does is it looks at the document and it sees up there is a bunch of
 text. And there is a video here. And I'm going to. So in order to summarize the document,
 I need to summarize the text and I need to summarize the video. So it will take the text
 and it will send it to a note that's dedicated only to text summarization. And then it will
 send the video to a note that's only dedicated to video summarization. The video summarizes
 summarizer in turn could do stuff like call face recognizers and call some databases in
 order to sort of get who is in the video or what's in the video, you could call object
 detection and so on. The text summarizer, in turn, it could call some word sense disambiguators,
 it could call entity extractors to also realize what is in the document. And then these nodes
 will send sort of so every node can call other nodes in the network. And at the bottom, you'll
 have these sort of AI primitives, like face identification, entity extraction, and so
 on. And they are not to be meant to be called by you directly, they're meant to be called
 by higher level nodes that sort of aggregate them. Okay. And this, if you look at this,
 and if you are a software developer, you, you think of libraries, like you think, of
 course, you know, this is this here, this stuff here is maybe that's hogging face. And
 this stuff here, probably in spacey that exists, right? If you are a software developer, you
 know, if you have to do subtasks, someone probably already solved that subtasks, I can
 just call a library. Now, the view of singularity net is that no, maybe you don't want to call
 a library. Maybe you don't know yet what's the best. So their view is a marketplace.
 And why is a marketplace better for AI than for regular programs? Because, you know, for
 regular programs, we don't need a marketplace, we simply call a library. Why is that not
 good for AI? I'm, you know, I'm trying to, I'm trying to sort of make sense of this right
 here. I am not convinced by this system either, but I'm sort of trying to make the best case
 for it that I can. So if you are this, let's go back to that graphic. If you are this text
 summarizer, and you need to do, you need to do entity extraction, right, you might have
 a lot of a lot of choice. So there might be, you know, entity, entity extractor, a, there
 might be entity extractor, b, and so on, there might be many of these entity extractors,
 and then a new paper comes out, right. And then entity extractor, f is somewhere on GitHub,
 you know, but so what you need to do every time a new entity extractor comes out is released,
 you know, someone makes a paper, maybe put some code, the code doesn't really work, you
 have to go fetch that code, you have to look, you have to plug this into your system, right,
 you have to test against your data sets, and you have to decide, is this better than what
 I had before? Or is it worse? Is it worth including and so on? So it is in the in the
 classic software world, if you have a library that does something, it does that thing, right,
 it cannot necessarily do it better or worse. However, in the machine learning world, it
 can definitely be you know, that this thing here is like 90% accurate, which is already
 good, but then something comes out with 95% accurate, and that's better, and you would
 like to sort of switch to the better thing, or the thing that meets your needs more, the
 thing that works on your test data set, and so on. So that's sort of the case to be made
 for an AI marketplace. Now, this singularity nets vision is that let's say, I'm a researcher,
 I come up with a new entity extractor, right? I have my so I have my paper here, I have
 it written, I have maybe a bit of code somewhere. What I can do is I can plug this into singularity
 net, right, and then I am say, here, here, I am entity extractor x, and you can advertise
 yourself to this network. And then all the other nodes like this text summarizer node,
 but you know, many other nodes could then come and sort of in an automated fashion,
 test some sort of test data set that they have against you, right, they tested against
 your system. And they can evaluate you and then they will switch to you to using your
 code. If you are better than the competition for them, or maybe if you're cheaper, right.
 And for that, if you're a researcher and do all that, for that you would get money, because
 every time a node calls you, they're giving you some money for analyzing their data. So
 that is the that is the sorry, that is the the core idea behind the AI marketplace right
 here. So the AI marketplace as a whole looks something like this. And there's a lot of
 stuff here. But we'll go through it sort of one by one. Okay, so it is so this, this here,
 it mixes kind of conceptual and technical and so on. But ultimately, you have is there
 a way I can draw this more easily? Yeah, maybe. Okay, so you have consumers, okay, and consumers
 can be people, or can be robots. And you have a whole network of them, right. And the robots,
 if it's a robot, the robot exposes an API, as we said, the robot exposes an API that
 says exactly what inputs it takes and what outputs it provides. And it can also do tags.
 So here are my inputs, here are my outputs, and it can it can have some tags, it can,
 for example, say, Hey, I am an entity extractor. My, you know, I do it, I do entity extraction
 in English, and, and so on, though, maybe the English would actually go into the into
 the input definition. So we could do entity extraction. So the input definition says I
 need a string that's called text. And that string needs to be language English. And for
 that, I can produce a set of a list of entities, and to T, something like this, okay, it is
 very much like you would specify an interface in regular programming, except that in singularity
 net, these types here, so the string with the language parameter, and like the definition
 of what an entity is, they are set, I don't want to say centrally, because it's on a it's
 on a blockchain. But in essence, they are on the blockchain centrally deposited, you
 can add your own, but you can also implement the the ones that other people have already
 defined. And what would be the good thing about not defining your own? Well, if if this
 is the kind of commonly agreed upon standard for entity, or entity recognition, did I say
 augmentation extraction entity extraction, I said, I put an A all the time, sorry about
 that. If this is the common definition for entity extraction, and you implement the same
 right, you have your new algorithm over here, and you implement the same API, you know,
 you have this this green API, and you implement the same types, then anyone who uses this
 API, can, if they want switch without any work to your API. And if you are better, then,
 you know, you get probably their business because they want to call the better one.
 The idea of singularity net actually goes further, because this is not only callable
 by humans, this is also callable by other robots. So here I have a other robot. And
 this is a special robot, because this robot is like an evaluator robot. So this robot
 can go around, and it has a little data set inside of it. And it will just do nothing
 else but scan for new AI's on the network that implement a certain API, it will recognize
 it will say, ah, this is the this is the API for entity recognition, or entity extraction,
 I will simply run my test data set against it. And I will run my test data set against
 this and so on. And I will report. So my API will be, I simply output, I simply so input
 would be a task name. So task would be a string or something like this. And the output would
 be a list of model and performance like model a model m 90% model x 95%. Okay, so there
 couldn't there can be robots that test other robots, and then publish sort of ranking lists,
 and then I as a, like, I as a human or the robot, you know, the the higher order robots,
 they can go read this robot, and then decide to which of the of the all the listed and
 things they want to go. So at central core to the system is this kind of shared type
 system. If you share the types, if you share the API's, your API's become replaceable with
 one another. And therefore you can enable sort of automatic competition and automatic
 matchmaking. So these robots, the there are evaluator robots, and there are matchmaker
 robots, where you can tell a robot, I would like to extract some entities, please find
 me the best node in the network that does it. Okay, and the marketplace makes sense
 because it's AI and it constantly shifts which one is good and which one's appropriate. That's
 the best case I can make for it. Like, I have my doubts that this is actually the case,
 like, but we'll get to we'll actually know let's make the case against it. So my case
 against the AI marketplace as it is listed here is twofold. So first, first point against
 it. Everything we know right now is end to end. The direction of research is clearly
 going into less structured data and more end to end. That means if I want to do a text
 summer or a document summarizer, I am right now much better off just training a giant
 model that does it end to end, rather than using many, many small models. Because if
 I call an entity extractor, right, and I simply only rely on that information, I lose the
 rest of the text and the nuances in the text, I simply get the output of that model. Now,
 I could combine that, of course, but this this idea of modularizing AI, I'm right now,
 research is pointing into a different direction. And second of all, I still believe, like,
 if I make a product, if I build a product towards a user, I want to know what's in it.
 Like even if I have to go with myself and test the stupid API, I would never use like
 a matchmaking agent that dynamically goes and finds me someone who can implement this
 API. Because implementing an API only goes so far implementing, you know, like I require
 image and I output value, that's an API. But that can be many. And then you know, maybe
 these tags here, maybe these tags could do something. But it is not like I think the
 system, even though it's, you know, thought out well with the types and the API is and
 so on. I don't think that's enough. I think that works for a very, very small subset of
 AI tasks. I don't think that works for most of the AI tasks that we have right now, because
 simply API definitions just don't convey what the models so wait API. So API does not convey
 what the model does function. In my mind, so I would ask yourself if you would if you
 were there to use a matchmaking agent, and then you know, sell that product to a customer.
 It's it's but I guess the goal here is that in the future, these matchmaking agents will
 be much more intelligent and so on. Yeah. So here's how it works on a more sort of technical
 level. So there is two components here, there's off chain and on chain. So if I'm assuming
 you know, what a blockchain is, if you don't know what a blockchain is, a blockchain is
 basically a distributed database, and in some forms, also a computation engine. So it's
 kind of a distributed computer that you can't fake. So you can't cheat, no one has authority
 over it, everything is visible. And so that's secure. The drawback is you cannot do hardcore
 computation on blockchain. So this is not AI on blockchain, the blockchain is simply
 there to first of all, register the AI's so register the types. So this this API is here,
 and register what AI's are available in the network. And second of all, to facilitate
 the payments to the AI. So how does that work? It goes via this sort of multi party escrow
 escrow contract right here. So there's a registry, by the way, that's where AI's register and
 put their types. So that's one function of the blockchain. The other function is to escrow
 money. And this, if you know, lightning network is very similar to this. So what you would
 do if I don't know, Alice wants to call Bob, Alice would sort of put a bunch of money like
 a big bunch of money. How do I do that? Alice would send money to this escrow account like
 this much money. And then that establishes a channel between Alex, Alice, sorry, and
 Bob. So there is a channel channel is opened, and it's tied to this money. And now Alice
 can sort of send incremental amounts of that money to Bob. And every time you know, one
 of these, like a little bit of that money is used up. And the way the reason you do
 it in escrow form and not so all of these could be transactions on the blockchain, right.
 But that's first of all, it's slow. And second of all, it's expensive. And if you do it like
 this, you actually only need at, you know, you need one transaction in best case. So
 if Alice spends this much money to Bob, there needs to be only one transaction to putting
 all of it to Bob at the same time rather than all these small transactions. So that's kind
 of the the channel principle. I think yeah, it's very similar to lightning network. And
 it's still secure. So there, it's still secure. The way it is done. I don't want to go into
 channel economics and security right here. But suffice to say, you can make this secure
 and fast to a certain degree. Okay, so that's how it works. Every time you call an API,
 you just send it some money in order to call it. So how does this look? This looks something
 like this. Sorry. Here is this AI marketplace, they've actually built it. And they have a
 bunch of services on there. As you can see, it's, it's, it's kind of they take some standard
 AI tasks, and they put them on here. And if you click on one, you can either, you know,
 pay a GI tokens. That's a thing we're going to get to in a second. Or you I think you
 have like 10 free calls a day if you make an account. So I've tried it out, you know,
 it works. But it's important to realize that the computation does not happen on the blockchain,
 you send money on the blockchain, and the AI service, it runs off chain. So this is
 off chain. Okay. So it is not a secure AI, you still need to trust the thing you're calling,
 um, it's not about privacy that much, but you, you can't verify the outputs, you can't
 verify the computation as you could if if it were happening on chain. Now there are
 methods to sort of do heavy computation on chain, but these, I guess wouldn't be that
 efficient. So just take that in mind. Now, the other thing is, I always sent say, you
 send around money. But what you actually send around is a token. So a token is a very special
 concept. If you if you don't know what a token is, it's like money on top of money. So it's
 like if you go to a fair, and the fair has like its own internal money system, and at
 the beginning, you pay like 20 bucks, and you get 100 fair coins, and you can use the
 fair coins inside the fair. And that just enables the fair to sort of have its own monetary
 policy. And it's usually done with these projects to at the very beginning, you sort of sell
 those coins to a lot of people and the people buy it not because they can use it right there,
 but they estimate they can use it later. And it's a way to found a project that's called
 an it's called an initial coin offering usually or initial token offering the coin that singularity
 that uses is aptly called a GI. And there is 1 billion. And you can see here, it's still
 active. So it's still being traded. You can see this is an hour ago, 20, 15 minutes ago,
 and so on. If you look at here is analysis. If you look at the activity on the network,
 it had a lot of activity at the beginning, it dropped and now it picked up a little bit
 again. I don't know exactly what that's related to. But so it is still alive. If you look
 at the price, however, this sharply dropped and is now actually below the price of the
 initial coin offering. And what you hope when you you know, buy the initial coin is not
 only that you can use it later, but you know that since there is only limited amount of
 tokens that that will be more valuable in the future. Because people want to buy it
 off you because they want to use the network here, it sort of looks like that's not exactly
 happening. And we'll get to what they're doing against it. Right in a second, the answer
 is inflation. So in a new blog post, actually, as I was preparing for this video, this new
 blog post came out yesterday. And here, they're announcing sort of the path forward Singularity
 Net phase two. And essentially, what they're doing is they're switching blockchains from
 Ethereum to Cardano. And I have my doubts isn't like I don't know much about this whole
 the whole crypto space, but isn't Cardano where massive amounts of the of the coins
 are like in some I think there are massive amounts that are just never moved and so on.
 And it's quite scary. But you know, they probably know what they're doing. And with that, they
 are doubling the amount of tokens like they could do it without increasing the token
 tokens. But with that, they're issuing another billion tokens, I think 50 or 25% will go
 to themselves. So that's usually you do that in initial coin offering, right, you keep
 some of the tokens to yourself, because as people buy it, it's valuable. And that's how
 you fund the operation. So here, they need to fund it some more. So they just inflate
 the currency with the new with the new token. And they project, you know, they project that
 the network is used is going to be used a lot more than double now. So I guess if you
 buy the new tokens here, phase two plan five years from now, there will be 2 billion instead
 of 1 billion tokens, my strong assessment is that in this case, the overall value of
 the network in 2025 is going to be far more than twice what it would be if we didn't release
 the new token. So they need money. They inflate the currency. It's you know, it's government.
 I guess it's valid, but just just to be aware. Okay, that's the network. There are a few
 crucial components that I have left out now. But that's essentially how it works. So one
 crucial component, so you the registry is where you register. One crucial component
 is the reputation system. And this is something that's quite difficult. So the reputation
 system is important, because if you want to sort of find agents that that perform well,
 you can also sort of rely on reputation. So if a lot of people have bought services from
 a particular node in the past, and they rated high, then you can sort of trust that node
 more than if if a node is lower rated or has dissatisfied customers. So they spent quite
 a bit here talking about reputation systems and how you could do them. And that is an
 open area of research is really hard problem to make a good reputation system that can't
 be gamed and so on. Yeah, there are various ways like, for example, a stake deposited
 by a consumer service owner to be forfeited should its rating in some dimension fall below
 a given threshold. So you can like put some money and say, Well, I I if my rating falls
 below a three, then that money is gone, I will like it's burned, it's automatically
 burned and that gives people more trust in you because you're now forced to uphold that
 rating. But it also allows some kind of mafia games like you could go to that, you know,
 service owner be like, well, it would be a shame if you had a bunch of one star ratings
 coming in, then you can sort of blackmail them in given circumstances. It's not easy,
 right? It's not easy. But that's built into into it. By the way, because this is on chain,
 anyone can participate in the market permission less, which is a really good thing. However,
 they maintain kind of a a DAP a centralized platform where they that they control. So
 you you sort of have this decentralized thing wherever you can participate, but only some
 people are listed on the central on the main hub, let's say, but you can technically build
 your own hub, like you can build you can build your own Android app store and so on. So think
 of it like, it's a marketplace for apps, but only the ones that are, you know, KYC compliant
 will be in the in the Google App Store, but you can build your own alternative app store.
 They also want to provide AI infrastructure as a service. And that I feel it's really
 irrelevant, like they say, okay, we want to provide this, but it really doesn't matter
 for the singularity net. So they, they, here is where they go into, oh, you could do this,
 you can do that with it, and so on, you can deploy it on embedded devices. So their idea
 is really that the whole world will be connected to this network. And whenever you require
 any sort of functionality, you just call the network, and the network solves your problem.
 As I said, I'm kind of doubtful, I still think it's probably going to be people just build
 the functionality either into a custom, you know, uni service, or they, they just build
 it on device. So the last component here is democratic governance. So they are, they are
 invested in, in sort of making this a community effort. And one thing is this governance,
 right? How do you govern decentralized organization? And that is also an unsolved problem. They
 do it in multiple stages. So they stay say, okay, in years one and two of network operation,
 basically the foundations, the foundation says everything in according to any any major
 changes the foundation decides. So the foundations are the maker of the network. In years three
 and four, they transition. So major changes, agreement of the foundation plus a majority
 AGI holder votes. Minor changes don't actually even require the foundation. And then there's
 also this introduction of benefit tasks. Yeah, so years three and four, and from year five
 on forward, this the the foundation is gone. And only there it's only done by votes by
 AGI token holder votes, which are logarithmic such that rich people don't have too much
 power. Yeah, so this was launched in 2017 at the end. So technically, we are in this
 phase right here. And I have searched for like an announcement that yeah, we're going
 to transition from this mode to this mode. But I haven't found it on their blog instead
 of what I found are like announcements that they're going to they're going to launch this
 supervisory council, which are like elected members that check the foundation. And also
 in this roadmap of part two that we've just looked at, they also saying, oh, progressive
 decentralization, making it real. They also talk about this supervisory council, and they
 now pay them and they release financial reports. But nowhere does it say that you see here,
 it's 3.5 years in so they should be in that second phase. Maybe they are, but I would
 guess they'd make an announcement if that's the case. Maybe I've just missed it. And they're
 actually doing this. But I have my feeling that if you you know, launch such a system,
 and you have power to do stuff, and especially this if the system doesn't grow as much as
 you expect, and so on, you're not going to give that power away. So that's, that is my
 my doubt here is that if you have the power, it's of course, it's always better for you
 if you say, well, I'm just gonna hold on to it a little bit longer. Eventually, you know,
 when everything goes well, but it's never that everything goes well. Like, yeah, alo
 communism. Okay, so enough rant. The benefits tasks. So they also have in mind, you see,
 there's a lot of stuff in this network, right? They also have in mind that this this network
 should benefit sort of humanity as a whole, which is, you know, a laudable task. But they
 have a system where it's some tasks are classified as benefits tasks. And the these benefit tasks,
 they are suggested by by a GIs by actors in the network that has so each agent gets a
 certain number of benefit votes, right? to cast each month based on its benefit rating.
 So the rating system is multi dimensional. One aspect is the benefit rating, someone
 can rate you beneficial if you like, do if you're a GI cures cancer or something like
 this. And, and then you nominate you vote. And then some of the some money goes to these
 benefit vote winners. Once a qualified benefit decided nominates a certain task, yada, yada,
 yada, yada, yada, if 25% votes are cast in the affirmative, then the task becomes a benefit
 task. Once a task is a benefit task, any agent capable of performing it and possessing a
 sufficiently high rating, and benefit rating will receive benefit payment for doing it.
 Okay, so the idea is the community nominates beneficial tasks, and these tasks will get
 benefit payment. Like, the only question is, where does this come from? Where does that
 money come from the benefit payment? So I guess it has to come from other people. So
 you have to have like some sort of a benefit tax or something like this, that you have
 other transactions that you give to the benefit tasks. And then this is like, you the whole
 system work, there's nothing about this that makes it benefit specific, you can switch
 out the word benefit by evil, like some you have an evil reputation, and then some tasks
 are evil, and get evil votes. And if you are especially evil, you get evil payments. This
 whole notion rests on the fact that people somehow recognize what's beneficial, which
 is a highly, highly controversial, right. And it's basically politics, right? Every politician
 advertises themselves as beneficial, every, every, you know, organic food is beneficial.
 But then you just do the bare minimum, you like cut, you take 99% of tomatoes, and you
 put a little bit of dirt on top of them, and boom, they're organic, like they're now labeled
 as organic. It's, it's, I this is, to me, this just seems like a thing that's going
 to be gained so hard, it's going to become irrelevant. It's basically a political game
 at this point. Because you cannot define benefit other than through human voting, and human
 voting is subject to money. And yeah, that's how politics starts. Okay, so they have, they
 have a lot of examples. So here you see sort of this network idea, they have a lot of examples,
 what can be done with this, I don't want to go into into these, because this video is
 already quite long. But it's, it's a lot of talk. I just want to say that it's a lot of
 talk. And, you know, they're basically putting up everything they have done so far, and they're
 doing on the network, what they can do with the network, which is all cool, right? It,
 but it's it's sort of advertising, what kind of research they do on it. And yeah, the last
 point. The last point. Yes, it's very long. So these people, for some reason, they actually,
 they're like two things they love or three, there's graphs, domain specific languages
 for some reason, they love graphs and domain specific languages. So their idea of AI, it
 all revolves around kind of classic notion of AI. So there is knowledge bases, and then
 there is graphs that and you can see this reflection in singularity net, right? This
 idea that lots of things by themselves network together can make up a bigger AI and so on
 that it is exact reflection and exactly goes counter to like the deep learning idea of
 let's do everything end to end. So the singularity net here is very much a reflection of what
 these people think. And yeah, for some reason, they love inventing DSL for new problems.
 Like why? What like, I've never understood DSL aficionados, but I guess if you are, you're
 having fun. Okay, so here they say, measuring modeling and extending singularity net. Okay,
 so this is sort of their research on singularity net itself, which is, you know, quite a, a
 important thing if you build a system like this. But what I want to, I wanted to do so,
 I've read through all of this kind of research suggestions and what they're doing, and they
 just make it seem great, but it's also very washy, in my opinion, and I was wondering,
 is it just because it's a white paper? And I you know, there's actual good research and
 for most things, I can definitely guess you know, they're, you know, they're also the
 people behind this Sophia robot. I don't know if you you know, like this Sophia robot and
 so on. They so they have a lot of success. So precision medicine and so on. There's a
 lot of research, but some things just sounded also just washy. So here that this is something
 that made me particularly just kind of stop. So they want to measure with this phi quantity
 for measuring integrated information in complex cognitive networks. So this phi, this number
 phi by this researcher tontoni is sort of a measure fundamental measure of the level
 of consciousness. And they themselves say, you know, maybe it's net, it's not, you know,
 the measure, but it's certainly an interesting measure, and so on. And they say we have experimented
 with measuring phi across time series generated by open call, by the way, open cog is from
 the same person that's one of the co founders, Ben Garth, so of singularity net, open cogs
 attention, allocation module, yada, yada, yada. While the while the system parsed and
 semantically analyzed a series of short documents, we have also calculated phi values while the
 open cogs system controlled the Sophia humanoid robot, as she led a person through a structured
 meditation system. So they like the extent of them describing the research is simply
 we have experimented with it. And we have measured it across time. And so I was wondering,
 like, what's behind this? So I went and I read the paper that's linked there. That's
 this using tontoni phi to measure the consciousness of a cognitive system while reading and conversing.
 And so this is a paper, it's quite short, but they let it read like texts from about
 different things. And they measure this phi quantity. And when you go and look first,
 what's this phi quantity, this is kind of a one of these papers, it's, it's very mathematical,
 actually. And there's a lot of information theory in there. So it has something to do
 with mutual information, there's a lot of ways you can calculate it, as you can see
 here on the left, and there's a lot of ways you can approximate it. So this is like a
 serious quantity. But measuring it is like super hard. And here, they let this open cogs
 system read short texts with, with respect to, as you can see here, poison and insects.
 And they look where the sort of, I guess the attention, the attentional focus of the system
 rests on which of these concepts, right? And then they measure the phi over time. And their
 claim here is I was okay, we also calculated five based upon the concept nodes. No, wait
 up here. As the system ingests each sentence, word nodes corresponding to each word are
 simulated as stimulated with this system, thus triggering attentional focus dynamics
 correlated with the reading process. One goal of the study was to observe whether after
 reading documents regarding insects, then poisons attention would spread to the concept
 related to insect to insecticide. This phenomenon did occur. So they say, okay, when you read,
 when you read insect and poison, after that, you got to put a focus on insecticide. And
 you can see so insect is blue, poison is orange, and you can see maybe the insecticide, you
 know, bumping a little bit after while you read poison. But honest, like this could also
 just be because it's associated with poison. This is, you know, I don't know that this
 is a bit interpreted a bit too much into that graph. And then what's even more astounding,
 we also calculated five values based on the concept node insect, poison and insecticide
 as figure three shows, there was an interesting jump in the five value when insecticide first
 became important, suggesting that the five increase was correlated with an increased
 complexity of attentional spreading within the atom space. So the atom space and so on,
 that's that's sort of this classic AI concept of knowledge bases and atoms. But here, so
 the claim is that the fire on the right somehow, somehow correlates with the insecticide attention
 on the left or with anything interesting. And that to me is a stretch. In fact, I have,
 I've put the I've put these things above one another. So in the gray background here, you
 can see the five value, and I've matched up the the time steps right here. And so the
 claim is that here, insecticide marginally bumps up, and then sort of this five spike
 is here. But if you look anywhere else, like here, insecticide bumps up, okay, but much
 delayed spike, and here, it doesn't bump up at all. But there's a spike still. And it's,
 it just seems, it just like that is just not a inference you can make right here. Like,
 I'm not sure. Let me let me know what you think. But if you know, you can't just nah,
 nah, sorry. This one, you know, this one, it was the one that that was kind of the most
 strange to me. But also, yeah, don't, don't, don't tell me that this does anything. But
 in any case, they, this is the type of research that they do. And so they measure these measure
 the intelligence of the system, and so on. Yeah. The last thing is these, what they want
 to do is this offered net economy. And you know, in researching this paper, I have also
 watched a bunch of talks from from Ben, and it seems like sprawling with ideas. And the
 talk about these offer nets is, is also so the idea behind it is that offer net is sort
 of an economy without money. The offer nets domain model, the other where is it? So huh,
 I don't I don't remember where it said, but offer nets is like an economy without money.
 So the idea behind it is okay, person A, person B, person C, or machines, they are sort of
 in an economy. And person A wants something that person B has, but B doesn't want something
 that A has. But instead, B wants something that C has, and C wants something that A has.
 And the logic here is couldn't you, you know, a cannot, a cannot trade with B, B cannot
 trade with C, C cannot trade with a but they can trade in a circle, right. And this offer
 nets, they do make this possible. And so that the idea is sort of everyone puts out there
 what they want. And the offer nets, they will sort of figure out, they will figure out who
 needs to trade with whom. And thereby, you could make an economy without money, right,
 without Yeah, you can make a money free economy. And is this the right paragraph? Because there
 was a fun sentence, there was a fun sentence that I've I've seen right here. So this is
 another another thing where I think that just like that the ideas they go a bit, they go
 a bit too far. offer nets analyzing the data, yada, yada, yada, open ender process. Okay,
 I don't I don't know where it was. But they say something like, yeah, offer nets could
 mediate this process. And I'm, and how do they mediate this process, you know, such
 that everyone actually gets their worth of stuff that they put out, they mediate this
 process by means of the offer coin. Okay, so the offer coin is now transferred from
 B to A, or sorry, or from A to B, let's say because a wants something that B has, and
 the offer coin is transferred from B to C, and then from C to A. So the offer coin makes
 all of this happen in an economic sense. And like, huh, are you saying there is an asset
 going along with a certain service, and the asset is sort of agnostic such that you can,
 if B gets the asset from A, B can then give the asset to C in order to obtain services
 from C. And that, you know, asset actually is what makes the whole economy work, even
 though no one directly wants to trade with each other. And you're doing all of that without
 money. That's crazy. So yeah, in any case, I think, oh, ah, there we go. Offer nets.
 A decentralized economy providing an alternative to purely currency based exchanges. This economy
 features a complex network of interactions that optimizes reciprocal changes of goods
 and services by finding agents with compatible and complementary preferences and coordinating
 their interactions dot dot dot by means of a coin, which is money. That's this is exactly
 what money does. Like that. That's what money is for. In any case, I'm like this. These
 people are very smart, and I'm probably too dumb to see what the exact difference is right
 here. So I just found it funny. If you know, if I'm completely wrong, then let it be stated
 that you know, that's what a semi only semi smart person would conclude from reading these
 things. All right, this was lengthy. But I hope you sort of got the idea. The base system
 is an a an API marketplace. Now the API marketplace in itself doesn't have anything to do with
 AI necessarily. But I've made the case that the API marketplace only makes sense in the
 in the world of AI, because if it was regular software, you would just hard code either
 the API calls or you would actually include the library. So the marketplace makes sense
 in the realm of AI. Okay, it's doubtable whether that's actually the case. It very much goes
 against the end to end principle, it bets on a form of AI that works on discrete graphs,
 it works on sub components divided into sub components, it works on networks, networks
 built together to achieve higher order functions, it could definitely be that the future of
 AI lies in this direction. It's just that the current direction is pointing away from
 that. The whole marketplace runs in on the blockchain, and only the marketplace so the
 AI processing is off chain. So it is not a on blockchain AI. And yeah, they've built
 it and they are in money problems. Currently, they're inflating the currency. But they're
 switching blockchains, because they think the new blockchain will be better and faster.
 And they project high growth and the token is actually active. So it's you know, it's
 not a dead project. And they are in the news quite a bit, especially with this this Sophia
 robot, I think that is that is a very it's a kind of a PR magnet. Alright, that was what
 I had to say. I hope you enjoyed it. If you did share it out. Let me know what you think
 in the comments. Let me know what I did wrong. And bye bye.