#233 Humayun Sheikh & Toby Simpson: Fetch.ai – an intelligent learning blockchain network

Transcript

[Music] this episode of epicenter is brought to you by shape-shift oh the easiest fastest and most secure way to swap your digital assets don't run the risk of leaving your funds on a centralized exchange visit shapeshifter IO to get started hello and welcome to episode and shows talks about the technology projects and startups driving centralization and global washing revolution my name is parent Robin crane and I'm the head ROI today we have on our show

home iron shake and Toby Simpson who are CEO and CTO of fetch AI respectively H dot AI is an ambitious project that seeks to merge machine learning and blockchain technology in order to build a collective super intelligence gentlemen welcome to the show thank you all right thank you nice great to be here so let's let's start with whom I own like tell us a bit about your background and how you got to be involved at this project at the intersection of machine learning and blockchains sure my background is in computing but I've spent the last 10 years of my life in commodity trading

building algorithms for the commodities market and seven years ago I I I was one of the investors in deepmind one of the early investors in deep mind because we were working on the gaming side with Dennis as well and so we met the team met myself and Toby met roughly 15 years ago and we've been on and off working into the intersection of commodity trading and price prediction models and al generating algorithms to predict the commodity pricing so that's been my background for the last six or seven years

in terms of how we got to the fetch what was quite interesting was that when you look at predicting commodity prices a very basic way to look at it would be what's happening in different markets but what is more interesting is if you start bringing context and various different information bases into the price prediction model and you could start building correlations then you realize very quickly that the the price prediction is effectively you know five or six times better very easily when you start building these correlations so what was quite interesting then is that if you extrapolate this whole thinking

on commodity pricing and actual physical commodities and then you start bringing in ways to form correlations in a distributed kind of environment the results improve considerably so we did some trials over the last couple of years on and off with our third co-founder which is Thomas hain there was a professor in Sheffield University machine learning in AI we we did some trials and we realized that to improve prediction models we need to have a system which brings distributed information to improve the correlations so that's really how I got involved in

starting fetch and and just brief interjection so regarding deep mind many have probably heard of deepmind which was you know start a few years ago and became a very big machine learning company and then go up all up by google for lots and lots of money but my kind of exposure to deep mine so Mayer recommended this documentary to me called alphago zero about something that they did you know deep mind gate to build a program that's very good at it go and got better than the best go player and so there's a documentary about that which is absolutely fascinating so I I highly recommend that so you you're one of the early investors in deep mind right so as

far as I'm aware like the company started out pretty small and then was bought out for Google for 400 million could you get into the story of deep mine and what they set out to do in the beginning and what they ended up doing and how they discovered that path well I just want to make it clear that my investment and interest came in because I've known Dennis for roughly 15 years now we we work together he was one of our advisors in a kind of a small game which was bringing the real life products into the virtual products and that's exactly how I met Dennis which was 15 years ago what was quite

interesting was the dumbest had a great well he's a great mind of our times and I think he has he's done a great job in building the company what his concept was that neuroscience which is the PhD degree he achieved he was looking to bring the general intelligence to life effectively and we were having lunch over in Cambridge and browns and when he proposed and he said well do you want to put some money in and I thought well there are three passions in which I have won one of them is machine learning and AI and there is no and the other one is the virtual worlds and the

decentralization so you know I couldn't have missed that opportunity so I invested in that and what the the concept was to bring the artificial general intelligence and the best way to show the benefits of it was in the gaming area which is where again demos and his background led him to so that's how it all started but the ambition was to build something great which is what they did and to be fair I mean we we you know deep mind is not just in the UK but probably worldwide one of the best AI companies which have come about and and

the journey has been obviously very ambitious and them has set the goal very high which you know although having said that you know we artificial general intelligence is not an easy one to crack but they're making some great inroads into it I mean it's it's quite a you know and I'm not I wouldn't say that they've cracked it but I'm saying in doing so they've achieved quite wonderful great things which go is an example of that now moving on to to Toby you've also worked at deep mind but your your background has been you found it quite a few different companies over over the

years so like walk us through because through your history well I started programming computer games in the early 90s back in back in the Amiga days and then have the the great privilege have been involved in product all creatures I was the the director and producer of the creatures series and what really interest me interested me about creatures was that the guy who invented the technology for that believed that if you modeled all of the biological building blocks of life and you put them all together you might actually get digital life and that's precisely what he did and we had a genetically specified little creature that was made up of chemical reactions emitters receptors and neural dynamics that would

learn by itself how to survive and and live in the environment that it was in and what was really exciting about that was that all of those components were specified in a genetic code so you could get a mummy creature and a daddy creature and then you would have a baby creature that was made of a combination of the genetics from from both parents an if that creature was better suited to surviving in its environment and more likely to make it a breeding age then it would be the one that was most likely to provide his genetic code to the next subsequent generations so effectively what the computer was doing was automatically fine-tuning itself to better work in the environment that it was in now I found this really exciting because it meant that the computer was

finally the bit that was doing all of the hard work and so long as we created a rich dynamic environment and had a big enough population of little bits in in that world we would have these these virtual animals effectively learning how to live in that environment without the need for human intervention and I was kind of curious as to whether or not that I'm thoroughly bottom-up development philosophy effectively in design philosophy could be applied to something grander and in particular the creation of very large-scale virtual worlds which has been a passion of mine now for more than a quarter of a century the idea that we could yes so we may ask a question on this creature swing because that sounds

really fascinating so I'm just curious if you know you said this creatures would be able to learn on their own and they have this DNA in and just so where does the player come in here like would you make decisions that then the creature executes well you can ask the creature to do something but of course there was no guarantee that it would choose to listen to you it kind of depended on what it had learned to associate with you and you could interact with these creatures so you could give them a little tickle and you could give them a little a little slap if you thought that they were misbehaving so of course in fact if you did that quite a lot then eventually they'd be quite frightened of your hand in the world and they would learn to associate bad things with with your

presence and that was that was kind of fun to watch happen I guess to a certain extent when human beings get involved in this it becomes more unnatural selection than natural selection but it was still quite quite quite in fact a 1:1 thing that's probably worth mentioning is people got so attached to these creatures that in the end we had to develop effectively a funeral kit at all to allow them to to write a few words to remember the creatures by when when they passed away and this came about because even back then in in in the mid 90s people were setting up websites and writing poetry and stories about the creatures that they'd had and the existence that they led and drawing whole family trees of how they went and

of course you know looking back now you sort of think well goodness if we were to do something like that today we'd get to collect the complete family tree of all of these things but back then that wasn't that wasn't technically practical those that were on the internet tended to be modems but yeah so the creatures effectively was a general-purpose problem solver we didn't have any rules in that system we didn't specify to the creature how to eat we just gave it an instinct to pick things up and stick it in his mouth and if the first ten things that it ate turned out to be rocks then eventually it would learn that sticking things in its mouth was a bad idea and that's not necessarily a good thing for the creature and and the idea that the weaker we could apply that boss montt

philosophy to creating virtual worlds was it was extremely fascinating and those worlds well we one of the things that was always my favorite was my world in a box where you started with one agent then he had two four eight sixteen and before you knew it you had a world that consisted of trees animals plants but the wonderful thing is all of those things were real they weren't painted on bit like the Simpsons episode where the fire exit was painted on the wall and they asked whether they could have a real one in future these things were all real so if you reversed a truck into a tree and knocked it over you could potentially build a bridge out of it or a log cabin out of it and we didn't need to know about bridges or log cabins or program any rules in advance for that to

be possible and this is great in virtual worlds because you can't predict what one person is gonna do in a world let alone tens of thousands and the idea that you can do anything that you perceive has been practical it's really interesting particularly when the complexity the really fun stuff the gray areas of the distance the difference between believable and not believable is entirely an emergent property and this is effectively the field of artificial life where you have a very large population of simple things that combine to produce more complicated behavior and Hermine and i had often talked about the idea that potentially it might be possible one day to build one of these worlds that was grand enough and big enough and could have a large enough

population of objects that we could actually do useful economic work in it and we'd sort of toyed with all these various ideas as to how that might work but the the technologies that I've been using tended to involve a great number of servers acting as a client-server type technology and then of course we'd bump into decentralized ledger technologies and it's just like click well this is it isn't it now suddenly we can construct a world of extraordinary proportions and we can fill it with an amazing population a population of things that represent humans that represent hardware that represent data or sensors or services and they can all interact on this network decentralized across the entire globe and we aren't restricted in the

way that you are with the real world we're if we're all looking at the same table we're all seeing the same table in a digital world there's no such restriction each individual could have that well tailored specifically for them and that gets really exciting because of course you can ensure that the things that you want or the things that you might want other things that you see okay so when you were talking about creatures it it felt a bit like crypto kitties have you have you followed that project which one sorry crypto kitties so cryptic KC's I'll tell you what I'll bet the creators of a theory of never saw that one coming yeah I think it's a wonderful thing and it's a great idea and and it introduced a whole generation of people who

suddenly wanted something that was unique to them - this whole crypto space but yeah that dub was a very cool thing and and the idea that potentially all of these little entities that work together could be more than just visual representations but could have personalities and existences and memories and behaviors and could live on a digital space that was optimized just for them is another layer of excitement on top of that I think but it's a great space I mean don't you find it's just like but for me I sort of see it as a bit like the Internet in the mid-90s everybody knew that it was going to fundamentally change the way that we live our lives and we're all experimenting with all these different

ideas but but nobody was quite sure how I mean we didn't see social network coming social networking coming back in back in the mid nineties but look where we are today and I look at the decentralized ledger technology the blockchain space the crypto space and it does feel that level of excitement that we all believe that the space that we're in is is extraordinary and it's gonna change the way that we live our lives and we're all trying out these amazing new ideas and exploring this space to see to see how that might work but one thing that came to mind to me when you when you talk about these virtual words it's something like Second Life right where you also had this this whole world where at some point you know people would sell these real estate or build businesses to create these virtual

things and sell them and you know for a short time it seemed like that could be become a big thing and then of course it sort of disappeared is that also something that inspires you or that you think about when it comes to the design or what you guys are working on now well that that kind of thing I know we were we were building our artificial life inspired virtual worlds at the same time that Second Life was out there and and I always take this approach of digital Lego that if you provide the low level building blocks then the physics takes care of your syntax errors so if you give people a pile of bricks and they build an upside-down pyramid sooner or later physics is going to knock it over if you provide people with

a whole load of aeroplane parts and they stick 100 engines on both wings then the physics of the environment is going to ensure that that thing isn't going to leave the grounds and even if it could the fuel consumption would be outrageous um so that that ability to ensure that you don't have to worry about consistency errors in in a world is extremely important and you do need that scalability it's not it's not for me anyway I always thought well it's not a true virtual space if I existed in a shard with 20 people I want to exist in a space with everybody I want to be able to look out on these worlds and be able to imagine that in front of me there are millions of people in that space all doing things that they choose to do but these these

are all pioneers in a second life and and creatures and all these other virtual world technologies in ways of presenting these environments that human beings can can go in and well for want of a better phrase live a second life in in in the case of the fetch course we're not restricted in the sense that we're not building a world specifically for human beings were building a world that supports the machine-to-machine economy as well and actually a great number of the entities that are living in that world and working in that world and getting stuff done they're actually digital entities that are responsible for themselves going out there trying work out how to get what they need and deliver what they've got let's let's segue into what what what fetch fetch is so I get the sense that

you're building another virtual world and you want to use blockchains in some way and sort of the inhabitants of this virtual world are who exactly do is are the inhabitants of this fetch system what are sort of the end Network points well we see fetch as a decentralized digital world in which useful economic activity can take place now that activity is performed by digital entities that we call autonomous economic agents now these these autonomous economic agents can act on on your behalf they can act on on their own behalf they can represent data or

services or any other number of things and we provide them effectively eyes ears and touch into a digital world that is tailored specifically for them so these agents connect to the fetch world through something that we call the open economic framework which provides them with their their visuals on the world and allows that that world to be tailored specifically to them I liken it a lot to the the ultimate the ultimate dating agency for value providers we ensure that when you connect what you see is precisely what you need to see or what perhaps you'd like to see and depending on what it is that you're doing underpinning all of that and making sure that we can run this in a decentralized global way and achieve the

scalability that we want is our smart ledger and the smart ledger is doing a number of things it's ensuring that we have integrity on the on the global state it's in and that includes of course all of the the transactions the interactions that are taking place between those agents but also its providing the ability for the network to learn how and under what conditions agents interact with each other so that it is better placed to ensure that the ones that are most likely to want to interact with each other are placed together and we also have a whole layer that is provided by that which is that the trust layer which lets you look at any transaction as it goes by and establish how likely it is to make it to

the global State which is global State sorry which means that when you're looking at agents that are involved in potentially droids or thousands of transactions very quickly in order to construct a solution for someone you can look at all of these transactions going by and you can make a call very very quickly in a matter of almost no time at all as to whether or not those transactions are going to make it through it's a big vision right and like we need to and at some levels like I've been trying to understand this vision like we I had a call with Toby in home I own prior I read the white paper I've been like thinking about this vision for quite a while and when it's a vision in which like they're like lots of components and like you have to think

about all of them and then how they connect in order to get to a level of detail that one is comfortable with so maybe we could start with so you have this idea of these autonomic autonomous economic agents right so so these agents are could basically be machines like they could be like devices mmm right you could think of I don't know a camera as an autonomous economic agent you could think of a sensor camera is a sensor you could say think of a sensor you could say think of a delivery robot you could think of a car you could think of a human and these are all off chain they have some kind of

processing logic of their own and they can sense the external world and you're building the virtual world for these entities right so what what kind of objects do you think are going to populate your virtual world is it meant to be like Internet of Things devices there's a number of things going on there I guess Oh what one of it is that a lot of the problems that we solve in our day-to-day lives are involving an increasingly large number of moving parts and it's becoming very difficult for us to to manage them transport is one and for the many areas where the number of bits and pieces that you have to touch it to juggle it does feel like pushing off water uphill sometimes trying to organize all of these moving

parts in a way that makes sense to the individual and actually a lot of these problems that are highly complex and involving so many things are actually best not solved from the top down because there's only so many things an individual central controller can hold in their mind in order to be able to manage all of this and one of the things that we thought was was was something great about fetch was that actually these problems could solve themselves in the bottom-up and again this comes back to my creatures background and the idea that a large population of things can work together to out of which a complex solution can can can emerge and fetch as you say fetches is it's not um it fetches enabling the agents rather than

them actually providing them or by giving them a world that tailors itself to them to ensure that they're able to do what it is that they want to do with the minimum of friction involved it really is about clearing all the junk away from between party a and party B so they can get on and do do what they need to do actually agents autonomous economic agents whilst they they might represent their data or services or sensors or people quite often they actually operate as populations as well and if you think about it if you walk around with with your phone in your pocket there's an extraordinary amount of sensor information there and why shouldn't that sensor information

actually be represented by an AEA and actually out there on the fetch Network attempting to generate value from the data that it has quietly in your pocket without you been aware of it whilst you're strolling along and going about your your daily lives so actually in some cases we're looking at populations of agents that exists together flight hardware or a car is another example where if you took a drone you've got the actual bit that flies around which is an agent but also the sensors on there might be another agent and the camera that's on there might be yet another agent and it may well be that the camera agent can convince the drone to take a small diversion in order for it to take roof survey pictures or somebody's house on route so together they're there

working together and communicating with each other independently of human intervention to allow better utilization of those things because that's one of the things that's most extraordinary about our lives right now it's not necessarily the data that we do use it's the data that we don't use either because we don't know it's there or that the costume to deliver it exceeds its actual value and when you can come up with an endless population of digital representatives to represent that data then the cost to actually put something like that into the fetch Network and for it to actually generate value goes right down and under those circumstances it benefits everybody because suddenly all that wasted information comes into play whereas previously it would have just

gone some people in the audience may remember this talk there was a talk by Mike Hearn from maybe in 2011 I don't know if you guys have seen this but he was back then the first person to talk about you know machine to machine payment on a blockchain and his micro payments so that was back in the context of Bitcoin and you know how Bitcoin could be great with like payment channels and you could have like you know a car driving on the road paying another car to pass them and you know that kind of vision now bitcoin doesn't seem like it's right blockchain for that although you know who knows maybe with Lightning Network and all the things on top some point it can get to that but at these conceptually I I

understand that vision right because you say okay you have all these individual entities they need to have some way of transacting I mean they can of course have some sort of wallet in their device and then they can send transaction and then we have economic fabric where then all of this activity and in a way applications can emerge and utility can emerge but you know if I'm a sort of blockchain perspective it's just a user right is it a person is it a car you don't care right it's not part of the core protocol but it feels to me what you guys are talking about here is something quite different where this the in a way this agent isn't just a user

but it's somehow it's intelligent decision-making is also part of this protocol do I understand this correctly yes yes you do the a this decision-making process is actually really important that to a certain extent we're putting we're putting geography on on the network a lot of the work that goes on between people or related or entities are related to what's near you what's around you and what's in a particular direction and been able to structure a network to put that dimension on to it is something that that is key to providing this this digital world this highly tailored digital world to the users of AFET I'll probably just add a little bit to

that in terms of if you compared it you know the transactional system let's say the block chains and everything and as you mentioned correctly Brian we're talking more than that what you have to work backwards from this is although we have a transactional system we also need to set a framework for the economy there has to be rules the economic rules which these agents work under and obviously learn from that all these rules are dynamic and they could be changing and they could be evolving as well just like humans did but there is a framework which sits which governs the the

interactions the economic value exchanges it's not just a transactional system we're building we're building the second layer up as well ok great so maybe we can narrow in on that because when you speak about the economic context of course you know that's right that's a core part of any blockchain but in Bitcoin you could say ok block size is an economic parameter the block reward another kind of economic framework is you know who gets to decide the transaction fees and and of course that is really the miners by choosing what transactions should they put in a block you know at the block time you know there's all of those things that in in the end and give rise to a particular economic dynamics you know and those look one way when blocks were not full

and then once they were full they they started looking another way so can you speak a little bit about what are those core you know economic parameters in fetch the eye and how do you think they will drive the you know sort of the behavior of agents on the system sure so if you if you take the economic framework that's also split in two sections one section is more the ledger section which is what you're talking about in terms of the block sizes the rewards for nodes miners and that's more a transactional layer but if you then move above that that's that that is only for the transactional

settlements that's the settlement layer you move above that then you have I mean just to give you an example you know you have marketplaces now that's a separate economic layer because in the marketplaces you have marketplace dynamics you have price discovery you have product discovery you have negotiations at the moment whatever you all the projects you look in the blockchain they mainly worry about the economics of the ledger we are more focused you know I guess it's a balance but we're more focusing on on the layer which is above that which which actually

defines the marketplaces itself which defines which gives you an opportunity to do price discovery but don't forget this is all happening in a very high-speed autonomous system so it needs to be able to adapt it needs to have its own principles it also needs to learn from those principles and evolve into a better system so so these are the two it's there is a split even in the economic framework there is a split and when I talk about the open economic framework we are talking about the marketplaces we're talking about how the economic exchange between the agents happen we're not talking about the economics of how they settle those economic values settlement of those

economic values is what the blockchain and the technology like technologies like that do we are also talking about something above that okay great so maybe I should have used to hear him before as an example because I think that would have been maybe maybe more analysis right so a theorem of course again we have like blocksize gas B's and in similar dynamics there are you know somewhat different parameters and then if you talk about a marketplace then I think what that would be in the soda veneer a terminology would be okay well anybody can ride some sort of smart contract on it you know you could have an auction contract other contracts that then allow emergent behavior or different types of economic

interactions we know it you just have it basically not a fundamental platform people can build applications on top to the extent that there is learning it's just well person one can ride the particular smart contract and maybe people don't use that and they're going to go to another one that's better and then over time there was going to be evolution and innovation and all of that but here you guys are saying that you are directly designing also those applications that in the case of of any hiriam would be you know user deployed smart contracts am I getting this right yes so let me take you through the whole so why why we're different to ethereal

and yet comparable to aetherium you use your spotted it absolutely correctly so you're looking at the smart contracts which is the which is the framework for value exchange on top of the settlement layer so if you look at the smart the smart contracts well they're not really that smart because you have to put the smartness into it so you have to come from the outside to put this much smartness to it where we are different is there that we are adding intelligence into two into both the layers where where as we're not stopping you bringing machine learning in AI from the agent side which is you can actually

build as much intelligence as you like into the agent but what inherently you the agent needs is some intelligence on okay what kind of transactions do I want to do now that information come can't come from outside when your ledger is doing and recording the transactions so the ledger feeds into as it as a as a toolkit it provides to the economic parties on who are transacting on the OEF it provides them kind of a prediction model which says okay well this is the kind of people you want to be transacting then these are the people who could be interested in your product

then the price discovery as well so the ledger provides the trust element which is okay what is the likelihood of this transaction to go ahead because if if you were thinking about millions of agents and an agent comes into the market how do you do search and discovery how do you do price discovery so inherently the ledger and the economic framework has to provide a search and discovery facility because you can't bring that from outside you can make a decision on what you are looking to choose but you still need a search now I don't think I clear if that right

because let's say only theorem somebody can build a decentralized exchange there are X and then people build a every layer on top and then they aggregate all those different you know offers and trades and then of course I can build sort of an agent right that it's gonna go and I have my smartness and logic and again I gonna you know be on the run a nodes get all the transactions I'm gonna maybe ping different services and then I'm gonna make some decision making right and bring that intelligent into that so I don't understand what it means for intelligence to be part of the ledger you're absolutely right you can make that decision but we have some framework has to provide you the tools

to make that decision you need that information to make that decision yes and and actually one of the cool things that by putting it fundamentally it's about the difference between stuff been around the ledger and Ashley honest and inside the network as opposed to around the periphery of it and one of the very cool things I may not need to really mention about the prediction model is one of the things that we can do with all of that is we know as a result of any given prediction we know how much on the network how much value was exchanged as a result of it this isn't even evolutionary thing because what we can actually do is we can we can we can reinforce those connections that are working and we can negatively

reinforced the ones that that are not so that means that those drop off and new ones can form speculatively so all sorts of interesting new ideas as to as insights that could be delivered to the network are created or all the time and on on a constant basis and all of that really is about ensuring that the users on the outside of the network get a bang up-to-date dynamic impression of what it is that works them what it is that does not work for them this episode is brought to you by shape-shift the world's leading trustless digital assets change quickly swap between dozens of leading cryptocurrencies cluding bitcoin ethers e cash gnosis manera golem or cur and so many more

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epicenter so you mentioned that this system it can assess the probability of a transaction going into the final state now you know we've done of course countless episodes about different blockchains incontinence protocol I haven't actually heard of any consensus mechanism that does something like that so have you guys invented like an entirely new consensus algorithm or how does that work yes we what would we've we've had - we started from the from the perspective of well goodness you know if we had ten million or a hundred million agents and they were all working

together to produce solutions to problems then we we realized very quickly that we were going to end up with a whole new level of requirement for performance and scalability and but also we couldn't afford to lose certain information about individual transactions because a lot of the learning invert our machine learning scientists work we're proposing on on all these systems relied on having individual transaction information going through the network are not compressing that up or losing it in any way and that that required us to take a different approach to how how we did this because you do need in particularly when it comes to organizing his transactions

he's very beneficial to have a blockchain type structure where you get the the well-defined ordering of all of all of the events but we needed a different structure to perform the the consensus mechanism we've we've chosen a structure that's a little bit similar to a directed acyclic graph because we don't need the direct ordering on the the proof-of-work tasks and we can scale that to record any number of results that we want and we sort of look at it as the the blockchain type structure which we've got scaling through multiple transaction lanes so that transactions that don't affect each other can be executed in parallel that forms effectively the knowledge and the the

proof of work system and the storage of results there performs or generates the understanding of that knowledge and these two things together give us the ability to create the predictions that we need but also to create the predictions so that you can look at any given transaction at a given node and see how likely um anything involving them is to be the in your in your benefit and working the way you expect it to but also to get the performance that we require from from a transaction capability because we need that to be able to scale and scale and scale as the number of agents in the system increases so that did require a different approach the the whole idea that we also wanted

to be able to use the computing power that was on that network to be able to do useful work be that actually part of the consensus mechanism of the network itself the construction of intelligence information about the network itself but also potentially general-purpose computation that require that uses for the network might might require and we've looked at a number of things that are intensely parallelizable if parallelizable is an actual word the particularly in biotechnology and research into diseases and genetic conditions you end up with things that can be divided up into lots of little chunks and potentially can be packaged up in executed by the whole network for

the benefit of the people who want that and in cases like that sometimes the information that is there also provided provides an interesting new understanding and insights that can be explored by the network to better connect people in in the future so I just wanted to jump in on that side point you said a you know useful proof of work I saw that in the white paper as well and you know you mentioned the idea of some sort of biological computation stuff so I mean of course the idea of useful proof work has been around same time coin was the first useful proof 11 I think yeah exactly so I would say that approach so far has basically failed because of course the

great the great benefit of something a bit coin is that you know you can very easily verify the work but it's hard to do it yes most other things you don't have that differential right so maybe it takes as much or a lot of time to verify it as to produce to work and then that that defeats the entire purpose so how are you guys able to do a useful proof of work yeah and of course that is as you can see quite quickly points out the idea that it takes a long time to do but it's trivially easy to verify is one of the key points that that allows such things such things to work and whom we we've cracked it and we've come up with

a mechanism whereby the computational packages that are put through the system we can create a verification purpose a verification method through that but also that none of the work that is done by the people who weren't involved and eventually choosing the block is actually wasted and that's one of the things that we thought was very important about useful proof of work that everybody who does some processing no matter how capable their their machine was is actually generating results for for the network and is actually receiving some form of reward for that and we've got a a a mechanism by by which the the people completing that that proof of work effects really

gets her to vote on who it is who makes the decision as to which block next goes into the into the global state so we're very proud of the results we've created our own virtual machine differently for this purpose that is biased towards solving machine learning tasks but is also capable of of general-purpose computing and a mechanism for packaging up those computational tasks so that it is possible to verify that the work and that was meant to be taking place has in fact been been taken place so this sort of summarize so the VA I understand it

is ultimately the customer of your system is is an agent so imagine like the agent is me I'm a delivery robot of some kind right and I have my own internal logic like I can I can make autonomous economic decisions like parties to transact with tasks to take etc basically an agent like like me can use the fetch system to a get work like ok deliver X Y Z here and you will get this much money so I will obligate like work proposals but I might also get work proposals that I might not be expecting right like so if I'm a

delivery robot normally I am expecting delivery work but suddenly it might be the case that I get a request so I have a camera and when I'm doing my work of delivery I observe that there is so there are some parking spots that are empty and then I have a I get a request for that data and I supply that data that this particular parking spot is empty that is valuable to a different agent that might want to park there in five minutes and so they let the ledger and an effete system recommends me to give this data and do a transaction on like parking lot data and so the way I imagine the faith's system is it is

recommending me these economic transactions these pairings with other agents that I could do but in order to build this system what you are doing on the technical end is a you are building a a blockchain a ledger B you are building the marketplaces and the logic by which these agents can match and they can transact see you are building like a useful proof of work for the base ledger itself and D you are building a new kind of scalability technology for the base ledger itself don't you think that's just too much like for example if you had like useful proof of work would

wouldn't why don't you use your useful proof of work and just build a standard cryptocurrency that will itself be such a big invention it's often etcetera if if we achieve even a small portion of some of these things but actually we set out to build this this digital world we set out to build this extraordinarily large decentralized world where huge populations of things could do useful stuff and yes it's a convergence of a great number of very very exciting technologies and actually you touched on something with the with the wrote there saying really you get you get some some job to do that you hadn't thought of and we love that idea the idea that some of the opportunities that are presented and

nan-oh yes and and this is one of the things that human beings are so bad at which is predicting the future we tend to extrapolate what the position were in now and imagine that well the future everything will just be faster bigger and better but eventually the same staff and we're trying to build fetch on fact we are building fetch to be an environment where it can adapt all of those things they can look at novel new combinations of markets and it can adapt to present opportunities that were were never understood before we're building this this prediction model but a mechanism for delivering those predictions and delivering them effectively to the users of the network who who have specifically said they want

them but also the ones who might want them and it's that might factor that's really interesting because there's all sorts of intersections of different marketplaces that we haven't even considered at this point and these are the kind of things that fetch will we'll learn and figure out and start delivering to users just as a matter of people performing work on that network I'll just add a little bit to it I think you asked a very pertinent question why are we trying to do too much it's the reason is not we are trying to do too much the reason is what is our objective and our objective is to bring this this decentralization to the real world now building a currency

cryptocurrency is great because it kind of kick starts that process but it doesn't deliver effectively what what the end result is because we our end result is we want to connect economic agents which generate economic value not just generate unlock which is plenty of economic value which is unlocked lockable there is no framework which allows you to do that now our objective is not to build a currency our objective is not to just build a ledger our objective is to bring these economic values to life so that's our starting point so anything we do in the middle

which either it's a new type of a ledger is a step towards the objective it's not the objective so that's our approach at the month sure is it's just I guess to us it seems like those like many of those steps or like gigantic even even like you said you want to use a cyclic I mean I think that's a nice idea we've done an episode before if if the Spector guys in in Israel that wrote a very good white paper or not but it's also entirely unproven and so far there's no I mean there's a sort of quasi cryptocurrency a Yoda right but they have like a central coordinator so it does not

actually it's not a real functioning cryptocurrency that does that right so that is I think interesting direction but even on its own like getting that to work you like truly work and decentralize will be like I think a massive thing so it feels like okay there's so many of these steps along the way yeah yeah it seems a big challenge that you guys have ahead at least we've been thinking about this for a very very long time I'm trying to figure out how to get all of these component parts together in a way that allows us to deliver their the thing that we were imagining and it's just that wonderful position where we were able to solve these problems by by drawing from all of these bits and piece

and then combining and with a bunch of innovations of our own to make something very very different that that's their to do something very very different and also this this framework is a framework where we can plug and play as well so we're building it in a modular fashion so if if knew various ways of cryptography comes about which are better than the rest you should be able to plug that in now at this point in time we believe our ledger delivers all the tasks we want it to deliver it's not which ledger is the best which ledger is the fastest it's can it deliver the

tasks which we needed to deliver and unless it does that that ledger is not useful for us so we have to build our own technology abstractly like like overall we've understood sort of the division at least like that division itself is clear to me it's like there's like lots of economic agents and you're building like this ledger plus marketplaces and recommendation systems that allow completely new pairings of economic activities to emerge between these agents right like the your your ledger plus economic sister is going to recommend things to the agents like do this and you'll be you'll get paid that

much and then there'll be another recommendation and then these agents can choose to act on them and perform some tasks and get paid and when a lot of like when millions of agents start to work on a system like that and they do their small bits of actions the whole system as a sum is greater than in some of its part it's it appears more intelligent than the intelligent of the intelligence of the individual pieces so you're trying to build something like that yes I think like in general from from like you come from a virtual worlds machine learning background we we come

from a very blockchain background we're probably like V and our listeners got into this space because we found decentralized money interesting right and we are here we are like we are like really the money nerds like we want new forms of money that's that's what what got us into this so I think the the main skepticism you're going to get from the blockchain communities that these some of all inventions that you're proposing it seems like a very ambitious target and and somehow like even reading your white paper like the the personal feedback I had was like a useful proof of work so that useful proof of work is a white paper in itself scalable ledger

scalable there is a white paper in itself the marketplace that's a white paper in itself and it's funny you should say that we recognize that and we are actually creating white papers on all of those those subjects individually and a bunch more as well because they they deserve the individual attention and there's simply not enough space to talk about them in the detail that that that people want to see as well in in the in the technical white paper as it is so they we are indeed working on on white papers describing all of those things in more detail but also I I don't want to be apologetic about it being ambitious I think we are

going to be ambitious it is ambitious and we expect to deliver the ambition what I'd like to just point out is although we are ambitious we're not insane all of this is done in a methodical way so for example the innovation in the ledger will in itself be deployable and and that's our first stage of deployment on top of that when we have the open economic framework that will be deployable as a stage so we're not trying to deploy the whole thing in one go and and for people who like money what this would do for you is that it

will give you a place to spend it as well because well with everything that is going on you need this currency to deliver some real value as well apart from we're not just coming in to do crypto trading but we're saying there is actually a utility for this currency and not just this currency any currency but you need to have that framework where crypto currency actually becomes usable this is a structure where you cannot deploy fiat currency and the reason is there's millions of transactions very low value happening at a very high frequency building in a economic

framework which is not governed by a centralized organization how do you deploy such a system what we want this project to do is to bring the cryptocurrency to the real world effectively so you just touched on exactly what we wanted to come to now which is okay so what is the sequence and the timelines that we have here so can you talk about okay so so you mentioned a whole bunch of new Viper band I think that would be very interesting to see more detailed explanation of this so you know when are those who least when is there going to be some kind of alpha or something to use and then and then these different parts of the systems yeah what what time frames do you think there will be

available well we have a a bunch of the kind of innovations up and running on a private test network in our office right now that includes the scalable ledger we have the virtual machine related to that it's going to be used for the useful proof of work that we're using to test in a variety of different things and get all of those systems up and working as we imagine we have a basic open economic framework that provides an environment to agents and agents that are able to transact and explore and interact on that from a whitepaper perspective we're looking at approximately give or take a week or two about a whitepaper a month from from now on in until the early summer where we're going to be scaling that up a bit because we've got a bunch

of things that we want to talk about relating to the actual economics of the space but also security issues and how to do that proper documentation on developing agents and and and all of the opportunities that exist there and we're planning on having a public test network that will be available in the summer and that'll be when absolutely anybody will be able to grab that code and they'll be able to build agents and and see things actually work on the network the quality of the digital environment that's presented and the kind of predictions of machine learning stuff that will be available will increase gradually in the months after that leading up to towards the back end of the year and we're looking at a main Network release in the

first half of 2019 so that's our current development and an unrolling plan so what functionality do you expect from this test network I think it'll be there in like June or July right yeah give or take and it what's important is for it effectively to be a a complete vertical slice where the the basic operations that you would expect on such a network are up and running the man includes all of the the key innovations working as one would expect but some of the detail not there particularly relating to some of the prediction stuff because the data won't exist on the network and we'll be looking to work with a number of

partners to bootstrap that initial data to get the the learning models working and that's an opportunity for people then to be able to to get hold of the the software and for them to be able to build build agents and experiment with what might be possible and actually see those agents working on the network that were particularly excited but of course of course we can imagine all sorts of things that people can do with the network but I I would I would guess that that's absolutely nothing in comparison to to what other people will imagine yeah so so so that was my sort of next question which is when you look at a vision like that you can see that yes if the technology were to exist the

application space might be huge because this is about agents they can recite on all devices they can represent humans etc and this is about delivering a set of interesting actions for agents that they can do and generate economic value and the generation of combinations that the agents themselves might not come across so abstractly this kind of system is applicable to a lot of things right but what do you think is is the practical application in the in the short terms if you have a main net release next year what do you think are the kinds of practical things you can use to build with you can build with this system even when there's not lots

of data lots of learning in the ledger itself yeah absolutely right bootstrapping a system like this is is is something that we have to pay an enormous amount of attention to I mean I like in the thing to virtual worlds you know nobody wants to be the first person to walk into a shared massively multiplayer online game and likewise you don't want to be told that well what can you do with this when you do anything because actually if you stand someone in the middle of a world and say you can do anything they tend to stand not really knowing quite where to start and where acutely aware that there are a number of marketplaces and use cases where fetch is particularly

applicable I mean I'll touch on on transport as an example because I've already mentioned it I'm sure her Minds got a couple it he'd like to talk about as well transport is one of those things as mentioned earlier it's got a huge number of moving parts they don't really play very well together it's operated by a number of centralized entities trying to solve complex problems from the top down and utilization is nowhere near as good as it potentially could be and coming up with an environment where actually those complex problems and all those moving parts could work together to solve themselves and the bottom up is is an amazing thing and it could really transform the way in which transport works for the benefit of everyone as well not just the users of transport but also the suppliers of all of those

moving parts because they can get better utilization but also better abilities to manage these things and smooth some of the peaks and troughs and bootstrapping something like that there's an enormous number of corporate partners that we're talking to and I'm going to be working with in the coming months to ensure that the kind of data that's in the network is the kind of data that we need for usable predictions to be able to be delivered in that subject area but also access to pre-existing data sources and sensors that are out there and we've got a concept of an agent class called an API a ei which is effectively an agent that bridges the old world systems and

computers to the new world and allows them to exist inside the fet space as an agent and those are all the kind of things that we can do to ensure that the kind of data and the richness that you need to be able to solve these problems in a way that's genuinely useful to people is is possible in the transport area right from from from the very beginning I will add to that so so my background has been in supply chain and predictive maintenance because it connects us all to the commodities sector so what is quite interesting is that we are where as we are building this dream we are

also very conscious of the commercial returns on such a system because we want to deliver commercial returns as soon as possible so as Toby mentioned there is a lot of traction in the transport sector but that leads into something bigger than that which is a supply chain sector now if you combine logistics shipping and predictive maintenance together I mean just to give you an example one of the one of the biggest returns which the which is expected in the short term from predictive analytics is based on predictive maintenance because we are starting to get towards condition based maintenance anyway so so it would be a very easy win or a very you know quick

area to get the returns from so we we're targeting some low-hanging fruits but then we want to build on top of that but they have to be in the right way where we can build the right prediction models and there is a lot of information which is very low value which is sitting with the academics which is sitting in the academic institutions which is not being utilized but this system will enable you to do is to bootstrap with cheap data and then build some very cool predictions on top cool awesome well thanks so much for sharing this and for joining us today now we're gonna have links of course to

you know your website white paper but if people want to learn more about it get involved get in touch what's the best way should they wait for the test night release or where do you want to send people to and we have a telegram channel and our website of where people can can interact with fetch directly and of course all we with we're there on Twitter and we're going to be publishing increasingly large amount of stuff on our website in more detail and and we're we're always interested to hear from people about and how they might be able to work with this with fetch

well her Maia and Toby thanks so much for joining us today thank you very much thank you guys for giving us the opportunity appreciate it and of course thanks so much for guests for once again joining us we'll be putting out new episodes every week you can either get all your podcast on any podcast application or you can get the videos on youtube.com slash episode of Bitcoin and if you want to support the show you can do so by leaving as an iTunes review that helps new people find the show and yeah thanks so much we'll approach being back next [Music] [Music]