• 00:03:33 How Mark Burgess started to develop CF Engine.
• 00:11:22 What is the genesis of ‘Promise theory‘? What problems does it solve?
• 00:30:51 How Mark moved between academic disciplines over the years?
• 00:34:51 What is ‘Smart Spacetime‘?
• 00:44:29 Does the universe have a direction? What creates that direction?
• 00:55:26 Are emotions necessary for an artificial intelligence?
• 01:07:07 How can a machine memory mimic the human brain?
• 01:16:57 Can ‘Promise Theory’ be used to describe ‘Intelligent Design’?
• 01:32:03 What are ‘low hanging fruits’ of scientific discovery in the next 20-30 years?

You may watch this episode on Youtube – #64 Mark Burgess (CF Engine, Promise Theory, Semantic Spacetime).

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Mark Burgess is the creator of CF Engine. He is widely known for his work on ‘Promise theory’ and ‘Semantic Spacetime’. He has published several books incl. Thinking in Promises: Designing Systems for Cooperation as well as Smart Spacetime: How information challenges our ideas about space, time, and process.

 

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Welcome to the Judgment Call Podcast, a podcast where I bring together some of the most curious minds on the planet. Risk takers, adventurers, travelers, investors, entrepreneurs and simply mindbogglers. To find all episodes of this show, simply go to Spotify, iTunes or YouTube or go to our website judgmentcallpodcast.com. If you like this show, please consider leaving a review on iTunes or subscribe to us on YouTube. This episode of the Judgment Call Podcast is sponsored by Mighty Travels Premium. Full disclosure, this is my business. We do at Mighty Travels Premium is to find the airfare deals that you really want. Thousands of subscribers have saved up to 95% in the airfare. Those include $150 round trip tickets to Hawaii for many cities in the US or $600 life let tickets in business class from the US to Asia or $100 business class life let tickets from Africa round trip all the way to Asia. In case you didn’t know, about half the world is open for business again and accepts travelers. Most of those countries are in South America, Africa and Eastern Europe. To try out Mighty Travels Premium, go to mightytravels.com slash MTP or if that’s too many letters for you, simply go to MTP, the number four and the letter U dot com to sign up for your 30 day free trial. Mark, thanks a lot for coming on the podcast. I really appreciate that. Thanks for taking the time. So when I go to your website, I find this incredibly interesting. You actually have four different introductions of yourself. So one is short, one is long, one is pompous as you call it and one is more historical. Which one do you like the most? I like the shorter the better. I’m not one for talking about myself too much but there’s some context in the longer ones which is sometimes helpful because it’s hard to put me in a box in a particular category of things because I’ve wandered all over the place in my studies and in my work and so people come from knowing me from different angles and so I try to present a little bit of that history. Yeah, I think it’s incredible how you say this, how understated you are and how humble you are. A lot of your co workers and people that know about physics describe you as the Richard Feynman of our generation which sounds like incredible on already. That’s far too much of an honor but I’m humbly thankful and if I would hope that has something to do with the fact that I try to communicate and share what I learn in popular writings as well as in more technical work because I think it’s important to share the insights that we have at a more popular level. Not everybody thanks you for that but I think it’s the responsibility of science to communicate to a wider audience. A lot of people coming from a computer IT background they know you’re from one of the creators, the creator correct me what’s the correct term there of CF engine which is described as way to control your computer’s immune system, correct? That’s true. It was when I was a postdoc at the university here in Oslo many years ago, 30 years ago as many people do in the natural sciences I got involved in managing the computers, the networks and all of the networking was coming up at the time the UNIX based systems and you know I’m a person who likes to get my hands into the system and really understand everything I need to take everything apart and put it back together and make sure I understand how it works and I did that for a few months and then after a few months it started to be less interesting should we say to to spend as much time as needed to be spent maintaining systems because not everybody realizes it but the computers don’t simply manage themselves they they need continuous assistance maintenance cleaning up you know garbage collection updates upgrades all of this kind of stuff and so to cut a long story short I decided it would be much more fun to try to automate that to write a computer program a smart computer program if you like a kind of artificial life kind of program to to allow the computer to become a kind of a living thing like a biological entity and manage itself as organisms to to make sure its vital functions are going well you know the heart rate is good it’s not overstressed all the garbage is being collected all the blood is being filtered etc and any harmful programs or or viruses or whatever might come into or even users for that matter because humans are often the big problem all of those things are being dealt with in a real in real time because life of course is is a real time process it’s not set it up set you on your on your career path and boot you out the door and everything’s fine it’s a continuous process of of learning adapting cleansing maintaining the system so cfng was a program that I developed over many years I wrote the initial version in just a few months to really try to turn every individual computer into its own self sustaining organism entity that would look after itself so that I didn’t have to do that and then over time I realized that there was a total lack of this kind of software in the industry and I shared it as open source CERN took it up first you know the particle accelerator labs took it up first and then gradually it spread around the world and became very popular and then many years later I even started a company around this but that’s the the long and the short of it yeah I think this is a complexity that few in the industry want to really look into right it’s kind of a liability thing and there’s just this automation complexity that people underestimate there’s so many different parameters and things that can go wrong on this level right so it’s usually left to human intelligence system administrator as you say to do all these tasks because I notice from my own experience if you say have the wrong version of the wrong library installed the whole system stops working and that’s it’s incredibly frustrating so nobody wants to really be responsible for this and it’s being pushed around in IT circles from the package management pushes it to a lower category so well you have to manually upgrade and then you’re like you well well it takes hours and you’re like we shouldn’t that be something the package management should do for instance and that’s that’s really disappointing so a lot of people try not to go into these levels of complexities how how big is that project now how many people use it how many downloads where they’re on github do you have any of those statistics I have no idea any longer what the statistics are I think it peaked around the mid 2000s and then towards the end of I think 2008 nine time frame several competing bits of software came up some sort of using a similar approach and others going back to earlier approaches as this infrastructure IT changed significantly you know that time CFM was designed in the old workstations and servers were standalone things there were thousands tens of thousands of them at a time and that’s one or two people were responsible for managing those thousands or tens of thousands but then something crazy happened you know the internet took off was ecomness suddenly every every student and their dog wanted to get a computer science learned to make a website and there was you know suddenly the market flooded with new talent in in IT of course that meant that a lot of people who didn’t have knowledge of systems came into using computers so these these systems were designed for people with knowledge to express their their needs and want um sort of went a little bit over the heads of the newcomers and at the same time they had skills in programming so they wanted to to make their own things they wanted to design their own systems and started to create new systems of their own to to do this and with this you know the rise of cloud computing suddenly everybody could get hold of computers and develop software in a very easy kind of fashion so you know in the mid 2000s you could go to literally anywhere that had computers in the world turn over a rock and underneath it you would find CFM running on that computer and to some extent it’s still true today you know that the giants of the industry um who shall not be named many of them still have CFM running in the enormous data centers so the footprint is there but the people actively using it sort of in a strategic way are probably far fewer today because there are many alternative ways of dealing with the managing of systems in particular cloud computing has kind of changed the way that we interact with computers and it’s become much more manual again because there are far more humans to do those jobs and you know humans love to get their fingers in the pie and and mess around with it so uh there’s been sort of a backlash against automation in some areas where now developers want to really have control again and push all the buttons themselves yeah the the other thing which maybe isn’t as much as an accidental discovery um or an accidental venture that you got into like cf engine is promise theory right this is a big theme that you’ve been developing over the years maybe you can explain it a little bit to us so we have a chance to understand it why it is something that people really need to know about why it is so important um it’s uh uh yeah great question but i promise there is something that i didn’t intend to to create it just kind of i sort of stumbled across it in an attempt to understand the monster i created in cf engine and when i i i made the software it was very intuitive i just kind of had some intuition about how this kind of artificial organism like thing should work based on my background in physics you know my my background is in theoretical physics and i tend to see the world in terms of equilibria things in balance forces fields of influence and and so on but uh when i shifted from physics to computer science in the mid 90s i spent maybe 10 years realizing that the way that we understand systems in physics doesn’t fully apply to the way that we understand systems in computer science it’s for a number of reasons uh which we can get into i’m sure a bit later on but uh one of the distinctions for instance is that in in physics we try to quantify everything and we want we’re looking at averages over time and over space we look for variations and trends and things like this computers are not really smooth like that they’re very you know sudden jumps it’s very they’re a bit binary you know the ones and the zeros but things tend to be on or off here or not there in this location or in that location they’re very discrete things and that smooth variation the way that we describe that in physics doesn’t apply so i spent 10 years unlearning all of those methods that i was hoping to apply to understand computers and eventually kind of studied how to understand some of those quantitative things in a different way but when i’d finished all of that and actually written a book about it i realized that there was a huge topic that was missing from computing and that was what it is we want technology to do because of course you know nature doesn’t have a purpose and society doesn’t have a purpose it’s just doing its stuff it’s doing its thing and different things emerge from that and and may become sort of popular or not popular so there are shifting shifting focuses on things in in these traditional studies like physics and sociology and economics and so on but in computer science things are much more deterministic much more determined we want this to be running here we want to solve that particular problem we have this goal this problem we need to solve and here’s the outcome here is the task to be done so we have specific desires or intentions that that we’re applying this tooling this technology too and and that sort of desire element is totally missing from physics you know we don’t physics doesn’t desire anything so how do you how do you describe that how do you encode that it’s not something that was is part of physics in in the same kind of way so i needed to come up with a way to to describe that intention that we have for systems what it’s supposed to be doing and then the extent to which the thing that we’ve made the monster we’ve graded is aligned with that purpose or not you know is it total total chaos or is it very very precisely tracking our intentions or what is it i’m promise theory was kind of um after struggling for a couple of years trying to figure out how the heck to to describe this venturing you know into game theory and graph theory and a bunch of other things like and logic you know all different kinds of things i i really i really looked into i realized that there was no story in computer science that was really suited to that problem in just the right way a lot of things were sort of getting there but nothing was quite right so i ended up coming up with this notion of a promise which is a kind of an idealized version of the promise that we have in in day to day um which is an expression of intention intention you know i promise that this computer will uh compute this job by the end of the day or i promise that it will be running this program and it will be available for downloading this or that or you know whatever the specific intention is we need to be able to encode those intentions into i’m sorry bless you you know we need to be able to encode those intentions into the system so that we know if we’re on the right path and if we’re not we need to correct course correct yeah so there’s this notion of drift you know systems go along they tend to drift off the path that we would like them to be drifting along and also the they tend to drift from their state of health their kind of average state of repair in which they are doing you know they have all the things necessary to complete that task so there are two ways the system can drift can drift from in the intention we have for it and it can also drift sort of dynamically because it’s a bit feeling poorly or unwell or it’s full of garbage or inundated with other stuff and we need to be able to course correct machines uh in both those ways so promise theory was was a way i came up with to describe that later of course you know as time went by we realized that uh probably from my because i’m a physicist and i tend to make things as general as i possibly can i constructed it as a model of agents which could be things people machines programs anything um and so it applied equally to humans as it did to machines or any kind of bit of technology it could apply to a book you know what’s what does the book promise to tell you by the end of this the final page so we can apply this to the notion of promises to any kind of entity human machine or otherwise and then promise theory describes the interactions between these things you know kind of physicsy way uh how collaboration ensues once you have these basic promises which are a bit like forces or charges in physics that allow you to understand how these things can come together and work together to form systems that are larger than the sum of the parts in some sense and this sort of came about at just the right time because as systems were scaling on the internet people needed to solve exactly this problem and realized that these these issues were missing from you know the classical ways of describing software so um promise theory began to get a bit of attention still not a lot of people were interested in this but it was a lot of people to get a bit of attention slowly at first mainly in the practical uh folks you know the engineering people in in it not so much in theoretical computer science uh academia as you know is very slow to change and and a bit indignant to change if it’s not one of their own ideas so uh so that’s been slower but but gradually this notion of a promises found more and more applications both in um IT study of money uh economic models um and even lately and more socioeconomic things and uh studies of leadership and how companies organize their you know agile development and how companies organize lean resources etc etc etc so it’s really a very general story about processes and i like this term processes because it appeals to my you know the my inner physicists uh if we can i if we can reduce everything to a kind of an abstract process whether it’s executed by a human a machine a book a collaboration of animals whatever the jigsaw puzzle of how we put all these things together is is the thing that promise theory tries to describe i think this is amazing and i think looking back i feel like why isn’t that the cornerstone of economic theory especially that that’s kind of where i see it applicable initially because maybe that’s the one i understand the best i the first thing that comes to mind when you when you told me about promise theory was either real like this part of the blockchain that specifically sets out to to have an additional contract so it’s not just the token itself but there is a smart contract assigned and this smart contract is smart means it’s just machines can read and understand what it is it’s not necessarily smart we could say it’s the unsmart part of a contract but it is a covenant that you form and and it gives you access to resources whatever those resources are and i always thought isn’t that a neat way to to kind of describe or tokenize the the way how the real economy works right how real capitalism works it’s this amazing supercomputer of price information and then we shift resources around based on this price information and i feel like there isn’t a lot maybe there isn’t i’m just too dumb to read it but there isn’t a ton of research that really goes into the supercomputer it has been running on in our minds for hundreds of thousands or at least a hundred thousand years maybe with a i mean we don’t need real money for it we just need like some kind of exchange exchange medium is that what promise theory and economics has been used for and people have made new discoveries or how does it apply to economics from your point of view right now when you’ve seen that maybe other people have picked up it’s a it’s a humbling experience to try to apply any any new idea i i recently realized that uh it’s it’s pretty daunting to imagine anyone could contribute to any field of knowledge today because so much has already been done there are so many people out there working on these things anything you try to imagine has almost certainly been imagined by somebody already somehow um in terms of promise theory it’s early days especially in economics it’s been far more widely used in it um let me just mention a couple of things that are characteristic of promise theory one is that it’s a model of agents that are working somehow autonomously by autonomously i mean independently an agent can only promise something on behalf of itself so i i can’t promise something on behalf of you because that would be imposing on you and you may or may not be willing to comply with my my wishes so i can jump things people you know can generally only promise about themselves and their own capabilities and that fits very neatly with this idea of agent based systems the blockchain the idea of independent actors collaborating entirely voluntarily in an economic or socio economic situation and to some extent this was the model that bitcoin and the blockchain folks tried to capture with this model of cryptologies there are all kinds of technological implementation details that i don’t want to get into but they kind of spoil that to some extent but the the idea that essentially all promises originate from self and then need to be understood by non self you know the outside world um is kind of the essence of economics or socio economic collaboration voluntary corporation and i think of it as the default state of all all systems you know in physics we have this notion of locality that everything that happens in a in a system is kind of in in localized in a particular location and doesn’t stray too far from that so it’s sort of a change that happens here doesn’t have a sudden effect very far away from it typically it’s not entirely true but it starts from that principle and in a similar way when i make a decision about myself it doesn’t have ramifications for the other side of the world without some in between process that propagates that from one side to the other and so this idea of things being very localized is is sort of at the root of it all now the blockchain tries to capture some of that by making individuals independent trying to remove the notion of a bank which is something like a relay hub in a network banks act as kind of the routers and switches of the economy when you send money out they will accept your money and route it to the to the next location and so on so the the banks sort of present the world of money as a kind of network in which money is a kind of promise you know i if you look at old currencies like the pound in the uk it still contains the text i promise to pay the bearer on demand the sum of one pound which used to mean that you could take this piece of paper to the bank of england and get the amount in gold equivalent to that doesn’t really mean that anymore but it’s still somehow the promise of of of value that can be exchanged for something else assuming that everyone else is along with the that that game but um uh it’s curious that because of sort of his for historical reasons if you like which perhaps go all the way back to moses and the ten commandments we have this view of the world which is based more around command and control and the idea that if i do this that must happen so i push this button that must take place like i give you this money you must give me this thing in return it’s entirely the wrong way to look at the world but we have this kind of bad habit of framing phrasing things in that way when i was coming up with promise to you i was fighting against that model of description of systems all the way and i think one of the things that people picked up on in the economic realm was also this fact that it describes corporation from this voluntary perspective which is more realistic rather than this force based push must happen kind of um idea that you know if if interest rates go up it must be true that this will be the effect on the economy it doesn’t happen in that way of course um and there’s a story around statistics and and scaled effects of all those tiny changes happening which may eventually lead to some of those things effectively happening that’s a totally different kind of probabilistic story on a different kind of level and yet we need to be able to describe the one from the other microeconomics from macroeconomics and vice versa if you will and in a similar way the tiny changes on one computer to the total totality of computation computation in the cloud or the idea of a single idea to the whole of human knowledge all of these are scaling problems that we need to be able to understand and that’s something that physics has a long history of being able to explain a lot of good techniques there to to draw on but the same techniques haven’t really been drawn on in the same way in these other areas like computer science and economics so the folks at the Federal Reserve and a little group of researchers that sort of stumbled across promise theory and the work I did on describing money as a network they’ve been kind of interested in other ways that we can use this to describe a new way of describing economics based around these principles which is perhaps more realistic than these neo what’s the term the neoclassical economics of of Milton Friedman and and company which are largely used by the the wider world and I think it’s it’s possible but no one has really shown exactly how to get that far in in that area it’s still still a thing to be done but it’s certainly true that the way promises work is very closely related to these cryptocurrencies that ethereum as you mentioned can be considered sets of promises encoded on this crypto ledger which you know for better or for worse are sort of immutable and will will continue to be promised forever more once you’ve made them which is both a useful and terrifying at the same same time so promise theory gives you a way to describe those things and understand the complexities of them and analyze them it remains there remains a huge effort to be to apply to to those things and really decide whether or not teaches you anything new or not even you come to a new discipline like that and you you’re a trained physicist and I think also computer scientists you have a degree in computer science I have no degree in computer science but I’ve the majority of my publications are have ended up being in computer science and I have my professorship was awarded in in computer science rather than in physics at the end of the day because I’d sort of made the made the switch and and had gone into that field when you go to a new discipline and we know that is something that a lot of thinkers who think more lateral who are not boxed into specific research or professorship who have that ability to look outside the box they do complain about this compartmentalization of science and that you find it incredibly difficult to go from one discipline to another and even mathematicians have trouble really breaking into physics and physics physicists into math which seems incredible because both disciplines seem extremely related to to the outsider to seem almost the same many times and is that something that you found true as well and how do you deal with this if if it isn’t as accommodating as you wish how do you how do you walk around this it’s it’s actually terrifying how hard it is to to flip between different subjects even when they’re quite closely related sometimes because there’s often a jargon associated with the with with the subject a way of formulating problems which is sort of traditional perhaps or or closely related to common practice or whatnot and there’s also an enormous stubbornness in in people in one field to to not think you know to see themselves as sort of special and everyone has the kind of their own special needs and and knowledge and they don’t want to be reduced to a general case of something else so there’s there’s often a lot of resistance from die hard academics in one area to to see what they do is being related to something else that they don’t do and ironically you would think that academics might be more open minded in that way tends not not to be the case but I do find it hard myself but I’ve also come to it’s something that I need to do on a daily basis because I I’ve got my finger in so many different pies that you know I’ll work for a day on this problem and then suddenly I’m working on a totally unrelated thing which I find both extremely challenging and extremely rewarding at the same time because I often learn lessons about both things by by being in the frame of mind of the other while looking at this unrelated apparently unrelated problem I will see it in a new light and suddenly realize oh you know what I do know something about that it’s it’s just like this other thing over here convincing other people of that connection is a lot harder than seeing it for yourself I have to say so it is challenging to to to take on board and to convince other people of and this certainly applies when trying to publish results because you know journals and academics or journals tend to be tied to sort of academics who are quite powerful in their fields and they’ve won their fame by being successful in one area and if you try to publish some new idea which doesn’t fall into that sort of classical view they may oppose that for you know political reasons or for human reasons or whatever or simply because they don’t follow or understand it so it can be enormously challenging I find those cross disciplinary projects that I’ve worked on have been the ones that have taken longest to gain acceptance but they’ve also been the most rewarding ones that have had the the greatest impact perhaps and the most applicability in the long run yeah talk to Patricia Farve and she just wrote a book about Isaac Newton and I asked her about is the age of the polymath coming back and she was very skeptical she’s like this maybe but there’s there’s nothing that suggests this right now so if anything it goes the other way right now which doesn’t vote so well for for people like us I consider I’m definitely far away from the the depth of science you do but I consider myself a generalist and that is definitely well it’s not a skill that’s as much in demand as I would hope let’s put it this way or maybe that’s just me right so that there’s a lot of a lot of avenues there the news thing you work on and you wrote a book about it a popular science book and it’s called smart spacetime and it is about semantic spacetime both are terms that I’ve never heard before before we actually talked last time what does that mean is that something that that changes Einstein’s theories is that a quantum quantum dynamic physics book what would should we think of when we hear the term smart spacetime so smart spacetime is the popular version of semantic spacetime that I used for my book because I thought it would be easier to understand and it turns out to have some connection to artificial intelligence and perhaps even our notions of consciousness and so on but that’s let’s not begin at that end this is also something that came out of promise theory and my attempts to describe ordinary processes in widely different scenarios in terms of this idea of the cooperation of independent agents and independent pieces coming together to form a whole now if you imagine you know an agent is a bit like a person it’s it could be any kind of person it could be an atom it could be a person it could be a nation state it could be you know any kind of a cell in a body an animal basically the thing that characterizes all of these systems if you want to call it that at different scales and to very different in somewhat different ways but just go with this sort of general idea for a moment the thing that characterizes all of these things is that they are entities sort of localized entities that receive input from outside information from outside and they process it in some way and they may respond in some way by giving message back or generating some output making a promise a new kind of a promise delivering a service and so on you know so an atom may absorb a photon it may emit another photon or it might interact with another atom to form a molecule the molecule might interact with another molecule and form in such a way that it be for example a virus you know it may infect a cell the other one might be a vaccine which sort of neutralizes the virus or you know an antibody which neutralizes the virus neutralizes the virus so all kinds of entities can be considered from this point of view that they both do things and they have some kind of a purpose or an intention of some kind which can be aligned with and then the way that we combine all these alignable intentions forms a kind of chemistry which again allows us to combine them into new things and those new things can make new promises again they will receive new kinds of input on a new level generate new kinds of output on a new level so no matter how primitive or sophisticated that kind of model is there’s something similar which is a system which receives something processes a little bit and spits it out again and what happens in between is kind of interesting of course depends on the resources inside this agent is it does it have a complex brain with a lot of memory or can it only remember a single bit you know one or a zero so the degree of sophistication matters a lot but the reason this is interesting is part physics and part computer science if you will the physics part of it is that you know we don’t really describe physics in that way the way we’ve learned to describe physics since Newton’s time Galileo and Newton told us that we have bodies entities particles if you like and they tend to move in straight lines and then they hit other things and they move off and you you follow these lines and you figure out what’s going on this concept of something being inside something else or something arriving at a location and being absorbed processed and emitted isn’t part of that story of physics or it wasn’t at least until the quantum theory came along and then suddenly became important and so there was this kind of missing perspective in the way that we tried to deal with physics and if you look at the way people tried to deal with quantum mechanics they also tried to start with this notion of things going in straight lines at certain locations and so on and and they got into a lot of trouble with that so it took a long time to figure that stuff out on the other hand in computer science people have this very centralized notion you have a computer it has input from the outside and it generates output it’s clearly centralized in a location similarly with biology you have cells that absorb and emit you have organisms that are centric that receive sense information and change behavior as a result so this centralized view of the universe almost the you know this um earth at the center of the universe the Copernican revolution that story was eradicated from physics to its detriment in some respects because we’ve forgotten how to understand systems in that way and go between that to this more straight line version of description and yet all of the interesting things that happen in the world are based around these more centralized configurations of information coming in process information going out so I wanted to see if we describe spacetime not in the way that Newton did as a theater in which things move around or and which Einstein took and corrected to add back some of those details which don’t work out quite right when you don’t take that central thing into account could you re describe spacetime by building it up from the ground level out of agents with the promises that they make more like a network so again you end up with a network description of spacetime rather than as we had for them for the money for the economic story rather than this kind of empty space with matter floating around in it what if everything is simply part of a giant network and all of these material excitations are just it’s their information running around the network could you describe the world in that way when you do that it allows you a neat way of combining the semantics the meaning the interpretation of things with all of those dynamical behaviors the qualitative and the quantitative come together much more naturally in that framework than they do in either the Newtonian view which tends to eradicate semantics or the the centric centralized universe the biological point of view if you will psychological point of view even where everything is centralized and this more objective view of the world is is demoted so this constant tension between subjective and objective in the world don’t go very well together except in promissory which unites them in a fairly natural way so i wanted to put those things together and try and describe spacetime and the interesting thing is that when you do that you have a story which although varies wildly in the details from the very small to the very large remains essentially correct whether you’re considering an atom or a country interacting with another country or for example a human brain a conscious brain and it tells you a little bit a little bit about the necessary and sufficient conditions of what must be going on inside in order for certain promises to be kept on the outside which i think is powerful of course it sounds very easy but it’s very hard to go into all those details so perhaps i make it sound easy it’s not trivial by any means but it’s a i think it’s a story that needs to be taken seriously and so i wrote this little book trying to explain that point of view it sounds fascinating to me and i i feel like these two views on the world as you outlined them they they’ve vexed me to this this one part of the world where we feel even if we can’t really describe it in technical terms there is a purpose there is a reason there is a creator even right something that definitely has already an inbuilt direction in life and i know you you’ve been quoting Goethe Sfaust on your on your homepage so he’s been pondering with the same question there is we clearly feel of the we i don’t know what science did in the last 200 years but we clearly feel there is this this positive direction of the universe and it goes somewhere even if the individuals don’t know where it’s going these are all part of this machinery they’re not just a cock in the machinery and to an extent they are but they all follow a certain direction more or less and then we have this as you say this whole objective universe that is basically like cold and and it doesn’t have any direction it’s just there and i find and you must have thought about this if you if you go keep and if you go keep this further why is the universe there and is they a creator and well that would be the first question i would be drawn to when i would think about that theory and you must have done this probably decades ago what did you find what is your gut feeling i think we we have no way of answering the question why is the universe here to do that we would need to somehow to be outside it and we don’t know how to be outside or if there is indeed anything outside of it we don’t even really know where it comes from although we have a lot of stories that are consistent to some degree with things that we see within the universe you know we are trapped within this universe so our ability to get outside of it and understand that part is is is pretty limited there are still certain things that we can say about how it might have arisen but when it comes to our purpose you know this is an interesting question for me mixing together these two sides of my my work which is the physics as you say very objective and the information science which is to some degree more subjective and intention based and relates far closer to things like human human intelligence and consciousness and so on all of those those ideas what physics teaches us is that the world is very different at different scales we tend to try to look at the world from our scale our human scale of course because that’s what we know and we’re far better rehearsed at understanding the world from that point of view sometimes that means that we we impress upon the world things that aren’t true on other scales and we make mistakes in the way we reason about it so for example we should never try to imagine that an atom behaves anything like a human being or that a galaxy behaves anything like a human being and yet the we know that the rules of interaction between things on those scales still have certain similarities there’s still things to have with inputs and outputs and things that happen in between so you can you can go you can to some extent use knowledge from one scale to infer what might be going on at other scales and in physics there’s this notion of dynamical similarity which expresses that point of view it’s also called scaling newton would have called dynamical similarity is the idea that similar systems may have similar explanations or similar phenomena may have similar explanations in some sense and to get it right you need to adjust all of those ideas you know to make sure that you’re not kidding yourself but it’s basically my belief that that idea can be extended in the way that we do in physics which is purely quantitative to the qualitative level as well where semantics can also be scaled in the same way that we can scale processes and when you do that you start to see things in very interesting ways the ultimate expression of that I think sort of the grand the greatest possible outcome of that would be somehow to understand our own ability to have a conscious understanding of the world around us some people think that’s impossible some people believe that there’s you know consciousness is something totally different from what happens in the physical world I don’t believe that I believe that we are just cogs in a machine but that we just haven’t understood the extent of machinery in all of its glory properly yet we we tend to imagine that machines are more limited than they probably are there must be well my point I take it as an axiom that we are machines and yet we’re nothing like you know machines that with cogs that we turn out like a car totally different animals animals things but nevertheless there are processes that take place in these things that can be described now one of the things that changes when you jump across large scales is that systems no longer are deterministic you you push the button or you turn the handle the thing the response of the system isn’t 100 exactly as you hoped as you scale systems it may only be halfway or it may only be a probability of any only a chance that turning the handle actually leads to the outcome and that’s not counter to the idea of of machinery lots of machines also behave in this way at scale take for example the global chain of logistics chain where we transport goods around the planet you place an order for a new car and it sets in motion a bunch of processes that result in a car being sent to you but you have no way of predicting exactly the time between you ordering the car and the car arriving at you it’s an entirely nondeterministic thing even being able to being able to trace its progress around the world on shipping containers that may or may not get stuck in the sewer’s canal you know that’s a totally unpredictable thing because there are far too many variables to take into account and the way that we see the world is not like the Newtonian picture an enormous theater in which everything is precisely deterministic it’s a subjective observation much more like Einstein’s view of the universe and much more like the quantum view of the universe where the observer has a very specific point of view a very privileged position but has limited access to information and it’s that incomplete information which makes the world unpredictable in our eyes from our perspective if you could imagine some kind of godlike observer as Newton did you know his his entire approach to physics was based on his belief in God that there was a creator who could see everything instantaneously we know that that’s not true when you’re trapped inside the universe and on different scales you have to deal with different kinds of delays and different kinds of challenges so we need to take that into account as we scale systems if you do that you find that the world behaves actually with quite predictable regularity but by predictable I don’t mean we know precisely and deterministically what’s going to happen I mean that we can generally write down some kind of our prediction as to what may happen and constrain that to some degree to make a kind of prediction I think we can do it in almost all cases and I find it fascinating that that is still possible regardless of the scale the place where people often get stuck and tend to object to this kind of view is the question of free will and consciousness in humans and I find this story fascinating I’ve thought a lot about it myself and it’s I believe it to be related to this notion of smart spacetime but if you could put a boundary around sufficiently complex resources and sufficiently complex processes on the inside of some your head basically right and then all of those things can be made to work in the way that we know that consciousness works and this kind of belief that those human aspects are somehow special emotions a point of view personality that they are somehow not machine like properties I don’t believe that for a second I believe that all of those things are highly natural parts of the processes if we only understand them in the right way so for example as we were as we were setting this up we were talking a bit about AI and what might be the next stage of AI some people think of artificial intelligence as being the intelligence whereby we we eliminate human emotion from the equation if you look at the history of science fiction this is fascinating notion that you know when the robots become super intelligent they will be devoid of emotion because emotion is a weakness a human failing I believe that that’s exactly the wrong way around that it’s those emotions that that lead to things being more important in one individual than in another individual that amplify and settle the question of should I or shouldn’t I will I or won’t I it’s those potentials that arise in individuals in different ways because of the different subjective processing of information that leads to precisely those things the uniqueness of the individual and all of those things that we tend to associate with humanity so any story about artificial intelligence or the processing of knowledge that excludes the concept of emotion I think will fail miserably to recreate anything like what we imagine human intelligence to be I’m fully with you when I think of these parts that we can’t explain in the human brain yet like emotions where they come from how they’re being inherited and there’s lots of them consciousness what morality ethics right I feel like when we talk about artificial intelligence those are all survival mechanisms that have made us a more successful predator a more successful animal so to speak right so that’s why we we rose beyond what else is out there and we actually have a successful story to look back to the last 50,000 100,000 200,000 years whatever we want to count and I don’t think that an artificial intelligence whatever the way it looks will be able to live without any of those systems yesterday might leave them behind and go to higher systems relatively quickly so it might not be 50,000 years might only be 50 years but they will go through the same problem of what are our values what are our ethics what do we prioritize how random are we as you say for that’s what emotions do how how do we derive an art from and is that is a huge problem and each machine will have the same problem in a high intelligence or not and our algorithms that we evolved which seem to be the best on the planet we seem we have we seem to have that impression right now I think there’s the good reason we’ll develop in machines too but I think what what comes next is the problem is that we have things like GPT three who were designed for something completely different and it wasn’t like a big deal wasn’t like such a great endeavor was a few million dollars but it wasn’t like you know Manhattan project but it suddenly could do poetry it could do html code it could do python code and even the developers were really surprised shocked maybe what it could do now it’s still very elementary and it has a different approach than we would assume and it doesn’t have all any of these advanced things we thought we just thought of but what people feel like there is this tomorrow you know someone in China comes up with the with an artificial general intelligence or super intelligence a human like intelligence it’s it’s difficult to find those words and yes they have emotions and yes they have consciousness and they will act like us so that’s all good and I think we all agree on this but only I don’t know 10 years later there is there’s something out there that is like on a scale of several billion smarter and better developed that has all these things we’ve developed but it’s like so far away from from our what we can see as a as a most smart human on the planet right now that we barely have any words and the the the worry is a little bit that this scales up in a in a very small time frame so we can’t see it progressing because it’s so quick in learning because that’s what we attribute to machines or machine learning that might not be true and I hope you you can tell me better but then within a short time frame maybe within our lives and people think about singularity as a popular science word for for for describing that that in say 40 years machine intelligence is so far out that they have all this would we know but they have so much more that we can’t even understand them anymore we don’t know who that is they go to the stars we can’t even send them letters you know there are already today plenty of systems that we can’t understand so I think that’s not the that’s not the benchmark for creating machines that we we can’t understand you know I I don’t believe in this notion of the singularity for a simple reason that it it is a story again based on the prejudice of human scale scaling it it doesn’t take into account the scaling of the world as we understand it it assumes that if we create a giant computer system filled with all the information we can feed into it you know we we feed all the Wikipedia into it and every bit of human knowledge that we can we can find that it will somehow behave like us but but think think about what makes a human like us we are the way we are because we’re surrounded by people like us we went to school with people like us we we learned slowly over time running around a playground climbing trees and doing things that that humans do on a very specific range of scales computers have no access to that world they don’t have the sensory apparatus for running around climbing trees they download information in an entirely different way from bookish sources right you can’t learn to climb a tree by reading a book you do it by imitating a physical process and mimicking certain behavioral emotions through actuators that behave in a particular way creating a specific kind of information stream that we are well adapted to it would be first of all it would be nonsense to try to recreate all of those things in in IT no one would do it even if you could do it you wouldn’t be able to get the same information to train it without you know literally creating a robot the same size as human being and sending it to school in the same way that a human being goes to school to learn to even care about us and our world but our world has changed so much I feel like we’ve cloudified ourselves so much who reads a book anymore nobody I mean nobody on the 15 ever reads a book right yes but but think about the the generation that’s growing up right now who still climbs a tree it doesn’t exist anymore these kids are literally in like a machine in a in a display in front of their head and they don’t move for 12 hours a day that’s it well now you you put your finger on something very interesting which is perhaps not so much that we should worry that artificial intelligence may exceed our best human beings that but rather that we might be turning our kids into robots by surrounding turning them into cyborgs you know by sticking their heads into these things too much of the time and and getting everything at the push of a button where you know instead of needing to use our manual dexterity to create something to use our minds we simply treat everything as an ATM where we you know I want a pot of noodles I press the button it arrives in my door you know like the the Star Trek food processor wait you don’t have one of those everything we want at the push of a button is the is the quickest way to retard our intelligence and go backwards because if we no longer need to be creative if we no longer need to connect together dots causal events and we’re not exercising those things that our brains evolved to solve my belief is that the the essence of intelligence not so much intelligence but well consciousness if you will is essentially a thing that our brain does to tell stories if you think of a brain as a kind of computer it’s not a computer like the ones we have but in you know it’s got a bunch of memory and that memory isn’t moving it’s you know some bits and bytes stood stored in some cell connection somehow but in order to recall those things and create a dynamical picture like for example when we’re dreaming or when we’re looking around us and seeing things changing in real time to create a real time picture from static memory you need a process that generates stories tells stories a narrative process and that’s the piece that we’re missing today we know how to take information the outside pattern it and store it in a static way in order to compare to patterns detect fingerprints faces etc so we know how to do the things that eyes do and the parts of the brain that that decode the patterns that senses decode and turn that into static imprints we don’t yet know how to read reconstruct dynamical worlds from those memories in the way that we do uh when we’re sleeping for example and when we’re asleep the main difference is you know brains are very active the main differences were cut off from our senses to a large degree and so the things that we’re imagining can only be coming from the memories inside us and yet what bizarre things that we we dream and it seems so real when we’re there in the moment you don’t question the reality of it when you’re dreaming only later when you you you compare it to the world that we were more used to anchored in reality do we start to question or that probably wasn’t real it’s a different kind of thing but that that illusion of reality that we experience in dreams i think is the is the crux to understanding the our conscious understanding of the world the ability to tell stories and and that again is it comes back to this promise semantics based on my dear that we need to combine dynamics with semantics patterns meanings with changes and that’s how we create stories that’s what we mean by stories right the a stream of semantics is basically a story so that’s i think the the great challenge for us to to approach some kind of a generally intelligent whatever that means organism and i think it’ll take a lot longer than 40 years to get there because i think we just don’t understand scaling in it there are two parties looking at this question i i read a lot of books by both of them so on the it side you got all the ai folks believing that the neural networks have some magical mystery that will just sort of make it happen i don’t believe that for a second because they don’t understand scaling and then you got physicists on the other hand all these usual suspects who fall straight into the trap of physics nv wanting to talk about entropy and information and and quantum computing and all of these things i don’t believe that’s got anything to do with it either because that’s about an entirely different level entirely different story where semantics have no place so unless you unify these two pictures the semantics and the dynamics and something call it smart space time or semantic space time if you will basically agent models and networked structures network structures the ability to constrain and allow freedoms in a constrained way around localized processes individuality different viewpoints all of those things have to come together then only and only then will we start to understand what we think of as um as consciousness you know um there a lot of people would say well when we talk about meaning it’s literally just and i know this word doesn’t fit there a compression and an index and algorithm so instead of saving the whole image which is maybe say in one gigabyte a big picture or a couple megabytes we just say well that’s a picture of a dock and it’s a happy dock and 15 other attributes so we just compressed it from a huge data set to a really small one so that seems to be something that’s going on in our mind right so we we only we save the shapes and we shape we save certain semantics meanings attributes and then we have an imperfect indexing algorithm it seems to be something that that is relatively obvious to be solved with artificial intelligence because it’s good at tagging stuff it’s good at at compressing it to two very small number of attributes and once you have in a model that’s the fascinating thing you can apply it once you feel it works for what you want to do and for your data set you can apply it anywhere you want it’s really cheap it’s really fast processing it only takes my milliseconds but building the model takes a long time and that’s kind of what when we describe ourselves as humanity we built the model that takes forever but once you figure out what we want to do and how we classify things it’s relatively quick I think this is what artificial intelligence researchers right now on one hand they know what they do is just pre or statistical models and it’s really boring and it does there’s no consciousness into it but they feel like once we make that step that we can basically classify everything or compress so much then the next step comes in and we have like this next level of AI or maybe a slightly different technique and we feel like well we even with relatively limited amount of space we can make sense in the sense of we can make a proper probabilistic description of how this what this could be and we can predict the next next step so when a cat you know what the cat probably does it’s an easy model to do and that seems to be something that even children have they know they can’t distinguish spiders from cats for instance and you don’t even have to run them through a big model they come with the model already that’s it’s in their brain when they when they are born maybe maybe they learn it over time but it doesn’t seem to be such a big stretch and then to to assume that we can describe the world and build an intelligence that can go through the world kind of like we do with an imperfect model it’s never going to be perfect we add some error correction like we do for children right don’t touch the oven it’s hot that figure they figured this out pretty soon not to do this again attention model I feel all the the switches are in place to get to something that resembles consciousness even if it doesn’t have consciousness but then again we look at people the scale of consciousness in humans is pretty enormous like we find people who are pretty obliterated what’s going on in the world if no clue they don’t want to know about it and then we find other people who philosophers who spent day and night just you know sharpening the knife of consciousness so there is a huge scale but if you say we we just want to build an AI that for now works like a I don’t know eight year old I don’t think they’re that far away from it yeah you I think you touched on an interesting point an important point which is that a key aspect of processing information is the ability to compress it into tokens or symbols symbolic and there’s this artificial distinction in AI groups between symbolic AI and non symbolic AI or neural networks typically machine learning if you like I don’t see those things as being distinct I see the machine learning tools as being ways of compressing information into certain representations which some of which are smarter than others in the sense that they can adapt or embody variations in a more interferometric way it’s kind of like an interferometry and the the mistake I think is in believing that you can reduce all of the cats and catness to a single symbol or word you know and it’s like in a book you read the word cat your mind immediately thinks of a million cats and all the crazy things that cats do but there is no sense in which having in gone in the one direction allows you to go in the reverse direction we don’t know how to do that yet and I think some of the most advanced brain researchers of today are starting to talk about the brain not as being something which is mainly fed by inputs but actually something which generates the world from within and rather anchors those imaginings those processes on the interior anchors those things to sensory uh interferometric sensory inputs combinations of sensory inputs and then tags those things in some smart way because we don’t know how it works yet and the ability to generate stories and recreate those worlds is again part of the problem that I think is missing but um it’s a really important thing that a cat it’s not a it’s not a symbol it’s a it’s a process the cat process it probably more like a living thing in our minds than it is a static memory and I don’t believe we’ve begun to tackle that problem yet you know my hobby is ever since I was a small kid the first thing I ever wanted to do in my life was music I was always fascinated with the orchestra and and the way that an orchestra comes together from all of the individual instruments and each individual player is playing with great skill and great individuality but the sum of all of those processes creates music on another scale which is the symphony itself which has a lot of interwoven parts themes interweaving with counterpoints and all of these complex musical things which I love to do as well I I compose music as a hobby as well but I see very much physics information AI all of these topics are very much like music in that sense that it’s the coming together and interfering of processes rather than bits of static data in the data model being looked up by a machine like retrieval process so it’s unlike that ordering that car from the other side of the world through the chain of logistics it’s far more like watching all of the cars driving around all over the place and figuring out what’s going on because suddenly because suddenly everyone’s driving to New York for a concert that’s you know that’s the expression of that thing it’s not a simple symbolic action that could be written down of course you can write down those things you can always compress and this has been enormously useful to us in language the fact that our our brains evolved language I believe is connected with this phenomenon that we’re able to compress things into symbols as part of the process of cognition so it allows us to serialize our knowledge and communicate it with others but of course by throwing the word cat to a person that doesn’t know the word cat totally unhelpful they have to have a process they have to have a cat process that they can anchor to that symbol in order to interpret it according to their own understanding and in order for us to resonate and find common ground and that’s the part again where you know AI and singularity thoughts are not really giving the human part of the process enough credence there is no counterpoint there’s no society of intelligent super intelligent computers from which to learn from each other to build that compositional framework for mutual learning and evolving the symbolic underpinning for that for that superintelligence model to emerge and I think that’s the bit that we’re underestimating or mis underestimating whatever the word is and that will be the thing that we come to solve over the next couple of decades probably because it’s always slower than we think yeah I want to go back for a moment to that point you made earlier and correct me if I misunderstood it but there is directionality it seems there seems to be a purpose that each individual level that we look at plays and it seems to be something where we have we have this time stream and along this time stream the or maybe that’s the in the end the question it’s the time stream of the universe and what happened to the universe it it isn’t all random so it started from from what we know out of a small soup of of energy and it created this this massive conglomerations of stars and planets and then intelligent life is there and there is this book about intelligent design when we look at just what happened in the early days on earth I don’t know if you ever looked into this we have this incredible odds for any life to exist we can say well it’s billions of years and there’s billions of stars and so it’s in the end the computational probability probabilistic approach and we can say at one point it must have happened but the strange part is is that we we go from something with very low complexity at least from from what we look we look at it maybe that’s the wrong way to look at it but we have this soup of into of initial proteins and we have more actually it’s just molecules atoms molecules and then produces soup and the proteins and the cells it seems incredibly complex and it’s getting more complex shouldn’t do you think there is agents at play and again this is probably the question how we look at this level there seems to be a certain purpose driven to a higher complexity that seems to be innate to the system so these agents we talked about a slightly about that before about the creator but it doesn’t have to be is there a designer is there at some level we are driven somewhere we are kind of just a you know the bootloader the vet there to to create artificial intelligence maybe that’s just one more step in this but it’s your assumption when you see this in in promise promises that each each of these agents make to each other that they seem to be they want to go somewhere or is it just randomness we see this the the 10 billion years of their universe they were all just random you know we have this tendency amongst people to anthropomorphize things to make things to give to attribute things human intentions i think it’s actually harmless some people don’t some people get very upset about about it if you know you say oh the electron wants to be attracted to the proton it doesn’t bother me at all because in promise there is this notion of a possible intention and as and the real intention or an intention that has been adopted by a human or some kind of mechanism a possible intention is really just a direction it’s just a way of describing direction in a space of possibilities so the fact that we we as humans can have intentions possible intentions or machines can have possible intentions an electron can have a possible intention on a simpler level it doesn’t require the notion of come consciousness or decision making free will so that i find that entirely unproblematic the question of whether there’s a god i also find sort of uninteresting on one level because i don’t believe this a human like entity that thinks like we do looking down on us from outside the universe uh guiding things because it would any being with those kind of powers would be totally unlike us we wouldn’t recognize it just as we may not recognize life on other planets if it’s on a different scale from us you know there’s a sense in which the amazon rainforest is alive if and and the trees talk to each other this amazing work done by german researchers 10 20 years ago showing how trees form these networks of communication then they’re talking to each other with fungi you know we don’t know what they’re talking about or how smart they are but how would we even know where how would we begin to decode that with no way of even connecting with it in the same way that a super intelligent computer might not even see us as a you know might not even see us at all so these these kinds of different kinds of entity whether we call them god super intelligent computer life on other planets it’s not clear to me that we would understand each other or recognize one another because we simply are too far removed from one another in the kinds of processes that we are and yet if we had long enough and had enough resources if we could collect that information and see the patterns over time who knows what we might find we might discover that the entire universe is thinking about something in some sense might not be a very interesting thought but who knows is your gut feeling and this is more faith than than what I’m writing a white paper but so I understand the science has a maybe deterministic view on this what would ask you about your gut feeling if we live in a simulation if all of these things come out of somewhere and we’re driven by intelligent agents so to speak like it like each of these agents has an agenda we don’t know and we can’t recognize it I agree but I think our human brain for better or worse has a certain kind of perception for other people’s agendas and other as you say there’s a very human way to look at the world in your personal intuition do you think there is something out there that’s guided this whole process or it’s just your randomness and we’ve just this pure little bit of luck that happened on this rock I don’t think there’s something that’s guided us on any scale that we recognize but of course we still have no idea about the origin of the universe and matter and energy and why it has this particular standard model of interactions but somehow built into that ability for these these key processes whether we call them electrons quarks whatever they are and the forces between them whatever created those kinds of processes that that palette of colors from which we paint the world around us yeah we have no clue where that came from but latent within that across multiple scales the ability to things to attract one another and come together and to represent information and create increasingly complex representations that don’t simply fall apart and decay into entropy straight away the fact that that is possible we have no idea why or where it came from but it’s latent within that everything we see around us which is remarkable but I know enough of physics and computer science to know that you don’t need to invent a mystery about getting from the very small scale to the very large scale it’s you know even even without becoming a total reductionist you can say that everything that happens on the large scale derives from things that happen on a smaller scale but that doesn’t mean that there are not new things on every scale that couldn’t be predicted from the nature of things on the small scale because because the nature of information is to decouple across multiple scales and to introduce new levels of information representation along the way like how that ends up producing all of these marvelous things is is truly awe inspiring and I have no idea where to begin to describe that but some people find it comforting to attribute that to a creator I don’t I find it marvelous to to believe that this could happen by by chance or if if chance is even because we don’t even know what chance means but we there’s so many things we don’t know that it’s hard to to give a name to that particular thing that’s missing from our understanding but I don’t I don’t need to call it a god or a designer yeah if we were ever able to create our own universe now this might that’s a big stretch right so we’re talking about hundreds thousands of years millions of years long time frame from now what do you think would be the thing we would most emphasize what would be when we design it when we had the opportunity to design it what would be what would be the most important factors we we really look carefully at the design and the rest we kind of leave to chance winning people always want to win like we create our own universe not just entropy like entropy is the opposite of winning or what would be the opposite well you define winning however however suits you best of course we do actually create our own universes right in computer games all the time yeah this is they’re becoming increasingly sophisticated of course they’re nothing like nothing even approaching the sophistication of anything in nature but still there are frameworks in which things evolve change over time they’re not fully determined and they have on me no clear outcome except that when we create these things we usually want somebody to be able to win yeah we like a game right like a game I have no idea what the winning move is in the universe game I hope it’s not the heat death or everything but who knows I suspect I will be there to see it I’m asking because when we when we make up our mind and feel it’s it’s a bit of a religious question if we live in the simulation it seems we everyone needs simulations and we use abstract dot as a simulation right instead of killing an animal we think about killing an animal and so we improve our odds when we actually go out there and get in a dangerous situation and anything that is more abstract than concrete doing is this is part of the simulation so I will we will just keep on simulating whatever we can and just run our odds and I think artificial intelligence to to an extent is just an extension of that when we the difference between the simulation we create and if we are in a simulation and we don’t know it is just maybe it’s just the amount of energy this this pure amount of energy that was the starting point of the of the universe and then one thing I don’t know what physics thinks about that maybe that’s something you you’ve looked into is this concept of time right so we know with Einstein that time is very relative but that’s a relatively new discovery it’s only a 100 years old a little bit more than 100 years do you think time from outside the universe could be a very different animal say to let’s assume for a moment we are in the simulation and whoever simulates us for that intelligence the time would only have been a few seconds but for us it’s 14 billion years so time could be so relevant so extremely relative from different parameters when we look at a time absolutely we know this to be true in the world around us today it’s actually a myth that Einstein showed that all time is that we can only measure relative times and locations and velocities and it turns out that we sorry I should I should say that differently Einstein it sometimes claimed that Einstein proved that there is no route there’s no absolute position there’s no absolute time he didn’t really show that he only showed that that view of the world is unavailable to us being stuck inside the universe as we know it it’s entirely possible in fact it’s easy to show that you can create we do this in the cloud the computer cloud that the time as experienced inside a virtual machine a virtual process is very different from the time experienced by the computers that they run on and the network the computers that they run on are they form a kind of an absolute spacetime with fixed locations absolutely not relativistic and yet the processes that run on them experience relativity in a similar way to the processes that run in in our universe material things moving in really at relative velocity like Einstein showed so it’s it’s simply a fact of processes running inside processes that allows things to become relative it in no way eliminate eliminates the possibility that there is an absolute universe an absolute spacetime underneath it all that we we can’t access at present perhaps you know one day but probably not but it’s entirely compatible with the the maximum speed of light for instance that we simply are running on some kind of process that has limited resources that finite resources immediately leads to us to a maximum speed limit in in a in propagating processes and we’re going to see and we’re going to rediscover all of those processes also all of that physics again in the cloud in the computing cloud as we become challenged with bigger and bigger computer systems bigger and bigger computer systems busier and busier computer programs running next to each other all of that unpredictability and weird the weird effects of space and time that we see in Einstein’s theory we’re already seeing them in the computing world they just manifest slightly differently and they’re not immediately obvious to everyone where we are because we are kind of the godlike people looking into the cloud from the outside we have a different perspective but from the perspective of processes inside the cloud they experience the world in a far more Einsteinian way so you know these things we like to mystify some of these these phenomena in the natural world and believe that they’re they’re magical they’re special we will never never see the same thing again anything that humans create couldn’t possibly compare to what we see nature has created i don’t believe that i think we’re seeing them already just on different scales and if we again pay attention to scaling and dynamical similarity and follow those principles as the as the old philosophers taught us we will rediscover the world around us in new ways in a computational way as an information world as we’re already starting to do and and we will find new levels of understanding which currently seem mysterious to us when you look at some of those breakthroughs and these rediscoveries when you look at the next 50 years so time and we are still alive very conveniently what well with already gray this might be much longer time spent if he is right he says to only 20 more years and then we can live forever if we get the right drugs what breakthroughs do you think are coming to the field especially computer science physics physics has had a long love affair with string theory that doesn’t seem to produce anything maybe it’s happening tomorrow who knows but from your point of view where would you feel the next 40 50 years have a lot of breakthroughs well what could we get at and we’ve already feel somewhat confident about it it’s hard to predict the future of course i i tend to think that the interesting questions lie in this overlap between physics and information but not in the way that people are talking about now not in this kind of idea of quantum computing or or even blockchain or any of these these kinds of technologies i think understanding the physics of computation as as we do it in computers today not on the quantum level but certainly incorporating that down the line because there’s so many so much we don’t understand about how computers work you know there’s this this odd myth that computers simply do what we tell them to it’s not true they do all kinds of things that we don’t tell them to do there’s emergent behavior on all levels you can’t possibly describe the behavior of the the cloud as we understand it today or all of the data in the world without appealing to some sort of weather forecast level understanding of the world you know it’s it’s it’s utterly unpredictable and to claim that it’s simply a a deterministic result or a scaled result of everything we’ve created is just pulling the wall over your eyes because it means you’ve misunderstood some principles about scaling and how new phenomena occur on new scales we are starting to understand these principles of course in complexity science and information representations and that it up but i still believe there’s a long way to go and people are resisting too much the idea that you know people still want as we said earlier they still want physics to be special they still want quantum mechanics to be that mysterious thing that only physicists can understand they still want relativity to be einstein’s property and there can be nothing else like it in the world until we can get past those prejudices and move on to a new understanding applying the same ideas wherever we may find them and be willing to do that to go out on a limb to take a chance i think we won’t uncover some of those mysteries personally i don’t i’m not a string theory fan or an m theory fan i don’t believe that stuff is i think it’s cool mathematics we may end up learning some interesting principles that can be applied in a totally different way but i don’t think that is the source of our understanding of the world going forward we need to get our handle on empiricism again we need to reclaim empiricism because we’ve kind of lost that in all of the theory you know i’m a i’m a theorist from top to bottom but unless i can get my hands dirty and compare it to something real i don’t believe anything about what i come up with right it’s only because i’ve been able to compare the theory that i’ve worked on with computer systems as of chrome and human computer systems interacting as they’ve grown that’s how i’ve been able to see that there must be something in this story around promise theory and semantic spacetime but with it’s it’s such early days you know there’s so much to do to so much to do to to place that on a firmer footing and i do believe that if we can really understand those principles we may even find the echoes of things that are totally unavailable to us like quantum gravity hiding within our understanding of network phenomena on an entirely different scale. Eric Weinstein put put forward that thought that there might be an entire generation of physicists lost on strength theory instead of doing something useful you know that’s obviously a very strong statement but i think what he meant is we and you just said that that engineering and a lot of fields that that physicists touch these days is lacking behind and the cloud meant be the exception to the rule so we we had hundreds of years where they kind of went a lockstep right so you could come up with a theory and you could test it definitely in your lifetime probably within a couple of years month maybe and then we went out and our simulation so to speak of these problems has gone far beyond of what we can actually implement would you from your point of view do you think that’s a that’s a problem of engineering because productivity growth has been too slow we don’t have the energy required the conscious build all these colliders is super expensive instead of just you know we can build it in our own lab or do you feel that’s more a problem of misguided directionality of research in physics and related disciplines a bit of both maybe i think what’s fascinating about engineering in applying science to create stuff is that a good engineer doesn’t try to develop systems to be understandable they develop systems to keep certain promises to have certain outcomes and even when we look at nature what nature has built from the technology of all these different processes the quarks and the electrons and and the going you know through biology all the way up there’s there’s no nothing you can point to in that which makes it easy for us to trace the the the nature of nature if you will and uncover those mysteries it’s hard because it’s way more efficient to build things that hide that information on each new level just as uh you you boil down you know terabytes of data into a single symbol or a simple model of a cat that’s efficient that’s what technology is about reducing the information down to small amounts that can then be the basis of new virtualized processes which is what we understand as engineering but because of that efficiency it makes it really hard for us then to reverse engineer those things i think if we want to study that relationship on a on a deeper level we now have a unique opportunity in information systems these huge computer systems because they’re becoming so complex and because we have full access to all the layers from top to bottom we could if we wanted to engineer systems to put that traceability to put that cause causal traceability into systems excuse me in order to answer some of those questions is what happens when we cross certain barriers of complexity and information um and how things interact across scales i do believe that’s an exciting challenge and maybe somebody if it becomes economically viable we’ll be able to do that but the probably the forces are against us because the economic economics of engineering tend to work in the opposite direction encouraging us to hide that information and reduce the cost by eliminating information from all levels yeah i feel that’s the problem with quantum mechanics it’s so promising but there seems to be like five people on the planet who actually understand it or maybe 500 but it’s a small community right and then they have trouble explaining it to anyone else because obviously there’s so much still going on that that’s my personal gut feeling you might not share that at all and i felt what if you have this discovery and it seems so amazing shouldn’t we come up with a machine that reduces complexity so much but we haven’t had that we haven’t seen it yet at least from from my knowledge maybe it isn’t a lot of machines we just don’t know about it i think some things are just complex and trying to reduce it will reduce our understanding of it as well yeah you know uh i was famous famous for saying that probably nobody understands quantum mechanics he he may be right but my understanding of understanding itself is that what we mean by understanding is that we can tell a good story about something right you tell me you you ask me a question about something i believe i know something about i tell you the answer you say but oh but why the answer i want to explain the answer in terms of something else you say but why that another point predictive models right so if you don’t know the why the predictive models might be okay too yes but crunch the point is this at some point we stop because we just run out of energy there’s all another why you could ask it goes on infinitely you can never get to the bottom of everything but at some point we simply say oh you know what i trust the rest of it it’s down to trust and when i believe i trust this bit i don’t need to question it anymore and then i understand it or then i believe that i understand it it’s an emotional response to resolve never ending regression in prediction and that is the function of i believe that’s the function of emotion in reasoning as well you know reasoning cannot work without emotion because it’s that emotional resolution that terminates the infinity of connections of causal connections that explain things and so that’s so interesting i never thought about that that’s so interesting that’s that’s fascinating i believe that that’s when we will come to feel good about the world you know we will eventually feel like okay you know what doesn’t matter the rest of you doesn’t matter but it has their own point so people will say they understand quantum mechanics because they can solve Schrodinger’s equation some people will say that you need to know the measurement problem and then they go to Bell’s inequalities and they say now i understand quantum mechanics because i can write down these expressions or i know coherent states so there’s always another level but until people make a peace with the story and where they are willing to stop then they never understand anything we never understand anything so it’s it’s really a choice understanding is a choice i have to think about that that’s fascinating i never thought about that the absolutely right i mean yeah it’s it’s it’s a way to avoid this indefinite work whatever we would otherwise have and we always have to live with this limited amounts of parameters that we can solve for limited amounts of variables we can solve for and and i can’t even build a fridge which doesn’t seem to be so complicated but i couldn’t do it if if all the fridge manufacturers go out of business i couldn’t do it on my own it’s just maybe the internet some youtube video will help me but there’s so many steps in between that would have to solve it seems incredible but yet i say i understand a refrigerator completely which is bogus right i don’t understand any of it mark thanks for doing this that was really fascinating i learned so much um thanks for taking the time incredible insight thank you for inviting me it’s been a pleasure