Hello everyone, myself Dr. Mithusha Verma, I am a consultant radiologist reporting PET-CT, CTN MRI at the Department of Imaging Nanavatti Superspeciality Hospital, Mumbai. First of all, I would like to thank Indian radiologist for giving me this wonderful opportunity and an excellent platform. The topic which we are going to talk about today is 3D, RTG has been lately. In this topic, we divided into two parts. In the part one of the lecture series, we saw the basics and physics behind it. Also, we saw the application of ASL in stroke imaging. In this part, we are going to see ASL and its application in various other pathologies. Just to revise in brief, arterial spin labelling is a gadolinium-free MR perfusion technique which uses magnetically labelled arterial blood water protons as an endogenous tracer. Here, we obtain two sets of images. One are labelled or tagged images and the second set is of control image. By subtracting these two data sets, what we acquire is cerebral blood flow maps. ASL is an echoplane imaging technique and must be acquired before gadolinium administration since gadolinium will cause T1 shortening, leading to a decrease in the measurable signals. This is how a normal ASL colour map looks like. Usually, in standard techniques, what we use is rainbow colour spectrum format and whatever is towards the red part of the spectrum is relatively hyperperfused while whatever is towards blue part of the spectrum is relatively hyperperfused. Coming to the potential applications. There are several pathologies where actually we can use ASL to substantiate our diagnosis. Application of ASL in stroke we already saw how it helps us in determining the perfusion-diffusion mismatch in the setting of hyperacute stroke also how it helps us to differentiate stroke from stroke mimics. Coming to rest of the applications like in epilepsy imaging, dementia, tumor imaging, CNS infections and imaging, few of these psychiatric disorders. Starting with the role of ASL in epilepsy, as per whatever studies and literature review, as well as our practical experience, we know that ASL is useful to detect and characterize the epileptogenic focus. In other words, the CESA onset zone, SOC. In acute deficit, ASL can confer seizure diagnosis and help to rule out other mimics. ASL abnormalities are well correlated with EEG findings and ASL is more sensitive than conventional MRI sequences in identifying ictal and post-ictal changes. As we all know that neurological dysfunction following epileptic seizure is a well-recognized phenomenon and this is what forms the basis of post-ictal change. This may involve transient and reversible motor, behavioral and cognitive manifestations lasting from minutes to days. Which means that post-ictal changes may last for few minutes after the seizure episode or it may even last for few days after the seizure episode, making this little variable. Ideally, the typical findings on ASL are seen when the patient is imaged 20 to 60 minutes after the seizure episode, which might not be very practical in our day-to-day patients and we usually image patients in a time gap as early as 1 to 2 hours after the seizure episode. Coming to few examples to understand it better, here we see a patient who is 21 year old and presented with acute onset right-sided weakness. On diffusion, there was no area of restriction to suggest hyperacute or acute infarct. On flare images, however, there was relative hyperintensity seen in the left cerebral hemisphere. ASL colour maps clearly showed clear hypoperfusion in the left cerebral hemispheres, which was in keeping with post-ictal change. So, this was a case of TOTS paralysis. This patient was followed up after 24 hours gap and this hypoperfusion reverted back to normal. Another example, here also we can see that diffusion and T2 weighted images look almost normal. However, on ASL colour maps, there is clear difference between the right and the left and there is hypoperfusion in the left cerebral parenchyma or representing post-ictal change. Another example from pediatric age group T2 and diffusion normal, but ASL showing clear hypoperfusion on the right and that on EG as well correlated with the rectal focus. So, if we try to summarize, we can understand that ASL is a safe, feasible and cost-effective imaging modality that can be used in post-ictal period to assist with the localization of seizure onset zone. In most of the patient that is like in this study, 80% of the patient what is seen is hypoperfusion. Sometimes, yes, hyperperfusion is also seen, but in those scenarios, most of the times inter-ictal baseline activity is also there. So, in post-ictal states or phases, the most common finding with ASL is hypoperfusion at the site of rectal focus. ASL correlates well with the spec and pet finding. While on the other hand, it is totally non-invasive and very easily repeatable as compared to these two techniques that is spec and pet. Another interesting example, here we see a patient who is 22 years old and comes with an episode of seizure. Post-contrast even as well as the routine T2 weighted image do not show any obvious abnormality. However, on ASL color map, there is clear hypoperfusion in the right cerebral hemispheres. At the same time, we see hypoperfusion in the contralateral cerebellar hemisphere as well. So, this brings us to a very interesting phenomena of crossed cerebellar diastasis. This is a phenomena where there is acute inhibition of function and metabolism produced by a focal disturbance in a portion of the brain at a distance from the original site of injury but connected via white matter tracks. Here in this case, it is the corticoponto cerebellar white matter tracks which are involved. Another example where the routine T2 weighted images show us the actual cause of seizure in the form of focal cortical dysplasia and the corresponding ASL color maps are showing hypoperfusion in this region. Another example where there is a small focal cortical dysplasia which is the cause of seizures and here we can understand the fact that though the spatial resolution of ASL is low as compared to the conventional MRI sequences, still we can see the small area as hypoperfused in the post-dictal phase, again making our diagnosis more confident. And for those radiologists who are like quickly looking through the images, they can actually get ASL as an important technique and use it to pick up the abnormality on the corresponding MRI images of conventional MRI sequences. Another example, here we see a very small area of abnormality in the right posterior parietal cortex cortical region in the form of few flow voids. Tricks was also performed which is time dissolved imaging of contrast kinetics. Here we could see small abnormal cluster of blood vessels and this was seen as area blooming on swan images. When we see the corresponding ASL color map, though this area is so small, still ASL picked up hypoperfusion in this region. And on literature review, we were supported by the fact that yes, ASL can pick up these areas of small vascular malformations like dural AV fistulas and these are seen as areas of hyperperfusion. Another example where there is a kid 14-year-old presenting with features of right-sided focal dystonia and ASL was showing relative hyperperfusion on the left. And this has been described due to the recurrent dystonic episodes. There is recurrent activity and what we can see on ASL is hyperperfusion. So this is effect rather than cause of this particular condition. Moving next to role of ASL in CNS infections. Most of the infectious etiologies are hyperperfused like we see in this case of a brain abscess. It has typical features of brain abscess with restricted diffusion. Not very difficult to diagnose on routine MRI as well but yes ASL will definitely support our diagnosis by depicting hyperperfusion and limit the differentials. Another example a very small tuber coloma. This example emphasize on the fact that though the area of abnormality is small though it is in the posterior fossa still ASL is sensitive enough to show the hyperperfusion. This is very useful in cases of not an infection per se but in cases of metastasis to pick up small small mess in the brain. Especially in those patients where contrast is contraindicated. One more important aspect of ASL in imaging infection is viral encephalitis. So though all other infectious etiologies are known to be hyperperfused viral encephalitis most of the times are hyperperfused. So in these patients when there is clinical support to the diagnosis of encephalitis. Though the routine MR images may be normal if ASL is showing asymmetric hyperperfusion we should confidently inform our clinicians that there is a chance and possibility of viral encephalitis. Coming to the role of ASL in brain tumor imaging. As we all know that perfusion the concept itself came from tumor imaging. So the conventional MR sequences which are based upon contrast enhancement that is just secondary to disruption of blood brain barrier and that does not correlate with the tumor grade. All these tumors exhibit a phenomenon of new angiogenesis which is based upon the grade of the tumor and perfusion is something which can actually determine the grading of the new angiogenesis. Therefore perfusion techniques are ideal to diagnose and grade these tumors also to guide for biopsy to avoid sampling biases and also to follow these patients after treatment to diagnose a condition like pseudo progression and pseudo response. Coming to the examples here is a well-defined focal lesion seen in the right parietal parenchyma with perillegional edema. On post contrast evaluation it shows heterogeneous predominantly peripheral enhancement with an inner cremated margin which is a very typical of high grade tumors and on ASL we can clearly see that this area is hyperperfused. Even on the corresponding DSC color maps this area is hyperperfused both the perfusion techniques are correlating and this turned out to be GBM grade 4. Coming next to few more examples now this is a case of ependymoma showing patchy hyperperfusion so this is the area which should be biopsy to get the highest grade on histopath and to avoid sampling biases. Small oligoastrocytoma hyperperfused a small area of post treatment follow up patient with a small area of peripheral enhancement and irregular margins on ASL the area is completely hyperperfused and this was nothing but post radiation necrosis with no obvious residue. Another example a large heterogeneous signal intensity lesion with midline shift quite a lot of mass effect and perillegional edema but what ASL helps us is showing us the highest grade of tumor is along its anterior aspect and this is what should be biopsied. Interesting example of a patient who presented with patchy areas of altered signal intensities on T2 and flare images no contrast enhancement and ASL showing obvious hyperperfusions which are so evident as compared to the rest of the sequences this turned out to be primary CNS lymphoma. Here is an example of a patient who after TMZ that is Timozolamide and radiation therapy came for a follow up and showed a area of focal enhancement on post contrast study there was this enhancement but on ASL this area was completely hyperperfused so which means that this is nothing but a phenomena known as pseudo progression very well seen with Timozolamide and that the treatment should be continued. Not only for intraaxial lesions but ASL also can depict hyperperfusion in something like meningioma so even if you don't give contrast you can confidently diagnose meningioma with this kind of a picture of ASL. Coming to the role of ASL in dementia and psychiatric disorders as we all know that imaging nowadays play a very important role in imaging dementia patients and as well as their treatment work up so ASL is a potential technique which without contrast and by adding just a four minute extra to your protocol can give you information about the brain perfusion. In Alzheimer's pattern of dementia hyperperfusion is seen in cingulate pre-cumulus inferior parietal and later prefrontal cortisis or symmetric hyperperfusion may be seen in hippocampus amygdala and ventral stritum. In vascular kind of dementia we are going to see global hyperperfusion in frontotemporal dementia we are going to see hyperperfusion in frontal and temporal lobes. We can actually quantify the amount of cerebral blood flow in these patients of dementia the perfusion status on these color maps by drawing ROIs or region of interest. Coming to the role of ASL in autism and autism spectrum disorder there are several papers talking about it hyperperfusion in children with ASD in region critical to social perception and cognition have been described so period temporal sulcus hyperperfusion in children with autism spectrum disorder is what is commonly seen. Even in patients who have symptoms towards schizophrenia there are studies and this is our own patient where we can see hyperperfusion typically is described in the medial cingulate gyri and maybe in the parietal lobes. So to summarize all these pathologies where we have differential perfusion ASL is definitely important and a very easy way to help us in diagnosis. So like luxury perfusion cases of infarcts from politic induced recanalization post-tictal states sometime tumors as per their grade vascular malformation as one of the examples we saw of AV facial other than migraine inflammation encephalitis all of them viral encephalitis will be hyperperfused otherwise ischemic encephalomalacia post-tictal state most commonly as we saw hematoma infections vasculitis cerebral atrophy brain net patients will be hyperperfused there are few pitfalls and limitation to the technique in the form of that it is very prone to movement artifacts there is low signal to noise ratio that is why 3 tesla will be better than 1.5 tesla a minimum acquisition time of 3 minutes is required ideally we do it for 4 minutes 42 seconds here at our place quantification of tissue blood volume is not possible and post treatment optimization automation and standardization is still in development there are few things which are going to come up in recent future maybe and help us like ASL in assessment of whole lateral circulation so we are going to actually see arterial territory wise perfusion status of the brain combination of DTI with ASL as a marker of early Parkinson there are studies going on again use of ASL in functional MRI assessment so a lot of things to be seen in future even ASL is coming up for abdominal inmiching to assess abdominal perfusion so ASL is non-invasive suitable for perfusion in patients wherever contrast is contraindicated safe in pediatric population repeatable easy add on to our protocols can be quantitative can assess global perfusion states and definitely add confidence to our diagnosis thank you all for your patient listening


Thanks everyone for having me. I like to talk a little bit about copyright and licenses, and then open source clients and responsibilities and finally about open change. If you do that, maybe a great background. I'm a lawyer by training. I started my career in licensing and especially open content and open source licenses a long, long time ago when I was international to add comments. Today, I'm a strategic advisor at Envision, which is a Daimler subsidiary working on software for the project. I run a company on the side called the Software Science Academy, and I'm also a strategic advisor to the OpenChain project, which is one of the reasons I'd like to introduce it here today. For disclaimer, I'm not speaking of Envision, because Envision decided to sponsor this event after my talks proposed and accepted. I'm grateful for being part of this today, but everything presented is my own. Thanks. When we talk about copyright, probably almost every post at software is protected by copyright. That means that the creator, the author, basically the developer or the exclusive right to decide what to do with the software. You may all be very familiar with this little icon, the C in the circle, which is originated from the US and it basically means all rights reserved. If you want to use the software, you need to negotiate and agree a license that gives you the rights to use software. You read that with many projects that are out there and available somewhere on the web, whenever there's no license attached to the software, basically the default is that you are not allowed to use it. So you need a license that you what you can do with the software. The particularities for open source licenses is that they are standardized and therefore they should be easy to read and to memorize, even if that's not the case for today's licenses. They are a direct license, meaning the rights are always granted from the developer directly, even if you may receive the software itself from a third party, which could be a vendor. So you can receive the software from any kind of people, but the rights come always from the developer, from the creator or the author of the software. In the European context, at least in the continental European context, an open source license is also a contract. That means that by offering the software under a license, you basically give someone the offer to use it and that person can accept the offer by using the software, which can be done comprehensively. So you don't need to sign anything, you don't need to officially accept anything that's being done just by using the software. This is the concept of open source license and this is officially recognized by many different German courts. I think the first court decision to deal with this kind of analysis was the District Court of Munich back in 2004. So this is a very old concept and there's no doubt about what open source licenses mean in the German context and the European context and especially also in the US context around the world. So most of the open source licenses grant very similar, even almost the same rights and that's defined by the Four Freedoms according to the Free Software Foundation or if you look up the open source definition, you will find that almost all open source licenses give you the right to use, copy, distribute and modify the software. However, most of them come with different obligations and some of the very familiar obligations are the copyright notices that you have to provide or you can provide the source code to make the source code available. One of the more complicated ones is the so-called copy left but there are different license obligations depending on which license you are looking at and that makes it somewhat difficult. Most context or most companies I've been working with is saying that, oh yeah, we understand most of the open source licenses, we know what we can do with it but they forgot that, you know, they also have to fulfill the license obligations. And here's just one example from the GPL which sounds very easy in the first, right? But when you look into the details and you start thinking about how to do this in practice then it gets quite complicated, right? So this says that you may copy and distribute Beberton copies of the program source code as you receive it in any medium provided that you consciously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty keep intact all the notices that refer to this license and to the absence of any warranty and give any other recipients of the program a copy of this license along with the program. So you are allowed to use the software but there are some obligations. And the same is true for something a little more complicated which is the copy left, right? So in the GPL again, GPL 2, it says you must cause any work that you distribute or publish that in whole or in part contains or is derived from the program or any part thereof to be licensed as a whole at no charge to all parties under the terms of this license. Now what exactly does this mean? And how do you handle this in reality? Well, when we talk about software compliance in a corporate context, it means that you have to look at many different software licenses, right? So you have a bunch of proprietary licenses there you tend to negotiate individual license fees and volumes and you have a big chunk of open source licenses where you need to understand and comply with the respective license obligations. When you look at the open source licenses individually you need to make sure that you also understand if they are interoperable, right? So different copy left licenses may not be interoperable with each other. So it's always important to look into all the details of the respective copy left license for example, GPL3 and Eclipse and so on and so forth to understand if you can use components coming from different sources under different licenses together in one project. So what happens if you forget or maybe intentionally neglect some of these license obligations that means that you are not only in breach of the license and the contract it also means that you end up in a copyright violation. And this is because most of the licenses or I should say almost all licenses are designed that way but some of the licenses explicitly stated for example in the GPL3 you have explicitly said that you may not propagate or modify a covered work except as expressly provided under this license. Any attempt otherwise to block a guide or modify it is void and will automatically terminate your rights under this license. So that means the moment you are not observing the license obligations, the license itself is gone. And that means that you are using a piece of software where you don't have the rights to use it, right? So in a legal analysis and the legal construction this means that rights are granted only under the you can call it resolutive condition that the license obligations are met. And when the license obligations are not met then the license is void and is gone. And the consequences you can find in the civil law and in the criminal law. And this is something more heavy I would say in layman's speech than just a breach of contract as a legal consequences. We can find possible claims by the rights orders. So for example, injunctive relief, the rights to information, compensation of course, reimbursement of costs. Perhaps you have to disclose some of your own source code if you have not worked with the GPL correctly or any other copy left license. And these details have all been enforced by German courts, right? So like the district court of Munich above but there are also other decisions by the district court of Berlin, for example and other courts in Germany. Now who is responsible for license compliance and for open source compliance? This is a tricky question. Of course, every individual developer who's working with open source should, you know understand the licenses and comply with the license obligations. In a corporate context, it is also important to understand that it's usually the management who's responsible for open source compliance. And we can find that in the German context again in the act on regulatory offenses which is in German, the Ordnumswidrigkeitengesetz but we can also find it in other laws like the Aktiengesetz, the legal acts on corporations in Germany and they all say the same meaning that the owner of an operation or undertaking or any company has to take the supervisionary measures required to prevent any breach of law. Let me just put it in close words here because the German law and the language used there is usually a little complicated. So it's not only the Ordnumswidrigkeitengesetz or the act I quoted before it's only the stock corporation act which says it more explicitly. The management board has to take suitable measures and it's responsible and it's one of the duties is to be liable for any damage resulting from not working on specific measures. So again, the legal language is a little complicated. I give you the full language here for your own studies but what you need to take away from this talk is that all of these laws have also been enforced by another court decision and in this case, it's the district court of Munich again who ordered a former Siemens management a member to pay 15 million in damages for the breach of duty with regards to an inadequate compliance system. So this was the first court decision in Germany that really detailed out the requirements for a compliance system. In that case, compliance was not about license compliance in the open source context. It was in the general context of what are the duties of proper management but you can take all of these requirements and apply it to license compliance in the open source context. It means that basically whenever you talk about open source compliance, compliance as a general fact is a matter for the boss. The management is responsible for a functioning compliance system and details and specific form of the compliance system depend on the type, the size and the organization of the company. So for example, a company like Siemens may have to fulfill with a different type of compliance management system than a smaller organization or a startup. It also depends on the type of relevant laws and regulations. And here I would say that copyright law is a very important law and intellectual property plays a big role in today's business. It also gives one another very important aspect of a compliance management system is the grounds for suspicion and the cases for non-compliance in the past. And in Germany, we all know that we have a lot of compliance cases in the context of open source going on. So any court would say, well, in the context of open source compliance there has actually been some grounds for suspicions in the past. Of course, delegation is possible. So the management does not have to do everything by themselves, they can delegate but they are responsible for monitoring and making sure that the compliance processes work. And that can again be justified through periodic internal or external audits and readjustments of specific management processes. Now in reality, I like to show this slide which I use, it's originally from the Linux Foundation and I think it shows quite nicely what today's status of the supply chain is. Basically suppliers are not really giving complete licensing information and this information is by no way in any kind of standardized. And when you have to deal with open source packages then the basic problem is that you kind of try to analyze everything that has probably been already analyzed by one of your suppliers before. So it's a double effort. And then depending on your customer they want some kind of information, a bill of materials. And then usually most customers have their own way of asking for a bill of materials. So there's no standard this way again. And this was the starting point for a smaller group thinking about how can we reduce friction and transaction costs in this current status. And this is exactly when the open chain project was born. You can find all the necessary information at openchainproject.org. Basically, open chain is an ISO 9001 like conformity assessment standard. So it provides a specification that asks you or asks you to know your own open source responsibilities to assign responsibility for achieving compliance to review and approve the relevant open source content and then to deliver open source documentation and artifacts. This is the important part that I mentioned earlier.  And that usually means that you have to provide a company documentation. And then finally, the specification also talks about contributions. They call it community engagement. So you need to have a certain measurement in place that shows that you understand how the community works. So in short, what open chain really gives you is a minimum set of requirements for quality, consistency and completeness in the open source supply chain. Today, the open chain project is very close to becoming an official ISO standard. So we expect that to be announced later in September or October, I think. Before that happens right now, there's a self-certification questionnaire available that you can find on the open chain project website that helps you as an individual project, as a company, as someone who's just interested in learning more about open source compliance to work with this specification. And it works like a survey. So you can go on the website, register yourself and then you will be guided through a survey that asks you different questions. And here you can see all the different sections of questions that match to the specification that I've mentioned earlier. And once you have answered all the questions with the yes, you can qualify as a self-certified against the open chain requirements. Before we go to the final slide, which basically just gives you the opportunity to ask some questions, I wanted to close with some general thoughts on compliance. From my experience, being responsible for compliance in any context is a very tough job. It's usually, basically it's run under the radar. Let me put it like this. So if everything goes well, it's hardly noticed, right? Because if everything goes well, there's nothing to celebrate. But if things go wrong, then the big fire has started. And then it's usually the compliance officer or someone in charge of compliance who gets all the blaming for that. So being responsible for compliance is a tricky beast. I would say these people really deserve our attention and our support. In general, I would say compliance is not an option anymore. It's a necessity. And that's especially true for the open source ecosystem. Compliance gives you a competitive advantage, especially on the context of corporations because it saves you time and resources once you have the compliance process set up properly. And then compliance nowadays is also a key requirement for most of the software vendors, if not all software vendors, right? So who would buy software or receive software including open source components from a vendor who is not compliant, then that basically means that you have to do all the analysis and the compliance work in the company. And finally, I would say compliance is a key requirement for the entire open source ecosystem. So if we as part of the ecosystem don't understand the license details and the obligations and are not in a position to handle them appropriately, then I think the whole system is basically at risk. And that's where I want to close and hopefully we have some minutes for questions left. And if there are any questions later on, feel free to reach me at this email address. Thank you very much. Thank you. It looks like we have a question coming in. So I will wait for that to finish typing out. Okay, so first question is, what are your thoughts on running LibreOffice software in non-Library operating systems? Oh yeah, that's a tricky one, right? So I usually use, I'm not happy to offer that, but working with different clients, I'm forced to work with most of the proprietary stuff as well in order to be compatible and exchange data. So only on my private time, I run LibreOffice and these kind of things. So it's very hard to convince people to move from proprietary products to open source alternatives. I think that's my thought. It's very hard and I include myself in that. So it's a long way to go still. All right. Well, I guess we can go in another moment, see if anyone has any comments or questions coming in. Yeah, I have to admit that talking about and probably thinking about compliance on a Sunday evening at eight is something that really only very enthusiastic people probably go into. It's okay. Okay. So from the first question it was, there is a comment saying, sorry it was not LibreOffice, but LibreSoftware. So I misspoke on that one. Okay, so was the question then something different? Like you wanted me to talk about what free and open source and LibreSoftware and the differences and all these kind of political talks going on in the background? Probably not. So I can say hi, that's nice. Okay, so we have another question coming in. I'll let them finish typing it out. And it looks like our second question is, is compliance hard to enforce in educational settings, such as schools and universities, any suggestions on regulations that would lead to a greater adoption here? That's a good question. So I have seen many universities really trying to be compliant on all sorts of levels. So we talk about open source and software and especially in the context of open access and content. So my experience is that universities and educational institutions in general are really the, let me say good guys, trying to get everything right. The only problem that they sometimes have is that there's not enough expertise. So what I've done in the past, for example, is to just provide pro bono advice here and there on how to get things right. The bad guys in this game are the publishers. Let's put it like that, especially in the university context when it comes to publishing and not making really relevant content and papers available under an open access license. And some of the corporations that want to basically squeeze out technology from university in the technology transfer project. And then you have to negotiate and argue about open source licenses and also patents, for example, how to deal with patents in that context of technology transfer. So my experience is that universities and basically the background in education, they are really trying to get it right. And there's a huge interest in using open source licenses and making stuff available and finding partners in that context, but we have to support them.


Live from Nassau in the Bahamas, it's theCUBE covering Polygon 18, brought to you by Polymath. You're welcome back, everyone. It's theCUBE's exclusive coverage. We're live in the Bahamas here for day two of our wall-to-wall coverage of Polygon 18. It's a security token conference, securitizing token economics, cryptography, cryptocurrency, all this is in play, token economics powering the world. New investors are here. I'm John Furrier, Dave Vellante. Our next guest is Nathan Epene, who's the Chief Investment Officer for Arcadia Crypto Ventures. Welcome to theCUBE. Thank you very much, John. Thanks for coming up. Thanks for joining us. I'm excited to have you on for a couple of reasons. One, we've been talking since day one, a lot of hallway conversations, small intimate conference, so we've had a chance to talk. Folks haven't heard that yet, so let's kind of get some of the key things we discussed. You are very bullish and long on cryptocurrency and blockchain. You guys are doing a variety of deals. You're also advising companies, and you guys are rolling your sleeves up, so kind of interesting dynamic. So take a minute to explain what you guys are doing, your model, and when you try to get some of your partners on later, you have a great team, experienced pros in investing, and you got whales, you got pros, so you got a nice balance. So take a minute to explain Arcadia, your approach, and philosophy. Okay, so Arcadia Crypto Ventures, primarily, we are a private fund. We invest our own money. We believe in the whole crypto space. We believe this market is expanding, and it is growing, and it's gonna be the biggest thing that ever happened. It's gonna be this fusion of internet and PC and mobile and everything is gonna go to the blockchain. We believe in the whole tokenization world. Everything is gonna be tokenized. So as a whole, we believe this space is gonna go very big, okay? So that's one piece, and because of that, we invest in the space, the whole space, not one Bitcoin or Ethereum, but everything in the space that makes sense, people who have a use case. Now the second piece of it is we advise great founders. We want to get founders to come out and build these new things because this is the new internet or the new era, and people have to come out and build these things. And so many of them are traditional businesses and we have to explain to them why this matters, why you should come to this space and be decent flies and reach the whole world. Because initially the internet came, the idea of the internet was everybody gets information. Now information did get everywhere. You don't have to worry whether mailman is there to deliver your email anymore. Even if it's a Sunday, your mail will get delivered. So that part was good. But now you have these few companies that's holding all your data. It's okay for most people, but they do censor a lot of people. So that is one point, that's censorship. So we want a censorship-resistant world where everybody's ideas get up. So that way, we believe that's how this whole internet space itself is going to change because of that. See this is, if I explain it in one word, this is the greatest socio-political economic experiment or revolution ever that has happened in humankind. In the history of the world. I mean this is important. I'd sit down in my opening today. Dave and I were riffing and Dave and I have always been studying. We are entrepreneurs. We live in Silicon Valley, he's in Boston. And so you're seeing structural change going on. So it's not just make money. There's mission-based, younger demographics. So you're starting to see really great stuff. So I want to ask you specifically, because you guys are unique in the sense that you're investing in a lot of things, but startups, pure playing startups, which had only one path before, or two paths. Cashflow financing, our venture capital. So that's a startup model. The growing companies that are transforming their growth business with token economics, those are what everyone wants. Those are the best deals. Then there's like the third deal. Well, we're out of business, throw the Hail Mary, pivot. So categorically you're starting to see the shape of the kinds of swim lanes of deals. Okay pivoting, that Hail Mary, you can evaluate that pretty much straight up on that. Startups need nurturing, right? So the VC model actually works really well for startups because the product market fits going to be developed. You got cloud computing so you can go faster. So you guys are nurturing startups. At the same time, you're also doing growth deals. We do. Explain the dynamic between those kinds of deals, how you guys approach them, what's the dynamic, what are the key things that you're bringing, is it just packaging, is it tech, so on and so forth. So with a lot of people when we are on the advisory side, so primarily we look at the founder and the tech. What are they trying to solve? That is key. You can't, if it's a third, you can't package it. No matter how you package it, that's not gonna work. You can't package dog, you know what. Yeah, exactly, okay. So that's one thing that we look at. The founders and their idea. Now, their idea, can it be decentralized? Some models are meant to be centralized maybe, so it doesn't work. See, it all boils down to, when we break it down, we look at it, okay, do you have an asset behind the scenes, is there an asset? Is that asset being transferred among parties? If you have an asset and it's being transferred, is there some central mechanism in between? Because if there is a central mechanism in between, that means you're gonna be paying rent to them, okay. You have these things, okay, great. Now you have your asset, you have that in between party. But in some of them, like, let's say you have money in your pocket. You walk, it falls down. Somebody else pickups the money. It's his, it's a bearer asset, okay? So that's where Bitcoin sawed a very big problem, right? It was a bearer asset. Unless they hack your wallet then they take your money. Right, that happens in real life too, right? Somebody can take money from your wallet. So it can happen in Bitcoin, they can hack your wallet. All right, so Bitcoin was solving that problem. Now the second piece is a registered asset. What I mean by registered asset is, take your car.  There's a record updated in the DMV, in the central database. If somebody steals your car, the car is still not his. It's only if they can change their record over there in DMV, then it becomes his. Now there, maybe you do want the DMV to be there. Or maybe we can, but the DMV being there, now you have a problem. They're gonna charge you rent.  John, I'm not gonna give him a license or a car in the state of California or they can decide, right? So that is where now you decide. Do you wanna go the centralized route or the decentralized route? So we break it down to the asset. So it's gonna be a fit for decentralized. I get that. Let me ask you a tactical question because I know a lot of entrepreneurs out there, they're watching, they'll hear this. A big strategic decision up front is, I'll say token selection. It's just pretty clear that security token works really well for funding and whatnot. Then there's a role for security token. I mean utility token. So do people, should they start from a risk management standpoint, a new company? So let's just say we're in an existing business. Entrepreneurs say, hey, you know what? We're doing well. We're doing $10 million in revenue and I wanted to do tokenize because this is where decentralized business, perfect fit. Do they start a new company or do they just use the security token with their existing safe company? I would suggest usually at that time, okay, that's more of a, it's very a legal question at that time, okay? I don't know if I'm a lawyer to answer that, but I tell them, okay, you have a business. The business model is going on. If you're happy with it, let that be there. Make a new company, okay? If your business model was not doing good, you might as well start from there because you figured out it's not working. But again, at that time, we tried to come up with this question. Are you trying to put out the old wine in a new bottle kind of thing? If the wine is old, it ain't going to work. You know, you have to get to that realization. So here, the answer- People are being sued. So mainly the legal question is, do I want to risk the- Yeah, all right. Let me hop in here. I wanted to ask, go back to something you said about censorship. Okay. I was in a conversation with my kid the other day. I was explaining, Google essentially censors your search results based on what they think you're going to click on. Yeah, they do that. And he's like, whoa! And he thought about it. He's like, okay, yeah, they kind of do that. Okay, so that's an underpinning of we're going to take back the internet, right? Yeah. Okay, I just wanted to sort of clarify that. From an investment philosophy standpoint, you're technical, yet you don't exclusively vet or invest in infrastructure protocols and dig deep into what you read the white papers. But you know, there are some folks out there, hedge funds, et cetera. All they do is just invest in utility tokens. They're trying to invest in stuff that's going to be infrastructure for the next internet. Your philosophy is different. You're saying, we talked about this. We don't really know what's going to win, but we make prudent investments in areas that we think will win. We like to spread it around a little bit. Why that philosophy may reduce your return, but it also reduces your risk. Maybe you can describe that a little bit. Sure. In general, picking winners in the long run has been, it's a proved fact that nobody could pick winners. Like if you take active hedge fund managers, active hedge fund managers in the long run, like if you take 10 to 20 years, they lag the S&P. So if you had money, if you give it to an active hedge fund manager, instead of that you just had to buy the S&P, you would have beaten 93%. That's Buffett's advice, buy a S&P 500. Buffett made a bet with Todd and they did a 10-year bet for a billion dollars or something where, you know, so take Warren Buffett for that matter. His fund is lagging too. And in reality, all his stock investments are down. Like he put it in IBM at 200 bucks. It's after eight years. It's at the 143 or something, right? So realistically, there's a lot of luck element, okay? You can do all the analysis and you could still end up buying Enron, Lehman and their schemes, right? Right, yeah. At that time, see they were using some models that they knew till then. Most people, investment comes from, you know, you have this background that you know, okay, this is what I look at. Cash flow, discounted cash flow, great. If that is their price to earnings, I'm going to buy. But then when Amazon and Arcane, most of the traditional investors, never invested in Amazon, they were like, oh, it's a loss making company, you know? They're never going to survive. But they forgot the fact that companies like that, there was this network effect and once the people are there, at any point Jeff Bezos can just turn off the switch and take off the discount. You're not going to change, you know, your shopping from Amazon at that point, right? Because this month I lost my 15%. You're so used to it. So people miss that. Nowadays they see that, but when it came to blockchain, they're like, oh, no, no, this is a fact. That's what most people do. So we talk about discounted cash flow as a class evaluation methods. I see guys trying to do DCF on these investments. I mean, we were joking about that. How do you, what's your reaction to that? Well, if anybody is saying that if they come to me and I'm like, I don't know what Kolein did you drink at that point, okay? Because what cash flow are they at this company? There's no cash flow. It's not like you're going to get dividends on these tokens. There's no dividends. It's like, can you find out how many people are going to use it? What is the network effect? And again for that, a lot of people are coming with a lot of these matrices or matrix right now, but I think even that they're trying to retrofit into it.  But really we don't know. So people tend to want metrics. Dave and I talk about it all the time. When people partner with their money, they need to know what they're betting on. So the question is, when you look at an investment, when you spend cash, when you write checks, what is your valuation technique? Do you look for the long, how do you play that long game? What's the criteria?  Okay, buying a token. It's got to be worth something in the future, obviously. So we look at the, we look at that space, okay? Where ever they are trying to disrupt, is there a big market or even if it's a niche market, okay? So we're doing an arrow chain token, okay? It's a very niche market. It's just the pilots, the maintenance folks, and the charter people or the plane charter guys. It's a very small market, but that's good enough. It's very niche. They can have an ecosystem between themselves rather than being incentivized to log the miles and stuff like that, right? It doesn't have to be a very big market. We just look at it, okay, founder is good. He has an idea. It is a space that can be decentralized and people can come in and they can feel that they're part of the ecosystem. See, the whole thing with the token economy and a traditional economy, like let's say I've been spending money to buy a stock. So I buy stock. As an investor, what do I want? I want maximum returns. The employee, he wants to get maximum pay. And the consumer who's buying the product, he wants to get it at the cheapest price. So there's a, it's not aligned, okay? The moment you're given the cheapest price, my profits go down. If I increase the employee's salary, my profits go down. So we have all three of us are totally misaligned. Super important point. I just want to think, do you favor certain asset classes, you know, token security tokens or utility tokens or you're looking for equity? I mean, maybe just- No, right now we've moved away from the whole equity bonds or any of those things. We are totally concentrated on utility or security token. We don't mind if it's a security token or a utility token. If it's a security token, are you looking for dividends? Are you looking for- At that point it's some kind of dividend. So you're not expecting equity as part of that security token? No, we're not expecting equity, but if they are saying, okay, my token, if people buy and if they pay me $10 and out of that, you're going to get $1 back, okay, that's fine. We don't mind that, okay? As long as it's legal and all those things, we are fine because it just makes the process easier.  At this point it's become so liquid at any point of time, within two or three months, the token is listed, people are either buying and selling. We know, otherwise, early when we used to do venture investments, we would get into a product once average times seven to 10 years to get out and in the meanwhile, they say great stories or we're doing great. How do I check whether we're doing that? I'm not getting any dividends. Nobody's buying this from me. How do I know where am I? I really don't know. I can make these values up and on my Excel sheet and say, okay, we value this company at a billion. So your technique is to say, okay, look at the equity play is the long game and you need an exit on liquidity, either an M&A or IPO. Now you have a new liquidity market so you play the game differently. I won't say spray and pray, but you have multiple bets going on and so you can monitor liquidity opportunity. So that's a new calculation. It's a great calculation also because, see if you're in the market and now we know at any point of time, we don't have things on our books that are like, we don't know what the value is. We know what that price is because the market exchange is there. Whatever people are willing to pay for us, that's a price. It's like saying, my house is worth a million dollars. Actually, it might be worth to me. It depends on what people are willing to pay me. If I have to synthesize this, you're taking high-frequency trading techniques with classic venture investing and looking at the token from those two perspectives. Yes. In a way, high-frequency trading meaning I'm looking at volatility and then option to abandon and get rid of whatever or whatever. The only thing is we're not exiting our positions. We are in the long game. We believe this whole market is supposed to at least reach $8 trillion. When we started this whole investing, at that time, the whole market was at $6 billion and we said, okay, this market, based on our thesis, supposed to reach $8 trillion. Until then, we keep buying, okay? But to your HFT now, so you're not really arbitraging. No, no, we're not doing any of those. Because they're applying real-time techniques to the token evaluation. So their game is try to get into a winner. Yes. Right, with the token. A lot of the funds, they're doing this arbitrage model, okay? They're trying to do arbitrage. But the problem is they're missing the big picture at that time. So arbitrage works in a very tight market, okay? So S&P, let's say, somebody's doing 5% return on S&P. The guy with the arbitrage is coming and saying, I made 5.3, 5.5% or 6%, that's great in the equity world. Now, our returns last year are 10x or 30x or 50x. And if somebody comes and tells me, I made an extra 0.2%. It doesn't really matter to me. I'm like, instead of wasting that time doing arbitrage and paying taxes, I'm like, just, you believe in the fundamentals. You believe in the fundamentals at that point.  Final question for you to end the segment. As new real pros come in, let's take New York as a senior in New York. The New York crowd comes into the Silicon Valley, comes crowd, existing market players from other markets come in here. How important is optics, packaging, and compatibility with the sector? Meaning, I just can't throw my weight around on a hedge fund saying, we do it this way, I got money. Because people here have money. So what's the dynamic of pros coming in with seeing institutional folks come in, we're seeing real pros come in? They've never been to Burning Man, so they get the Burning Man culture, but this is not a Burning Man industry. Businesses doesn't run like Burning Man. Maybe it should, maybe it won't, that's an debate we'll have. Your take. So the new funds that are coming in, so they have a fear that they have missed out. They are missing the picture that this is just the beginning, okay? So they've seen that this industry has gone from six billion to 500 billion in a year, or a year and a half. They're like, oh my God, I missed it. It's gotta be over. So I have to write these big checks to get this. We don't write big checks, we write much smaller checks because we believe that if a founder is raising money, he has to raise it through smaller checks from everybody. That means all those people are really interested in this. And all of them really want the token to go up. Whether it's the investor, the user, and the employee who is working there because all of them, their interests are aligned. The moment you give a big check, so let's say you could raise 10 million from 10,000 people, or you can raise it from 1%. So when the big check is there, let's say I go to raise my money, there's this fund who's missing, he says here's $10 million, okay? Now I've got me and the fund and my tokens. Nobody else knows about my tokens. My tokens are as good as valueless, right? Now the fund is looking, okay, I need to exit. Nobody knows about my tokens. The fund is the only guy who has my tokens. He's trying to exit. Obviously either the market is going to crash, or there is no market. And he's like, why did I get into this? So he missed that point that you need people around you. It's not just you alone. See, earlier days- This is your point about understanding how token economics work. Yes. So having more people in actually creates a game mechanic. It is. For trading. Because then you know, you're not the only guy interested in this. In the earlier venture capital space, there were these bunch of few venture capitalists who wanted to capture that whole thing and try to sell it to the next guy. Here, what I'm saying is, we all have to come in together. We all can be together at the same price. Which is good, because the small person has, the common man has a chance to be a VC right now. Earlier you could never be a VC. I could only see Google after IPO. I could never get it at what KPCB or Sequoia got it at. I had to wait till they go through CDC, CDB, which they bought at 5 cents. I would get it about $40 maybe. In this case, the big fund has a lot more money than me, but I can have my small 5,000 and 10,000. I can invest in the ICO. If you pick the right spot and you're there at the right place at the right time, because you are seeing guys come in and trying to buy up all the tokens early on. They're trying to do that. They don't get it, but they will understand.  Even they will evolve. They're like, okay, this is not how it works. And you have to make mistakes. All right, so I got to ask you one final, final question that you brought up about this, more people the better.  Possible? It is possible. So we see some of that behavior. Dave calls it steel on steel. You know, you have groups, I'm going to take this whole deal down. We see that in venture capital. It used to be syndicates. Now you're seeing Andreessen Horowitz doing the whole deals. That kind of creates some alienation, my opinion. But what's your take on that? I'm a big whale. I'm taking down the whole allocation. It's okay. Some of those things are going to happen. It is fine. The only problem is usually when that happens, the big whale who takes it, he will realize very quickly. He's got to get more people. He needs more people. Otherwise, he might be able to exit to his five buddies who were always taking it from him. Now those guys, they also have to exit at some point, right? Nobody knows about the project. Might as well just take a small piece. Even the founders in this case, typically in a token model, founders who have taken 20% or 10% have done better than founders who took 60% of the whole token. Nathan, great to have you on. Love your business model. Arcadia, crypto ventures. They got real pros. They got a whale. They got people who know what they're doing and they're actively understanding the ethos. I think you guys are well aligned and you're not trying to come in and say, this is how we did it in New York before. You get the culture, you're aligned and you're making investments. Great perspective. Thanks for sharing. Thank you so much, John. Hey, this is theCUBE bringing the investor perspective live here in the Bahamas. More exclusive CUBE coverage. Token economics, huge opportunity for entrepreneurs and investors to create value and capture it. That's blockchain. That's crypto. That's token economics. I'm John Rodeve-Vellante. We'll be back with more coverage after this short break.


All right, greetings everyone. It is September 9th, 2022. It's the 15th meeting for Cohort One and we are discussing chapter six. We're in the first of two discussions on chapter six of the textbook. We're going to go to the questions page and start there where we have one question that we explored a bit last week in our welcome back. And then there's at least one question prepared but anyone is welcome to add more questions here. While we turn first to this question, what are the four steps in the recipe to construct an active inference model? And can we make templates that facilitate people walking through some of these stages? How much model building do people prefer to be engaged in this section of the textbook group? Do people want to have one or more model that they're personally developing? Do they want those to be scaffolded in a shared page to help increment models long and see models at different stages? Yeah, Brock and anyone else. I just, it's the same problem as like the first half of the textbook without math. It's not going to go well. This half without modeling, it's not, it's going to be quite difficult, I think. So I think we should just get that out of the way that it's, yeah, it's not about whether you want to or not or whatever. If you want to just understand it at a cursory philosophical perhaps level, then maybe not. But if you actually want to understand it, then we are going to have to model something some way. It doesn't have to be the most complex, most precise one. But yeah, I wonder if we could develop like maybe a couple examples or simple ones that people might like to model. I don't know, yeah.  Thanks, I agree. I wanted to frame it as a question, but I agree with that perspective. And following along, as we structure a few specific cases, the rat and the T-Maze, the isocating, which are used in the textbook, then some of the examples that are used recurrently in the literature, like Birdsong and a few others. And for these many scripts exist and helping people who might not have the experience to set up either by with some walkthroughs. Okay, here's how you get octave running. So you can do this MATLAB script or here's the standalone DEM demo because this textbook really only gives the pointer to MATLAB scripts and methods. Like when they say that the standard schemes can be applied, they mean a MATLAB script. And that's exactly what the step-by-step guide, model stream one is built around. And chapter seven on modeling in discrete time is going to be akin to the step-by-step guide, but it's not step-by-step-by-step. It's more like it takes two bigger steps. Okay, how is this four step recipe for active inference modeling similar or different than approaches that people have seen for systems modeling from other frameworks? For example, in a reinforcement learning or cybernetic modeling. So something more recent and computational or something more pre-formal or computational. Should this recipe be surprising to people coming from a certain background? Are there sub steps that are relevant to consider? Brock, yes. I don't have extensive formal modeling experience, but I just necessary kind of most of the things that I've done for less half decade, like entail some form of this. And I don't see how you could model anything without going through these basic questions here. Like this is, it's a bit, I don't see how this is specific or exclusive or whatever to active inference. It's a bit ambiguous to me how that, I mean, besides specifically the generative model, I guess, but which system are we modeling? Another way to ask that is what questions are we trying to answer? Or who, I think Lyle brought this up, like who are you making the model for sort of thing? Because the same, like you were saying just earlier about the particular choice of how you're modeling it, it's just a choice on that system. And that entails particular kind of hidden states, markup, blanket, et cetera, that may be relevant or may not be, depending on the questions, what you're trying to model. But that arises from those questions, I guess is what I'm saying. Thank you, Ali. Well, yeah, I just wanted to mention that referring back to your previous question, I also am currently developing a model, actually an emotion perceiving agent by pretty much closely following those four steps. But I'm getting some helps in that regard in how to actually implement those four steps in my particular situation. But on the topic of modeling, well, at a very basic level, we can categorize different approaches to modeling in terms of answering to three different questions. The what models, the how models, and the why models. At least that's the kind of approach mostly done in neuroscientific modeling. So in my opinion, these four steps and this recipe can be applied mostly to the how and the why, what models are mostly descriptive models of phenomena. So I think my connection, yeah. What models are mostly descriptive models. But when we want to model an agent and self evidencing agent which tries to, which behaves as if it infers something about its environment, we have moved beyond just the descriptive stage and we're dealing with the how and probably even the why questions there. Thanks. When I hear why I always think of Tim Bergen's four questions, the four whys, Aristotle's four whys. Why is a subjective modelers pluralistic playground? And it doesn't lend itself to one specific even type of answer, let alone a framing or a specific parameter, a combination. And I think that's a huge aspect of the move from like, what are systems to how am I going to model the system, which is a part of the turn that happens when we start to model is we dispense with absolutism. All of a sudden these questions about what we're trying to do, our preferences and affordances and constraints and limitations and everything come into play and we might make a portfolio of models that are shining light on different aspects of even the very same phenomena. So it's kind of like an empirically grounded pluralism and pragmatism when we start to be engaged in real useful modeling. And that is something that I feel that one learns on the field, not necessarily bystanding. So that's just to say that the modeling is really important, even for philosophical reasons, to say nothing of some of the things that are coming to play in chapter seven and beyond where it's like, yeah, the C vector has this many entries because why and how many rows and columns does this have? But once we understand why the rows and the columns and the shapes of these different entities are the way they are, then we just have to use this scaffold to think about certain systems, consider their form at a very structural level like is it in discreet time or in continuous time and so on. And then we might set up not just one generative model, but again, families of them. In the future, we hope and expect that active block friends will facilitate us doing parameter sweeps across families of cognitive models and generative models. But even in this boutique phase of model construction that we're in, in current year, one would still say, okay, I wanna have one model where it's either acceptable temperature or too hot. And I wanna have one with a three-state model, too cold, just right, too hot. Here's gonna be a four-stage model. And just being able to specify that again instills pluralism through action about how we're modeling a system, which is the philosophical point that it seems like sometimes isn't grasped by those who don't engage in the specific discussions around modeling a system. Okay, so where does figure 6.1 come into play? It's a rehearsal of some of the figures we've seen earlier in the book and many, many other places. This is the particular partition. The particle is the internal and the active sensory states. So internal plus blanket states equals particular states. Those are the particles. If we were looking at dust under the microscope, like Brownian motion was describing initially, the particles were the dust particle, and then the external states were like that, which was not the dust particle. Partitioning the generative process and the generative model off from each other. We're dealing in Bayesian mechanics with particular states with particles, with things that are able to engage in sensory motor type loops. This is just bringing in a few more pieces than that, which is just to call attention to the direction and the existence of the arrows in the work. How particular is the free energy principle of Aguilera at all? And in follow-up work, there's discussion around which topologies on this particular partition facilitate or enable which kinds of formal claims to be made. For example, one might find it kind of interesting that sensory states have a bi-directional arrow with external states, or might find it equally interesting or differently interesting or less interesting that also active states have an arrow pointing back to internal states. And of course, the questions that we have raised all along the way of like, okay, let's just say that we're gonna model a person's arm. What are the active states? What are the sensory states? What does it mean that there's a bi-directional arrow connecting them? Is this the only topology for this particular partition? Could we have the around the clock model? External states influencing sensory, then internal, then active, and just going around the clock without any crosstalk in the blanket, without any back arrows. Is that plausible? Does that change the formalism? On one hand, how could it not change the formalism when it's changing the structure of how variables are related to each other? Or maybe there are formalisms that are actually abstracted away from any topology choice because what is being described are the flows and the gradients on these variables. But then what are those variables? I think, I mean, that just highlights this need for modeling. But if we just really briefly go back to that, since one, it's like the fact that there's this bi-directional arrow between external states and sensory states is a bit confusing in that, again, the language, like, well, if it is acting on the environment, isn't that an action state, you know, or something like this? In what way is it bi-directional? So could you not draw that as one arrow going to the sensory state and one arrow from the sensory state back to the environment and then say what functionally does that entail? And then you're doing this particular selection of how you're modeling, right? But I think you have to do that to understand why that is bi-directional there, right? In that particular framing of the generative process, generative model there, right? Which is still, to me, also slightly nebulous, but I think, yeah, just the bi-directionality of it between the action and sense states that kind of makes sense almost that you sense your actions and your actions are a response to your sense, but that's not really what's being done there precisely either, right? So... Dis-explicating that is what, yeah, we need to separate and explicate what, yeah. Yeah, another really important question is like, this action perception loop is appealed to early and often. It's the first picture in the textbook. Before the high road and the low road in 1.2 is 1-1. We're talking about agents and their interfacing with the niche. And we're gonna end up calling how we model agents or generative model, and we're gonna end up calling the niche and the dynamics of the niche and how it's influenced as the generative process. And we're going to call the interface between the generative model and generative process a Markov blanket, and it's gonna inherit this technical definition from Bayes graphs, and we're gonna add these variables, and we're gonna connect them a certain way, but we're talking about the feedback loop with the agent and the niche. Oh, so what is this then? Are the observations the sensory states? And this isn't just like a low hanging fruit, like, well, then why doesn't it, like, you know, why couldn't we call them? I mean, they are called as X and Y interestingly in the continuous time formulation. And I get it that they're being distinguished here because this is the continuous time, POMDP and the discrete time POMDP, but where do we see active states here? We see policies, so our active states along the branch influencing the B matrix, how the hidden states change through time. So there may be a very simple trail from this action perception loop framing to the discrete and continuous time POMDPs. Yeah, I think just to me the simple, if you instead of thinking of this as a circular thing where you're literally going from one state to the next state to the way you might do it in a normal state machine, if you're thinking of, no, the active, when you have an active state that however it is changing, that is necessarily entailing change in the sensory external state and the internal states and vice versa here with the sensory states, like that if you perform some action of some kind, there's some active change in that state, like you're necessarily changing the internal hidden state. So like if you grab a glass of water or something like this, like what you're modeling now has changed slightly and your internal states and the sensory states are doing the same thing where for the glass of water or whatever was this object just sitting there doing nothing, but now it's become a thing to drink out of and that's changed in some sense the kind of external states that are being fed into your sensory states, like it's changed the Markov blanket that you're kind of selecting for or relating to. And I mean that, yeah, the PMDP it's, I mean I think that is visibly there, same sort of thing where if you have this observation, you're necessarily creating a new internal or sense state that is gonna change your palsy selection. So and that's gonna change your sense states. So returning to the recipe one question that I think will reduce our uncertainty about as we really do the modeling is how do we account for residuals and understand which modeling residuals, which is to say variability in data that are not explained by the model? Which and what amounts of residuals are acceptable? So if we were doing a linear regression and the only data we had on hand was height and the only data that we wanted to predict were weight, then if we could just say we've used all the data and whether the model fits with an R value of 0.1 or 0.9, we know what the residual is of height on weight regression. And here's a number that describes what fraction of the variability was described with this regression, R value of one being like, it's all perfectly on a line, R value of zero being like it's just a total scatter plot. We can say how much variability is explained. And then we're done with the empirical data that we had so we can't explain any more variability. When we're looking at empirical traces of behavior to explain even a small fraction of behavior for some systems, it might be the case that the generative models already must be quite complex. In another scenario, it might be the case that a very simple model like looks to the left are 90% of the time followed by looking to the right. I mean, on average, isn't that true? So then when we are evaluating models, whether in block prints, sweeping across models or just qualitatively or in a boutique way, are we looking to reduce the residual to zero? What traces of data are we looking to explain variability in? And then if we're not going to be with empirical traces of behavior looking to reduce our unexplained variance, what is gonna be the criteria by which we do model selection on? The AIC and the BIC, which are information criteria, balance variability explained across families of models with their degree of parameterization, penalizing having more parameters, rewarding better explanations. So seeking to find a balance with models that are on the kind of Pareto frontier of explanatory and simple. That's great. That's like one extra nuance on top of merely explaining more variability with a smaller residual. It also adds in the penalty for having more parameters. So that in theory and in practice, stops the modeler from just, okay, well now we're gonna add in the temperature in this other part of the world because 1% of the variability just happened to be explained. So now we're just developing these totally spiraling, looking for more and more data sources to explain a decreasing amount of variability that's left. So it's good to pull back from that edge through the use of information criteria. But at the core of the criteria and is still the imperative to reduce the residual, to have a model that fits within a model, the way that we fine tune it so that it fits data well, but not overfitting. And then across models, we can see an analogous process of wanting the model structure that fits without, for example, overfitting or over-including parameters. So I think one question, which might be added here is like, what data do we have? If we're doing a didactic model and we just wanna sketch it out and run a simulation and just purely generate data, that might be useful in certain settings. But the question of what behavioral data we have is very non-trivial, if we're going to be approaching this as an empirical analysis problem. Brock? Just another question there for us, what observations must we make or something related to what data do we have? If you don't have the data that if there's not sufficient affordance information contained in the data to model the problem, then... Do we have to model our action selection at a second or a third order? Yeah, exactly, yeah, to realize the right epistemic value. Okay, so that was figure six one. Discrete and continuous, shallow, deep and hierarchical. So just to address this, which might come up, shallow versus deep is describing how iterated the model is through time within one type of time. So you could have the one hour shallow model where hours are our units or you could have the 10 hours where it goes one, two, three, four, five, six, seven, eight, nine, 10, where you could have a hundred depth of 100 with a time click of one or one could have a hierarchical model where there's 10 hours that make like a deca hour and then there's 10 clicks of the deca hour to reach a planning horizon of 100 time steps. So deep is describing within one unit or counter of time how iterated that counter is. Depth is describing strict hierarchical nesting. And so they can both be used separately or together to describe events over longer and longer time horizons. Crucially, this is in the context of language processing. The duration of the word transcends that of any phoneme and the sentence transcends that of any word in the sequence. And if we assume paragraphs are made of multiple sentences, paragraphs always transcend and encompass sentences. So anyone else wanna just add a point? I'm sure that you fellows are familiar with this distinction but just wanted to point it out since the temporality in these models and doing model selection on different forms of temporality is going to be an essential piece of the puzzle. Yeah, and it also reminds me of the Schenkerian analysis theory that if you remember we talked about earlier and it's hierarchical, I mean, it's temporal hierarchy that tries to describe the whole musical piece as a kind of hierarchical layers which I think maps perfectly unto this specific example of language parsing because that also is a kind of thing that should ideally be modeled in a very hierarchical way. But the caveat here is unlike language which is at least in some languages are necessarily hierarchical. Music is at least tonal music or even non-tonal music are not necessarily, they don't necessarily have this hierarchical structure. So depending on the kind of music or the genre of music, we might have either a very shallow and non-hierarchical structure or a very deep one. So that's something that I think, again, refers back to the kind of system we're trying to model but even that kind of system needs to be more, I mean, specified in a more granular level in order to have an effective modeling strategy there. Thanks for bringing in that domain. Like it makes me think of the intro, the chorus, the bridge, the verse. Perhaps these are only in certain genre of music but analogously and then the measure transcends any beat in the measure with a special case of like a one-one time signature or something and so on. And then although language is described in a strictly hierarchical sense, I'm wondering how syntax and grammar actually create kind of a strange loop. For example, it's trivial to say that phonemes are nested within words and sentences and so on. But what about the semantics of a sentence that contains commas and exceptions? And then at the end, the speaker says the previous sentence is not how you think it is. That is also causing kind of like a deep linguistic recall that can be trivially modeled as containing nested units but will a model that narrowly considers the semantics of words, then the semantics of clauses, how will it be able to address some of these questions? Here, I think we can point to a really excellent attribute of the active inference modeling framework. So in this discussion where the LW paper was cited, we could just marvel at nested systems and draw blankets on blankets and so on. And they could be drawn around every organelle and every little lipid granule in the neurons and or the brain regions. And so we discussed a little earlier like doing structural model selection on which one of those are relevant. However, this does not mean we need to attempt to model the entire brain to develop meaningful, pragmatic simulations of a single level. For example, if we wanted to focus on word processing, we could address some aspects without having to deal with phoneme processes. This means we can treat inputs from parts of the brain drawing inference about phonemes as providing observations from the perspective of word processing areas. So one could say, I'm gonna make a figure 4.3 where S is the true word. And phonemes are being passed as observations to my word inference engine. And someone can say, but aren't phonemes the result of inference? And someone can say, great, make that module in blockference, make that module and pass me that data. And then we can do a nested model and we can graft those models together. But through the Markov-Blanket formalism, not even the whole Markov-Blanket bounding a specific particular entity, but just the trivial Markov-Blanket that exists insulating any two unconnected nodes in a base graph, we can use that broader, still technical concept of a Markov-Blanket and just say, all right, phoneme observations come in. And surely we could make a model where another type of information comes in and the inference is on phoneme and that's what's passed out. So I think, again, to point to this advantage of active inference modeling, it allows us to situate phenomena as complex multi-scale nested systems and also bite off what we can chew and makes it an empirical question of how grafting together which different structures might be useful, but we don't need to go like turtles all the way down. We can just say, even if it were turtles all the way down, we're modeling the third through the fifth turtle. And the fifth turtle gets a top down prior and the third turtle gets sensory input from one below it. And that's the part of the stack and this is the lateral width that we're modeling. Another advantage of active inference which not even saying this is a unique advantage is that this goal directedness and corollary capacities like counterfactuals on goals and the way that counterfactuals on world states influence our goal selection, nested goals and transient goals or conditional goals can be resolved in an unfolding action perception loop. That's something that as this example of like wanting to enter an apartment, of being goal driven over multiple timescales and then being able to again zoom in. So it's like, oh, grab the keys. Okay, well, here's the key grabbing module. And so it says, well, the key grabbing is actually accomplished through this kind of grasping behavior. Then that could be modeled or you can just say we're subsuming finger grasping kinetics in the grasping module because the phenomena that we're trying to explain and the data we have is just about when things were grasped or maybe the only observations we have is when apartments were entered. So we can actually, I expect frame systems extremely expansively and clarify where we're doing formal modeling. I think maybe this is just a philosophical or ontological framing of it, but I don't see how you can model anything well without admitting that it is either incomplete or partially consistent or... But there is some hidden state, which is probably not included in the model. And to me, active inference kind of natively has an affordance, at least, at the very least a bookmark to do that. And so whatever depth or hierarchy you modeled the system, it's okay, it's okay that there's still hidden state and it's incomplete if it sufficiently models, answers the question at hand, then you can always add another layer or make the model more complex and that's just what we were going to do anyways. Nice comment, I totally agree with that. Again, thinking back to the linear model example, where is that extra information about like adjacent possibles for the model? It's unstructured. It's kind of like either the structured information is going to enter your linear regression just so and everything that's outside of your linear regression is we're gonna need to go back to the square one. If we include a second observable, we're gonna have to go to square one and do the model selection all over again and test for all the repeat interactions again. But in these multi-scale models that we're discussing, like you said it was a bookmark, it's kind of a nice way to frame it, which is like the phonemes. You're bookmarking or leaving an open USB port or something, anything that outputs a phoneme will plug in to that part of the model. Anything that is able to hear can be plugged in to the frog riveting. Anything that can see can be plugged into the visual component of the frog. And so it's almost like consistent with all of this blanket talk. We're being clear about what is modeled formally and the borders of the formal model are degrees of freedom as formulated to be expanded upon and composed. One other, I'm not sure the context is something here Ben Gert will say, attributed to Peng Shui, but Pei Wang that in circular reasoning, when your circle gets big enough, it becomes coherent. Maybe another way of saying like that if your model is insufficient, that if you add enough states enough, if you expand your blanket enough eventually, if you're modeling something correctly, I guess, it can, the general process can match. Your blanket states can come into alignment. Nice, some more active inferences. So extericeptive, proprioceptive, and tericeptive, we're going to use active inference modeling. Memory, attention, anticipation, planning as inference, we're gonna use active inference. That is what it looks like to integrate disparate fields using one model. One family of models. So, others can work out some fun ways to communicate and frame it. But I think when people are like, how is this simplifying? When it might feel like it's bringing in a lot or even stepping on namespaces that people already have familiarity in, like we discussed earlier, you could ask how is memory related to attention? Or perhaps more saliently, how will you model how memory is related to attention?  Are you going to read a book written before computers were invented and look at pros? Are you going to dive into the psychoanalytic tradition or something that's maybe even clinically biomedical? What's the move? And active inference addresses that. In these theme of, where's the people use that mean different things? Or 400. We have Bayes optimal behavior. Well, then why do things go wrong? And there's multiple layers to unpack. First is going wrong is about your perspective on how the system, perhaps how you prefer it to be or how you expected it to be. But like given where you put the dish when it fell off the table, that was just compatible with gravity. So it was finding its optimal position and fragmentation where you placed it. The ball rolled downhill as a consequence of where it was placed and the slope it was on. And framing behavior in that light, we're modeling behavior as rolling to the bottom of a bowl. What's the bowl? What's the ball? Those are the questions. But it's like chemical reactions proceeding under Gibbs free energy minimization when we have policy selection driven by variational and expected free energy minimization. But how can things that go wrong be optimal? Well, it has to do with different parameters in the model. Given how they are, the model performs optimally. That doesn't mean computationally efficiently. It doesn't mean adequately. Doesn't mean it's satisfices, but it may be framed as Bayes optimal. Fixed and learned behaviors. This is gonna come into play in chapter seven. There's an amazing continuity in theory and in simple examples with perception and learning, which is to say that perception happens over faster timescales or perceptive like processes happen over faster timescales while learning like processes happen over slower timescales or even just more generally, perception and learning refer to parametric update processes. And you might even be able to situate the same example both ways. Like if we see a ball move across our visual field, are we perceiving the location of the ball? Are we inferring the location of the ball or are we updating and learning our position of the ball? For those with a computational background, learning often equates to parameter updating. And so in that sense, any kind of dynamical perception is learning. So for some backgrounds, the difference between perception and learning will be seamless. For other backgrounds, those are gonna sound like totally different processes. I mean, isn't perception when you see the book, but then learning is like when you understand something about the book. And then speaking from a more modeling perspective here, a fixed model or fixing a parameter of model reduces the model's complexity immensely or another way to say it, taking a fixed parameter and making it learnable introduces multiple hyper parameters. And there's no single way to address parameter learning. You could say, well, we're doing moving average learning like a Kalman filter or just a simple sliding window. We're taking the average of the last three. But now you have to parameter sweep across how many? And with a single trial of data, there may be, it may not be clear which learning strategy is implemented because especially if we want to differentiate learning strategies empirically, we would want to see like multiple comparable trajectories within and amongst individuals in similar and different contexts. But now, you know, our lab has gotten quite big and we're doing quite large model selections even for learning and updating parameters that might just conversationally seem really simple like preferences being updated. So here's this description of when it comes to inference and perception as being fast changes and learning as slower changes, though they're being modeled in a really analogous way. It is worth noting in this book, we exemplify a rather simple generative models that are defined using tabular methods, e.g. with explicit matrices or tensors for priors and likelihoods in small state spaces. In comparison, more sophisticated GMs are being developed in machine learning, deep learning, robotics, et cetera. So is active inference deep learning? Okay, good fellow, generative adversarial networks but with Benjio and Mirza. But are we saying those are formally active inference systems or are we just saying that we can think of it like bird song conceptually and then are we just adding a wrapper or a descriptor to the GANs that are non-active colleagues or building? Is it shining any new epistemic or pragmatic light on GANs? I mean, here's a whole paragraph on GANs. Home is behind. Oh, that's chapter 10. What happened? But there's still a whole paragraph on GANs. Did you see that? How did that happen? I'm not sure what the optimal way to view a PDF is or that one exists, but I don't think it's a browser. It's not bad, but it's not optimal. Yeah, I'm interested in this, again, the generative model. Oh, I know what those are, so generative model like, and how specifically GANs here in active inference is one, is the rectangle a square, sorry, square rectangle, rectangle not a square here, is that what is being said or is there a degree, a index to blanket here of varying degree? Yep, yep. As far as I know, and also you fellows might know, like this is being pointed to variational auto-encoders and variational Bayesian inference are directly implicated as potentially recursive cortical networks are and world models, but if these bridges can be made solid, like you said, with squares and rectangles, so that we can say all some are none of X, R, Y, or here's how you project this model to that model, it's going to just break a dam with possibly certain applications of active. Any final comments on six? And I think certainly by two weeks from now, let's look at the SPM implementation and look at some of those that are pointed towards in the book as well as some of like the epistemic chaining and PMDP type models as well as anything that anyone else builds. So any final comments? Yeah, I just wanted to point out the recent work by Fields et al, especially one, the two papers that first and was also as a co-author of is one of their main objectives is to cross that bridge between active inference and VAE via the CCCD system. So that bridge they're talking about in this book is already being investigated to be filled. So yeah, they're pretty interesting developments going in that regard. Okay, any last notes before I stop the recording? What is that paper? I'll send you the link from the discord. I forgot. Maybe just one, I don't know. We haven't tried to make a effort on the code to lay down some basic examples from the first half. But should there be some, if we're gonna model stuff is should we have a subpage for that or do put something somewhere in the code for that? I don't know. We've had this code section from the beginning. Yeah. And just however it should be totally modifiable full control. So however this and anything subpages need to be modified and the GitHub link to a textbook, anything that people suggest, just let us know. Any hails?


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