So, hey everyone, welcome to today's Protocol Labs Research Seminar. Today we are joined by Alexander Viond, who's both a doctoral student and a research assistant at ETH Zurich. Alexander works with secure computational technologies, including FHEs or fully homomorphic encryptions, trying to make techniques more accessible to non-experts. Today, Alexander will be talking about HECO, automatic code optimizations for fully homomorphic encryptions. So, Alexander, I will let you take it from here. Cool. Thank you for the introduction. So, yeah. Hi, I'm Alex. And today I'm going to talk a bit about our work on compiler designs for fully homomorphic encryption. And of course, as is usually with most of this, we do a lot of work with a lot of collaborators, including some lovely students, Parthik Amiru and ETH, and also my superlative Amber, who is also here today. So what is fully homomorphic encryption? So what is FHE? FHE enables computation on encrypted data, specifically, it allows us to delegate the processing of data to a Parthik, but now also having to give away access to the underlying data to that Parthik. And so that obviously has a huge potential to transform privacy as we know it today, because it will enable us to provide end-to-end security for a much wider set of applications than what we can do today. And the good news is that thanks to a series of dramatic performance improvements, FHE is finally faster than that to be useful. And in fact, we're already starting to see it used in practice. For example, Microsoft has recently deployed fully homomorphic encryption as part of the Microsoft Edge Browser's password monitor service, which checks whether or not your credentials have been found in one of the deep databases that Microsoft knows about, without you having to submit your credentials, nor them having to give you access to the deep databases they know about. But we're also seeing smaller startups increasingly looking into how to commercialize FHE, mostly around the area of privacy-preserving machine learning, where you have a server that owns a machine learning model, and you as a client can send an encrypted input and get back an encrypted inference result on that model. While there is a lot of problems in FHE, there is one big caveat. And that is that right now developing FHE applications is notoriously hard. It just isn't generally usable yet. And there's a lot of complexity in trying to make things work. And so for the moment, we're mostly seeing things like this password monitoring service being developed by a whole team of cryptographic experts. But of course, if you want to see wider use of this technology, then we must work on making it more accessible. And so today I want to briefly touch on what exactly is that makes developing FHE applications so hard, and then go into how compilers and tools like compilers can help to address these complexities. The reason why it is so complex to write FHE applications is that good performance tends to require fairly extreme optimizations. How we express our programs and how we optimize them is kind of crucial for the performance. The sort of trivial straightforward implementations in general will be exceedingly slow in the context of FHE. And even fairly small changes in the application can result in drastic differences in how you should implement it and then how well it will run. And so FHE imposes a lot of constraints on developers. And that in turn then results in a fairly unique programing paradigm for FHE. As an example, data independence is sort of built into FHE because by definition, computations in FHE are a black box from which no information must leak. And that includes things like bits about whether or not we branch. And so we cannot do data dependent branching. That means no jumping, no dynamic loops, and not even real if-else statements. We actually can emulate things like if-else statements by doing this multiplexing light thing that you can see here on the slide. But when we do that, we actually have to evaluate both sides of the block, right? And so in general, data independent means that the runtime of your secure computation is always at least the worst case runtime of the original algorithm. But in practice, it's frequently much, much worse even before you consider the actual overhead from the crypto in the map. Obviously that's maybe not the greatest news, but there is good news as well in the world of FHE. For example, a lot of the major FHEs right now offer a very powerful, restricted form of data parallelism. So you can encode many thousands of integers into a single side of the text rather than just a single one and then operate on them in a single instruction multiple data fashion. That can obviously give you a massive amount of speed up. But the catch is that you're very limited in terms of data movement. So you cannot treat this like an AVX type 12 vector interaction. It's much, much more restricted. And so using this in order to enable actual improvements in the latency of applications rather than just sort of paralyzing and improving throughput is actually a fairly significant challenge. But it's worth tackling this challenge because doing so tends to give you several orders of magnitude in speed up. So it's not unusual to see 10X, 100X or even higher amounts of speed up from applying Cindy parallelism to a problem. And of course, I'm only touching on a few highlights here. In general, the FHE design space is relatively complex. For example, most of the schemes only natively support additions and multiplications. And while that obviously allows you to compute any polynomial function of the integers, that isn't necessarily the kind of things you want to compute. And so you can use polynomial approximation to sort of get around that. But it turns out that in practice with the restrictions of FHE, it's very hard to find the balance between the accuracy in your polynomial approximation and the performance of that approximation. And so alternatively, if you need non-polynomial evaluation, you can look to emulating binary circuits, right? So you can, if you imagine addition and multiplication in Z2, or that just X4 and AND. And so with that, you can do arbitrary computation, but the catch is that now every single operation in your input program is getting turned into dozens, if not hundreds of actual energy operations that compose these, let's say binary adders, binary multiplier circuits that you are now having to emulate. So at the very least, when you're using this approach, you're seeing about auto-magnitude slowdown compared to doing things directly over integers. And in practice, it tends to be much worse, actually, because of sort of compounding factors. If that wasn't enough, then you also pretty much always, in both of these settings, have to consider things like parameter selection. This requires balancing security, performance, and accuracy. And so it's not just the thing of looking up the standard. It's really per application. You need to figure out what are the correct parameters for this specific task. And you also have to deal with this very unique cost model of FHE. It's somewhat like MPC in that additions are much cheaper than multiplications. But in fact, in FHE, most of the runtimes are dominated by these same specific operations that don't actually modify the message on an application level logic, but they just require to maintain the site or text as the computation goes on. And so this is also something that here is just how to wrap your head around. In summary, developing FHE applications is hard because it requires looking beyond the overhead of the underlying FHE operations and really the application design has a significant impact on the performance. And so you need to spend a lot of time in conduct. As a result of that, we see a massive performance gap between naive implementations done by non-experts and first-time users and those that were carefully designed and fine-tuned by experts with maybe a PhD or even the faculty position in this field. And so the design space, obviously, is very complex. There's a variety of different approaches, and they all have non-obvious trade-offs. So that even for experts, it's not even entirely clear what would be the best approach for every problem. But for non-experts, it's usually completely and transparent where to even begin with your implementation. As I said, I want to keep it fairly brief on the challenges today because we actually have a paper about this from last year where we go into a lot of the detail, a lot more detail about the challenges and what it means for sort of everyday developers. If you're interested in learning more about that, you can either check out the paper I linked below or we also have a talk about this recorded or that's available on fhg.org in the list of previous talks there. So yeah, but today I don't want to focus on that. And instead, I really want to focus this talk on asking how can compilers help in addressing these complex settings? And that basically means asking ourselves, how can we make fhg accessible to non-experts? And we propose that that requires combining concepts from the crypto side on the other one hand and then programming language design in order to be able to bridge that gap and to develop tools and abstractions that facilitate fhg development. And I personally have to believe that compilers are the key to democratizing fhg because delivering on the promise of fhg requires tools that allows non-experts to actually develop, you know, turn the existing code into efficient fhg solutions with ideally minimal changes. And in that world, the compiler should be taking care of most of the complexity, allowing the developers to express their computation tasks that they have in a high level language and then the compiler automatically generates and optimizes secure and efficient fhg code from that. And towards this vision, we have focused primarily on two challenges in our research. The first one is automatic translations. Specifically, we're focusing on mapping high level imperative code, which doesn't really mesh well with the programming paradigm of fhg. And how do we map that to efficient batch fhg solutions automatically without requiring user input? Well, minimal user input, for example, we do require the user to imitate which values are secret. Okay. On the other hand, we're also bringing in a new architecture for fhg compilers that we believe much better reflects the different optimization opportunities that the fhg stack allows for. And so I wanna have a closer look at what we actually mean by an end-to-end development tool chain for fhg. Traditionally, we might think of an fhg compiler, and this was definitely true, let's say two, three years ago, as a tool that converts a program in some more or less high level language into an arithmetic circuit. And the arithmetic circuit is sort of the natural and traditional mathematical model of an fhg computation. If you then wanna have an optimizing compiler, well, that means probably rewrites and simplifications on the circuit happening before you're able to run it. But in probability, we see that fhg applications come from a whole range of domains which all have different paradigms used to express the programs. And so when we directly translate these various high level paradigms into circuits, what happens is that we lose a significant amount of information and making it much, much harder to actually work with and optimize the program. And so one of the first things we do is we propose to extend the notion of an fhg compiler sort of outwards towards the front end by introducing program optimizations that make use of the high level information that is present in that input program while it's still a program and not a circuit. And on the other end of the system, we acknowledge that really what the end user wants is not an arithmetic circuit, what they want is instead a secure and efficient fhg implementation, right? And so that can mean having an executable that works on CPU, but also increasingly GPUs, FPGAs, we've seen work for that in fhg and then looking towards the future, fhg specific ASIC accelerators that are currently being developed as part of the Dalai Deep Drive program by a couple of competitors, including for example, Intel. So in order to support all of this, we also need to extend the compiler on the other side to the right here with the ability to target hardware first by lowering fhg operations to the underlying map, and then going further and scheduling these low level operations appropriately for the target hardware. Okay, so that is sort of the zoomed out view of what we think an end-to-end fhg development tool should needs to be. So how do we actually go about building such a compiler? And for that, I want to take a very brief look at the evolution of compiler architectures. So the traditional compiler is this monolithic tool that takes in, let's say, AC for C++ and then through a number of transformation crunches it down into assembly for specific types. And this is basically the architecture of GCC. But we shouldn't realize that there are significant advantages to introducing additional intermediate representations like the LLVM IR that is used to enable the client compiler to retarget to a variety of different backends from the same starting point. And this means that each of the translation steps that they now have to do is significantly smaller than the large gap that something like GCC has to cross. But it turns out that for high-level language, especially higher-level languages than CC++, this is still a pretty large gap. And so we see compilers for SWIFT, for example, introduce their own high-level intermediate representations but then lower, for example, to LLVM. And we're seeing this trend towards an increasing number of abstraction levels. For example, with Rust, we're now seeing a high-level and a medium-level intermediate representation before they're targeting LLVM. And this trend really goes to its conclusion, sorry, with domain-specific languages. So with domain-specific languages where we have a large number of intermediate representations and that means that the individual going steps between them are much, much easier to express to the point where you can sort of fairly easily express as a domain expert, rather than as a compiler expert. And so this is exactly what we're also using for our compiler, ECO, which, well, homomorphic, a different compiler, sorry, we don't have the most creative acronym there. And in our case, this looks like this, right? So from the input program, we first go into a high-level intermediate representation and here we still have our control flow, our high-level operations, like linear algebra operations, right? So even higher-level potentially then C. This is then lowered into this eventually optimum optimization into scheme-specific intermediate representation. And this is basically sort of representing the native operations of an FHG scheme. Most importantly, this means not having all of these convenient functions like equality, comparison, etc., that being sort of stuck in this multiply, add, etc., and then when we want to go lower and actually target hardware, well, then we translate to the polynomial intermediate representation, which actually is there to represent the underlying map of these schemes. And then there's another step, which is the RMS or Retinue Number System intermediate representation, which is representing an abstraction of how to efficiently work with large-degree polynomials and large-big-width polynomials in sort of fixed-width hardware. So all of these obviously are designed to translate and lower down to LADM and then inherit from that the existing targets. But we also envision that in the future ECO will target the writer or instruction set architecture of these upcoming data deep private accelerators specifically built for FHG. I think I said the word intermediate representation a whole bunch in the last minute or so, but let's actually look at what is an intermediate representation. And specifically, I'm saying this in the context of the MLIR compiler framework, which is what we're using to realize the ECO. So here, an intermediate representation is basically a description of the, let's say an API. You have an operation that belongs to a dialect. So standard.end, it has operands, it has results. It also has types, very important for the actual implementation of things, but I wanna completely ignore that for this presentation. And then this basically defines the syntax in some sense of the intermediate representation. But whether or not this actually means anything isn't defined here, right? We can expect that standard.end probably adds two numbers, but this actually isn't defined as part of this. It's defined through low rigs. So to give you a bit more of an interesting example that isn't just an addition. And so let's have a look at the VFB multiplication operation. So this is quite a bit less trivial than, let's say, an integer multiplication because it translates to, first of all, a series of polynomial multiplications between the polynomial elements of the psychotext. And then actually each of these polynomial multiplications translates even further into, let's say, an entity or an element-wise multiplication and then inverse entity, which is a way of sort of performing large-degree polynomial multiplications efficiently. And in fact, this will then, like one each one of those will actually translate even further into the RNS level IR, but that gets way too verbose for this sort of overview. So I've sort of zoomed in here a bit, so let's zoom out a bit. Let's have a look at what's actually happening here in terms of architecture. So we have a Python front-end and we also have currently a C-like, sort of domains with a language front-end and we're very open to adding more front-ends if people can sort of show us why they would prefer one. All of these front-ends they translate the input program into our high-level intermediate representation. This is made up of our custom dialect or set of operations around FHG, but it also includes, and this is the advantage of using something like the MLAR framework, built in dialects and operations like the tensile vector of dialect, ones for basic arithmetic, for control flow in terms of the fine function calls and all of these things that we get to inherit. And by cleverly connecting our FHG dialects with the built-in systems, we actually get to benefit from a lot of the built-in optimizations and we basically avoid reinventing the wheel for some of the compiler one-on-one stuff. So as I said, and just showed earlier, this then gets translated to scheme-specific IR. So for example, this BFV.mult operation that I just showed is from this BFV dialect and then we have dialect currently for BFV, PGD and CKKS, which are the really based FHG schemes that will have this SIMD capability that I mentioned earlier. So when we then want to translate that into an actual computation, the easiest way would be to just translate it into calls into an FHG library like SEAL, which implements all of the crypto by itself. But increasingly, we're seeing that there's interest and demand for being able to actually lower to hardware directly. And this is then when we do what we just saw, which is lower into polynomial operations, those, and that was the thing that didn't fit on the slides, get lowered into RMS, Resilient Number System Operations. And then finally, this gets lower to LAVM if we want to target something like H86, or in the future, it would get dispatched to a deep-private accelerator. I've already given you an example of what it looks like to take something from, let's say, BFV through to poly, but now I want to have a bit of a closer look at the high-level transformations that I mentioned earlier. And in order to be able to do this, we need to have a quick recap of this Sydney-like parallelism which I already mentioned. Obviously, it's amazing, it allows us to do things like replace many operations with a single Sydney operation, right, great, but the catch is that it doesn't give us a data movement. So in normal operations like AVX-512, which are very common server-sized vector operations, we can actually fairly efficiently commute or scan together, and this is very heavily relied on in-optimization targeting this. In FPG, this is not an option. The only thing we can do in terms of data movement natively is cyclic rotation. So we can take a cybertext and we can add significant actual runtime across the rotated in a cyclic rotation. And because of this restricted data movement, it doesn't really work the same way as computation paradigms like, you know, we see like AVX-512 or other vectorized products. And so really how to use this to get latency improvements, right, so how to accelerate the single instance of a program is very, very tricky and sometimes not very obvious. For example, this is a fairly simple program that computes a simple image sharpening, right? It takes a kernel and it iterates it over the image and what it may not look like it, it is actually very FPG-friendly. So currently it's in a very unfriendly form because we have all of these random accesses into these image pixels, which is very, very bad. FPG cannot do this sort of data movement or extraction of a single element from a vector efficiently. But this does actually have an efficient FPG implementation. The problem is that it's a rather dramatic transformation and I hope I'm not presuming here when I say that it's basically unrecognizable from the original program. For example, this inner loop here or this inner loop nest even has been turned into a single instruction in the FPG program and these outer two moves, well, they just completely disappeared because what we're doing with the FPG approach is we're no longer iterating a kernel or an image, we're actually duplicating the image a few times, rotating it appropriately and then jamming it through the kernel in one big Cindy operation at the end. And so this is an example of the kind of transformation that we need to do in order to get decent performance out of FPG and it's also an example of how drastically transformations can be. So it might be natural to resolve two things like synthesis or other sort of heavyweight tools to achieve this, but our goal here really was to do this with simple and efficient translation rules that you can actually use in everyday development of program. So in order to show you how HECO would tackle something like this, I'm going to drop to a slightly smaller and simply example that will actually fit in the slides and that's the hamming distance. Very simple task, calculate how many positions of the vectors disagree, right? So the hamming distance of this example is two. So this is like the C++ representation of the program. This is what it would look like in the high-level intermediate representation. I've omitted all the typing information here just because it gets very messy if we do that. So right now, this has a lot of these random index accesses and as I mentioned earlier, these are very bad for FPG. We can emulate them by rotation, multiplications with mask and all these kinds of things but basically they're just prohibitively expensive. At that point, we actually show it's much better to not even bother with batching and put every data even into its own cybertext but that's not exactly a great solution. And so what we do is we want to try and minimize these operations. But before I can show you how we do that, I want to quickly unroll this program just for exposition, it makes it a lot easier to follow along. So let's give this a few unrolls, there we go. Now we have a program, double the hamming distance of size four, maybe not the most practical program but it fits on the slide and it currently has a cost of eight index accesses and sorry, that should be four multiplications. And we want to make this better and the first thing we're going to do is maybe not going to be super obvious why it's the way we're doing it but we'll get back to it later. Because the first step in what he could do is it combines what we call sequential operations. For example, all of these additions here that we see they keep adding to the result, right? But it's one series of additions and we can combine these into a single big addition operation and we can obviously drop the plus zero because that's not relevant. And now instead of having a binary addition operations we have an every addition. And this is actually quite important later on but for now I just hope you will believe me that will become a relevant. Okay, so this is the next step. It's much easier to see how this relates to batching because what we do now is we look through the operations and we try to apply an operation instead of two individual vector elements to the entire vector. Well, that's great. We can get rid of all the other ones because they've essentially been pre-computed by this operation. But the catch is that now what we need to do is we need to add an index access every time we use this result, right? So instead of actually removing index accesses we nearly move them around in the program. The good news is that if you do this replacement in a let's say clever way and you continue doing it, for example doing it to the multiply here then we're starting to see the first improvements, right? Now we only have four index accesses rather than eight that we had in the beginning and we've also removed three or four multiplies. In practice, in like reverse programs it takes a bit more than just hand waving to make this always work. And so in the compiler we actually have a good amount of what we call target slot logic which decides how and where to vectorize these things so that this works out in as many cases as possible. Okay, so having done, we've made the program I guess not just 50 but actually more than 50% faster. That's really nice. And in the world of like playing that would be like a best paper or whatever. But in the world of FHE that's just not even table stakes. 50% faster is no linear sufficient in a world where the performance gap between naive and expert is many orders of magnitudes in the worst case. And so I promised you we'd get back to this interesting additional operation and that's what we're gonna do now. First of all, we need to translate these index accesses into rotations. So that means that we now instead of fully emulating that this is a scalar we're just moving the value that we want to be on the same slot in each of the cybertexts that we have.  And then we can get rid of the last index access in this program by just saying, look if we're trying to return a scalar we'll just look into the first slot of the zero slots of your cybertext. And that gets us to a program that now has no more index accesses to single multiply if you rotate this is much much faster than the input program was but most importantly it's still linear it's still all of n in the size of the input. And we can actually do significantly better here. And that's by exploiting that all of these inputs here have the same origin. Like so all the things being added have the same, they originate from the same cybertext they're just different rotations of the same cybertext. And here we are the first, as far as I know to exploit, automate and sort of generify this epigy focal technique that is used to compute the sum of elements in a cybertext. And the way we do this is fairly straightforward we take a copy of the cybertext we rotate it by half we add them back together to get the partial sum and then we rinse and repeat this like a logarithmic number of steps until we have the sum in every slot. And obviously in the complete example here where we went from three rotations to two rotations because at size four the difference isn't significant but you can I think clearly see how for larger sizes and in every tree we're talking 8,000 to 64K elements per cybertext going from all n to all n can make a significant difference in runtime. And I will have some evaluation results on this later. Okay, so this is sort of a toy walkthrough of what HECO does in terms of compilation. When we zoom out a bit the compilation pipeline looks like this. We take in an AST for the program from the front end we then convert it into the SSA high level intermediate representation SSA here being single static assignment it basically means we don't have variables that are overwritten again. It's a very standard form for compilers and makes it much easier to write optimizations. We also then apply a bunch of standard out of the box simplifications constant folding common sub expression elimination sort of standard techniques that just help us get the program size down a bit. Then we do what we call type separation where we split the sort of vectors and tensors that appear in the program into the ones that are irrelevant because they're operating on plain text and stuff and the ones that we actually care about that operate on secret values and that allows us to then do all of our optimizations only on the things that we should actually be optimizing because it would not be good to apply FHD style optimizations to standard sort of plain text number crunching. After this we do another round of simplifications and then we do the vectorization parts which is what I spent most of my time just sort of walking you through because the vectorization parts could unlock more redundancies we do another set of simplification before then finally doing this cyclothex folding parts which exploits this focal technique that I just showed you. And then from there, if it's sort of continuing this train we go to the scheme specific intermediate representation and that would be the point where we do what's called noise management which is inserting cyclothex maintenance operations and all these other sort of slightly more technical intro to related aspect of the compilation. Okay, so that's what he could ask. Well, how does it do? Well, if we look at some actual evaluation results of the optimization, we can see that when we compare eco solutions against naive non-veg solutions we see about auto-magnitude speed up. So that's a lot of access, right? And we see that in pretty much all of the cases we get at least an auto-magnitude speed up. And that's great in itself, I think as a result but in order to show that we're not just sort of doing some speed up or that we're actually finding sort of what the experts would have written we're also comparing where possible to a tool called Porcupine. So Porcupine is also an energy compiler but it's a synthesis-based tool. So it really only works for sort of toy sizes and it takes even then up to like 20 plus minutes and sometimes just absolutely fades. But when we could evaluate it we could show that eco and Porcupine actually produce essentially the same output. There are some situations where Porcupine is ever so slightly better than our tool but it's basically within the margin of error on this kind of scale. And so what we're showing the key clue is that you can in fact achieve near expert batching solutions for a lot of different programs the same way that the synthesis-based tools have been doing it but actually do it in less than a second of compile time making it actual practical to give it to non-expert developers and ask them to integrate this tool into their sort of developed, compiled debug cycle because obviously you cannot go wait for a 20-minute synthesis compilation every time you want to do something. Of course, eco also scales to more real-world example sizes and in the paper which is currently on submission but we have a pre-printed up on archive you can see more about how eco scales to actual larger real-world size problem instances. This is me nearly finished but I want to do one quick diversion before I conclude which is that I want to briefly talk about FHD standardization and so maybe not in the way you're thinking about which is the scheme standardization. This is actually going amazingly well for several years now we've had a great de facto community standard which specifies sort of what levels or security are considered acceptable what parameter sets are considered safe and so on and as a result of this being very very stable over the last few years this is now being turned into an official ISO standard and it's being driven by Microsoft and Intel primarily right now but obviously as an ISO standard has a huge community around it now working on it and I'm very optimistic that within the sometimes glacial pace of an ISO program this will come up fairly soon and then we'll actually have a very fixed and nice description of all the standard schemes. What we don't currently have and what I obviously as a compiler person care about much more is standardization in terms of intermediate representations. We did have this draft API that came out around the same time as the original community standard for the schemes but I think it was just a bit too early at that point so what we ended up with instead is just a de facto conceptual API for example SEAL has an evaluator object but then offers add multiply et cetera functions corresponding to the scheme operations and pretty much all the other libraries follow a similar pattern. The problem is that well first of all they're different but that's just an engineering question but also conceptually it's not quite sufficient to really think about and argue about. So this is just one layer in HECO whereas all the other layers don't really have an obvious standard of representation and so now that compiler efforts are accelerating and I don't just mean our compiler but there's also the transpiler by Google, Zama has an internal compiler and there's various other parties and stakeholders actively building compilers for FHE. I believe it's high time to revisit the standardization and expand it beyond just this one level. And so we actually have a round table an informed round table on unifying FHE abstractions that I'm moderating. We meet roughly monthly and it includes stakeholders from Intel, Microsoft, Google, Zama and variety of other people and companies working on FHE compilers. And so if you happen to be watching this and are interested in this area please reach out to us or to me because we'd love to talk and hear your ideas even if you're not necessarily working on FHE compilers but maybe also on other kinds of advanced probably compilers like zero knowledge groups or NPC. Okay, now it's my actual final slide. I just want to refer back to the paper once more and also mention that you can actually find HECO in a more or less ready to run state on our Github at github.com slash marvel.he slash HECO one caveat currently you need to go to the depth branch not the main branch in order to find the company. Thank you.


Welcome everyone. Hi. Before we begin today's event, we want to take a moment to recognize that Berkeley sits on the territory of the Hushan, the ancestral and unceded land of the Shishenio Alloni, the successors of the historic and sovereign Verona Band of Alameda County. This land was and continues to be of great importance to the Alloni people. We recognize that every member of the Berkeley community has benefited from the use and occupation of this land since the institution's founding in 1868. Consistent with our values of community and diversity, we have a responsibility to acknowledge and make visible the university's relationship to native peoples. By offering this land acknowledgement, we affirm indigenous sovereignty and we'll work to hold University of California, Berkeley more accountable to the needs of American Indian indigenous peoples. We know that many of you are spread throughout the world on the land of other peoples and we would like to take the time to acknowledge that ancestral history as well. So for those of you who are learning who I am, my name is Aidan Walker. I am a junior here at Berkeley. My major is conservation and resource studies at the College of Natural Resources. My concentration is in constitutional law and food systems. I love it, it's wonderful. And something that I love about Cal is the culture. As you can see, I'm part of the rally committee and a big part of our job is promoting the culture. We had a rally earlier today, we had a rally last night and we're gonna have another rally tonight. And I just love it. It's so much fun. I highly encourage all of you to come and see it. And stemming off of that, I would like to extend the message of gratitude to all of our donors. Everything that we do could not be done without you, especially my favorite part of the rallies. So thank you. So now I have the wonderful honor of introducing our chancellor. Carol T. Christ began her term as Berkeley's 11th chancellor in 2017. A celebrated scholar of Victorian literature, she is also known as an advocate for high caliber, accessible public, higher education, and a champion of women's issues and diversity on college campuses. Chris spent more than three decades as a professor and administrator at Berkeley before serving as president of Smith College. At Cal, she works to foster community and prove the campus climate for people of all backgrounds, celebrate the institution's longstanding commitment to free speech, strengthen Berkeley's financial position, address the housing shortage, and develop a 10-year strategic plan for the campus. Please welcome me, join me in welcoming Chancellor Chris. Thank you. Thank you. It's wonderful to welcome you here to a conversation that I'm very much looking forward to. It's a joy to introduce you to one of the most exciting transformational initiatives happening on campus today, Berkeley's Discovery. Launched in 2020, this effort aims to ensure that every undergraduate can pursue projects beyond the classroom that cultivate their deeper talents and passions. This initiative was founded in ways to strengthen our undergraduate experience at Berkeley by reinforcing the ties between the academic environment in the classroom and the real world outside. And the important for this is so that we better prepare our students for the future and to prepare them to be lifelong learners. So we all know how to do this. There's two pieces to it. The first piece is to make sure that our students have lots of extracurricular activities with lots of experiential learning experiences built into that. We are currently working on doing that. We have a new Discovery Hub that was just implemented this past year. We're adding new opportunities to it every week. And our goal is that every unit, there's over 100 departments, hundreds of research labs and hundreds of units affiliated with the university that they will give us their experiences for our students and that all our students will go to this place to find the experiences they need to make this experience at Berkeley as rich as possible for them. The second thing we're doing is we're working with instructors to try and improve their classrooms so that instructors will have more intellectually engaged in material in their classrooms, that their classrooms are more inclusive and that also the connections between what the students are learning in the classroom and outside are also fortified as well. So this is a huge project. It started in 2020. It's something that we're very proud of and we're something that we're very, very excited about and it's something that we feel that the need for it continues to grow every day. So I'm joined here today by four students and if you pardon me while I put on my glasses, middle ages is not kind. First, let me welcome Fira Fatemah who graduated last spring in astrophysics. As a student, she participated in research on supernova at the Lawrence Berkeley National Lab under the supervision of Dr. Saul Perlmutter who won the Nobel Prize in Physics in 2011. She now helps develop educational programs and trains facilitators at the Lawrence Hall of Science which is right up the hill. You can see it is this concrete building. It's an icon for all families in the Bay Area. Everybody goes there as children and it's one of the hallmark pictures is to get a picture of the whale with the Golden Gate Bridge in the background. Next, we are joined by Dominic Wittz who is a second year student in the College of Letters and Sciences. He's participated in the Berkeley Connect mentoring program and volunteered as an orientation leader. He is also active in the Orthodox Christian Fellowship and is a student lead in Berkeley Dining. He's going to continue to explore a plethora of classes until he declares his major. Ruby King is a third year student in psychology and Native American studies. She works at Cafe Aloni and is a Fiat Lux scholar and a Berkeley Hope scholar. As a member of the Karuka and Yukor tribes of Northern California, she is interned for its natural resources department as well as helping organize prescribed burns in the remote climate mountains. Stephanie Cuarez is a third year student in data science and cognition. She's a violinist for Mariachi Luz Del Oro and a database assistant for the Student Discovery Hub that I mentioned a minute ago. As an intern, she's developing innovative methods for measuring diversity in films. Her experiences as a first-gen Latina student has fueled her passion for expanding opportunities that nurture students' potential. So I'm gonna start with a few questions for the students and then you're welcome to join in with your own questions. So, Fira, can you tell us about your research you pursued as an undergraduate? Yes. Now, is it? Yes, it's working. Okay, great. Hi everyone, as mentioned, I'm Fira and throughout my time at Cal, I graduated a few months ago, back in May, but when I was here, I pursued two research experiences. The first one was with the Space Sciences Laboratory or SSL, it's also up in the hill. I worked with Dr. Solene Lejeune where I was studying the Van Allen radiation belt and if you don't know what that is, it's essentially like a region within the Earth's magnetic field where there's a lot of like high energy particles but the thing is like we don't know how to like best model that area. So when satellites have to kind of fly through that region around the Earth, they can like run into technical issues when like the high energy particles hit the satellites and so my research was trying to find ways to better model, to better parameterize the Van Allen radiation belt and I worked on that research since like my second semester of my sophomore year until the summer right before my senior year, so it's about a year and I was fortunate enough to be able to publish a paper on that research. I was the first author paper on the Journal of Geophysical Research Space Sciences so I'm very grateful for the opportunity and then the second research I pursued after I finished that one was with Dr. Sol Per Mudder who was the 2011 Nobel Prize winner in physics and that was with the Lawrence Berkeley National Lab and for that research in particular I was helping them study supernovae. Essentially that's when stars reach the end of their life and they go kaboom and because of this, because they're really bright we can actually use them to measure distances and also measure the age of the universe. So I was essentially working with like the data of all the supernovae spectra and I was trying to find a way if like if the data is super noisy and it has a lot of just interference in the data like how much I can still retrieve information back out of that data. So that was the work that I was working on after I graduated. I still work on that research for a little bit but I finished it very recently like the end of September and so now I'm continuing my work as a facilitator coach at the Lawrence Hall of Science. So yeah, that's the work that I've been doing it's been great and I'm very grateful to have all of these research opportunities during my time here. Thank you. Yeah. So Dominic, we hope each student's discovery journey at Berkeley includes a connection with an immersion in exciting opportunities inside and outside the classroom. So could you describe how your experience in Berkeley Connect has helped you to find your community and explore potential majors and perhaps if you could give a little bit of background on Berkeley Connect that would be wonderful too. Yeah, of course, Oliver. So Berkeley Connect is essentially a program that a number of different disciplines offer here at Cal in which graduate students acting as mentors will meet with small groups of students usually around 20, yeah, I wanna say 20 or less and they'll kind of mentor them. So that's for both like first years and transfer students especially and there'll just be like weekly meetings, maybe like discussions on the topic at hand. So I did Berkeley Connect twice in English. So we talk about maybe like editing, revising. One time we had a game night, another time we went to go see a play at the Berkeley repository theater, which is a lot of fun. And so I think Berkeley Connect has been great because not only has it helped me kind of find a smaller niche within the larger UC Berkeley community because in a school of 30 to 40,000 students it's a lot to kind of juggle like trying to navigate all of these things. So Berkeley Connect has been great for like kind of helping me focus on like a smaller part of the community. For instance, like there are some students from like my Berkeley Connect English classes that I'll still see in some of my other classes which is really fun. Like for my English 45 C class I have another student who it's like, I'm like, oh yeah, I remember you from Berkeley Connect. And so that just makes the university feel a little bit smaller. And yeah, I think, and then also with graduate students too. So fostering relationships and getting advice from students have kind of tread that path before it can be very helpful. Whether that's like just with like life stuff with like student discounts or just with like a larger things like jobs and other community things. So Berkeley Connect's been invaluable for that I think. And I definitely highly recommend it. And it actually did get its start in the English department. So I'm especially proud that I was in that sector of Berkeley Connect. But they're all I'm sure great. Yeah. So how many students are in Berkeley Connect? Like total or like in one? In one pod. Oh yeah, in one pod. So it can kind of like vary. I know I had one that had like less than 10. And then another, that was when I did it with like transfer students and another that I did with like mostly first years. And that one was like larger. I think around 20 to 25. But still like way smaller than like your larger lectures. And you can do stuff on like a smaller like seminar scale. And it's just really fun. It's a lot of fun like community building activities and whatnot. Thank you. You're welcome. So Stephanie, you're a student in one of our new majors. And we, this year we're forming a new college, the first college for the 50 years in the history of Berkeley. It's a computing data science and society. It's the big hole that you see on the southwest side of campus. That's gonna become this extraordinary building in the next three years. Could you talk to us a little bit about why you chose this field of study? Yeah, can everyone hear me? Okay, cool. Yeah, so actually it's kind of a funny story. I originally came in as an intended film studies major, which is pretty different to what I'm currently pursuing. I mean, data science in a lot of ways is more technical. And there's a lot of computation involved. And it's a pretty demanding major compared to film studies. You know, I love both, but yeah. It's a lot more demanding. But I originally took data A because I wanted to get more exposure to things other than film. And that's when I really realized that I enjoy learning about the world through data. And, you know, data is kind of this journey of learning about the world. And, you know, it's so complicated in different ways. I mean, data science is kind of engraved, engraved, not the right word, but it's like in every crevice of our lives, it's like an everyday decision making. Like we process information about the data and the world around us. And it was just really interesting and I wanted to learn more about it. Yeah. And so how did you find it? Sorry? When you enrolled here, what were your first set of classes? Yeah, so I was taking mostly writing classes, you know, completing my general recs at first. But I realized that, you know, data science was such a, you know, big culture here and like computer science and data science. And I didn't want to just limit myself to learning about film because, you know, films are great and I love learning about films, but, you know, there's just so much more at Berkeley that I wanted to explore. And yeah. Thank you. Thank you. So Ruby, how have you cultivated a sense of belonging on campus? And does it make it easier to connect with extracurricular activities? Hey everybody, my name is Ruby. Is it off? We can't hear you. Hello, can you guys hear me this time? No. Okay. Is it good now? Can everybody hear? Okay. Hey everybody, my name is Ruby. So I have cultivated a sense of community here on campus through the organizations I'm in. So as Oliver mentioned, I am a Berkeley Hope Scholar. I'm a native student and I'm also a Fiat Lux Scholar. Through those organizations, I've been able to create large networks of really great people that have helped me. And during my time here at Cal through like some trials and tribulations I've had, I lost my grandma first semester here and it was really difficult, but through those communities, they really helped me get through it. And also with that being said, they have also connected me to a lot of extracurricular opportunities through the Fiat Lux Scholarship in particular. They have a faculty mentorship program. And through that, I was connected with two different professors for both of my majors. And both of them are really great and have connected me to really wonderful opportunities that I wouldn't have without the program. Thank you very much.  So could you talk to us about how that's enriched your Berkeley experience? It's not working. Okay, there we go. All right, yeah. So being part of the mariachi has been one of the most fulfilling things for me here at Berkeley. You know, at the same time, it's like I'm expanding my artistic horizons and representing my culture. Which are two of the biggest things. You know, it's part of my identity and it's the two biggest things that I mentioned to anyone that I come across. It's really helped me become more confident and a better communicator in ways I never really expected. It's really helped me hone my emotional intelligence because art is just a different, it's completely different to data science and I think it's important to have both an emotional and more artistic side to kind of, you know, such a technical major.  Thank you. So Ruby, could you talk a little bit about how your experiences before coming to Berkeley shaped your time here? Okay, okay, okay, okay, okay. So my experiences prior to Berkeley actually is exactly what shaped what I wanted to major in here. I came in to Berkeley knowing that I wanted to do psychology. I grew up in a predominantly native town and with that I learned so much like rich traditional knowledge and I grew up in such a beautiful place and I'm so grateful for that. But I also seen a lot of struggles. So deciding to major in psychology was wanting to understand that struggle and to go back to my community after getting my degree to help help my people, to like deciding to major in Native American studies was a little bit different. I wanted to minor when I came in and I didn't decide to major until my sophomore year. Like I said, I lost my grandma first semester here and that was something that impacted me really deeply. She was actually the person that had raised me. So losing her was like losing my mom and she just had such a huge impact and influence on me as a person that she had gone to UC Davis and graduated in 1978 and she graduated with a degree in Native American studies and a minor in art. She did this right after the implementation of the Native American studies programs in the UC system in the wake of the red power in the civil rights movement. And I think just the influence that she had on my life that was something that I also wanted to carry on with me. So in a way it was kind of like carrying on her legacy. Thank you. My condolences. So for Dominic, Dominic you belong to an Orthodox Christian Fellowship and have participated in various clubs. Could you, one of the things we were trying to do the discovery initiative is to have this synergy between your classroom experiences and your extracurricular experiences and to try and make better connections between those two. So could you talk to us a little bit about how your extracurricular activities complement your studies? Yeah, of course. So let's see, where do I start? I think I'll begin like, I've always kind of had an interest in doing either English literature or like history or something that had to do with like reading, writing. Like I can do math, but math is not my strong suit. And so I think a lot of it for me was, a lot of like religious faith and like from whatever background you come from is kind of thinking about the past. And so, and there's lots of different ways you can explore that through both like history and literature. And so one of the things that I found meaningful was like being able to take history classes that kind of explore a little bit about like regions where Easter orthodoxy kind of developed further. So for instance, I'm currently taking a class on the Russian autocracy, which is fairly outmoded, but like religious faith is like an interesting like kind of undercurrent within that region. And so the hope is like by better understanding history and the things that kind of surround it, the better understanding I can have of like my own religious background. And I think that can be true for anybody regardless of like what kind of path you're kind of walking on. And so yeah, I'm also taking like another like Slavic comparative literature class examining literature and revolution. So during the kind of communist era right now we're looking at like 1917. So the February and October revolutions. And that's really interesting because at this time where you have kind of like different political actions and change, religion is still kind of sometimes an undercurrent there because it's so ingrained in Russian culture. And so I think a lot of those things like my background kind of then ends up informing what classes I end up deciding to take. But I think it's, you know, important to mix things up a little bit. So also just taking classes in like English literature, which is heavily like Western Christian or maybe even agnostic is also helpful for kind of changing and shaking that perspective. And I hope to do that further with other history classes I take and kind of to just broaden the scope of things that I know. Yeah, does that answer your question? Yes, it does. Thank you, Dominic. You're welcome. My next set of questions are not specifically for any of you, whichever you would like to answer them. So the first question, I think everyone comes when they have come to a campus and have their undergraduate years, there are things that they've discovered about themselves that were sometimes are sort of surprising actually. So could you talk a little bit about something that you discovered in your time here that surprised you? Academic? I think, I guess, I know I just said that I'd always had like an interest in doing reading and writing but I was surprised that I enjoyed history as much as I did it when I entered college because one of the unique things about approaching history at the kind of undergraduate level versus like the high school, middle school or even like I don't know, elementary school level is that so much of history is kind of subjective. It's kind of like filtered through these specific lenses and that's oftentimes through the background of the professor. And taking high school history, a lot of like classes have certain curricula that you have to follow and so there's not necessarily as much flexibility and you don't necessarily get to hear that kind of personal voice. And so that, I don't know, that was something that surprised me about myself that like I was still interested in history because it ended up being like so personal. And then I guess like on another note, like I guess I also learned you can't continually function on like six hours of sleep. You do need to get sleep. That is important. But yeah, academically and also personally, yeah. Yeah, I agree. Stephanie, would you like to? Yeah, sure. So this kind of has to do with more like the beginning of my undergrad. So in high school, actually coming into undergrad, I was kind of excited that, we all get accepted here and all the freshmen are kind of the same because we start fresh and we come in not really expecting too much. But when I started meeting people, it was kind of a lonely time just because so many people seemed more prepared than I did. And I went to a high school where APs weren't really a thing. We had honors classes, but APs weren't a thing at all. And so people came in here with like so many transfer credits and I was like, oh, I don't, I think I'm behind. But that didn't really have to, that wasn't really my fault. And even though, you might compare yourself to everyone else and we all come from different backgrounds, but we're all still here. So I think that's really important to consider when you're first getting accepted here, like during your undergrad that, I mean, we're all here and we're all getting an education. And that's really important to consider. Yeah. Thank you. Thank you. Thank you. Vera or Ruby? Yeah, sure, I can go. I guess one thing that I've discovered is my capability coming into Cal. Like it's such a big place. And just like Stephanie was saying, like I didn't, we had like one AP class. We didn't have any honors, like coming in with other students who already have like, they're declared already because they had so many transfer credits or there was so many APs out there, like high school offered, like it was a different experience. But as like my time progressed here at Cal and I was in more rigorous courses and I was doing more things than I was in different kinds of spaces, I kind of realized that like, I am capable of being here and I think that's really important for students to understand, especially from like lower income or like marginalized and like people of color to realize that. Thank you. I guess I can. All right. Pess? Okay. So I think for my own experience, something that I didn't expect is that I didn't expect astronomy to involve coding at all. So when I thought about like, oh, I'm going to become an astronomy like astrophysics major, I figured I'll be like looking through a telescope. And I did get to do that. But then I didn't realize that a huge part of astrophysical research is the coding part. So that was something that like threw me off and I was really scared at first because when I started as a freshman in Cal, I did not have any like prior coding experience. I mean, maybe some like really small ones, but nothing like, I did not know Python at all. That's like the coding language that we use. But I was very glad that there are resources that like for example, there's a decal. If you don't know what a decal is essentially like a student run class on like Python. And so I was able to take that my first semester and I didn't expect that I could find coding fun. Like I thought it was something that's really tedious and I didn't think I would be interested in it. But it turns out that if it is motivated by something that I like, which is astronomy and like the pursuit of astronomy, it can be really exciting. And I actually do now enjoy like the process of coding to like solve a question or something like that. And then kind of related but not related is that I used to kind of think that once you're in college, you should quote unquote like only focus on one thing. Like you shouldn't kind of like think about other things, you know, like once you choose your major, like that's all you do. Like you don't really get to think about anything else. But I'm really glad to discover that it's still okay to like have other interests as well. Like Stephanie, you're still in like mariachi and like I'm also doing dance like in my time here as a student. So I think it's really comforting to know that, you know, you don't have to like fully let everything go. Like you can still have like be your own person and like do something that makes you happy like as your time, during your time as a student. Thank you. Yeah. Yeah. So could you describe someone, could you describe the most rewarding experience you've had at Berkeley? So I think for me, it was, it might, this might sound really vague but just the experience of like realizing that I could like go through my classes here at Cal. I feel like it was really intimidating as a freshman to think about like, oh, I'm going to UC Berkeley. Like I don't know if I quote unquote have what it takes to like take all the like really hard like math and physics classes.  I would feel kind of like scared but then at the end of the day, I realized that I was able to push through and that was like really rewarding to think about that. Also like thinking about my background, I don't think I've mentioned this but I am not from the United States. Like I'm from Indonesia and I used to live in like a village when I was really young. So I did not have any resources to like telescopes or like observatories or stuff like that until I was a lot older. And to think about that, that little girl is able to grow to be like who I am today and actually have a bachelor's in astrophysics is crazy to think about. So that is, I think all the like things that accumulate to where I am today, that's all like really, really rewarding and I'm really grateful for. Thank you. Stephanie, Ruby. Oh, okay. Yeah, I can share. Yeah, so I think, sorry, what was the question? It was the most rewarding experience you've had at Berkeley. Yeah, so I think Berkeley is such a unique place. I mean, it's much more different than a lot of universities because Berkeley puts you in so many different situations. I mean, just existing and being here and walking through campus makes you more politically conscious and really has taught me to have more empathy for the world and for the people around and people that live in Berkeley. And yeah, I think that's the biggest thing. Sorry, that's my resolution. Thank you. Yeah. Dominic or Ruby, would you? Okay, I guess one thing for me was I had the honor of being able to be an orientation leader for Golden Bear orientation this past semester. And that was a lot of fun. My group was amazing, my partner was amazing and I think kind of trying to give back to the campus community and that like every time I've kind of like moved from like one space to another. So whether that's from middle school to high school or high school to college, there's always kind of this like, I think awkwardness that comes with settling into a new space. And so I think for me, it was meaningful to try to I think give back to the community and help other people's experiences be hopefully a little bit less like awkward than it was for me and settling into the campus because it's a lot to take in. And so I mean, I hope I did my part but I found it was a really great experience not only just to see community develop among the students but me amongst my peers. So the other orientation leaders I worked with with the students I encountered and with the orientation mentors and staff that I had the pleasure of working with. And so for me, that was really meaningful just because communities are hard to come by and I think finding them wherever you are and taking advantage of wherever you can find this community is super important whether that's like a religious organization or a club like a quidditch or like a fun class or just anything like to kind of seize the day so to speak with those communities. Thank you Dominic. I think a rewarding experience for me is being able to help my younger sisters know how to navigate higher education. I didn't really have that. As I said, my grandma was the first person in our family, the only other person to go to university. So I applied in the pandemic like completely all alone and that was something that was really difficult for me because I didn't know like the right documentations. I didn't know like what to put for a lot of things. So like being able to like take my knowledge that I obtained here and bring it back to my community and to my younger sisters and to other people in my community, it's super important. Thank you. So thank you for being here today. Thank you for entrusting us with your loved ones for their college experience. We're very grateful and we're very honored and we also wanted to thank you for your help with us too. I hope this session has been informative. I'm available to talk if you have any questions about the Berkeley experience. In closing, I wanted to thank Susan and her team for putting this event together and also to thank these extraordinary Berkeley students. For spending the time with us today and just being extraordinary. So thank you very much and go Bears.


The Benio State Police Command has arrested and paraded seven suspected criminals over offenses ranging from kidnapping, armed robbery, and other offenses. The State Police Commissioner, Okoro Alawari, who briefed newsmen at the command headquarters in Makurdi, stated that five of the seven suspects were herdsmen arrested following intelligence of their nefarious activities. C.P. Alawari noted that they will be prosecuted according to law. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


SOMIZA Celebrates 16 Years Anniversary With Vesinova SOMIZA might long go and Vesinova celebrates 16 years of being friends with some exciting photos. Amidst rumors about their relationship, the two acknowledge the 28th of March to be their friendship birthday. While Sam announced its 16 years, Vesinova marked it by saying they are inseparable. 16 years and going strong looks like this, the radio star captioned photos. The two have served amazing goals, and they've been supportive of each other's careers. Over the weekend, Vesinova was with Sam to help push the sale of his cookbook. It's been rumored that the two friends are now in a romantic relationship, since SOMIZA and husband, Mohal have reportedly divorced. Well, the couple is yet to confirm the rumors, but Vesinova and Sam have been serving friendship goals since the year kicked off, as they go on vacation from time to time.


I think the vast majority would say transitions are tough. Change is hard, and for the Tana Hill family, it's no different. After spending seven seasons in Miami with the Dolphins, Ryan Tana Hill joined the Tennessee Titans. That means his wife Lauren and their two kids were also along for the ride. The community here is so good, like amazing. Well, we just moved here from Florida, but we're from Texas. There is an incredible community in Nashville, and I feel like they wrapped their arms around me and just made it really smooth. What started out as a football move turned into a great chance to experience a new city. There's hard stuff that comes with every life, right? But I looked at this as like this is a really fun opportunity to live in Nashville and see what this community is about, and it has been so rich for us. With a new city comes new friends. How are you? I'm good. And through the Titans Women's Association, Lauren has found a group of women who understand the highs and lows of the NFL world. There's nothing like the community of women around you in the league, period. We all just kind of understand each other, and there's parts of our life that only we understand. I think that's important for everybody in the world to have that, you know, to lean on each other. And I'm super thankful for, you know, our community in Florida, our community here. And I feel like it's really exciting that if we hadn't come here, I wouldn't have this, you know, new community. For Lauren, this group of women provides more than friendship. She has found a group of like-minded women who enjoy doing the same thing she does, including giving back. Just being able to get out and do some stuff in the community, and you're doing it with your friends. So it's like it doesn't really get much better than that, and there's not a lot of other organizations in the world that do that. Like you're going out and you're doing good together with people you love. It doesn't get much more rewarding than that. Lauren Tannehill is investing in a new community with a new team, and a new group of women, and enjoying life as it comes. We're just stacking the boxes for these tables, and then we're stacking on this table right here. And then they'll come to your tickets, and then we'll walk. What's your name? I'm Lauren. Ryan and I both believe that God puts you in a specific place for a reason, and He gives you a platform for a reason to be used for outside yourself. And so I think that just using it to reach out and make a positive impact wherever we can is our goal, definitely as a couple. It's really cool that just because you have talent in the athletic arena, people want to be around you. And I feel like that's like a superpower that you only have while you're doing it. So you got to use it and enjoy it and make all the good difference that you can.


Now I want to take it from machine learning to our next topic in the financial technologies We have two excellent speakers here with us They will tell us all about digital currency and fintech Trisha Kemp is a general partner at Oakland At Oakland healthcare and fintech. She has over 15 years of experience in investing in financial technologies And before that she worked 11 years in big companies Michael Casey is the senior advisor of the digital currency initiative here at the media lab He was working as a journalist for over two decades in financial technologies for within others the financial the financial times Also, he has literally written the book about Crypto technologies So I look forward to this panel's and would like to ask the speakers on stage Okay, so we are the the gap between Refreshments after the classic last line. So we're gonna make this a nice brief chat. We've got some rolling that we have to do Thanks very much David for that introduction. Yes, so I'm Michael Casey I'm my world is defined by blockchain. That's a word I think you might have heard once they obviously a bunch of you probably know what this is But often it is a technology that is still really quite nascent And so I do find that we're talking to crowds sometimes that don't really understand what it is But that's gonna be a theme we'll deal with here You know, I personally think that it is a It needs to be thought about as the next wave of profound disruption in the sort of the the architecture if like the infrastructure of our You know our digital economy basically because it just changes the framework and we'll get to that in a little while But but fintech is is kind of like the the overarching theme in many respects and it's quite a it's quite a buzzword of them at the moment it seems to be that everybody is wants in on it, but Trisha give us a sense of what the Investment environment is sure So so I'm a general partner with the firm oak hcft and we've done maybe 25 fintech investments over the last decade let's say Fintech investing and the term fintech is can be you know different definitions of what is fintech But it has exploded over the last three to five years. So 2015 2016 both saw numbers like, you know, nine billion in each of those years a little bit off fourth quarter of 16 in US in Europe and that's about ten times what fintech investment was in 2010 2011 just to understand So it's greatly exploded, you know about 40% of those investments have been in both the lending portion And the payments portion so they sort of been focused in certain areas But you know the amount of money that's gone in is going, you know, it's greatly expanded couple other interesting facts to think about when you think about fintech is the valuations have Have remained frothy and are just starting to fall So we kind of track how many down rounds and you can pull it all up in pitch book and you know general tech companies 2016 I think the number was 19% were down rounds, you know 81% were up rounds, you know fintech It's still well sub 10% so they're all still having higher and higher valuations And you know something like health care IT, you know the portion of our business is 30% down rounds So the valuations have kind of started tracking down. So again, this is just kind of all interesting It's still frothier than tech in general even though it attracts a lot of tech investors But our expectation is it's going to start to fall. I mean one other interesting fact that we look at is And again is just of interest if you're thinking about fintech company is Strategic exit is most likely is the highest percentage of exits So if you actually kind of track it, you know 27% of companies sell for under 50 million dollars of the other 73% the vast majority a percentage like 89% Sell between 200 and 600 and sell in a strategic sale to a big legacy player So this is very different than you know a direct to consumer uber or you know guilt or whatever it might be You know there are strategic sales to legacy infrastructure players, you know in the 200 to 600 million range So that's kind of how that market has looked over. Let's call it the last five years I mean, it's interesting that the froth is there because I think that You know we can call that hype or we can call it You know a sense that this something big is happening, right? So I think that you get a lot a wave of interest in something when when the word is out that we are doing something big I think in terms of fintech, you know, I like to I try not to hype But I do tell you this is the first time really in 500 years that we've that we've really just tried it to tackle and truly disrupt Money, right? So it's it's a it's a big deal So inevitably the enthusiasm and the story and people like me who write books about it We sort of help to breathe the hype, but as a result where where is the value? We're you guys looking to invest within this kind of rather, you know Like big vision picture, but there's actually gonna have to be some up some real life opportunities coming to market the next two Or three years. Where are they well? All right, so we invest in largely B2B companies So we're not doing the next direct-to-consumer robo advisor like a better man or a wealth front or trying to come up with the next mobile bank So we think it's far less risky and actually easier because of what I said earlier about valuations To kind of look at the world in terms of all the financial services ecosystems So we kind of frame it if you think of all the stacks, you know banks payment processors insurance companies mortgage processors asset management Companies capital markets companies etc. They all have ecosystems in and around them And they all have stacks from custom acquisition to onboarding to pricing to risk management to you know count management to profiling to claims Processing they all have kind of legacy infrastructures That that is is you know dramatic change is happening a wise dramatic change happening We all know you know cloud AI that we just all heard about you know data and data being used for AI is One of the most compelling cases is within financial services Industry within the financial services industry, you know mobile all those stacks have to have now mobile and instant Uses so all those new technologies are being brought to bear up and down those stacks So what we like to call this is kind of Enabling the ecosystems right so we think one large change and one large trend that's going to happen in you know 17 through the next decade is you're gonna see everything from You know think of your insurance carrier I mean we all see the Geico and the progressive commercials But besides that you know you get eight and a half by eleven pieces of paper and envelopes in the mail once a year and your priced once a Year and your engagement is once a year. I mean you're not you know, there's no there's no mobile engagement There's no online pricing. There's no real-time pricing and pulling in data on you There's no you know take the photo of the fender-bender and send it in and get your claims processed immediately So you can kind of go stack by stack across all those and there has been money put into payments into banking But you're gonna have solutions up and down those stacks being brought to bear by you know cloud-based solutions data, etc Stack is a word that I've suddenly realized as I left the journalism world and came into the tech world So what I love because I it allows me now to think about In a business and and the and the architecture of where we build Applications in an interesting way. So I think really again a little bias But I think that of the blockchain actually as the ultimate underlying stack, right? It is it is the bottom stack because what we're doing here, you know for those of you who may not know much about it I mean we're grappling with a fundamental problem, right? So there's this core issue that is again 500 years We've had this centralized trust model where by Banks and for that matter other institutions have acted as the intermediators of our trust problems We've relied upon these institutions to represent us when we haven't been able to exchange and appear to appear away For whatever reason we don't trust so money is is is the most important application of that It's we trust each other to transfer Cash with a stranger because we've got this one bearer instrument that we all understand is the conveyance of the value That's being conveyed there, but once we move into you know a remote environment How does the world know that the dollar that I'm spending? I'm not double spending somewhere else the monetary system requires some Understanding of how that that exchange happens and therefore we have to invest the trust if you will in a bank And this goes back for 500 years. So now what blockchain does which is the underlying technology that enabled Bitcoin is to create a Decentralized architecture a distributed system in which that trust can now be deferred to a consensus mechanism That's driven by cryptography and a bunch of other things You know there's some game theory involved, but it's it's proven itself Bitcoin is actually the first use case if you like of this and as much as it's it's had its hiccups as As a digital currency people don't think like well, why do I want something that's you know? I've got my dollars and I think of Bitcoin as a digital currency and that's all it is in many respects It's the first use case and it proved itself because it hasn't been hacked It has not been destroyed the ledger is complete even in this decentralized environment So once you start to recognize that you can see it as this Important foundational layer like Mark Andreessen when he was sort of first Confronted with Bitcoin said something like wow This is the distributed trust architecture that we always wanted when we were first rolling out You know the internet that this was this was the stuff that would truly allow peer-to-peer exchange And if we'd had it maybe we wouldn't have these behemoths like Amazon and others that end up Re-centralizing control right so so there's something quite profoundly Opportunistic about this moment, but it's it's very very early days and so Building that architecture is is I think some people compare it to the internet is something like you know Maybe the early 90s right we still haven't got to we've maybe we're grappling with email But we certainly don't have the worldwide web or we don't have mobile You know smart phones and we certainly don't have uber and everything else yet, right though Those equivalent things don't yet exist, but what's also interesting is because I think of this recognition that the track that the The disruption could be so big and the opportunities are so large We are rushing forward with attempts to build applications on top of it And you know you heard you heard, you know the the presentation from Cromwell Romway earlier Which is a really interesting idea that you use this immutable ledger now to be able to sort of provably transfer title and exchange mortgages in ways that Cut out all of that reconciliation But the question is what architecture you're going to build that on and it's it's there's some interesting ways around that You know there's private blockchains, which aren't quite as disruptive as these dental decentralized models But all of that's coming along and and I think that it's going to be very interesting to see how these interplay between what are ultimately like fintech applications and the the rollout of the of the Of the of the underlying infrastructure is I mean the opportunities are huge At the Media Lab. We're looking at things like as a project. We're doing that Mark's actually involved in Mark Webber the Warehouse receipts and so this idea that you could really bring into our financial system is individuals and entities who have been locked out of it because that centralized trust architecture is difficult to get into there's huge Requirements to do so and the the case of warehouse receipts of those farmers that we're looking at in in Mexico who have will get a receipt for the grains that they submit to a warehouse which should be they should be able to use to then get Alone to be financed but because there's no way to prove that this hasn't been double-spent that the same Receipt hasn't been re pledged three or four times They get locked out of financing banks don't want to finance them So what if we place that into this immutable record that nobody can change this decentralized trust architecture you start to see the Possibilities so so these are the things that we're exploring in this in this realm and I think it's it's very exciting But as I say where we're kind of at the very early TCP IP stage of development But nonetheless in in FinTech itself some of them being built on Centralized systems at this stage at least there's a lot going on what what are you? What are some of the other I would just add to what you're saying so you know blockchain has tremendous Promise as we all know and trans use cases There are all these other Things that may or use cases that may or may not use blockchain that which in within financial services need to be fixed You know I gave an example of insurance earlier. I'll give an example of you know identity authentication You know when you show up to open a bank account We all know you have to show up you have to bring like a utility bill You have to you bring you know two forms of where my address is all this sort of stuff That is just 30-year-old technology that needs to be changed Invoicing CFOs the CFOs you know they receive payments in and seven different ways including you know dog and pony And they send them out eight different ways, and it's all you know electronic AR and AP it's all very You know old antiquated is what you'd say so that there so blockchain has tremendous promise But there are also use cases and solutions and services that are needed kind of up and down these stacks We can talk about you know the digitization of payments which everybody's familiar with and I don't know if you know the numbers but consumer payments And this is gonna be approximately right, but let's say 2005 41% and you guys all look too young so you're not remembering all this but 41% of consumer payments were digital You know 60% 59% were cash and check by 2015 that had completely flipped and it's close to 77% digital, you know 30% cash and check B2B payments are still well over 50% Be a check so US companies send around their 70% of the check volume United States They are sending round checks and something like 55% of the cases so you're gonna have you know the continued digitization of consumer payments and you know many people think 2017 is when we're all gonna start putting our our leather wallet away and start using our phone as our full wallet because we all Want you know one touch purchase, etc. We'll see if that happens, but many people think it's gonna happen but you're gonna have this continuation of The digitization you're not only up and down these stacks But across payments and then across B2B payments, which is a much you know larger volume and consumer payments So so digitization of continued payments, which brings up what I kind of say a third area of Lots of promise, you know in 1718 which is what I'd call reg tech regulatory technology So it's fraud. It's risk. It's identity. It's compliance. It's know your customer I don't know if anybody saw that the there's a Wall Street Journal article recently You know HSBC as you know 8,000 people in a tower in Long Island City, New York, and they're literally doing you know Manually I'm gonna call it manually doing background checks you go open up an account They are googling your name and making sure you know you're not the axe murderer or the terrorist or whatever it is I mean, it's all that whole world of compliance is now somewhere between 15 to 18 percent of banks day-to-day costs and As everything becomes more digitized and as all we all have more and more devices those needs and what your identity is and You know the positive history which is your I'm sorry the negative history Which is your background check and your positive history is that you know? Patricia camp paid this utility bill for this many years at such and such address is all going to start to be organized in a fashion Such that it becomes easier. I'm glad you mentioned identity. That's one of the areas that we are Very interested in as a potential blockchain application one of the reasons why is because you start to Think about was a couple things one is one is That compliance problem could be Better resolved by the transparency of the data that resides in the blockchain right so in a real-time way I can start to look at where my flows are I might anonymize that data because we don't necessarily want that data to be Publicly identifying people but at the same time if I can sort of we talked about machine learning and big data earlier on If there's a sort of a big data analysis of what a blockchain flow system looks like you can start to manage Nodes rather than individuals and that's an interesting way to think about this because what what obviously the India's facing this massive problem right now with this you know this demonetization or they're calling it they're sort of moving away from from cash and It's a massive backlash partly because people depend upon cash as An anonymous form of exchange and there's some actually kind of human right almost to this capacity to exchange And so bitcoins very interesting because it says it is digital cash. It's not it's not a new way to do Digital banking. It's literally digital cash You the anonymity that that obviously gets its in truck gets it in trouble in the one hand is a very important feature as well Because it allows for fluidity of commerce. So once you start to think about that Architecture you then think of how might we apply compliance to that how what is what other things that we could do with this? Information and there's all sorts of interesting applications around, you know KYC and you know your customer concepts that or digital identity concepts that that don't necessarily Identify people so knowing your customer is not knowing their name and their social security number and what the utility bills is But it's knowing that this particular entity Whoever it is that you're dealing with has this provable record of transactions that makes them a good credit or a bad credit Or a high risk or a low risk and you have this sort of more automated interaction between financial institutions and the customers And then you start to see okay So we could have potentially a safe world where these these flows are managed in a way that doesn't You know undermine the financial system and we can identify risk from a kind of a pattern perspective But also protects anonymity and privacy and these things which are becoming increasingly important And hopefully that is the sort of thing that that really reduces that massive compliance risk because by the way That compliance problem is is is creating a global problem, which is this de-risking issue There are there are countries all over the world that have had remittance corridors shut down because banks are terrified about being sued For somehow, you know unwittingly funding a money laundra, right or a worse like a terrorist I mean you I don't know if you saw but Western Union was fine. It must be a month ago now like six hundred million dollars You know it's not an insignificant number because they are AML and a money laundering techniques We're not and the answer is to put up all the more compliance officers Correct and or shut down operations with whoever they see risky Which is either way is going to cause the same thing because that just adds to cost Which means the barriers to entry to the financial system higher So we have a duty for the sake as far as I'm concerned to the global economy to resolve this problem And I do think this is where a lot of the great opportunities. Yeah No, and it's a little bit related to the last panel where you know where is data and then you know good use of data AI? applicable, you know financial services is a great use case I mean just as we all now walk through the security things of the at the airport, right? You know ninety nine point nine percent of us who are being double-checked before they let us hope open our you know Merrill Lynch account right and it's only the point one percent that are in trouble You know a lots of AI and lots of data can be used to make all of this a lot smoother It's a great. It's it's a terrific match of use case But a lot of it also in my mind is it is leading us eventually to a Complete reorganization of what we consider to be a financial institution. What a bank. I mean, what do you? How long before you know, we start to think I love asking these questions What it went when will banks be the same as they are now in ten years twenty years time?  I suppose is the question. Yeah, and they're not wishing that upon the banks. Although I quietly am but no So I don't know the answer and I guess our investment you know time horizons kind of five to ten years And we're trying to do is get them efficient enough right so that they can compete I Mean it is very interesting and Michael mentioned this you know the Indian Demonitization I mean it's as if somebody in the United States said okay every five ten and twenty dollar bill is worth zero December 31st Right and you all have an all merchants stopped accepting it because they're trying to dramatically move that country to being completely digital payments And they announced it. Oh, they now said the night of the US election November 9th or 8th 9th And and they said, you know, whatever value of rupees as of 1231 are no longer of value There's zero and so you had this enormous, you know Massive chaos right so what's my point my point is that you know Does something like that in the United States is this digitization continue, right? There's a concept of a bank how many people walk into a bank, you know If you no longer have to walk into the bank with your utility bill to show who you are then you know What happens, you know, you're gonna see dramatic change or obviously gonna see dramatic change I would counter Michael that it's people's money So I have always said, you know, it sounds great to have, you know Wealthfront and Betterman and Robin Hood and all these sort of things that all sounds great when you have $5,000, right? If you have, you know, whatever number of dollars you're handing it to Vanguard or you're handing it to Chase or you're putting it some place You know, there is this is people's money. So I don't think it's quite the same as get rid of taxis and get into Ubers, right? I think it's I think that it I think there is a difference to this because there's a sense of security There's a heavily regulated environment. I mean, you know, the whole the company has to be heavily regulated and licensed So I don't think it quite shifts like other, you know industries because I'm at risk of losing your fortune or losing your life Big checks on how far this technology can go but at the same time, you know There's a there's a mountain that's slowly starting to move and I think it's gonna be interesting to see how it goes We're gonna call it quits because we've actually both have to try to make it to Transport to get us out of here and further south Sorry, there's no other no questions, but I hope that was interesting and useful to you to you all and thanks very much


Now that we have seen how the network layer takes packets from one end of the network to the other and the transport layer ensures reliable delivery. What we have to see is how do these packets actually find their way to the correct application. For example, you may have multiple applications open on your desktop. You may have a browser open. You may have some other application, maybe your remote logged in into another workspace or you may have a third application which could be your antivirus, which is running and so on and so forth. Now all these packets are coming to the same machine and TCP has to now figure out whom to hand over these packets to. How does it do that? For this, there is the concept of a port number. So every application is associated with what is called a port number. Port number is not a real entity, but a virtual entity. It's an idea wherein we say that HTTP is port number 80. So when any packet comes to the machine for port number 80, TCP knows that it has to hand over that packet to the HTTP application. So out of all the applications, so whenever an application is started on your machine, it's associated with a port number and then when it makes a request, that particular port number is tracked so that when the reply comes, the reply can be handed off to the appropriate application. While this sounds complex, it actually simplifies our life. Imagine if we had to write applications wherein each application would have to take care of this sending of packets and keeping track of the acknowledgments and doing all the work on their own. So because of the layered architecture which we saw and the modularity in the system, each application does not need to do that. What does the application need to do? Let's say you're building a new application, which is going to use TCP. What does your application need to do? Your application simply needs to say that I'm going to use TCP and as soon as your application starts, automatically TCP comes below it to provide all the services that are required for reliable communication. Once a port number is assigned to your application, TCP also knows what to do with replies or packets that are intended for your application. So at the application layer, which is the topmost layer, which is what we are often concerned with, it looks very simple. There is a client, there is a server or in today's world, there is the cloud where there is a cloud of servers. It does not matter. The idea is still the same. So you have somebody who is going to generate a request, which goes to a server and you have somebody who is going to send back a reply. So this request reply scheme is the basic form in which communication happens. If it is reliable communication, the request reply scheme is supported by TCP. If it is datagram communication, which basically means some loss is acceptable. In those cases, the communication is built on UDP. That's basically how the application layer works.