Hi all, I'm Anand Chandra Shekran. I'm the CEO of an artificial intelligence and computer vision company called Math Street then. We're based out of Madras and the Bay Area but I'm not here to talk about the company today. I'm here to talk to you about something that I used to do when I was a postdoc at Stanford and it's and what I'm hoping to cover over here is a very brief introduction into a field called neuromorphic engineering and I'll hopefully convince you that it is one of the things that you should be paying attention to and it's of relevance to the world. So we heard Sailesh bring up some very interesting points in the previous session and some of those had have some very important implications to the way we build the architectures of how the intelligence we're delivering to the world comes about. Deep learning as he pointed out has some inherent flaws in it and I tend to agree with them. You don't want you don't need to be training things with a million images to actually come up with useful and generalized intelligence and neuromorphic engineering isn't directly concerned with that except that it's in its underpinnings it draws inspiration from neuroscience where we know that we don't learn that way. We know that as humans we don't learn by training on a million images. So hopefully this this is a bridge into answering some of his questions the questions that he raised and poses extra relevance because of that. Okay so about the title of the talk so that's that's actually fairly straightforward. Moore's law most of you guys are aware of it expresses this observation that was made a long time ago that and I'm paraphrasing here that compute capability will keep doubling every 18 months or something like that. Interestingly this this term Moore's law was coined by a person named Kaurav Mead who went on to become the father of neuromorphic engineering and his lab in Caltech pretty much spawned every professor out there who does neuromorphic engineering today and so we are fast approaching certain fundamental limits in physics and so Moore's law is getting harder and harder to maintain. It's more of a wish it's something that the world is collectively trying to keep true because we see the benefits of doubling our compute capability but we're going we've seen the staggering costs that it entails that that has to be put into building the next level of technology. Every time we try to decrease the process size you're talking about billions and billions of dollars invested into that and only companies like Intel and IBM can actually do that. Well there are alternatives to that if you think about the goal or the hope behind Moore's law which is about doing more with every passing year then what we need to be doing is looking at alternate architectures thinking in parallel rather than series not worrying about von Neumann architectures and working on neuromorphic architectures instead for example. So I'm going to cover some very basics to make sure we're all speaking the same jargon and and most of you are familiar with some of these since the underlying principles over here are neuroscience so I'm just giving a brief primer. We can very simply think of a neuron in your brain as a fundamental unit of computation it doesn't have to be you guys can argue with me about whether that's philosophically true but for convenience sake let's just consider the neuron to be the fundamental unit of computation that happens in the brain. We have our analogy most people in this audience are familiar with these in machine learning you either call it a neuron you call it a convolutional filter a feature extractor whatever you call it it it shares its basic capabilities. You have a cell body which has these things called dendrites the dendrites essentially serve as a filtering mechanism through which you collect input from other computing elements. Those things are then passed through some kind of non-linearity essentially the same over there which allows the computing element to decide whether to pass on that information or not and that information is then transmitted through the brain using these long cables extremely long wires called axons that take the signal which is essentially digital and pass it along to other compute elements and the process is repeated. This shares the perfect analogy with the kind of basic compute elements that we have in machine learning today. On a separate level what do you do with these connections how are they actually connected there is an analogy over there as well. In your brain this picture over here is an image where a very tiny subsection of a cross section of your brain has been labeled so these green little things are individual neurons and what you see is in a complex and elaborate pattern where their dendrites have a very particular structure. These are the things that filter the information which is computed the non-linearity sitting somewhere close to the cell body and then you get the output out. This is a familiar deep convolutional network kind of diagram over here so you have very similar analogies you can think of the width of your convolutional filter to be the same as your dendritic arbor or axonal arbor. You even have cells in the brain that are fully connected so these things match up pretty well but it's at this point that things start deviating because how this information is processed the architecture that goes to make use of this information deviates very rapidly and existing machine learning techniques are pretty much just scratching the surface. In fact most deep convolutional network for example is a purely feed-forward network and Neuroscience 101 tells us there's 10 times more feedback in the brain. So there are lots of deviations and the analogies break down but we're not here to discuss that or rather I'm not here to discuss that but what I would like to point out is that whatever is true for what we do with Neuroscience will be translatable. We can take what we do over there and apply it over here and so it will be relevant regardless of the philosophical underpinnings. So going into the crux of the matter now when we talk about scale of these systems in your human brain you have something like 100 billion neurons give or take a few billion I'm sure and these are connected with around a hundred trillion connections. Whether all of these are useful or not completely different discussion but that's the order of magnitude over here. Now when you talk about the scale over here in state of the art machine learning that's actually not that easy to answer because it actually depends on the architecture that's being used. So you have extremes. So at one extreme you have somebody like Henry Markerman in Europe who's bringing who's building extremely elaborate models of neurons so you have only 10,000 neurons but you still use supercomputers to compute that because they're extremely elaborately modeled up to the ion channel levels and at the other level you have Google and Baidu and Andrew Eng and everybody competing to build the largest deep learning networks every other year and regardless of which architecture which you're talking about you're off by orders of magnitude and scale. You're still talking about extremely small networks and while Silesh brought up a very important point saying that the size of the network is not important not necessarily important a lot a lot does depend on how big a system you can actually build. The number of things that you can do will obviously increase with the number of the size of the network that you build. Simple analogy from nature would be well we are the higher mammals so-called higher mammals we essentially have a much larger brain much more cortical regions a much larger cortical region that can support much more abstract associations which is what we would associate with higher forms of intelligence. So regardless of what you do and regardless of what architecture you're actually building what we have today is orders of magnitude off from what we have in biology but this doesn't really convey the picture as much as when you think about it in terms of power. This runs every one of your brain runs on 20 watts or give or take right it's 20 watts and to simulate anything even at that lower scale you're talking about mega watt range in power right so this is like a blue gene rack where probably an older blue gene which did a petaflop of computation you could probably use this to build something that's 100th the size of this in terms of computational units. We're still not talking about making them useful just talking about what you can fit in here that would run in relatively the same time scales and you're talking about six orders of magnitude difference in power okay so if you want to ever build an artificial intelligence you want to build a brain you want to put it in a robot chassis you can't cover you can't connect it to a hydroelectric dam to power it you're not going to be essentially you'll have one robot per earth or something like that right so this is completely not scalable and so we need to be learning something from here something about the architecture and something about how to build this in an efficient manner to ever bridge the gap in scale not talking about capability not talking about function just talking about scaling in power okay so well how do we do that and that is the field of neuromorphic engineering okay so starting the 80s carver mead has essentially been pioneering a lot of work where instead of simulating the brain you emulate the brain right so general purpose computing von Neumann architecture or GPUs of today it doesn't matter they can only simulate the brain so even though you have great advances in GPU technology and your clusters are capable of doing a lot more you're still so maybe you've reduced the order of magnitude by one but we were talking about six orders of magnitude difference in power a GPU cluster may save you maybe one one order of magnitude savings over here so you're not going to get there right so neuromorphic engineering on the other hand is about building custom silicon taking the architecture from the brain and morphing it onto silicon neuromorphic engineering right so I'm showing you two examples over here of two such systems that have come out in the last decade the first one neuro grid was built in Stanford in the lab of a guy named Quabana Bohan so I was very lucky to be part of the team that built it minor part but a part of the team that built this so what you're seeing over here is a 16 chip system you can see the size of it we're not talking about kilowatts or megawatts right so this 16 chip system emulates a million neurons with it with a billion connections yes I know it doesn't match up with the billions and trillions we were talking about but this is a stepping stone towards it and each of these chips essentially has transistor logic that allows you to emulate how neurons in the brain function okay so I'm not going to go into great details but just give you a teaser saying we can we can morph the analog properties of neurons in the brain by using sub threshold analog logic so this is the this is the part of the transistor physics and the IV curves that you guys are talking about which people usually throw away as leakage current okay so pico amp range currents which are usually not even well characterized so there's still a bit of sorry there's still a bit of an art form but you can harness those extremely tiny currents which have exponential math and and mimic the the kind of biophysical properties that a neuron have so what this ends up giving you is extremely low power transistor circuits that can emulate the nonlinearities that are captured by neurons in your brain okay so that's part of it and then to communicate this information we use asynchronous digital logic so as opposed to clocks that power all of your chips today all your CPUs there are no clocks on this chip but can you think about what would happen to a chip that that's running circuitry that's that uses pico amp currents and you have this massive chip wide clock that's going up and down you'd essentially have a clock it would completely screw up all this so the the communication infrastructure is using asynchronous digital logic and that's that's kind of the teaser into the kind of technology that goes into building such a chip so this new this neurogrid chip back in 2009 ten ish was at that time I believe the largest neuromorphic system ever built several years later we have this thing called true north which came out of IBM Alamedin so they have a group called the cognitive computing group over there so my fellow postdocs who actually built neurogrid a part of that team and they they built true north which is essentially 16 times the size so here you have each true north chip having a million transits million neurons essentially some differences in architecture some differences in technology used they they in fact don't even use the sub threshold analog the entire chip is digital but it it it's still a path towards building these kind of networks at and at scale and IBM is investing in it Qualcomm is investing in it there are several other big companies that are investing in it because they all see the value of these new architectures in actually pushing the envelope on how much compute can actually be done okay so there are a whole bunch of others such neuromorphic systems out there I obviously in an introductory talk we can't go into all of the details and all the flavors and variants there are but this is an active area of research in most of the rest of the world and we need to dump it up in India as well and that's part of my agenda for this talk is to drive a little bit extra interest into doing some of this ourselves because this is part of the future that's coming okay so for the so I've given you a brief introduction on why we need it power that's only one answer it's power and about what's out there using neurogrid true north the spinnaker there are a bunch of systems that's fine but I wanted to give you a little teaser into how such architectures are built so I'm gonna walk you through an exercise with some building blocks there are multiple ways to build neuromorphic systems but I wanted to walk you through an exercise in building one such system and use that as a template to give you an idea of what in what it entails to build this these kind of systems right and what it what is different about how they compute information so a reminder the structure of a neuron you have your filtering bodies the dendrites which collect information integrate it in the cell body the soma and then transmit that information to other computer computer units right so and this is again pictured out of neurogrid what ends up happening is they do the VLSI design that allows you to build the individual components that mimic all of these parts and put it together in like a metapixel and then a and tile it in huge chips which can then have a huge number of neurons now I'm gonna add one extra element over here this picture over here is our nano wires okay this comes out of HP labs they built something they that they dubbed the memrister the memrister is essentially a device that mimics the synaptic connections that you have when one neuron is connecting with another neuron through these axons what ends up happening at the very tip the connections is a release of chemical neurotransmitters that allows you to transduce that electrical signal now in neuromorphic architecture you could do that with CMOS you could actually just make circuits that do this but those circuits are going to be very expensive and very big if you want to capture capture have a lot of them remember that the number of connections which means the number of synapses is a thousand fold the number of actual compute units so we're talking about a lot of lot of connections and you're trying to fit that into this the CMOS architecture so memristers are potentially the way of the future so what these things allow are these nanowire junctions allow synapse like synaptic properties to to be expressed over here so they are capable of learning meaning weight changes just like you have in your back prop and machine learning and stuff those weight changes can be captured by altering the the electrical signals in the the the source and the target side so these things are capable of learning in a way that's analogous to the brain which and similarly machine learning weights and you can capture a lot of weights by by tiling a raise of these nanowire devices on top of the chip itself one such architecture so brief architecture you have your computational units down here they they are the ones that integrate and have the nonlinearity and all that kind of stuff you have your dendrites which are the filtering units the they are what transduce the signals that are coming in from the inputs and convert that into signals that can then be integrated and on top of that you have a fabric of connectivity all the connections from all the millions of billions of cells that you have have to somehow interact with this dendrite to clear to allow such information transmission so how is this information transmitted so what I explained over here is the integration part but there's also the transmission part right that's missing over here and I'd like to briefly touch upon that for several decades now the entire neuromorphic community has been using something called address event representation it's not unique to neuromorphic engineering but it's a very convenient way of actually transmitting data so this this pixel over here this single neuron essentially has to transmit information that pixel is styled in an array of neurons it's just like any layer of a neural network you have a whole bunch of neurons and you want this layer to communicate with another layer okay so the way you do it if you take the address of that pixel as I mentioned earlier when a neuron fires so essentially when you've you've integrated the information and it it gives you one essentially that the address of the cell that gave you that information is translated into into a packet that you can transmit to other such chips so neuromorphic chips or systems can communicate by just sending information when something happens so the event essentially the firing of a neuron the address of the neuron that actually fired address event representation is transmitted to another chip where it's delivered at pretty much the same well it can be delivered at the same location or with some logic into a larger arbor remember what I said this is the equivalent of your convolutional filter width right this is the region of interest for a cell at that target location so the effect of one neuron firing is fed by its arbor if you can just transmit it somewhere to the vicinity of that arbor and have this substrate that can transmit this information to the rest of the machinery the dendrites the soma so on and forth so this is the basic building block this is the this is the higher level how do chips communicate kind of architecture okay so I hope you got a feel for that now diving into that pixel let's look at what happens into that individual pixel and see how we can actually combine this information to come up with something so inside that pixel you have that cell body the nonlinearity for which you put put a bunch of transistor logic to emulate that you have your filtering transistors essentially they serve as the dendrites which are collecting information from all the input that coming through and then you have the equivalent of the source cells so these the the source cells axonal arbor where that address event representation the individual event actually arrives okay and similarly when the cell fires you just send it out of that soma so this is kind of like a zoom in of that previous slide now now I'm going to bring this together with the membrister logic how would you put membristers remember the nanowire synapses so that you can get high density connectivity along with the neuromorphic architecture like this to actually give you an immensely connected learning system okay so that's what we're going to go over in the next one or two slides okay so you have oops sorry you can have this crossbar this bar essentially represent one nanowire so one way to think about it is from your silicon substrate where you have received your address your address event you pump it up to the top of the chip and send it down a nanowire okay that's oriented in this particular direction similarly you bubble up a connection from the dendritic connections these are the guys that are going to collect that information straight to the top of the chip and have nanowires run through that connected to that okay the crossbar essentially gives you a synaptic connection okay and we're talking about nanometers here that's that's kind of what you're trying to buy here what I end up happening if this is how we'll end up looking so this is what happens with one dendritic crossbar and there's a one dendritic nanowire and one external nanowire meeting up over here you have one connection but what happened to your neighboring pixel and the neighboring pixel in every direction it'll end up looking up something like this okay so you have this crossbar array where your original so your your input cells have their target destination that allows you to bubble that signal up and spread it across a certain distance okay and you have your collecting wires the dendritic wires in blue which form a cross grid to start collecting this information from a whole bunch of axons now you take this at scale you essentially have a grid of these pixels and so the particular architecture in which I've laid out these wires is actually called a liquorif liquorif crossnet look it up if you're interested it's pretty cool so you have all your all your connections offset by one one location so you notice this particular crossbar doesn't touch this one so obviously you're not shorting connections here and so and and and a why and a nanowire from such external connections from way down way below the screen over here would end up terminating prior to hitting the dendritic crossbar so you have a grid of these alternating crossbars that don't intersect except at synaptic connections so you don't short them essentially so what this will end up doing is give you that axonal and dendritic orbit structure so to illustrate that you I'm going to point out at this one blue wire over here this blue wire ends up over here it came out of a dendrite of a particular cell this obviously intersects the axonal wire over here but doesn't intersect it over here which means each neighboring pixel is looking at a slightly shifted version of this of the input that's coming into the into the chip and this is essentially a non-overlapping convolutional filters that's the analogy would draw out of your machine learning kind of studies so the analogy comes back in right when you're trying to build your machine learning systems and you want to put them into architectures that are that can allow them to scale to extremely large size you could reuse this kind of technology to get you that kind of connectivity at that kind of density and and make it extremely useful so I'm going to wind up this exercise I hope I've given you a feel for the the kind of elements that would go into building this architecture there's no way I can make this exhaustive so I'll leave you with okay so just a little bit more so I'll take out all the confusing extra wires and you can see that the input coming in over here is actually hitting these cross the crossbars that you see over here for the cells that were in vision okay and and just to summarize what we're talking about from both sides of the picture right so we have our brain which is extremely small and uses extremely little power we're trying to build brains electronic brains so infinitely this is this slide is out of a DARPA synapse project report so this is the kind of thing that true north and neurogrid was supposed to help build so your your brain we're trying to make the equivalent electronic brains regardless of the exact architecture you need to be able to connect a lot of information from a lot of chips using long-range connections this would be dealt with by things like the address even representation I meant I mentioned earlier in those individual chips you would be building dense grids of neurons so essentially neural network layers would be built into that using extremely little power and having a lot of them per chip for each of those neurons you'd for each of the neurons you'd end up having a lot of crossbar junctions delivered through things like membrister nanowires what does this all give you it gives you the the the beginnings of an architecture that will take your GPU clusters and shrink them to the size of maybe a single board with a few of these chips okay so the the story for all of you guys is when you are building your deep learning algorithms or hopefully building things that go beyond deep learning algorithms you have the hardware architecture that's coming out from labs around the world and companies such as IBM that will allow you to then translate all of that into single chips or single systems that are orders of magnitude several orders of magnitude smaller use much less power and you don't even have to change your computing paradigm right now these boards could sit in the cloud just as your GPU cluster sit on an AWS instance and you could still do the same kind of computation right so with that I'll leave you I'll I'll stop talking and I'll leave it open to questions from you guys yes TrueNorth is built by Dharmendra Moda's cognitive computing group okay and are these chips also capable of allowing you to train or they take the pre-built neural network and yeah unfortunately TrueNorth does not have learning on board they don't use memory still devices so the goal for the Synapse project was to use learning capable devices and put them on the silicon substrate unfortunately it's there are practical difficulties to putting this kind of technology on chips it's not yet a reality at least it's not it not yet a reality at scale but it'll come so you still train your neural networks on GPU clusters and use the pre-built ones right so the I think the philosophical philosophy for Dharmendra's group is to train it on their blue gene computers where they have the exact same architecture mapped out as the TrueNorth system so you can do all the training on their blue genes once the learning is done have the deployment done on the TrueNorth chip that's I think that's the that's the way they're approaching it for the saturation I doubt the TrueNorth chip is going to be on a mobile anytime soon but having said that companies like Qualcomm started a neuromorphic group specifically to try and build chips that will eventually go into cell phones so you can have a cognitive co-processor potentially that would sit on your on your phone and do some of the tasks that that are out of out of scope which you'd need a GPU cluster in the cloud for hi when you're when you're talking of nano level the transistors physics also comes as a limitation right sorry the physics of transistors yes that will be limitation nano level so there will always be limitations but but the kind of density you can achieve with what already exists the kind of nanowires that they're using in HP labs and other places is that you can hit the equivalent of 10 part 10 intersections per centimeter squared so that kind of matches what's in your brain the connectivity we're talking about using a memrister crossbar array with today's technology if they can scale it matches your brain in the brain is actually potassium ions which are traveling sorry in the brain and the neurons it is the potassium ions which travel lots of different things are happening but yes yes they are biophysical processes that use ion channel kinetics yes the limitations of transistor physics in the CMOS fabrication are they not affecting that's how for a high speeds optic optical come semi-current optical switching was used because of the limitations of the physical sure so so there are two ways to approach this one you can push the boundaries of physics by trying to come up with new ways you can try to do optical switching for example but there are there is an alternate path where you can use existing CMOS technology because both true north and neurogrid used existing processes in fact both of them were built in IBM foundries right so existing CMOS can get you a long distance towards meeting a lot of these requirements without having to actually invent anything new so it's a very powerful argument that the architecture can trump the limit limitations of physics which is the basic premise right there are two ways to keep Moore's law alive one you continue hitting the boundaries of physics and push for innovation there which should happen of course but you can augment that by exploring alternate architectures that can actually give you that boost because of the way they compute as opposed to how many computing elements there are such that the compute throughput is much higher hi so I have a couple of questions for you one of them is what kind of neurons are these systems able to see able to you know implement so are they like are they limited to say integrate and fire neurons or do they go up all the all the way up to you know right so that's there's a very straightforward answer right there in my slides neurogrid for example modeled ion channel kinetics in using subterracial analog neurons true not for example it's probably more a much closer to integrate and fire neurons because it was all digital so it doesn't have to be one versus other because and they share the fundamental architecture which is how you put these systems together if you if you believe that there's a lot of power in the exact method of integrating and fire the firing for a neuron a neural element you can definitely try different variants and different people are trying different variants but what is the what is actually important is the richer dynamics that you know other neural models are actually able to accommodate right sure you could you could try a whole bunch of different models for for your basic compute element there is no limitation if the limitation is to start trying somebody has to do it and several people are trying several different neural models and thanks a hi so one thing you told us that you want to motivate people to start doing this in India sure right so what's the cost and what's the scale that we are looking at sure is it really possible to do things here right yeah good question so I was part of an academic lab right so in Stanford neurogrid was built in an academic lab it didn't have to be deep inside IBM with millions of dollars to actually be able to do that so a neurogrid chip still in Stanford right it's funding is not logic is no let me give you actual numbers right so a neurogrid chip we built them in batches of 40 or something I'm trying to remember you the way it's done is academic labs give give their designs and they're all packaged together so that they share a silicon wafer and before they go to into like an IBM foundry and and it's all done right so doing a batch of 40 or something like that probably costs $40,000 that's cheap and with and a neurogrid system if you wanted to build it from scratch and when you're not doing it to scale probably will cost you $40,000 but you're talking about a pocket-sized supercomputer for $40,000 and if done at scale okay the cost obviously plummet once you can buy the entire wafer and you're doing millions and billions of these chips or something like that then the actual unit cost will drop down so this is within the realm of possibility today yeah so a single chip could be the same price as a Pentium which I think brings it within the realm of reality so one analogy that's often drawn is between the brain and AI approach and the airline and bird the flying between the airline how an airplane flies and how a bird flies is often compared with how a brain things and how a computer should think I'd like to know what you think of this comparison if you're sympathetic sympathetic to it and what the community has seen to convince itself that the analogy does not hold in this case sure in that it can guarantee success in the longer run right no I'm I'm I'm not trying to punt the question but I don't think it needs an answer because let's just take something that Shailesh had brought up and I kind of believe in so deep learning today is draw some analogy from a lot of neuroscience definitely but it has its limitations because it's not adhering to some of the other principles which we think are important okay but are they useful yes absolutely otherwise you won't have this deep learning case in the world today so at every point in time there there are going to be abstractions which are going to throw out a lot of the the way it's actually done to build something intermediate which will be very useful so I see nothing wrong in that the only problem is we shouldn't go into the soul you know like AI and neural networks dying every 10 years or 20 years because the public gets disillusioned it doesn't have to happen that way as long as the as long as people understand and keep moving on it so hopefully people like myself and other people will continue evolving and building new architectures going beyond deep learning while the world exploits deep learning including ourselves we will exploit deep learning to build useful products I don't see a reason for the world to stick to one versus other you'll always derive some benefit at every level of abstraction any other questions okay will superconductors make any difference to this I know that superconductors feel is pretty nascent it's still yet to evolve a lot but will it make a big difference to this sure I see no reason why any advance is not going to help over here I mean when cover me didn't always starting with these things nano wire members test did not exist so any any new technology once it reaches a certain state of maturity the world will find a way to morph it into this kind of use case so there is no straight answer until it actually is practical to actually morph it right and the second question was we tend to kind of have different abstractions like let's him talking of a dog dog depending on the context if I'm talking of a dog and cat I would treat every drug the same and when I'm think talking of in relationship with respect to something else but when I'm talking of breeds there's a hierarchy and no longer looking at only at a dog I'm thinking of labradars and poodles all within the dogs so and then there is a different abstraction of males and females so which applies to different species and breeds and then it applies also to a labradar also to a poodle so there's some sort of cross connection between these hierarchies in the way we store the knowledge so would it be possible to emulate those hierarchies here sure so let me paraphrase your question if you don't mind so it seems to me that you're asking me whether what we do in our brain which is having a whole bunch of cross associations can be ported to something like this the answer definitely is yes the reason for building some things like this is that you could build larger systems and and be able to model such phenomenon so yeah the answer is yes the advantage of these guys is that they're not sticking to like a deep learning architecture necessarily where you can't necessarily combine information across different networks or provide feedback in a way that works very well yet these on day one are being built with applications like that in mind so without going into further detail I'd recommend that you go maybe check out what coven as lab is now doing with neurogrid in terms of applications such as those things right thank you I see there was a there was an experiment it was reported in Hindu I think this mentioned that they were using they used some portion of rats brain cells and they connected visual inputs and they connected the outputs to a two robotic motors and they were able to make the robot move around in the room sure I think that they called it wet wear sure because it contains the biological processing units and the the electronic hardware I yes to mention it's suggesting that we use wet wear instead of hardware suggesting anything just wanted to mention this and and it's definitely possible though the problem is maintaining organic systems you know the reliability and keeping them alive essentially it's tricky yeah sure no but still if you're talking about cultivating cells in having cells in culture and connecting them to networks one day you know a virus infects your colony your you've lost your chip your wet wire right these are by very definition you can you can duplicate this clone these make millions and billions of these chips without ever having to worry about things like that about losing an entire system yeah the other benefit so there is one additional thing wet wear by very definition you're talking about it having synapses and connections which you can't necessarily measure or capture right the same the reason can you read your brain if the answer is not yet definitely and it's not an easy task to do because how are you going to actually map out those 10 nanometer or extremely tiny synapses trillions of them and actually be able to copy and paste them somewhere and clone it well these these can be done right because everything over here can be read not entirely sure of a membrister array can be read but potentially sorry yeah


Craig Clapperton, I'm the commanding officer of USS Mount Whitney. So the ceremony today is the 69th anniversary of the first Allied deaths in the liberation of this part of southern France. It was actually members of the French resistance that were trying to set up a beachhead to enable the American forces to land here. And both French resistance and American forces died 69 years ago in this day. It was heavily defended in mind area and there was a great loss of life. So it's very important that we remember this day today. It's wonderful to be here. It's a great opportunity for me, for my crew, and for the civilian mariners and sailors alike on Mount Whitney to get out here, be a part of this history, be a part of this remembrance, and share in the great hospitality and the friendship with the people here in Tales of Marin.


ని నాంలి నికింరి. సిఎ అలి ని ాకూసి సిందినీ.


Hello, everyone. Welcome to COOP Conference and the Cloud Native Conference China 2021. Thank you for joining this session. I'm Lu Yue from Shanghai. Ludwig and myself from HPIT will talk about end price and harbor, a good partnership in helping each other. This is our agenda. I will start with an introduction about Ludwig and myself and what we are doing in HP. And then we will describe the differences on the end price strategy and open source strategy. Why it's important that we should build a partnership between them. I will also briefly share the existing adoption and the status of harbor service in HP. And then I will hand over to Ludwig to share more about how we contribute back to the open source community and what we have done to turn harbor into an end price great docker-grade strategy. Both Ludwig and myself are from HPIT. I'm an IT developer manager that has automation and cloud native practice. Ludwig, do you want to introduce yourself? Hi. Good morning. Good evening, everyone. My name is Ludwig Lim. I'm an HPIT cloud architect. I mostly focus on cloud cost optimization and moving applications to containers. Back to you, Yeh. Thank you, Ludwig. In HP, IT delivers engineering and software development solutions for R&D communities to create great products, short the product life cycle, lower the cost, and improve the quality. We're also catching up in budget chant, modernize IT practice with latest technology, cloud native tools, and frameworks. IT supports over 17,000 developers from R&D, labs, CTO, and IT organization across the world. Ludwig and myself start to attend the CUBE conference from 2018. And we know Steve and build a very good relationship with Hub team. There was a gap on the Docker-registered service in HP. So we decided to adopt Harbor into HPIT. It's a very interesting journey. And we feel some essential gaps because of the key strategic differences between end price and open source. For example, end price requires the service to be stable. In HP, the essay of our Harbor service is 99.5% availability. And the customers are very sensitive on the performance. Operating teams care about the maintenance effort, such as patching and upgrading. Every end-price service also has some metrics like time-to-response, time-to-resolve on issues. So how to gather right support on right time is also critical. For HP Harbor service, time-to-response is one working hour. And time-to-resolve is four working hours on critical issues. So you can imagine the changes. Each end-price environment substituted is always an important consideration, be it vulnerabilities, authentication, authorization, encryption on rest. And meanwhile, and if you look at open-source work, it usually demo-cheats the flexibility, innovation, and latest technology. And the community is also a very good place for learning and collaboration. But there are no guarantees as there is no support because there are no dedicated resources. Each open-source software has its own roadmap for new features, but they're not always put the end-price requirements with high priority. So we definitely need some good approach to build the synergy between end-price and open-source. And we are going to share you our learning from the journey deploying the harbor service into HP from 2020. Today, there are 2,600 repos with 11 TB images on the 300 projects. Every day, there are about 30,000 images approved and deployed to internal and external platforms. OK, now let me hand over to Ludwig and share more about how we contribute back to the open-source community and what we have done to turn Harbor into an end-price with great Dr. Rajesh. Ludwig, your turn. OK, thank you very much, Ye. OK, so let's start with how enterprise can help in Harbor development. For this, we want to share our contributions to the Harbor development. Our first contribution to the Harbor development is disabling anonymous access. Our open-source and enterprise have produced great quality software, although the focus of both is very different. For open-source projects, the focus is more on collaboration and openness. Well, for enterprise, they have their own intellectual properties to protect. We contribute to the Harbor project by submitting a pull request that, optionally, this allows anonymous access to Harbor. This way, Harbor can work for both enterprise and as well as for open-source. Our second contribution to the Harbor project is OIDC testing. HP is one of the first companies that use OIDC integration feature of Harbor. Being an enterprise, we have access to authentication tools that have complex mechanisms such as OIDC. Also, HP has thousands of employees that can help in testing this feature. We help by submitting feedbacks and bug reports to the Harbor team on the OIDC integration feature. This is something that is very difficult to do if we just rely on the open-source community. Next, our next topic for the talk is turning Harbor into an enterprise-grade internal Docker registry. For this topic, we have three subtopics. First, the technical aspects. Second, the end user or customer aspects. And third, the security or compliance aspect. So let's go to the technical aspects. First, we need to understand the Harbor components. During our initial testing with Harbor, one of the challenges that we met is understanding Harbor components because it seems that every time we install, something different came up. For example, when we change the exposed type to load balancer, the Harbor installer will create an engine X controller. But if we set the exposed type to ingress, the engine X controller will not be created by the Harbor installer. So you need to be very careful. You need to understand how the configuration works because in the end, this configuration will affect how your Harbor architecture will look like at the end. Tip number two, leverage auto-scaling, okay? So for this tip, I'm going to show you a demo how HP uses HPA for Harbor. One of the challenges we have in maintaining Harbor is our customers are located all around the world. With a small team, we cannot have 24 by seven support. So how do we manage heavy workloads when they come suddenly? For example, those heavy workloads that come on a Saturday when we're both out of office. One of the solutions for this problem is to leverage Kubernetes horizontal pod auto-scaling or HPA. So let's start by showing you the current setup we have for this demo environment, okay? I'm going to show you the current pod replica configuration and as well as the HPA configuration. So let's do a K get deployment for the Harbor, okay? You can see that for Harbor core, we have two replicas and for registry, we have two replicas. And if we go get the HPA configuration, okay, let's do that again. We start with minimum of two pods for the engine X and for the registry, but it can scale to maximum of five pods for both the engine X and for the registry, it can scale to 15 pods. Now I'm going to run few test stress test scripts. These scripts will push Docker images and do multiple Docker logins, okay? So we're going to do multiple push and multiple logins. Let me run the script. Okay, there you go. This one and finally this one, okay? So since HPA will take some time to kick in, let us proceed to the next slides, okay? Tip number three, don't ignore pod restarts. You might be curious, Kubernetes has a self-healing function, so why worry about intermittent pod restarts? Well, because pod restarts are just symptoms of a bigger problem. So if you don't fix this problem at the early stage of your deployment, when your deployment gets bigger, more complex and there are more users, chances are you will have problems every day. So let me share to you one of the experience that we have, like for, we have like rare occasion where in our Harbor pods crash. And although it automatically restarts, we still want to fix this problem, okay? After long investigation, we found out that the cause of this was because it ran out of memory and after the pod run out of memory, it crash. Although Kubernetes was able to self-heal, users still notice it. Our next tip, don't treat containers as block boxes. Although Kubernetes provides layers of abstraction for your containers. To make your Harbor installation more robust, you need to understand the configuration of these containers. Different cloud providers, node architectures, instance types and container runtimes affect how your containers are created. Low limits will cause performance issues and in the worst case cause a pod to fail. Look at the screen that I'm sharing. You can see that both of these are for Harbor instances, okay, on different Kubernetes cluster, but on the same cloud provider. You can see that for this one, it has an open file of 1024. This issue actually caused our Docker registry pod to fail when people are doing multiple push because the number of files that it can open are so small. So we fix it by increasing the number of open files, okay? Okay, next tip, pay attention to external components. Harbor uses the following external components. Number one, Postgres SQL. It uses the Postgres DB to store like user credentials, project information like the project name and the repositories that you have. And if you have multiple users trying to do multiple Docker logins, the number of connections will increase. So you need to check the number of connections, especially if you have many users. If your number of connections hit the ceiling and your Docker will say that Docker login failed, okay? Also, if you have engine X as part of your Harbor installation, you need to check the worker connection settings for engine X. The default worker connection setting of engine X is very low and it's not suitable for production workload. You can check this by editing the config map of the engine X pod. Okay, now let's look at the HPA results. First, we go and get the HPA again. You can see now that for the registry, it has five replicas, okay? From the initial to replicas, we now have five replicas. And for the engine X, it has now three replicas. So let's do a cube CTL get pods to see if those pods are indeed created. So K get pods. You can see now we have five registry pods running. Let's go and check at the engine X pods. You can see that there are three pods and you can see that the pod here, it was just created about four minutes ago. So this is the proof that autoscaling indeed work, okay? For Harbor. Okay, now let's go to the next section, customer and end user aspect, okay? Our next tip for you is you need to know your customers, okay? What kind of images does your customer produce? Different type of applications will produce different kind of darker images. For our, in our experience, we have a customer who reported to us like he's having issues doing darker push to his registry. And when we investigate, we found out that his Docker image just are about two gigabytes. It turned out that his application is a machine learning application. And it has a lot of like machine learning data in this image. That's why it took a long time for the image to push, okay? And you need to check like, how often do they pull their image, okay? The number of pools can help you in estimating the workload that your customers will be doing in production. When you know their workload, you can do in advance like the tuning for the database connections, for the networking, okay? For global customers, are they pulling their images at the same time from a different location? So you need to check this one because sometimes the problem might be because of Harbor's performance. Other times it might be because of networking. Let's say you're pulling your Docker image from an on-premise network. So if you know where your customers are pulling their images, you can help them in troubleshooting, okay? And the last tip is involve your customers in testing. Different customers use Harbor differently. And in the end, your customers are the one using Harbor, not the IT team. So you let them be involved in the Harbor testing even at the early stage. Next section, security aspect, okay? We'll look at the security aspects in security aspects of turning Harbor into an enterprise grade internal Docker registry. First, when you deploy Harbor, you need to work with your cybersecurity team. For HP, before we move Harbor to production, we had lots of consultations. We have lots of reviews, architectural reviews, cybersecurity reviews with the cybersecurity team. They check our architecture. They make sure that the HP's intellectual property will be protected. So for enterprise such as HP, the IT team has responsibility of making sure that the HP's intellectual properties are properly protected. So this is one of the reasons why we submitted a pull request to the Harbor team to disable anonymous access. Number two, integration with official or security tools. So usually big companies like HP have partnership with cybersecurity companies that sells product that protects their assets. Let's say image scans, antivirus and stuff like that. So in the beginning of the Harbor project, when we tried to adopt Harbor, we worked with the Harbor team, we worked with the anchor team in the anchor plugin for Harbor. We not only test the anchor plugin, but we actually submit pull requests and code to the anchor plugin for Harbor. You can see our contribution at the GitHub repo below. Next, intranet or internet facing. So for intranet facing or internet facing cybersecurity, your cybersecurity might have different policies for this. You need to check with them before launching the Harbor application. Okay, that ends our presentation for today. In summary, we show you how we apply open source software like Harbor to suit HP's need. We also shared our experience and tips on making Harbor enterprise ready. Yeh and I want to thank you for your, for attending our presentation. Thank you everyone. We hope our Sherry can help you in your open source journey. Cool.


Good morning, everyone, and welcome. Thank you for being so obedient right off the start and coming to your seats. My name is John Palfrey, and on behalf of the Harvard Law School, just delighted to welcome you here to the first public meeting of the Internet Safety Technical Task Force. We have been convened over the course of the, much of the course of the year, by 49 state attorneys general through a joint statement that they entered into with Myspace and others. We have been joined over the course of the year by about 30 wonderful companies, NGOs, others who care about the safety of children to work on this topic. And we're delighted to have a point in the process where we're able to welcome the public into our work and to show off some of the technologies that we have been assessing. So the run of show for today is that we have solicited over the course of the summer for anybody who has a technical solution to help kids stay safe online. We received about 41 submissions over the course of the formal process. And of those, we have selected about 15 who will be presenting to you today. That's a lot of companies and a lot of technologies to take in. I beg of you, those who are presenting, to keep to the few minutes that we've asked you to present in so that there will be time for questions with each presentation. And I will be a harsh taskmaster, I'm sorry to say, as we go through the course of the day to get us out of here by five. One to say also concurrently, we've invited anyone who submitted those who are presenting and those who are not to set up a poster session out to the side, as well as a chance to show off technologies. And we hope many of you concurrently or otherwise will take advantage of the generosity of these presenters in setting up a poster session off to the left. So I wanted, before we began with our technical presentations over the course of the day, to welcome two extraordinary public servants who are at the heart of this effort. We have the great pleasure of welcoming both our Attorney General, Martha Coakley, and the Attorney General of Connecticut, Richard Blumenthal, who have joined us here today out of extraordinarily busy schedules during campaign season no less. And to help give us some of the context for why they took this unusual approach of not using a regulatory process but rather a collaborative process with some of the key social networks in the space and key technologists and even some crazy academics like us to see what we could do by working together to see if we can keep kids safer online. So without anything further from me, I'd like to welcome an extraordinary public servant, our Attorney General, Martha Coakley, to give us some opening remarks. Thank you very much, John, one of my favorite crazy academics, as a matter of fact. I am going first only because I have the home court advantage. But I am delighted to be here with my colleague, Dick Blumenthal, and he's going to have a chance to speak to you also. When I became an Attorney General almost two years ago, one of the things I quickly realized is that among my colleagues, there were two tremendous leaders in this field that I cared so much about because I had so much to do with it as a district attorney in Massachusetts, including Cambridge, and a quarter of the state. I had spent a lot of time investigating and prosecuting cases where children were victims. And it became clear to me through the course of the 90s that we had a whole new frontier to worry about, not just the guy in the raincoat in the schoolyard who could go after children or the father or boyfriend who had physical custody of a child. But now we had a frontier where there was absolutely almost unlimited ability for predators to reach out to children. And frankly, very little risk of being caught, being identified, and it created enormous challenges for us on the front lines for all of you who are here today. And when I became Attorney General again two years ago, there was already a tremendous amount of work being done by A.G. Blumenthal's office, by other A.G.'s including A.G. Cooper, and I know someone from his office is here today, for some of the staff at the A.G.'s office in Massachusetts. And I think we all realized what a tremendously challenging problem this was. And I will say that the fact that we are here today talking about this in this forum, as John referenced, I think is an incredible salute to the common sense and the goodwill and the collaboration of everybody to get the right result. Would that we had one-tenth or one-hundredth of this relating to what's happening on Wall Street, for instance, we'd be way ahead of the game. But I think we saw the wisdom of trying to address this as a problem and figure out what was the best way to get a good result for the people we serve. And we appreciate that you all have contributed to that, particularly here today, but in an ongoing effort to get cooperation and good results for what we're looking for. When 50 Attorneys General, 4950, formed this coalition, we worked particularly around the social networking sites and we quickly recognized that there were going to be technical issues that would require solutions, particularly around age verification and implementing design and functionality safeguards to protect minors on sites. We had seen, for instance, from our own work in this field that regardless of what a particular site might say or not say, children still had access to, and more importantly, predators still had access to an unlimited range of contacts. We saw the results of those contacts in terms of solicitations, in terms of unwanted material, including pornography, and in fact, we saw contacts that occurred offsite as a result of online sites that resulted in child abuse and sexual assaults and physical assaults. The key principles around what the joint statement involved talked about developing the online safety tools, the design and functionality changes, which we are quite interested in, educational and other tools for parents, educators, and children in law enforcement cooperation. And the leadership in the industry, acknowledging that these were important goals, I think was key, and it was a crucial turning point in our negotiations to make sure that we were here today working on the solutions we are instead of being involved in legal or paralegal squabbles over where we should go. And so the fact that this piece today and the public offering of it is occurring here in Harvard and in my old county seat in Massachusetts is a matter of great pride to me, and I appreciate that we are able to continue to work with you to get some results, not just to congratulate ourselves that we've been able to reach the joint statement, but by undertaking a comprehensive assessment of all the technologies available, we expect that the internet's safety technical task force will offer recommendations on the technologies that can be used to develop age and identity verification tools for social networking sites, and indeed for the internet as a whole. I look forward to receiving your recommendations in the next few months and how we have as a task force proceeded, and most importantly, I welcome you here to Cambridge today, and I thank you for your work. John. General Coakley, thank you, and thank you to your staff. Scott Schaefer and others have been extraordinary to work with, and we're very grateful for the leadership you've shown in your office has as well. Now, to introduce Attorney General Richard Blumenthal, as General Coakley mentioned, this is one of the nation's leaders on this topic, someone who has, during his tenure as Attorney General in Connecticut, really been one of the outspoken leaders to protect kids online, and he's gonna give us a slightly broader statement about his goals for this task force and a charge for today. General Blumenthal. Thank you very much. First, let me thank John Palfrey and the Berkman Center and all of the staff there for the great work that they have been doing on this very difficult and challenging and crucially important issue, and I wanna thank General Coakley for her leadership from the very first day that she was in office. She was a national leader on this issue of internet safety and has devoted a great deal of time and attention and great creativity to it, and I want to thank members of our staff who will be here throughout the course of the day, and hopefully in contact with you as well, Anthony Genata of my staff and Assistant Attorney General in Connecticut, and Jay Chowdery of North Carolina, Roy Cooper's Chief Assistant on this effort, and of course Scott Schaefer, all of them have done great work. Roy Cooper and I have co-chaired this task force of Attorney General and he could not be here today, but he will be following I'm sure very closely the results of these deliberations and the report that will be produced. As I was coming here this morning, walking through the square, I had one of those flashbacks, I'm sure you've had them an exam or a paper that is due tomorrow and you haven't started it yet. I was an undergraduate here many, many years ago, and I had one of those flashes and then could calmly reassure myself that I didn't have to produce anything at the end of the day, but John Palfrey does at least at the end of the year. And as much as we may be sharing ideas and being creative today, we want a product from this task force. It's not an academic exercise, forgive the term. It is very goal oriented. The Attorney's General established this task force because we want better means to protect children. It's really about as simple as that to increase the level of internet safety. Many of you who have talked with us know the passion that we bring to this issue and it is a passion that has united Attorney's General as I've never seen before in 18 years in my office. Other than the tobacco effort, which Connecticut also helped lead, there has been no other multi-state task force that has attracted so much support and interest as this one and I want to really very sincerely thank Myspace, which was the first to reach a historic agreement with us as Attorney's General that led to this task force as well as Facebook, which became the second to reach an agreement with us and the Myspace initiative in putting together this task force I think has been critically important and so I thank them as well as the Berkman Center. The task force I understand is now becoming rather than the Myspace task force, the Berkman Center task force, which I think is a good thing because we have tremendous trust and reliance on the Berkman Center and on John and his staff.  Part of our passion, quite honestly, is the result of that grassroots feeling on the part of parents and citizens and law enforcement officials. This issue is very much a law enforcement challenge and another part of the passion that we bring to this issue comes from our local and state police who are deeply concerned about the lack of age and identity authentication and verification because it leads to opportunities for child predators. Myspace has taken the initiative in eliminating about 50,000 child predators who have established profiles in their own names. Child predators who are registered in the state, go online using their own names, having been convicted of a felony involving criminal sexual assault. And of course, as we've said again and again, for all of those 50,000, there are a lot more that don't use their real names. The anonymity of the internet is one of the great threats that law enforcement sees to apprehending and also preventing criminal assaults. So we have a very direct interest in our law enforcement role, as well as parents. I'm the father of four children, ages 22, 19, 16 and 14. Roy Cooper has young children, many others do. We see this problem very personally and closely as citizens and parents as well as law enforcement officials. I am tremendously excited about these 41 submissions and about the 15 presentations that we're gonna see today. But even more, I'm excited about the dynamic effort that's involved here. John was telling me that some of the submitters contacted him after a few months, after their initial submission, and said, could we update what we gave you? This whole area, the new technologies, I think are obviously, you know better than I, a work in progress. And what's exciting is the focus and the work being done. We don't expect to have a magic or final product, a final solution. On December 31, but this task force, I think has stimulated a lot of progress. And I think we'll have a very, very positive result. And this process that has brought us to a point on the trajectory, I think we'll go on and we hope to continue to support and encourage that progress. Ultimately, of course, the goal is not only to enable better law enforcement, but also to empower parents. We've said this again and again and again and again. When we're asked, how can you possibly replace parents as a source of responsibility? No, the objective is not to replace parents, not to in any way substitute for them or absolve them of responsibilities, in fact, to enable them to be more responsible and to empower them. And of course, to bring a greater measure of accountability to the social networking sites and to the issue of internet safety. Accountability is the antidote to the dangers of anonymity. And whether it's holding schools accountable, holding parents accountable, all who should be held accountable, we hope to do. So I think that this task force's work is really gonna be very profoundly significant to the State Attorney's General, to our constituents, to the parents of our country. And I want to thank all of you for participating, for your contributions here today. And for the product, for participating in the product that eventually we will have that is so important. Thank you very much. Thank you, General Blumenthal. We have about 10 minutes until our presentations begin. And General Blumenthal and Coakley have kindly agreed to take a few questions if anyone would like to ask them. Just a reminder, this is totally recorded completely on the record streaming as we speak. So just as a fair warning. But they'd be delighted to take a few questions and maybe at about 10.28 or so if the Aristotle team might start making their way up, we'll get started at 10.30. So maybe take any questions for... I'm only here in case General Blumenthal needs a lawyer. Does this question... I see, oh good, excellent. He's got a fabulous one. A local lawyer, in fact. Someone who knows the Cambridge scene well. I can't believe, after having led this task force for eight months, that there would be nobody standing up to have anything to say. We have put everyone to sleep. No, I don't believe it. I don't believe it. Yes, please. Do you mind coming to your mic or can I give you one? And you have a right to remain silent. Yeah. Though being accountable and saying who you are would be great. I'm James Carmichael from Southern California. I represent ChatSafe. Something you said earlier, are you going to have representatives that we can speak to later during the course of the session and into the afternoon? Why don't Tony Jay, Scott, if you could just stand up. So people know who you are. Thank you. Where your hostage is. Absolutely. Tony Janata of Connecticut, Jay Choudhury of North Carolina and Scott Schaefer of Massachusetts, extraordinary assistant. I'm Kevin Trilley, Cert Ideas of the Company. I just had a question about, you're bringing the US perspective obviously to this, but yet we all know the internet's a very global forum. And just, how does that fit into your thinking with other countries in the world that are looking at this or just as the solutions that are proposed, how they can work within the US, without the US, and how's that criteria going to affect kind of how you look at this? Well, I'll attempt an answer and then if General Coakley wants to give a better answer, probably she will. You know, obviously there are global ramifications and we are not an island. It's a little bit like, forgive me for oversimplifying the financial crisis. You know, we can't deal with the whole world's recession or the whole world's credit sees up. We have to deal with what's happening in this country even though we are inextricably linked in the financial world as we are in the internet world with other countries, but we can set a model. And that's partly what we're doing in the financial area. We're setting a model of hopefully a new system that works better than the old as well as a temporary bailout. And we are very focused on what can be done with new technologies to authenticate age and identity or impose other safety measures. That will protect children and stop inappropriate content in a way that can set a model for the whole world. And you know, again, at the risk of seeming much too simplistic, Roy Cooper and I have both said, and I'm sure infuriated many of you when we said it, that if we can put a man on the moon, we can make the internet safer. And I think this task force in a way is living proof of it because you've made such progress, those 41 submissions and there are others that are not in the book, partly because folks didn't wanna make them public right now. I think a test to the progress that we can make moving on. So we need to set a model for the whole world in my view, even though we can't impose on the rest of the world these solutions, we can set standards and we can raise the bar. I agree with Duke, I think that's the right answer and I had a little bit of time to think while he was answering, but it seems to me that we've done that and we do that in other areas. The most obvious parallel is what we're trying to do to protect online identity theft. We know that there's a lot of rings in Eastern Europe, for instance, that trade in information both from here and around the world. Well, we're maybe not gonna be able to go after them, but we can make those transactions safer and we can give tools to consumers to make sure that their own information is protected. We should do a better job of keeping lead paint out of China, for instance, and so if we up, we're not gonna stop what happens in China around those toys, but we do have things we can do around the borders and what we do to protect what comes into the country, both literally and online and so the technology is there to do it and I think you keep in mind what we're trying to accomplish is a global in communication network, but we see it as a challenge, certainly, but not an overwhelming one and I think that the dick is exactly right, it's the technology that we're talking about now that will let us focus on keeping people who use it safe as opposed to worrying about what else is out there. If I might just respond on behalf also of the Berkman Center Task Force and how we've approached this in consultation with the attorneys general, we've adopted sort of a concentric circles approach here, which is, as General Blumenthal mentioned, he's most focused on identity authentication tools as the technology solution that we need to answer to, but we also came back and said, look, there's a lot of other stuff going on in the technology space, maybe more broadly even, identity on the internet or some of the other ideas we've heard about. So we've drawn a broader circle around that. Likewise, we've heard that social networks are their area of primary interest and that the joint statement makes that plane. On the other hand, we've said some of the research shows that in instant messaging or chat is where a lot of issues are happening. So we've drawn a broader circle around social networks to say the internet. The same is true with national and international. We came back and said, plainly, these are state attorneys general covering the United States, but we have some international participants. Bebo, for instance, is on the task force, and we've also recognized that this is a problem you can't just border around the US. So again, the broader concentric circle has international in it, so your concern has been noted and concerned. So I'm gonna do two more questions. Wendy Seltzer of the Berkman Center and then John Morris, and then we'll let you off the hook if that's okay. Thank you, Wendy. Thanks. So I hear a lot about keeping children safe while using social networking tools and one of the ways that it seems that these tools help children to make themselves safer is by giving them outlets for information where they can find things that their families or their local communities might disapprove of to learn information from their peers and from other trusted sources for which even children at times need anonymity and need privacy. How do we strike that balance, particularly as we hear some government pressure through cooperation to close down on anonymous channels? How do we keep enough room for anonymity that children and adults can explore? Well, you know, anonymity has its place even in a world of full disclosure and we live pretty much in a world of full disclosure or a much greater disclosure than I can ever remember and then I've ever read about. Ultimately, everything becomes public in the world we inhabit seemingly anyway these days, but there is certainly a place for anonymity when it comes to personal advice. You know, even in the law, there are privileges against testifying. There are confidentiality protections for documents and even when it comes to the government, there are protections for certain kinds of national security secrets. On the internet, I think there is a role for anonymity but it ought to be constrained or at least limited to accommodate the safety concerns that have been raised. Right now, it seems very much like Times Square where everyone moving around is anonymous and that's the analogy that is frequently drawn and I think what we want is perhaps some greater accountability where it can play a role in effective protection of children and also not only is it anonymity but it's actually outright falsehood. If somebody says he is 17 and he's actually 37, there ought to be a way to stop it or stop that prevarication, misrepresentation before it has damages. If a child says she is 11, if a child says she is 17 or 18 and she's actually 11, there ought to be some way to check that. Same with obviously identity, false identity. So I don't think that accountability necessarily is always adverse to anonymity where it's appropriate but the role of anonymity is a limited one. I think that's a fair question to ask and I think that that discussion is gonna continue as we have this new marketplace. I hear in the question, the concern about First Amendment and freedom of expression that's always been the discussion. That's always how it gets framed as it should be. And so I think and I know the attorneys general agree with me is that we're not trying to tamp down on that. People may disagree where those lines get drawn and that's why we continue to have the conversation but that discussion about the marketplace of ideas in this particular marketplace will continue. And so we have a job to do. We are advocates for safety. We know what the limits may be or should be and it's just a much bigger marketplace and we're gonna continue to have that discussion I think. Yeah. John Morris. Another question. Just one, John asked me very short so just I'll do it one sentence. Do you anticipate taking recommendations from this task force and attempting to implement them through mandatory legislation at the state level? Legislation regulation of social networks at the state level. That's a very good question and let me try to be brief. First of all, the idea of regulation as General Coakley just said, I think is one that is kind of a last resort to us governmental intervention or regulation or dictates from the government is really not a goal here. In fact, the whole purpose of the task force and of our agreements first with MySpace and then with Facebook and perhaps with others eventually is to achieve voluntary solutions. A lot of what we're doing here is simply enforcing the rules that the rules of service or the rules of participation that the sites themselves have and we want them to be enforced. So we see ourselves as partners and to answer your question, I don't envision these recommendations as being the basis for legislation. If they work, my assumption is they'll be adopted without regulation. Regulation is sort of the sort of Damocles that hangs over all of us. And by us, I mean we as well because when there's a regulation or a law, we got to enforce it. And as we've seen all too often, laws are only as good as they can be enforced and there are a lot of laws that are not enforced which I think undermines the credibility and structure of our entire legal system. So my hope is the recommendations will be credible and sound and will be adopted voluntarily, not by governmental fiat. All right, that's great, concise statements. General Blumenthal, General Coakley, thank you so much for your leadership and for being here today.


This is covering the spread part of the fan dual podcast network Week eight in college football may not be quite as monumental as week seven But still some really really fun games on the docket for this weekend We're gonna break down those games and with Dr. Ed Feng get his read on those games and his favorite bets across week number eight here today This is covering the spread right here on the fan dual podcast network and number fire calm My name is Jim Sonness I am a senior writer and analyst for number fire calm here with Dr. Ed Feng to get his read on This week's game you can find Ed's work over at the powering comm and find Ed on Twitter at the power ranking Ed Week eight is coming in hot week seven though was a delight lived up to the hype. How you doing today? I'm doing pretty well. It was a lot of fun here in Ann Arbor where Michigan unexpectedly for me at least dominated that game and I wish it would have been a situation in which I trusted my numbers more but I thought the market was pretty fair in Michigan minus seven and They really dominated right? I mean, we were sitting there watching in the first half and Penn State was actually up 14 to 13 But there was one long run from Sean Clifford. Otherwise they did nothing on offense And the other score was a pick six off of a helmet and And then you go into the second half and you think it's got to regress to something a little bit closer And it just never did so Really good performance by Michigan in some sense. I I still think it's probably a little bit of an outlier performance, you know So we'll see we'll see I mean we'll see really what happens when Penn State plays Ohio State in a couple weeks Yeah, Michigan 41 to 17 the final score there and that game was a noon game So did you get a chance to watch the Tennessee, Alabama game that night given that the Michigan game was earlier on? Yeah, I got a chance to catch a bunch of the second half actually the family joined me for actually both of them Yeah, but that was it was pretty incredible going back and forth and I Was telling my children as well Knoxville might burn tonight of Tennessee Like dad, what why would you burn your city when you win game? He's like, well, I know doesn't seem very smart. Yeah They had fun Riot in Knoxville Amazing time Yeah, I mean like it was just I think that it was one of those games were like you went with pretty high expectations But you always like think through at least I do you think through the paths to like it being disappointing, you know Okay, like I've got these high expectations But maybe Alabama were underestimating them stuff like that they wind up rolling But then like it's like oh Tennessee's answering blow for blow. Yeah, not only was it like a legitimate win where like it was back and forth affair But it was entertaining the entire way like I know you you like defensive struggles and like those are fun, too they had their place and I think that they have their value as well But like having a back-and-forth affair like that where it's like the product of good offense rather than bad defense I I think it's really hard to top that Yeah, and especially late in the game. I thought Tennessee really Really got screwed by that penalty in the end zone. I thought that that was an offensive passing interference Couldn't really see it on the replay But I mean it didn't it didn't look particularly good and then to lose that fumble on the the match point and Recover from it. Yeah, kind of unbelievable. Yeah, really was but kudos to them I'm sure we talked about Tennessee down the line here in the very near future once again Really fun team really fun game and a fun slate on tap week 8 as well I'll break down what Ed's numbers are saying about this week's biggest games and more in just one second First a reminder to make sure you are subscribed to covering the spread wherever you get your podcast We had our NFL week 7 first look up yesterday our full breakdown of week 7 is tomorrow and our player prop vetting preview We'll be up on Friday as long as JJ Zachary's and so get those by subscribing To covering the spread wherever you get your podcast and while you're there if you like what you hear leave us a rating and review As well the NBA season is now underway And it's the perfect time to download Fandall America's number one sportsbook because right now new customers get a no sweat first bet up to $1,000 that's up to $1,000 back in free bets if your bet doesn't win Fandall has all your favorite bets from the money line to point spreads to player props You can even combine your bets for a chance at a bigger payout with a same game parlay Plus with live betting you'll get updated odds on games that have already started the Fandall sportsbook app It's safe to cure and super easy to use so download Fandall today to get your no sweat first bet up to $1,000 make every moment more this season with Fandall official sportsbook partner of the NBA must be 21 plus and President select states first online real money wager only refund is it is non with throwable free bets that expired 14 days restrictions apply See terms at sportsbook dot Fandall comm gambling problem call 1 800 gambler over the Fandall comm slash Rg and Arizona call 1 800 next step or text next up to 5 3 3 4 2 in Connecticut 1 888 7 8 9 7 7 7 7 or visit ccpg.org slash chat in Indiana 109 with it in Kansas 1 800 5 2 2 4 700 or visit KS gambling help calm in Louisiana 1 877 770 stop in New York 1 877 8 open wire text open Y in Tennessee called the red line at 1 888 9 9 79 in Wyoming 1 800 5 2 2 4 700 or in West Virginia 1 800 gambler Dot net let's go to week number eight now here in college football before talk about the actual games I want to do a futures market check-in because we saw the Alabama loss in Alabama They'd had some tight games before that as well. So not to stay a one game blip there for them So let's talk about the futures market here right now Ohio State is the favorite to win the national championship at Fandall. They are plus 170 Georgia is plus 190 Alabama still lurking. They are four to one right now And when you look at these championship futures any team stand out to you as being undervalued right now Well, I look you point out that Georgia is the second most favorite because all the chatter in in arbor is about Oh Tennessee versus Michigan yada yada yada And let's not forget that Tennessee has to play Georgia is going to be at least a touchdown on a dog In that game. So so Georgia really still is a favorite to come out of there. I think Tennessee is still still the underdog Yeah, so for me, you know, not not too much value anywhere I mean, I think that that sounds just about right to me I don't don't have any precise calculations on that but I mean clearly those three teams are still gonna be there Alabama controls on destiny and I guess I mean, I guess they just can't slip up against, you know, pretty tough division. So, yeah, we will see We will see what happens, but But yeah, it seems just about right to me Yeah, Tennessee 20 to 1 right now at Fandall Sportsbook Michigan 16 to 1 Clemson is 12 to 1 so some big games this weekend. Let's up at that Clemson game They got Syracuse this week Syracuse 13 and a half point dog a total is 49 and a half and we talked about Syracuse last week and Pretty skeptical of them entering that game and they didn't blow the doors off NC State But like they did they did cover they did get a win there Did they do enough in that game Ed to convince you that they're capable of covering a 13 and a half point spread against Clemson? No, not really. I mean, so I talked last week about North Carolina State plus three and a half and And and that didn't win but did get a little bit closing line movement because it closed at three It did, you know, I was really surprised how well Syracuse was able to move the ball against a really good North Carolina State defense That's really what happened there. They had a fifty two point seven percent success rate on offense and Yeah, you know Syracuse offense looks pretty good. They're 19th when I look at my justice success rate They're not quite as good on defense. They are 49th Am I convinced about Syracuse? No, not really I mean, it was it was kind of one game against the North Carolina State team and maybe I mean, obviously North Carolina State didn't have their quarterback I tried to argue that that didn't matter so much. Maybe it does. Maybe I was wrong about that You know only time will tell And you know and when you're talking about Clemson, I still think they're like, you know When you look Clemson was a 14 point favorite earlier. It's 13 and a half now when you have Clemson as a 14 point favorite against You know a respectable division opponent I think you are making the assumption that Clemson is going back to the Clemson of three years ago And I'm not sure that that is true as well either. Um, you know when they're they're the offenses You know, they're 45th when when I look at adjusted success rates 74th In passing. So yeah, they had some success. Sure, but Though those aren't, you know, that's not reminding anyone of the Trevor Lawrence and Deshaun Watson days Um, the defense has been better.  They're up to 19th when I look at adjusted success rate When um, columnist was on we talked about I asked them how much they missed Brent Venables And he said, well, I think Brent Venables misses Clemson And and that turned out probably be the more accurate statement with just the the free fall that Oklahoma has been in over the last month so Yeah, I don't know. I I'm um I I think there's probably I would lean so my number has to have uh Clemson by about 10 So that suggests some value on Syracuse And I think it is difficult for a team to cover two touchdowns when the total is in the high 40s So is there value on Syracuse? Uh, probably, um, I'm not too interested in it just because of I think there's Just so many question marks about both these teams. I really don't have a read on where Either one of them is going to head in the next couple weeks or for the remainder of the season So probably to stay away from me. Yeah, I think that for me I've had a lot of this year where I've seen value in my numbers but had no interest in betting in I think it seems it's been a lot of situations where it's been teams I can't get a firm handle on um, like, you know, and I think that Potentially Syracuse can be one of those teams. I'm going back to Oklahoma really fast Because they'll kindly wrote a piece about how Sports folks couldn't catch up to how bad Oklahoma was And I was like, you know, I was like, oh, yeah, that's kind of funny to think about but I then thought about your model That has like the more aggressive updates for recent data Do you think that better equips you to like handle a situation where a team kind of seems to be In what you deem to be like a free fall for them? No, I mean my model hasn't been able to catch up to michigan state Yeah and Yeah, no, I don't know I I think So when when you kind of tune the parameters of my model you you're kind of making a guess on on average how How hard you should move these teams after results and you're going to be too low on some teams And you're going to be too high on others and the art of it is to figure out when that is the case um You know, I'm more familiar with michigan state.  Yeah And we'll obviously see um in there and uh in michigan's next game here in n arbor, but um So, yeah, it's it's tough to know. Yeah, for sure Well, syracuse the clumps in the stay away for ad Let's dive now into all miss at lsu where we've got lsu is a one and a half point favorite total is 68 and a half It was 66 and a half yesterday now at 68 and a half So this movement towards the over there all miss Seven and oh, but now facing lsu in baton rouge pretty tough spot there Who do you have winning this game? Yeah, I mean, I'm pretty close to the market here. I have lsu by about a point and Still happy that they beat florida outright. So Uh, that was one of the the best that I liked last week. Um The thing that interests me in this game is the total, right? So you sent me the email about talking about this game. It was 66 and a half yesterday and now it's up to 68 and a half My model says about 59 I think there's I I think I would lean towards the under but you know almost is definitely an up-tempo team that is Under laying kiffin always going to be better on offense than they are in defense right now. They're eighth In my adjusted success rate. They're better with brushing their seventh in the nation. Um, they have a quarterback in jackson tart That's been a running threat so They want to play up tempo there, you know, and and they're facing enough lsu defense that has that has been pretty good Over the course of the season not against tennessee, but otherwise been pretty good so And I do think lsu can score too against an Ole Miss defense that that doesn't look particularly good. Um in my numbers so But still, you know, that's a pretty big gap. It's like a nine-point gap and um Yeah, be interested to see where the total goes in this game. Um, I mean, I'm staying away from I'm I would probably lean towards Towards the under there, especially that uh is continuing to get that high But but I do see where points come from in this game Yeah, uh 68 and a half right now I think that it would be wise to keep an eye on that given that it has moved already Towards the over and maybe its situation where if it keeps on rising You circle back later on this week. It's a minus one 10 both ways So it's possible it's it's settled in at 68 and a half and it might not be Rising anymore, but if you agree that's reed and what his model is saying under could be the play there So keep an eye on that keep an eye on where the if it keeps moving. Maybe you want to dive into that one eventually, but um I think that the under does make a lot of sense there, especially if you are about nine points below where that's at Let's swing out to the pack 12 right now and talk about ucla at oregon Oregon six and a half point favorite here total is 69 and a half in this one and we talked about ucla quite a bit and They've pulled through in some really tough matchups. They've had impressive wins, but oregon They've been on a roll ever since that auburn game. So or the georgia game. I should say uh, how do you see this one playing out? Yeah, my number is like oregon by about a touchdown It sees two really good teams on offense Oregon is fifth when I look at my adjusted success rate and their success rate was actually pretty good against georgia It looks like they were able to dink and dunk and and kind of move the ball But uh, you know, obviously that didn't show much I show up much on the scoreboard But you know no explosive plays at all. So and and they've been pretty good since then. Um, ucla has also been, uh, Pretty amazing on on on offense as well over the course of the season. They're six in my adjusted success rate And then you know at this point in the season, you know I still tend to rely on both the success rate and the yards per play Adjusted for opponent and both these defenses are better When I look at adjusted yards per play. So they they look better by yards per play, which means Okay, so maybe they're not that good of a defense at stopping Opponents, but they've been able to eliminate to at least reduce the effect of big plays We expect the the kind of success rate numbers to be better. Um So that would suggest that these defenses are worse Uh, than than what they look like in yards per play. So, uh, it could be a lot of points in this game And uh, it should be a fun one out west 69 and a half's a big number. I'm guessing you're staying away from that based on your read on that Yeah, I mean my total is like 67. So it's not too far off that it probably should be just a little bit higher because uh, uh, because of what the success rate says about the defense. So, um Yeah, but Oregon by about touchdown so, well, uh Yeah, I mean, I think the market is pretty sharp on this one. So pretty good markets across the board and our three big games Let's open up the board now and talk about other games in week eight where you're seeing value What are you seeing that you like right now for week eight? Yeah, so the one game which I like is uh, it's probably Texas A&M minus three at South Carolina So this one's kind of interesting to me Uh, well, first of all, we have Haynes King who the Texas A&M quarterback who Uh has maybe the most peculiarly throwing throwing motion of any quarterback that has made it to the Made it to the college level But you know, he was the starter Had a terrible game against ab state lost job max johnson got hurt Haynes king is back and he got hurt at the end of the Alabama game. It looks like he's gonna play Um, it looks like there was a foot injury, but it looks like he will play And you know, it's one of these things where uh earlier this season I talked maybe not on this show but but I I definitely my number suggested that the The offense was awful and the defense was pretty good making them an under team and Since uh, so that was before the Mississippi State game since then the defense has gotten significantly better Um in in the last two games, uh, obviously it helps that they didn't have to face bryce young in that game against xan against alabama but um, so So they they've been better and I I just think there's a significant talent gap here between what's going on on texas a&m Uh compared to south carolina. I mean, this was a program that was really bad in the last few years of Will must champ in 2019 and 2020 they're bringing chain beam last year who, uh You know had a pretty good turnaround at seven and six and you expected Better because they they brought in spencer rather the quarterback Um, you know, the offense has looked again. It's one of these situations where they've looked better by yards per play than success rate so the 39th In terms of yards per play by my metrics after scheduled adjustments. They are 64th and success rate Again, the latter is probably going to be closer to the truth And then you know their their defense is uh is is solid, but It's again another unit that's a lot better by yards per play 30 seconds than they are by success rate 108 So and south carolina was a defense that was good last year and they brought a lot of their talent back so The success rates number suggests that they're they're not as good I just think there's a big talent difference. My primary model likes texas a&m by six and um But a couple of other models that I look at uh market model where I'm looking at what the market has said this year And also only data from the current season Both of those Suggest that texas a&m should win by at least four here And the data from this year is accounting for the fact that their offense is really struggled Which I think makes it even potentially more important that you're seeing value there, too Right and and it also accounts for south carolina and the fact that they've played some pretty terrible teams outside of the sec right so it accounts for all that and that also says that texas a&m minus three is a good bet a lot of places have gotten off of three already so I'd be like texas a&m minus three Obviously like it a little bit less at three and a half But my model says six and I think there's not either. Yeah, it is currently three at fandall sportsbook minus one ten On the minus three right there. Uh, I think it was minus one or four three and a half at some other books so you could look around try to get that three somewhere but um It is right now three at fandall sportsbook again minus one ten on that one That's all we got here for week number eight again pretty fun slate night Maybe not the same firepower as last week, but still some pretty fun games We'll see how these things play out across the week add plenty of stuff for you going on though Across the power rank and across the football analytics show. What is going on this week there? Right at parker fleming on the podcast. He's been a guest on on this show as well. He's a quantitative college football analyst Uh, he's been doing a lot of good work for the for the hammer as well So yeah, we talked a lot more about his models talked about some of the key games this week And uh, yeah, just check that out at the football analytics show wherever you're podcast And then uh, still working on my newsletter over at the power rank dot com Uh, seven nuggets saturday is full of tips and bets and news and and humor Uh, so check that out at the power rank dot com Awesome and check out add on twitter at the power rank I am on twitter at jim saunders You can also make sure you are subscribed to covering the spread on your podcast platform of choice And also you can find these up on the fan dual youtube page After they're posted over there as well If you got any questions for us again, uh, check me out on twitter at jim saunders back once again tomorrow with ryan williams breaking down nfl week number seven player prop show coming up on friday with jj Zacharyse and we'll talk to you all then this has been covering the spread right here on the fan dual podcast network