of you as tomorrow starts today super glad to have you all hanging out with us on your Sunday January 28th or I don't know it might even be Monday for you at this point if it is I'm very sorry but we're glad to have you aboard my name is Jared I'm gonna be your host today also got Ryan all the way over there as I point completely in the wrong direction doing my best Jamie impression yeah sorry me in the head man yeah sorry about that and then also of course you've got Dada in the middle calling the shots with everything and we've got saying hello to some of you all this morning we got Roger checking in and saying hello you've got Tommy checking in and saying hello nerds and we've got Damian also checking in from Clifton Ireland so top of the evening to you I don't know if that's how that works in Ireland but you can you can correct us in the comments we're used to that kind of stuff but I figure we might as well go towards very close to an area that Damian is where Ryan is in this place called England so Ryan we're sure that you've got quite a lot to tell us about today what have you got for us to get started with on tomorrow today well if we're saving the bet well it's not really the best is it the we're gonna save ingenuity to last right yes make sure everyone understands what's happening because that's how YouTube attention works so you stick around to the end basically all I've got to talk about is that the the facilities in Starbase look practically the same as they did last week which might not sound that interesting but it's a good sign because in the background that means that SpaceX is working ham at getting everything ready for flight three of Starship which fingers crossed should only be two to three weeks away we've seen ship 28 doing some wandering about in the production site moving from one of the high base to the rocket garden it's just kind of sat there at the moment booster 10 is getting a little bit of work done on it as well and also looking ahead we've seen booster 12 getting some work done on it booster 13 getting some work done on it and we also have ship shuffles happening all the time it's getting so hard that even NSF our most informed writers are getting the ships mixed up because there's so much stuff going on at the moment at Starbase so even though the launch site looks empty like it has done since November 18th last year there is SpaceX actually doing a really really good job at making sure that all of the kinks and upgrades and everything is all sorted until or for the next flight which as I just said should fingers crossed becoming in just a little bit of time here so yeah it's not the kind of manic mayhem that there is usually before flight two before flight one but it's kind of coordinated progress towards what will hopefully be a successful flight three excellent so slowly but surely that progress is being made and of course all of us here tomorrow hoping for a successful flight three it would be great to you know they were so close to making it to orbit on flight to just seconds away from it before the the rupture so man I'm really really hoping that they get it this time because we we need a W right now besides you know the it feels like the last W that we had for the Artemis program was Artemis one itself and then we've been you know watching Starship which is going to be a part of the human landing system go through its growing pains and then with the clips program the commercial lunar payload services with Perrigan we're off to a rocky start it's just we we could really use a little bit of the good news right now with with the way everything's been this is it's been a rough start to the year so I'm really hoping really hoping that things go go well what the world needs now is launch sweet launch I mean we did get a little bit of that from from Vulcan right Vulcan worked perfectly but but yeah that's I don't want to say that's like the only successful thing that's happened so far this year because India you know they got their solar emission where it needs to be an L1 and they're they're sort of they're getting ready to do it a test flight of their crude vehicle so it's just this is I mean we've got a lot on the horizon that's very exciting it's just it seems like he's first this first month has been you know we got Vulcan but it's a little rough well what was it was a launch what was the launch of SpaceX recently did where they posted the video the fairing returning from space oh I'm not sure they post videos yeah they posted the videos I didn't realize that they still posted videos I don't know I I saw a video I it wasn't I don't even think it was on I think it was on Facebook actually just gorgeous gorgeous footage of looking from the nose of a fairing to the app and watching the the plasma plume swirl around it was just if that's the video I'm thinking of then I think that was like officially released like a couple years ago oh okay let me see yeah let's take a gander at it because I mean let's just look at it anyhow because pretty so yeah that is looking right there oh come on yeah yeah that was the hottest and fastest we've ever attempted but it's also I believe it's also been superseded by who was it or is or is this the one from last year with with it was via sat whichever one fully expended so I can't remember there's been there's been so many Falcon heavy launches I don't know which is not which is not something we used to save before last year so not not hearing you John that's really that's really cool I still love it look at the look at the dynamics of that also this is okay so Sakura is confirming that this is the via sat Falcon heavy launch gotcha look at that Mach 15 not even Maverick moves that fast so there was actually an interesting kind of little tidbit that was released maybe should I say by William Gerstemeier in the Cygnus NG 20 resupply press conference the other day and so if you're unaware Falcon 9 this Tuesday now is going to be launching Cygnus for the first time which usually launches on Antares apart from when Antares isn't available when in the past it's launched on Atlas now it's launching on Falcon because Atlas being tied blah blah blah blah anyway they're launching on Falcon and Cygnus's contract with NASA requires them to have late-low capability so they can put things like live animals like mice on board of Cygnus and it is not put on their like weeks in advance it's put on their hours before launch and because of that SpaceX have had to develop a fancy door in the fairing so it's about one and a half to 1.2 meters so SpaceX has gone out of their way to develop a door for their fairing for their competitors commercial resupply vehicle which I think is just another prime example of how SpaceX doesn't really care about having competitors they'll just do whatever their customer wants them to do because they've launched countless one-web satellites they're now developing custom fairings for Northrop Grumman I mean this company clearly just you know they know they're not going to be beaten or that that's what they believe so they're just helping everybody along the way which I think is fantastic think about it if you're if you're if your competitor is asking you for launch services that's that's something that they obviously can't do on their own yeah so so if they're paying you for launch services you're reusing the booster you're using an existing infrastructure and it's aside from you know labor costs and a little bit of fuel it's all profit yeah and I do I do want to I don't want to be the downer here that I often am on the show but here I go which is that one of the best things that I ever said to me is that you're only good until you're not so just I always try to temper expectations and other things like that because they're you are great until you have that one slip up that causes it but also I do want to say that holy smokes that isn't that is an amazing thing where they were literally just like you know what sure we'll put it open a door in the fairing sure you need it how Northrop week we got you we got you Northrop you're good so how hard can it be yeah how hard can it be can't be that difficult can it so yeah JTTV is actually asking a really good question here even though dream chasers can be flying on Vulcan the dream chaser use the same door and JTTV I don't know if you're asking about dream chaser flying on like Falcon what would dream chaser have to fly on would it be able to fly on a Falcon 9 or would it have to fly on a heavy so I'm not sure but it's a Crew Dragon because Falcon 9 can comfortably take Crew Dragon to the ISS yeah let me I was about to say I will look that up to figure that out so you're gonna hear me typing very loudly here I don't have I don't have the numbers in front of me I'm not one I'm not sure if dream chaser would fit in the fairing of a Falcon 9 but even if it did I would imagine that Cygnus and Dream Chaser have their access hatches in different locations on the vehicle one being a tube the other being a space plane so I would imagine that hatch locations would be to need to be different perhaps but I'm not an expert on Falcon 9 fairing yeah yeah I was gonna say I don't know if I don't know many people who are experts outside of those who work at those who work at SpaceX and I don't want to necessarily say they're not talking but you know they're not exactly nobody is exactly saying a lot with that there so yeah pretty cool I so I can't I can't find an official wait but I can say that in order to meet the commercial resupply services to conditions the guidelines for it dream chaser did have to fit within a five meter payload fairing so take that take that as you are with it there so I'm seeing a lot I mean I'm seeing I'm seeing a lot of imagery that has dream chaser crew dream dream chaser without a fairing stacked on top yes that was that was that was sort of the original idea of a crewed dream chaser but now we're in cargo dream chaser so which they they have said it should be easy to turn it into a crew dream chaser I know I easy and turn it into in the world in the world of spaceflight is typically not used you just you just put some oxygen candles in it and you're fine so yeah I do it at home all the time I just open up I open a door and I get more oxygen immediately so how hard can it be right look if you could pull the door off of a plane and get a nice breeze inside of it anyways I should I'm gonna stop right there before I do it that's those are those are an engineers for favorite words it's easy you just yeah tell me about it well this could be fine on Vulcan Centaur to give you some idea I'm not sure which variant of Vulcan Centaur it's flying on simply because I have just like it's so heads down unfortunately okay it's going on the VC4L so we're going so we're gonna have four solids on it there yeah I'm going to fairing so according to Sakura the max payload for Falcon 9 is 22 ton to what orbit is this 22 tons to what orbit and dream chaser is nine tons so dream chaser is nine tons but you also have to remember that it's going to be carrying more than just itself it's gonna have the shooting star cargo attachment on it as well which that alone is going to have oh my gosh I'm on the order I want to say five tons of cargo capability just by itself with with the shooting star module that still doesn't seem like it exceeds 22 that still doesn't seem like it exceeds 22 yeah and I was gonna say then it's just a matter of the physical space being able to fit inside of the fairing yeah and I was gonna say I just looked up Vulcan Centaur and what VC4 can lock then that can do about just a little under 25 tons to low earth orbit so yeah it'd be pretty cool I you know I just want to say this which I think is this is really really cool sorry my dog was like she had something to add she actually just reminded me that it that a lot of things like sickness are designed to be launcher what's called launcher agnostic which basically means that they don't necessarily have to watch it on an Antares they've watched it on an Atlas 5 and now they're launching it on a Falcon 9 which I think that that is a fantastic way to design your vehicle why make it something that can just stay on your stick let's stick let's put it on everybody's stick and see how it does with it there that you know he's up crazy Jared sorry well somebody's got a phrase that here and I'll absolutely take the blame for it but I just think that's so cool that like this is a cargo vehicle that has that is now going to have road three different types of launch vehicles in order to do its job how awesome is that in the space industry that's damn near universal you know not it doesn't feel like it's been universal for for commercial cargo and especially for commercial crew it just feels very awesome that they actually are doing it because they could design it to do it but there's a difference between designing it to do it and then actually going out there and doing it and Northrop Grumman has been very much pushing very hard to make themselves a leader in space flight since since the purchasing of orbital sciences and other stuff like that orbital sciences and space division of Northrop Grumman innovation more words so yeah and as JTTV is pointing out as well Starliner is actually launcher agnostic in case you wanted to know so if you wanted to you could potentially stick Starliner on something if you feel like it oh and you know Jack Barnett's bringing up a really good point too which it's actually four different ones because we've had two different versions eventaries and we're about to have a third version eventaries come out with the new engines from Firefly Aerospace being used for the first stage because the Russian invasion of the Ukraine has caused them to lose access to those first stages other than Tari's typically flew on so yeah holy moly this is actually a really cool mission coming up and I'm just excited because I mean like hey it's really it's really cool right like I just I think the sickness to me just looks the part of like a cargo spacecraft you know like it's a very it it's very sci-fi looking to me and I think it's just really it just looks really cool so especially now that they're flying the enhanced versions that are much bigger than the original version so yeah I'm super I'm super excited to see yeah there's actually shows that there is more space in the Falcon 9 fairing so when sickness grows by another segment theoretically that could fit in the fairing of a Falcon 9 as well yeah why don't we why doesn't SpaceX just throw some starlings in there how hard could it be it's easy just yeah air spikes actually bring up a really good point in the engineering aspect of things which is that when your biggest constraint is NASA it's easier to be larger agnostic but the size the volume is more limiting with things you know that was sort of the issue with the with the j-dub which wasn't necessarily the mass but it was the fact that it was just so damn big that they ended up having to fly it on the aria on five because that was only fairing that had enough volume to actually fit it which then added on some very interesting constraints right because then you've got to deal with the vibration of solids and other things like that so yeah and satellite origami yes and 300 plus individual points of failure and and several small heart attacks over the course of a January and early 2022 but also as I will just always remind people it works to shut up so and it's working brilliantly so you can also shut up so yeah well it won I just gonna say it won the Collier trophy so that's of course that's like the big award in aerospace so which we will talk about something that one also won the Collier trophy a little bit later as well so yeah very very cool and then it's a girl what are you showing down there you show is that the is that the dream chaser I can't I can't see that graphic it's really really tiny on my screen unfortunately cargo dream chaser with a second stage inside of an atlas with five solids yes because they were going to fly it on atlas five but I believe that is not the case anymore I believe the Vulcan five huh it's it's like somebody did a bulk purchase on those I have no idea who could have done that so I at least I hope Jeff got a discount for that so do you think if you buy 12 to throw in a 13 for free so next time if we get Tori back on the show I will ask him personally if we buy 12 Vulcans will we get can we get the 13th at a discount so of course I don't I don't know I don't think we're I don't think it's like an airplane we're not wet leasing a rocket so I don't think we can do that so JTTV I like that would they want Starlink on a international space station orbit I don't think so no I was just I was being a smarty pants this morning with it by the way SpaceX please launch the Starlink from from Vandenberg on time tonight I want to see it it would be nice to have a Vandenberg launch actually go when it's supposed to after this last one that got scrubbed like what five times or something yeah it was a lot ish times and then on the final attempt when it did actually launch the webcast died because you know Twitter live streaming is fantastic and only regained at T plus two and a half minutes so you know good job like I gotta admit that I was I was thinking about it last night and I realized that when I go and I film SpaceX launches with my Canon t2i which is 14 years old when I film it and I put it on YouTube I'm providing a view of the launch at a higher quality than SpaceX does now so yes so yeah I guess if you like using Twitter to watch stuff you can but it's I feel bad because it just doesn't look good on there of the of the 30 to 45 seconds I've watched of one where before I went this is garbage turned it off and then just went to watch NASA spaceflight do a fantastic job of covering them so to be fair have you have you watched one recently I I watched the one that happened the first one from Vandenberg this year but it was on my phone because I was I was out filming it and I wanted to make sure that I had you know everything good to go on there so I was watching it just to me I typically will have the stream open when I'm filming them just because I want to make sure that launch actually happen and they tend to have the least amount of delay with that there so even on my phone it didn't look great I would say it was maybe 720 but like compared to what it used compared to what it was on YouTube just is that your band or theirs and then it will drop out to 480 and then pop back up to 720 so I don't know so yeah but is it I don't know yeah so minor space X's you do you do need to give them a fair shake and things do change and do evolve so they they may be improving but I agree that that their initials were rubbish potato I can first impressions or everything sorry so I don't I don't know I don't know to say outside of that other than also it's on Twitter which is not somewhere I want to watch video at as well so yeah yeah it's not a platform designed for video sorry I'm just gonna say it so yeah that's my take and you can't convince me otherwise so show me show me someone who's had a successful video career simply relying on Twitter and I I think that list is probably going to be short if that is a list so but also I feel like this is much more a members show discussion so and I feel like this will also be something that I will be slammed into the ground and and demolished by Jamie will just come in and and and have 10 to 20 talking points that will absolutely obliterate me and I will be banished shadow realm with my opinion on video anyways we love you we love you Jamie you're you're amazing as always so now that we've now we've had a hell of a tangent with that there what is secure what do you have what is that is that Vandy that being in Berg it looks like Vandenberg on that they're a map of Vandenberg at least yeah so that's that's slick too which is where Firefly launches from nowadays yeah that's where arguably the one of the greatest rockets of all time the Delta 2 launched from so yeah very cool very cool indeed all right so should we you guys want to talk about one one thing before we get into our our sort of you know goodbye to our dear friend and this was something that I have been wanting to talk about for like almost a month now if y'all don't mind which is that I don't think y'all heard of this mission called Juno it's out at Jupiter right now and it's it's doing an incredible mission we're learning a lot about Jupiter from it and it's recently been flying by Jupiter's Jupiter's moons because it's changing its orbit to kind of set itself up to dispose of itself at some point next year it's that it's run through its extended mission we're starting to see some of the instruments on Juno reaching a point where they're they're getting fried enough by Jupiter's radiation belts that it ain't it ain't looking pretty anymore Juno does these incredible close-ups of Jupiter's atmosphere and I mean we're coming down to like gosh 5,000 kilometers above Jupiter's cloud tops that is so that is absurdly close that is threading a needle through a needle in order to do something like that and as a part of this extended mission they have been having Juno use Jupiter's moons as gravitational assists to help sort of tighten up its orbit bringing in to orbits a bit more often and we end up seeing you know views of those moons and it flew by I know one of my personal favorite moons in the solar system and it has returned some credible images of Io and I'm gonna go ahead and let you take a look at that there and just be absolutely blown away by what Io looks like with it right there so thank you Gander is not amazing so and I'll zoom it in a little bit here too so you can kind of get the detail of Io coming through there but yeah Io is amazing because it is close enough to Jupiter that the gravitational pull of Jupiter stretches and squeezes Io a tremendous amount which causes the interior of Io to essentially remain molten so Io is the most volcanically active place in our solar system is more active than Earth there are in excess of 20 active volcanoes at any given time on the surface of Io and in fact just recently there was some enhanced imagery that was pulled out of the data that Juno here took and they were able to see that that sort of up in this area right here up towards the North Pole there is a active plume from a volcano that was erupting at that moment spewing it out spewing up material somewhere on your over about 30 to 40 kilometers above the surface of Io here they are the volcanoes they are huge they're potent and Io is just a nasty place just covered in sulfur sulfur dioxide be bombarded by the rate that radiation belt of Jupiter contributing a little bit to it as well it's volcanic eruptions with the sulfur man it is just it's just a hellish world but dang it's it's pretty and we haven't had imagery this good since the Galileo mission in the 9 in the 90s and early aughts so one of the things that they have been doing is they're taking the imagery from Juno and they're going to be comparing it to the imagery that Galileo took to see what are the geological changes that have happened since then because even though it's only been 20 years which is sort of not even not even a blink of an eye in geological terms that's it's I don't even see came and really quantify that in terms of a of a time period in geology it's it's almost it's still with it there yeah with some magic some magic sound that but yeah it's seen big geological changes over something like 20 years the absolutely astounding but that's what they're looking at with that there so that's cool wow isn't that a great image so I absolutely love that and my favorite my favorite thing about these images that the Juno mission puts out there is that they are publicly the images are posted publicly you are allowed to grab the raw data and they encourage you to play around with it and send it into them and just kind of do what you want with it I've seen people who have taken so that was the official one from Juno I've seen people who have taken more of the images and stack them and able to pull greater resolution out of it there so are those are those just our RGB captures or do they are they capturing any infrared or other wavelengths those are RGB on there and then those are also true color as well so a lot of people a lot of people the images that are put out there are color enhanced you could understand it a little bit better so yeah arrow spike is saying at least the image on Wikipedia made it look yellow or yeah like images of Jupiter also make it look significantly more spectacular than it actually is I could find a quick one from Juno to show you what Jupiter actually looks like it is it turns out it is not as it is not as spectacular as it is even the ones that you see coming out of NASA are frequently colored hands you know they always show that one of the South Pole of Jupiter with its with its storms there and it's like it goes from like this like beige ish to blue and like no that is that is that's not that's not at all what it actually looks like it's very I don't want to see I want to call it lackluster because it's still absolutely gorgeous but it is just it is not at all what you are going to be expecting it to look like because of that so so I'm I'm waffling here a little bit because I want to make sure that I can I can show you all with this that's right we're always here yeah no it's all right I under I understand that you all are just kind of taking our time with this here this is just obviously a lot of imagery to pull from so I have to I have to kind of figure out what I want what I want to have aero spike says the next thing you're gonna say is that your urines isn't blue oh don't even get started on that one that way I don't I don't care what anybody says that's far that's far enough out in the solar system that I just don't care anymore at that point so who you how dare you acute how dare you accuse me of being a Pluto hater I have planet Pluto hater I have been pro Pluto so much you have no idea how pro Pluto I am about all these things you don't you don't you don't you did you you're wrong so I have to say about that is you are wrong I am so for Pluto that I you I will fight people who say that Pluto is not a planet so mostly because I've seen it almost happen all right here you go here's Jupiter how Jupiter actually looks you can see again particularly lackluster in what it actually looks like so it is isn't the those those belts of clouds and stuff that you see that most people consider to be absolutely spectacular yeah it's a lot of it's been color-advanced unfortunately because I would bring out the detail better I would best describe this photo as a dirty computer case from 1983 yeah actually you know I don't think you're wrong about that at all it sure does it sure does look not like like Jupiter I will say that it's just like a lot of people look at it and they go really that's it this Jupiter this is a good so and I mean I it looks it looks just like Jupiter to me through a telescope so I don't know what the big deal is but also I've been like looking at this stuff for like over a decade stuff like your stuff so yeah yeah actually we're still we're very game over we're still trying to figure out what took that chunk out of Jupiter there it's just a it's just absolutely massive that's the thing I don't know there might be a whole bunch of oh my god why am I forgetting it now there might be a whole bunch of monoliths that's what it is so and they're multiplying there's so many of them and I'm telling you forbidden things so yeah so of course remember everybody everywhere else is yours except for Europa it's have no landing there so that's the rules of the solar system so but that's not going to stop us we're we're gonna go land on Europe at some point so but that is a bridge that will be coming yeah aero spike right there thank you very much just confirming what we were all thinking monoliths or galactus as well for all of our our comics friends out there at our same the simulation takes time to load wait just give it give it a minute we'll load Jupiter fully for you there I don't I don't know who's running the simulation I would like to speak with the simulations manager so all right so we're gonna we're gonna head on forward a little bit here what have we got Ryan are we are we jumping back to you on this one well we're getting to the last part of the show so I I'm expecting everybody knows by now that unfortunately a good friend on the Martian surface ingenuity has damaged one of its rotor blades upon landing from flight number 72 and because of that JPL will no longer attempt to fly the helicopter anymore because apparently more than 25% of this specific blade is missing and now this is the only photo and you could see that there's a term in this just from the shadow of one of the rotor blades on the surface so yeah and this is the only photo of a rotor blade that they've shared so far I believe which means that more rotor blades could be damaged if we think there's there's two blades and then another set of blades on top of that so there's four blades in particular and this is just one of those blades so yeah it's it's certainly not a it's certainly not the site that we wanted to see however as everyone's been saying it is incredibly important to note that this was Flight 72 of 5 so ingenuity has flown 14.4 times more flights than designed it's a technology demonstrator and the fact that 72 flights were even achievable on Mars remotely is absolutely mind-blowing so this is certainly it's certainly a bit of sweet ending we don't want ingenuity to kind of go out this way but the achievements that have been surpassed on this mission by ingenuity it's just you know it's the first powered flight on a world other than our own there is no bigger feat than that I don't think and it's proven that for future Martian missions helicopters and drones and things like that are 100% possible as a point of curiosity if I were JPL I would want I would see if I could give program ingenuity to give each motor a short pulse to rotate the each blade 180 degrees and take the same picture and see if they could get a consensus on what those shadows look like from each of the balls no that is nothing that JPL is actually has already done or will be doing oh sweet that the data either hasn't been shared to the public or they don't have the data yet it's I know that process has happened or will be happening I just you know the the the last like official update we had was a was a teleconference on Thursday night I think yeah and I was gonna say there's actually some interesting stuff that people have pointed out which is that in the imagery after flight 71 and before flight 72 on some of the images that were taken of the blades were very some of the shadows on those blades were very inconsistent with what had been seen before again so it wasn't you know people are kind of asking as to whether something happened during flight 71 that may have actually damaged the blades as opposed to a hard landing on flight 72 now it's because we don't know if it's entirely like shadows playing tricks on us here or things like that but it definitely that like does not look like a good rotor blade if you ask me I'm wondering whether that could be the solar panel because the solar panel is a rectangle that sits on top of both of the rotor blades so I wonder if that's just shadow trickery the shadow of one of the blades and the shadow of the solar panel are kind of like stacked on top of each other like this and then it's kind of shining a shadow like a 45-degree angle kind of thing perhaps that would make sense and then this terrain that that this happened on with ingenuity was noted for being particularly flat which flat on Mars still means that it's probably sort of what we would consider an undulating sand and that would make sense as to why the shadow would have that little like curve on the rotor there then it may it may be a dip in or a rise in the regolith of Mars there that would cause that so but I just thought it was interesting enough to just bring that up and see if maybe with it what's going on with it but of course JPL will ultimately give us a really good good report on it with it there so yeah somebody in our chat room was asking how yes Graham W was asking I wonder how fast ingenuity would fly in Earth's atmosphere and I can tell you that it did move at 10 meters per second roughly 22 miles per hour at its fastest during in flight in the Martian atmosphere and it was basically optimized for the Martian atmosphere so I don't I don't know how fast it would fly here on Earth it would be extremely OP on Earth let's just put it that way because when you design a helicopter to fly in a 1% atmosphere and then take it to 100% atmosphere you know that's a that's a huge difference in air density that doesn't translate exactly the way you think it's going to you the blade is over geared for the motor so you would be drawing more current than you could provide to the motors in order to be able to spin that blade enough to be able to get vertical thrust so it just it just wouldn't it wouldn't work it's not compatible you have to you have to gear the motor and the blade together for the conditions in which you're flying yeah and you got to remember too that those those blade tips are moving at an incredible RPM about 2,300 RPM when they're flying they're approaching supersonic speeds and that is like absurd with what's there so yeah that is that is really difficult to design something to to work with that so yeah it's again ingenuity was just a whole bunch of absurdities and I absolutely love every second of it and Frank's got a really good idea which is that let's send in another drone to check it out that's very very true I quite like that one and then aero spike is also saying that I'm so looking forward to ingenuity's successors and frankly I am too because ingenuity is opened up the doors for missions that beforehand were just sort of in the in truly in the realm of science fiction where people were just entirely disinterested in it you know it's almost like when that that announcement about the gas and Venus's atmosphere happened all of a sudden everybody cared about Venus and now all of a sudden everybody cares about exploring Mars via via air and in basically aircraft in some sort of way it's not that nobody's ever thought about it there's been plenty of proposals for aircraft in the atmosphere of Mars it's just that nobody's really actually pursued it simply because it's always been considered something so far out of the realm of possibility and I do want to say too I found this really interesting which is that when I was researching this last night just to make sure we're talking about ingenuity correctly and everything there was a tremendous amount of opposition from the perseverance team to having ingenuity there in the first place they said that ingenuity was going to be a waste of time it was just going to slow things down it wasn't going to help out the rover it was just a toy this was just a pet project that somebody's trying to get money to do like this was stupid we don't want it this is this is just completely it's the antithesis of this mission it's going to ruin stuff for us it's mass that we don't need it's a waste but then it turns out that a lot of these criticisms that were levied against ingenuity turned out to be incorrect it actually did keep up one of the things they said is it's just not going to keep up with perseverance it's not going to be able to fly like the rover is just going to outpace it well ingenuity has been able to keep up with perseverance like it didn't follow perseverance's path exactly so it's a little part where perseverance makes a big U but ingenuity just flies right across terrain that perseverance couldn't do or yeah perseverance couldn't do but it's just it's incredible to me that that that it was so there's such a massive amount of opposition to the existence of ingenuity that it was nearly to some people on the perseverance team it was almost offensive to have it there but then it proved it's worth and it turned everybody's opinions around about about aircraft and rotorcraft on Mars and now you know it's now they are literally thinking that okay maybe we're not going to do rovers or maybe we're gonna do rotorcraft so like we're done with wheels so roads where we're going we don't need roads you know situation going on here they're now saying that for Mars sample return if it happens that they're no longer going to have fetch rovers they're going to have fetch help rotorcraft to go out and grab the samples and fly them back to wherever they need to whatever is going to take them off of the surface of Mars they're already talking about making a massive version of ingenuity something with like 8 to 10 rotor rotor blades to fly with something like a 50 to 100 kilogram payload on board like they are like go big or go home baby so and and and I also wanted to say that there was a really cool mission proposal that under NASA's advanced studies that they're actually getting funding for since it's something that is in the future but they're looking at it on and it is such a cool one it's called Mars aerial and ground global intelligent Explorer or Maggie I'm sure that was a back that's not a back of me at all and it is a vertical takeoff vertical landing electric powered aircraft for for use on Mars and basically they're talking about flying this and then basically those wings rotate to the vertical and come down like a helicopter and land it and then it does its science wherever it needs to be at and then it flips its wings back up and turns them around and then it flies elsewhere as you can see it's just got solar panels all over the top of it to power it and yeah what a really cool thing the ingenuity is done it's you know I think the aerospace earlier said that this is like the sojourner mission where we realized that oh what if we put the instrumentation on a plot on a mobile platform why do we have to stay in the same place to study something let's drive it around and see what happens now we're at the point of why do we have the instrumentation on on a platform that drives and is limited by the surface why don't we just put it on a on an aerial platform and we could go wherever the hell we want amazing that that's exactly what happened with this so enough I've brought up the map here from JPL so you can kind of see the yellow line is the flight path that ingenuity has taken and then the kind of white you like gray line is the path that perseverance has taken and you can see how the helicopter has been kind of following the rover since deploy but later on if I zoom in here you can see how it has been getting ahead and going over from this 2d image terrain that looks like it would be unfavorable to a rover like these looks pretty kind of intense and dunes here so yeah that's hop skibbity do over in front of the road that's go ahead that is certainly in I'm sure in the engineers eyes of JPL horrendously unfavorable terrain for a rover to drive on the last thing that they're gonna want to do is have it attempt to hit dunes like that it's it's it's it's a tremendous risk so so that's right it's not it's not just a Cybertruck that would have a problem with it but an actual rover on the surface of Mars would have a problem with it too so there's all there's also a risk to vehicles like ingenuity air powered vehicles or air flight vehicles with sandstorms and high winds that which they also have on Mars the wind I don't I don't think it's a one or the other like I don't think one is is inherently better than the other an air powered via or air flying vehicle obviously allows you to go farther and track more distance but there's also risk in being tipped over damaged by a storm or damaged by a windstorm or sandstorms so I don't think it makes sense to just go oh well now the helicopter worked we're just gonna do flying vehicles that doesn't that doesn't track to me yeah I feel like the winds on Mars are so the winds on Mars are a bit faster in terms of velocity than you would get here on earth but in terms of force because the atmosphere is only one hundredth density of what you have here on earth it is if it's sitting there I don't think it's gonna end up doing as much as say it would here on earth I would find it very hard to find wind tipping a rover something like ingenuity over at any point a sandstorm yes you don't want to find one of those if it's if it's motive and control surfaces are geared towards the atmosphere there then any winds in that atmosphere can adversely affect its ability to stay upright or stay controlled and I mean I would hope that they would have some sort of portion of their design or software that can help compensate for that I actually don't know about ingenuity if there was anything involved in terms of wind and turbulence mitigation with that there so yeah that's really okay Ray I mistakenly said air sorry we mean I mean it's atmosphere I would call air on Mars as well so Ray says when did they discover air on Mars well yes sorry not air atmosphere thank you thank you to our most pedantic audience today here is your award for that look we'll crowd here is your award for the biggest pedants most pedantic comment of the day with that where you win no points may God have mercy on your soul anyway in case anyone seen that movie I did like to people saying too that maybe we could use big flyers for for the lava tubes and other things like that on Mars I think that's gonna be a little difficult for caves and things like that just because the first of all even here on earth the aerodynamics of helicopters and their blades and how they operate is unbelievably complex and still difficult to understand even when you simulate it on some of the most potent stuff that you can nowadays there's a lot like I don't want to call it unknown there's a lot going on it's hard the basic the basic principles are understood but the like precision that you can find in a lot of other areas of aerospace is absolutely not there without the helicopters of rotorcraft at all so yeah so I don't know I don't know if they would have something flying go in to the lava tubes but yeah we'll have to we'll have to see with I don't know so JPL maybe like oh yeah we could we could design it to fly into the lava tube so I think they'll ever do you think they'll ever not do sky cream if you can figure out how to get a bigger payload to the surface but also I don't know that would I think that would require just straight up retro propulsion so it's something like a starship kind of retro propulsion so so yeah yeah that's a good point to battery longevity you also gonna have issues with battery longevity and flying vehicles that won't exist for ground vehicles very very true so so yeah what's bigger thermonuclear radiation generators yeah just how hard can it be right it's easy right it's exactly and I do want to remind everybody that we actually do have a full-sized rotor craft getting ready for a NASA mission called dragonfly which is going to be going to Saturn's moon Titan it is the size of a car and data as you just mentioned a nuclear battery to power it this one will have a radio thermal isotope generator powering and it is expected to do multi-kilometer long flights with there and in this it is so cool in the skids of it is where the instrumentation is so so it so when it lands it just samples the surface it's landed on through it landing here in a sense so and I think that's just so cool it's like it's like a fly it tastes with its feet exactly and and Titan is really great you could do that at Titan very easily because Titans atmosphere is thicker than Earth's atmosphere is and the gravity is so low on Titan that you also it's just significantly lower than you've got here on Earth the thickness of Titans atmosphere and the gravity of Titan is so low that with those two combined if you could build some wings that you could actually put on your arms you have enough power in your arms to actually take off like you could fly in the atmosphere yourself of Titan if you make wings that are big enough and light enough you could you could pull it up so and I think that sounds like a ton of fun if you ask me so I would I would love to give that a shot with that cool so do we have anything else so I know we had a slim which sort of tipped over a little bit unfortunately I don't know if we have that ready to ready to go quickly for the members show okay we're gonna talk about it in the members show okay there's some of the images that we got back from that are absolutely astounding a scene of spacecraft sitting on its head I guess on another on another body so yeah pretty fantastic we'll talk about that if you would like to become a member you could actually go to youtube.com slash TMRO slash join right now and join and become a member and get into the members only show that we're about to do right after we wrap this one up in just a few minutes like instant access to that so of course we also do want to thank our members of tomorrow as well hi Jamie that's right so if you as I just mentioned would like to become a member of tomorrow we've got different levels you get different things at different levels our escape velocity members here for example they get access to a exclusive channel in the tomorrow discord where you can see us talking back and forth to each other you like you got to see a little bit of put the viewer with us this past week you got to see a little bit of planning some of the jokes that we make and other stuff so very a very interesting look into a very interesting place for a multitude people the ultimate behind the scenes and if you would like to become a tomorrow member it's literally all it takes is as little as 99 cents a month that's a quarter of a cup of coffee per month become a member of tomorrow and we're supported entirely by what you do with us and again if you would like to become a member of tomorrow you can head on over to youtube.com slash TMRO slash join and of course liking the video subscribing to us commenting and sharing is always helpful for us here at the show so that's it we're wrapping it up we'll see you next week unless you're a member because we're about to jump right into the member show but I'm gonna be here next week how about y'all yeah should be okay all right well we will see y'all next week thanks for joining us on tomorrow today and members we'll see you in like 30 seconds or so bye everybody


Hi, I am Dr. Manjaraj Shastri. I am a general laparoscopic and cancer surgeon practicing for last 25 years. Lumps in the breast are a quite common problem among the females different age groups and most common breast lumps are most of them are benign. Benign, what I mean by benign is they are non-cancerous but at the same time they can present with painful or painless conditions. Just the presence of breast lump can be a finding most common breast lump that is benign is fibro adenomas that is what we call mouse in the breast because they are well defined and they can easily be moved in the breast tissue the other condition which is quite common in the among the females around the 30s and 25s to 30 age group is the fibro adenomasis which is basically a nodularity of the breast and many a times it is painful associated with nipple discharge either which can be serious or thin fluid like or it can be greenish or slightly yellowish rarely it can be slightly breast-tinged also but these are the conditions which should bring the patient to the doctor at the earliest and further investigations need to be done and for the according to the evaluated according to the evaluation we go ahead with further treatment. Lump in the breast is a common finding among the women of different age groups and they can be painless or painful painful breast lumps are commonly seen well in the puberty and menstruating age group where the pain comes or before the menstrual cycle about two to three days before menstrual cycle and some people have it after the during the menstrual cycle also this is usually associated with nodularity feel in the breast and they are quite tender the other common lump in the breast that is seen is a fibro adenoma or what we commonly call as a mouse in the breast by which what I mean is that they move in the breast tissue very easily and they are painless another word of caution nowadays is that the breast cancer is being seen quite commonly in the younger age group also so my suggestion is any breast lump in any age group please come to the doctor at the earliest so that we can investigate further and take care of it here at north side manipur hospital malaysuram the facilities are available round the clock and the nursing is very good they are very patient friendly I would suggest you should come here to see the difference and experience the treatment thank you


Hey guys, I'm here. I'm going to see what we're supposed to get as much as we can and see how much ground we can cover for a lot of things that we have to do. So let's go check a couple of things out. So yeah, so we're diving to the whole straight away. First, there was a Lupo Sufo panel. I got to see the Lupo Ultra panel pro with their full color 30, which was interesting and also the full color 60 which is like the one by two panel. And it was interesting that the output of those panels were comparable to the native output of the new released SkyPanel X. But without the only difference that we got was that the SkyPanel X had the additional enhancer which actually turned the soft light to a very hard source that could actually go far. At that period, it's only at that point that the SkyPanel X would perform the Lupo Sufo panel. There was also the Aladdin battling lights. And that was interesting, but it was not too interesting because there are other materials. I mean, there are other manufacturers who actually also had this technology like Pipelight and Lightstar also had like bottleneck technology. Then we had a very interesting vendor which was like Black Lightning Console that actually democratized CRMX across different brands without actually using a specific gateway. It was actually software sold on an iPad and it actually spoke like a router, a controller that could actually speak to every light and make them programmable given each of them. I believe they sold it via a unit versus and each universe had the ability to program the SkyPanel with its full DMX capabilities for about 25 units of this kind of fixtures. There was Lightstar. They were showing the 2500 watt with a 25 degree beam and it was actually, I'm kept in a 4 array grid and that was very interesting because it had like this glass lens system that actually had focus through and put together in the 4 array grid. I could see what the possibilities were, but there were no photometrics for me to actually understand much of the value in terms of to compare it to regular fixtures or what it can compare to. They also had like 9 degrees barrel lenses that was also available for their fixtures and their units. So I guess that's like a new system. We'll probably get to see more when they will put out more information and I think they're very popular in the Europe region, maybe not in America, but in the Europe region. Past that we actually got to see 9 solutions and checked out the grip solutions and the C-Panel which is actually quite interesting for tabletop and the current C-Panel that I actually captured in this video like the grip solutions were available. Also could handle like payloads up to 5.5 kilograms that's somewhere like an AUSA G2 with like a Canon CNE and your V-Lock battery and can actually do a proper counterbalance. You could use it for like your product shots and some other things. So then there was the DZO film that went to the DZO board and we checked out the couple, saw the Pavel which actually is a Super 35 anamorphic that covers full frame with vignetting and it was interesting. I had like a couple of experiences and I found some of it pretty interesting from what I saw. And we did get to see the genesis, the other range of lenses that existed, the tangles which were quite heavy and they were like a couple of more lenses I got to see and try out for the phone. I was really impressed with the DZO retro. It didn't feel any person, nothing jumped out at me. Then we went to the DJI board for a bit, checked out the transmission system. Then there was like the Athena company which was from NISI checked out their lenses, their combination with the running 40 and so many other things that was listed in terms of like adapters like using like PLD or adapters to be able to get it forward. It was like a comfort system. It was interesting. I got to like run it into the digital photo pro cine steadicam. I got to try it out. It was interesting, surprising. The amp can actually support about 25KG, that's about 70 pounds. And that was like a fun surprise. This led everything was not a lot more standard than the pro cine X. Then I got to try out the airy trinity. Now, a word of question. The rig is quite heavy. You have to have balance fit because in a steadicam mode the weight is usually towards you. But perting the airy trinity it keeps the weight and the javelin mode away from you. So it could be you could run into situations where you're being pulled by the weight and if you're not careful you could lose your balance if you don't have like great fits. If you're like a steadicam operator you would know this, but you would really feel the weight transferring. The airy trinity can take up to the alexa 65 but I think it's where you ask yourself what can you do, what can you take and how can you pet phone. And something interesting is that all the rules of steadicam goes out the window. When you know you have a normal steadicam you're using gravity to balance you control your movement, you control your breathing but the airy trinity has a gimbal head that actually levels the horizon takes care of the balancing so it's almost like a gimbal and all you just focus is the grip and how you actually use it and how you actually can move. If you even get to like when you get to work right you have like your watching controls that allows you to like do rolls, do tilts and do pants independently so you can actually do that and have like a work session where you actually going into like you can go through spaces, you can go through people you could actually do a lot. So everyone when you actually get to like use it in javelin mode and flip the camera and actually like look back and try to do a donno one it actually just goes seamlessly without thinking of it everything just cuts, cuts, cuts, cuts very seamlessly. So it's quite interesting to actually see that that was actually possible like using the rig so it actually opens a couple of possible interesting possibilities but that now said it is quite heavy, what I mean quite heavy, quite heavy if I was supposed to compare it I would say a 240 watt battery on an Amira that is a balanced and steady camera that has two more 240 watt battery and a 10 battery that's like the weight range I believe the trinity so you should be prepared for that kind of weight that you can that's not the same what you can carry because the lady who was beside was also a goofy operator and she also operated the trinity so there's also the getting used to it and maybe worrying for the first time just being shocked by the amount of weight after the trinity we stopped that I stopped by Techno Crane's boot, the Techno 22 and also like the Techno 17 and Super Techno 45 and we had like a conversation with understanding the process and a lot of things that I think were which was quite interesting. Interestingly I stopped by Godox's boot and they actually just released the 2400 by which is actually 2600 watt pull-in fixture that is constant throughout the range of Thompson to Daylight putting out constant power and that was very interesting, they do not have a price for it yet it's actually comparable to like a 4K power and it's mind-numbing that these cobs are just getting bigger and stronger and you can take like the spot fixtures, you can take those type of intensifiers that could actually intensify the output of the beam so it's just interesting how things are growing pretty fast, it's very interesting and that is more specific because of the technology and how you could use like battery power solutions to be able to like get a lot out of it you get a phoenix hat like a 2400 watt battery that could actually run that unit for like an R so if you're like working at 50% then maybe you get 12 hours of it out of it if you work with 50% of these intensifiers it will like get more punch out of it it's interesting, Goddus also had like a new prototype CRLX reflector system that had like divisions from like 1 to 4 that you could use just like CRLX from Light Bridge, a company actually also made it so it was interesting, they had like a full kit some of the pitfalls that most people are talking about in CRLX which is like the clamps that are like plastic they fixed it in the ass and made the ass aluminum to show the prototype, I didn't get to test it in the future but from what I saw I think it's quite interesting maybe it looks a little bit rough around the edges but there's a lot of interesting potential so I do not know if we just like a patent or whatever but clearly they can actually compete in that space and be able to like introduce that kind of material into the industry in terms of and they could like run from the 7 inch to like a 100 by 100 often custom sized compared to your orders so after that we did get to check out the 9000 dollar director if you find that which is quite cool and quite interesting which can also like be adapted to PL and EF that also happened and we saw cheap lenses that had like close focus for like 10 inches they are like lenses that you could use for like macro close focus and this was also interesting to quickly look at then we quickly stopped by NanLite boot which had like the regulars the four light kits and all the regulars that existed in the NanLux line after that there was Pipeline which was like the sister to the other didn't buy NanLite they actually existed that came across at the beginning of the show and they demoed their own inflatable light and how light it was to read like an 8 by 8 to read like the 12 by 12 wall which we saw there was like the 1 by 4 there was like 2 by 4 and this was interesting because of the applications you could use them how light they were and easy and safe you could actually like read them to several solutions it was interesting to see the quality of light and how rapid it is and it looks like it had like zones when you looked at them when you programmed them with DMX because they had like different channels and it channels like a 100 watts so if you can count the ripple lights and the balance you could probably almost guess how many watts each light source is and it does have like the entire cloud fixture too that also you could use in bringing in soft diffusion into the room so that was actually like interesting to actually see and actually know then we went to Angel Nudex which and they actually showed their optical system that they could take apart, replace, customize or then change the iris and interestingly they had a different approach of using spherical glass to actually achieve oval bulking which is different from the Payton system that the Hawks have you could actually install internal diffusions into your glass and you could do it like on your walk bench in a clean environment but maybe not in the field but in a clean environment you could introduce like diffusion fields into the lens you could introduce microcontracts ability you can change the iris shape from like a normal iris to like a triangular iris to be able to give you like a triangular bulking and you can also introduce like they have like a rare filtration system which you can also put more interesting fields like streaks ultra contrast or open clear glass so that was that and we also spoke like some of the veterans who was speaking from the explaining perspective of the lens while the engineer also was actually tearing apart the lens showing us how easy it was to be able to take it apart and actually run or customize there's a lot to your specification I guess most of the expense of the lens comes from the fact that it's been designed in such a way that anybody who is interested enough and bold enough would be able to like take advantage and actually customize the glass into something that is a lot more fun and that could actually like work and also the value that a lens carries was a camera that would actually lose its value every three years so those kind of conversations happen for a bit and it was interesting to hear other perspectives of professionals in the industry then we actually have grip renec factory GFM we saw their dollies and their lightweight cranes which is quite interesting and the dollar was rated to be able to pick up about 250K so that's like a single operator a camera and some more and it could actually get really super compact because it's actually a tall call of dollies so it was quite interesting to see some of these things and how they work and you could actually program your speed where you could do repeatable moves and stuff like that, that was actually interesting then there was a company called, I think it's called Rocky I forgot the name, but they actually created a show that allows you to like plug in your RS3 Pro which can carry up to like a 670 and probably a CNA lens on it, I think it was interesting, this is the first iteration I can see how this would be great for people who do reality TV work and how it would be to actually work for them so we actually got to like try it out a little bit and in front of me we just got to like experience it I think it still needs to be like cleaned up because there's so much jerry rigging that's happening that I feel it's still like in the first iteration maybe on the version 2 it's a lot more mature there's a lot of streamlines that would actually happen or when that happens so after that I went to Flanderside Football then also Cannons Boot and we explored the DPV 2720 and they had like a contrast ratio of like 200,000 to one and the other DPV 31 series had like a contrast ratio of 1,000,000 to one then I briefly dived into the virtual production Cannons offering and how they were using their cine flex zooms to like take the lens data into the lens and actually send like the focus the iris, the zoom information to actually match what already Angie was doing so I can trust in the conversation to see how that played out and by the time I was done they got really got fast for me and it was actually ended so today is day 2 I'll go about Cannons Boot and get like check out a lot more things and hopefully we'll be able to like have more fun and see what can happen okay so that's like day 1 IBC and until next time I see you improvise, adapt and over go it's been a fun experience talking to most of these manufacturers letting you perspective, letting you set in applications and yeah it's been amazing so far


for today of this wonderful workshop. For the first talk this morning, we're very privileged and fortunate to have Professor Manfred Oper visiting us. I don't think Professor Oper needs an introduction in this community, he's made seminal contributions to topics that span the range of what this workshop is about, and this morning he'll tell us a bit about the replica method for approximate inference. So please. Thank you very much, I'm really happy to be here and would like to thank the organizers for inviting me, and when I was invited I thought, hey, this is great, I can learn a lot of new stuff and I'm not disappointed, there's a lot of new stuff going on. But then in second thoughts, goodness, I haven't really worked in recent years on structured data, I did a few other things, what shall I talk about? And so, I got the idea to speak actually about older work, if I allowed myself to do that. Older work with Dürte Malzahn, that work came out of some work that we did on approximate resampling methods, and but possibly got a little bit overlooked, and was briefly mentioned on the first day by Professor Takahashi, and I hope that I can give a little bit of a complimentary point of view on the question of Gaussianity, and so let's see if that could be useful, but this doesn't work, oh it does work, yes. Okay, so my motivation came of course from earlier work on statistical physics using the replica method, and I still believe it's a very powerful method, and I know that by many of you, the results have been made rigorous in many cases, not the technique itself, but I still believe it's a very nice tool that can give you answers, and in this talk I will combine it with approximate inference method, but the usual typical ingredient, the replica method, the way it's used very often is we need to have a tractable input distribution that somehow leads to Gaussian densities of certain random variables by Gaussian equivalence, and then at the end it allows us to do explicit expectations using Gaussian integrals. The second typical ingredient, and that made me always a bit worried about when it comes to comparing to real data, was we need to have also a tractable target model usually called the teacher, and in the past of course there had been so many teacher-student scenarios discussed, of course mainly in simple neural networks, but we see now also yesterday that it can be done in a much broader way, but it seems we have to define a teacher also, and yeah, that together with the input distribution leads to some tractable random variables. And so if you want to use a comparison on real data, and I think also yesterday we saw this is nicely possible in the unsupervised learning case, but maybe it's a bit non-trivial in the supervised case where we need this teacher, and I read a very nice paper learning curve, so generic feature maps had been discussed yesterday as well for realistic data sets within a teacher-student model, so it was shown that I think this defining an appropriate teacher is okayish in the regression case, but already on real data for some classification cases, there were significant deviations between computed learning curves and the ones on real data, so I felt maybe this older work could give some alternative picture on that, so in this talk we try to avoid these analytical averages over the data that usually lead us to these Gaussian random variables, but we use an approximation to the replicated and average measure, any approximation is Gaussian, I mean there will be Gaussians of course, they had been so successful, but they come from a different point of view, so we use approximate inference method like the variational approach to approximate the replicated and averaged measure over the random variables of the students, if you want so, and always leave the expectations over data and the task within the formalism and work on that average sort of on a final stage, and as I said, we focus on the Gaussian approximation of that measure, and I hope nobody was really shocked this work is from 2005, still in the new century, not in the old one, and so, but we give, but we gave, haven't done essentially anything new on that, but we present the application to old machine learning models like Gaussian process regression problems and support vector machines, essentially kernel things in the kernel world. Okay, so I just give a brief review sort of on the standard way, possibly many of you know that, so the statistical mechanics applied to single layer neural networks, so usually we work on a statistical ensemble of networks W that defines, is defined by some Hamiltonian H, which is usually the sum over a loss function, summed over training data, we have also here a regularization term, and that can also be viewed as a Gaussian, a prior measure over the couplings of the neural network, and the lambda mu are these activation functions, and so that formalism can be used sort of to model a Bayesian approach, so if we set this inverse temperature parameter beta equals one, and interpret the loss function as a negative log likelihood, then, and we're interested in a Bayesian framework, so that would give us the posterior of the network parameters W, and then a student who wants to make predictions would be given by the conditional expectation of the data, of the weight vector condition on the data using this distribution, but as is also important for applications, very often we want to have not an ensemble, but look at sort of the minimum of the cost function, and so then we would take this beta parameter to infinity, and then this measure P of W given data would concentrate on that point, so this is the standard thing, and very often we use two simple assumptions, so the inputs X mu to the network are some D dimensional vectors, and we assume that they're independently generated at random, and the simplest case would be that the X, the components of each vectors, again would be IID random variables with zero mean and unit variance, and of course the simplest case in that case would be they're all Gaussian, but even we in the old days believed that maybe this assumption is sufficient to show Gaussianity for the activation functions. The assumption too is that the training labels that are kind of the, the training labels are generated by some kind of teacher target rule, so this is given by a weight vector WT and the probability distribution of the labels given the teacher activation function lambda T, and under that condition, we are interested in computing the log partition function, the free energy, which then can be used as a generating function for certain expectations, expectations over the ensemble, but then also expectations over the data when we compute the expectation over data of the log partition function, and we use the replica trick in order to compute that, so the expectation of log Z can be written as a derivative of the log of the expectation of Z to the power of R, when R is a real variable, we can take the limit R to zero and take this derivative and so we're happy, but in practice, we are able only to compute this expectation to the power of R for integer R, and then we try to bring our computations in such a form that we can easily do an analytical continuation in the simplest case in the so-called replica symmetric framework, and then in the result take the limit R to zero, and of course we can do the expectation of the replicated partition function and use already something very simple where we did not make any Gaussian assumption, using the fact that all the data and the labels are independent from each others, so we get N data points, so by moving the expectation over data inside, we get the expectation essentially over single data points to the power of N, and this outer thing is the Gaussian prior measure, usually I denote that by P zero, and in the applications where we say we have this nice Gaussian equivalent, we can perform the expectation inside here and in this term by saying, okay, everything depends only on that L lambda A, which are linear combinations of the IID random variables X, and if we have a central limit theorem or if the X's are Gaussian themselves, then essentially the high-dimensional integral over the X is becoming a finite-dimensional integral over jointly Gaussian random variables lambda one up to lambda R, R is the number again of replicas, lambda T is the teacher, so they are jointly Gaussian, and here is the sum over the labels that we have conditioned on the teacher lambda, so this is something that we usually do and we write that as exponential to G one of Q, where Q is the covariance matrix of that multivariate Gaussian, I think most of you have seen that, or many of you have seen that calculation before, so the only thing that remains is of course to integrate now over the weights conditioned on these values Q, and that is again, I'm sorry, here's again with jointly Gaussian, and these are the mean zero, we have covariance QAB, and so then we can express essentially the whole expectation of the replicated partition function by an integral over these matrices Q, where we have... So we have inside an integral over all weight vectors, replicated weight vectors, where we fix those inner products of the W's, so essentially, so the W's here are still coupled to each others, but they are coupled only in a macroscopic way that they have these conditions QAB fixed, so it's a kind of really a mean field model, very weakly coupled Q's, so essentially you say they are, if you take a finite set of the couplings, they are essentially independent, they would be even Gaussian, so you have resulting from this Gaussian average, you have a Gaussian distribution over the W's in this case, and so in practice then we can decouple the whole thing using simple, I would call them large deviation techniques, and then represent this whole integral again as something that is exponential in the dimensionality of the weight vectors, and then finally we can use subtle point methods to find the most likely essentially Q that we have here, but the only thing we need to do is of course something, we have to work on what's gonna happen in the limit R to zero, we have to say something about replicas symmetry or more complicated stuff, so essentially replicas symmetry would be to say if two replicas are different, two students are different, we have, we say all these matrix elements in the matrix QAB are equal, if we have, if A is equal to B, we have the self-overlap and there's also an overlap between a student and teacher, so essentially we plug that in and that high symmetry of this, of the joint Gaussian distribution that we have here allows us to rewrite the whole Gaussian average using, so we can decompose the Gaussians into a bunch of independent ones and the ones that couple them, and then the important thing is we have something, a Gaussian, an inner Gaussian integral that is to the power of R and now it seems like we can analytically continue R to real values and then live happily thereafter, so that is essentially the very old story and how this is done if we do first the average over the input data X. Okay, so now what we want to do is postpone that average a little bit and don't think about too much about Gaussianity or Gaussian equivalence of inputs that be a little bit motivated by this result, so we say okay, what comes out is essentially weakly coupled W's and maybe there are a lot of more cases where the W's are weakly coupled and become essentially independent Gaussian so does say okay, we approximate these replicated posteriors by independent Gaussians to start with, maybe we can use more complicated Gaussians later, so the idea is then saying okay, again we have training data, so start fresh, training data, now I call them M, the total number of them for a reason, now we assume that these guys are independently drawn independently at random from some joint distribution of inputs and labels and well, we work on that later, but maybe there's also a goal when we want to make comparisons with real data, we say okay, maybe we can sample from them in some real data by doing splitting of the entire data set into a training and test and then do cross-validation, so this is somehow that something we take as an input parameter to the computation, okay, so then we have this Hamiltonian for M data, here's again the regularization of the prior and to make things look a little bit nicer in the computation, I add another Poissonization trick saying okay, possibly in the large, I don't want to talk about a thermodynamic limit, but let's say for a sufficiently large number of data, it doesn't make sense, it doesn't make a big difference to assume that M is not fixed but is also a random variable with a bit of fluctuation, so we assume it's a Poisson random variable, so we fix N the number of data on average, so that makes the end result a little bit nicer, so remember that's what we get if we replicate the partition function and we have an inner average that comes from the independence of the data, so there's something to the power of M and here is the average, the expectation over essentially a single data point and here we have something to the power of M but we would like to have something nice in the exponential that we can use for the variational approximation, so we use that artificial Poissonization step of the number of data points and then we write this replicated average Hamilton partition function in a new way where we have this effective Hamiltonian for the R replicated variables and this H is now given essentially by an expectation over a single data point where we have the loss function here, so that's a simple way and it's nice to have this expectation in the exponential, you see that in a minute, so it's a relatively simple way of writing things down and now, okay, at some point we have to go further and do averages or whatever, but in general, let's say in finite dimensional cases for general non Gaussian axes, we cannot solve this or simplify and so we have, in a sense, we have this replicated model that defines as an intractable distribution over these weight vectors, W, so we treat it as a variational inference problem, we say we have this intractable problem that is defined by this prior part and here is this intractable Hamiltonian and so we would like to approximate this by attractable density, of course, Gaussians are usually a good choice in a case where the Ws are continuous random variables and so Q would have a theta in it and the theta would be the so-called variational parameter, before we had a problem where we said we get order parameters that come out of the calculation and we find their most likely, we find their macroscopic values, in this case, we say, okay, we introduce from outside certain variational parameters, theta, we have to optimize them by optimizing, when we want to optimize the approximation, so they replace, in this formalism, the old order parameters. So then we would say for fixed R, we would optimize these variational parameters by minimizing the Kullbach library divergence between the measures Q and P, so P is the intractable one, Q is the tractable one and so essentially it's this Q look, Q over P and integrated over all Ws, this essentially means we get a negative entropy term for the approximating distribution and we get something nice because we have to work with log P and log P has this, by taking the log of this thing, we get the intractable Hamiltonian down and we have to calculate the expectation of this object and there's also the expectation over data but now we do first the expectation over the tractable approximating distribution and fix the average over the data for a second. So of course if Q is a Gaussian then we get Gaussian integrals that would replace the Gaussian integrals that we had in the standard formulation. So as an exercise, you go back to this single layer network and we would start with a factorizing a Gaussian approximation. So each component of the weight vector, so all weight vectors are still independent but there is a dependency between the different replicas of the individual variables and so we would say, okay, they have a mean of course and but all these means are the same for all the replicas so that is replicas symmetric assumption or the simplest one but we also work with a covariance matrix, the R by R covariance matrix that has the replicas symmetric structure that we had used in the other approaches essentially copy and paste and now if we do the computation of the expectation of that term before we had an expectation over data now we have the expectation over the approximate distribution with a little bit of work you see, oh, there's the same structure, a bunch of Gaussian integrals essentially a Gaussian integral to the power of R and we can also take later the replica limit R to zero. And I haven't talked about any thermodynamic limit here but we see there is a little bit of a dependency on these variational parameters here, rho that has mi squared, xi squared in it and then we also can take the data average here if we take the standard teacher student scenario from before we can also carry out then the average over the data and we get agreement with a previous calculation let's say if we simply take xi to be plus minus one so then all the nasty exit dependency will go out and if we take all the teachers by replacing them all to be equal to one and arguing a bit with equivalence possibly that's okay so we essentially get then the same result as before I think if you would use other distributions you would have to discuss again a bit why this might work so in some sense you can get the old result by assuming that there is actually very weakly couples no couplings between the weights after the replica and so on and so forth and so this maybe it's a good variational approximation so now to do that for something slightly more complicated we looked into so-called Gaussian process models but if you want there are nothing but single layer networks in a sense so you would work again in the Gaussian in the Gaussian prior world where you have a Gaussian distribution over the weights and so if we define functions to be functions of the inputs as linear combinations of the weights with certain given features phi i then this induces a Gaussian distribution over function so a Gaussian process usually this is written as F is a realization of a Gaussian process with zero mean and kernel K and the kernel K is essentially this weighted sum over features and then you can show in practice everything all the calculations only depend essentially on the kernel and not on the individual features and then you go into a function a presentation where you say I have a posterior function over a posterior distribution or in function space so it's conditioned on the data which is given by a prior over functions that has this covariance kernel K and there is a likelihood term and so lots of people have done work on it I think it's still a popular method used in all kinds of applications and so you wanted to play with that slightly more complicated model and so this is a bit of an illustration so these would be samples from the Gaussian process prior so this blue thing is essentially the prior covariance and the prior variance of the function and if we have now one, two, three, four data points so we see the posterior concentrates more especially where we have data but there's still some posterior uncertainty and that would be samples conditioned on the data so that can fit functions and but still these functions can be non-linear so we are not restricted to simple linear cases so now if we do this program of a Gaussian variational approximation for the replicated Gaussian process models we would have again that measure and we would have this effective replica Hamiltonian it's just the same story as before so we have this E of X and Y and the average over E to the minus sum of replicas of the loss so very similar to that before but now since we are working now in that function space of course there will be dependency there's dependencies in the prior between two different input points so it would make sense of course now to work with correlated Gaussians and so that's what we did so we were assuming there is a Gaussian process measure that approximates this measure over replicas where essentially we have to define a mean function and we have to take a covariance essentially a covariance kernel for the approximating model that has the replicas symmetric ansatz in it and so the interpretation of that variational parameter functions would be R of X would be the data averaged posterior mean of a function at an input point X and these other two guys would then be the correlation of two expected functions on two sides or kind of that self overlap so they have an interpretation and of course in practice it's a bit horrible right we have to deal now with functional variational parameters so in practice we make a few further approximations there will be operator inverses but we make approximation that are justified for a larger number of data but we have not assumed here explicitly any thermodynamic limit but so we work with that formulaism and so the nice thing with working with these replicated distributions is we can also easily represent not only expected things like here the expected loss that we can now then evaluate in terms of the variational parameters but we can also work with fluctuations of losses easily in the replica pictures so if we are for instance interested in the expected loss to the power of two averaged over data sets we need four replicas and take the replica limit to zero but since all averages are Gaussian they can be evaluated approximately in that framework so here's just an evaluation on a simple one-dimensional toy data that means the input to the whole system is one-dimensional so and it's a uniform distribution over input variables so very far away from original Gaussian so and the loss again as I said is Gaussian process regression so it measures essentially the square loss between the predictor and the y and the kernel is not a linear kernel so it's not really one of the old problems that we had so it's a periodic radial basis function kernel that can fit nonlinear functions and here's two comparisons so when we simulated essentially functions from the prior added a bit noise to them and then did Gaussian process regression and compared the empirical training error and the generalization error with the predicted results that we got out of the replica theory so it's a very very simple toy scenario where we say okay many things because we have a uniform input distribution so a lot of things don't depend actually on X but we can also get decent results for sample fluctuations of measures for instance of the training error so how do they fluctuate between data samples and also the uncertainty of the generalization error so the replica picture is very nice doesn't assume that everything is self-averaging you can get fluctuations decently in that framework okay so now yeah there anything we can do with real data so there's an interesting thing so if you go into the this computational framework with this approximating Gaussians so you might be interested in saying something okay I want to evaluate certain functions that depend on posterior averages in a complicated way and we want to evaluate them on training data so this runs sorry that should be a mu it runs over all the training data so we want to know what is if we have an average over training data how can that be converted into something over test data so if we go into the formalism it's a bit complicated but essentially we can transform that into something that contains the average over the data generating mechanism we haven't specified what that is but within the Gaussian approximation we can relate an empirical average to an average over x, y drawn at random from the data generator and this is a complicated things evolving a bunch of Gaussians but for certain things they can be evaluated and there's one example that we tested it on so of course this is the uncertainty of the Bayesian uncertainty of the prediction of the function at the data points in the data set so this should be usually small but here is something that relates the variance, the uncertainty of the prediction at arbitrary input points x averaged over all possible over the density of input points so that would be the prediction that this method gives and so we can use simply a data set split it into test and training data and check if this relation is fulfilled and you see it seems to be fulfilled very well for well I mean that's what we could do there were data sets that you would do in the early 2000s so it's not really huge but it seems well possibly if they're bigger it gets better even so it means we can this method leads us to relations between certain things evaluated at data that are in the training set and they can be represent as I said you know this expectation over the data generating mechanism is a part of the formalism I don't perform it analytically but we use data splits for doing that okay how much is left okay okay fine so as a relative to Gaussian process methods we also looked at support vector machines and so they can be viewed as some sort of Gaussian process model so we have the problem of a classifier with binary labels plus minus one there is also a feature an implicit feature representation in terms of weights and the support vector training can be formulated as a minimization of the length of the weight vector such that these things are always greater than one and so it's also known as a maximal margin classifier and the nice thing is based on the kernel trick everything can be expressed in terms of kernel so in many cases that would be a sum over infinitely many features but we can do everything with kernels and so at the end of the day to relate that to the previous things that we've done with Gaussian processes we can work with a pseudo posterior P over weights using that pseudo likelihood and a prior that essentially says we make the prior over weights smaller and smaller so that enforces the minimization in the limit epsilon to zero so we work with that pseudo posterior do all the things that we've done before then take the limit epsilon to zero to reach the support vector limit and finally we rewrite everything in terms of measures over function space and then have again the replicated measure over functions and here's this prior measure and as before some thing that has this expectation front of it so we can essentially use really apply the results that we have developed before for Gaussian, similar to Gaussian process regression and so some of the predicted relations would be for instance, there's a number of support vectors that are the data points that end up on the margin so this can be represented by an expectation over the data generating mechanism phi is the cumulative Gaussian and this involves a bunch of the what I would call the order parameters or the variational parameters that we had and they can be also computed from the data sets or that would be the zero one loss the number of misclassification you can do more complicated things but they would fill sort of the whole page and that's why I didn't give it so again, we can use try that on real data so there was a data set which is nine dimensional here, not infinite dimensional so we get decent approximations here this is not so nice but for a small number of example data possibly the approximation is not so good so yeah, essentially this is all I wanted to say so I try to explain a framework where we have where we approximate the replica measure for a given data distribution but not performing the average over the data explicitly not making a explicit Gaussian assumption but the Gaussian assumption comes in of course in the variational approximation what I also liked was we did not have to define explicit teachers in a supervised learning case and the approach predicts certain relations between expectations on training and test data that could be of interest and they can be applied to kernel machines and of course, yeah, can that be useful? I don't know, first of all, can it be extended to interesting things? I mean, I don't know what, we all have different interests, I mean, I like dynamics and I always said, you know, how can't we apply this to dynamics to get something that we can compare on real data? That's what I want to do. Well, neural nets, who knows? I mean, we know we have Bayesian neural nets essentially where we do, where people approximate posteriors over network weights using also variational techniques and make all kinds of approximations. Why not put replicas on top of that approximation and do something with it, maybe it's possible? Of course, I mean, maybe there's nothing for the mathematically minded among you. This is, I don't know, when is this good or bad or it might even become exact under certain scenarios in the thermodynamic limit? Is there something that can be said in that cases? And of course, there will be hidden assumptions on data distributions. When does it lead to effective measures over these weights, W? But I sort of circumvented that problem, but what I got was something that might be easier, computable without assuming specific teachers. But of course, it's easy to construct counter examples. Essentially, if you wanna do it in a practical situation, so I mean, the idea was be to sample data from the generating mechanisms or relations between training data and hold out data, but let's say we have a relatively small data set, then essentially you sample from a discrete distribution. And we had done work before that. I was also mentioned in Professor Takahashi's work. Yeah, so we would have to introduce extra random variables saying, oh, is this guy inside of the training set or not? And then it turns out the simple Gaussian approximation is not good. We have to do something a bit more advanced, though for instance using expectation propagation things. So we have to go beyond the Gaussian approximation. So it's not something that is always working, but yeah. And then at the end of the day, I thought, oh, maybe what I'm doing here is maybe a little bit related to somehow averaging over TAP equations, but I don't know the answer. And I think now I'm done with my talk. Thank you very much. Thank you very much. Very good talk. Yes, the one note, the good point of the variational approach is a guarantee of the approximation. For example, we can have a bound or something. Yeah. Yes, yes, yes, this is my question. So if the R equals zero, it does not hold, OK? So we cannot expect it to create some bounds for R equals zero. OK, thank you. Yeah. I thank you a lot for the talk. It was very interesting. First, let me say that here. Sorry if we have overlooked this work. I think it's very interesting. I personally didn't know it. And I think some of the approximations you make, I think, are related to some of the stuff that we have been doing in kernels, and some colleagues have been doing in kernels. So we should definitely try as a community to fix this overlooking. I have two small, maybe more technical questions. The first one is if I understood correctly, the average over the teacher, you just defer it to the end. Yeah, and we're never doing explicitly. Yeah. But when you want to plot these learning curves, somehow you need to take this average to be able to plot these theoretical curves. So how do you do that? Do you approximate it in the whole data set as a population average over the whole data set? And then if you want to know the performance, I don't know. OK, thanks. And the other small question was, I was really curious about this grand canonical approximation like or this Poisson introduction like where you let the number of samples to fluctuate. So do you have any intuition of why this is important? Why does it make it easier? OK, I'm going to take a look. Looks like a nice trick. Thanks. Sorry, I had the equation, but that was his second question. No, it's fine. I guess I have maybe a naive question. But so is there an example of a system where you understand this approach to be exact, but maybe it's not exact if I don't consider a variational approximation? Yeah, is there a simple example of a model where you understand this to be asymptotic? Then it reproduces the replica calculation, right? OK, if there are no more questions, let's thank Professor O'Pher again.