board members are present except board members gallantine and fennel and vice chair percer so we have no minutes to approve it this time so so number two board business complete number three board business cultural heritage board this is our this this is what our role is what we do so I'm gonna read this because we asked for those of you who may not know we asked that we we asked ourselves to actually make this available so we can read it so the public knows what we're doing so the cultural heritage board shall consider the following matters standards guidelines and criteria to the extent applicable in determining whether to grant or deny a permit whether the proposed change is consistent or incompatible with the architectural period of the building whether the proposed change is compatible with the adjacent or nearby landmark structures or preservation district structures whether the colors textures and materials, venestrations, decorative features and details proposed are consistent with the period of significance or way out of that and or are compatible with adjacent structures whether the proposed change destroys or adversely affects an important architectural feature or features the Secretary of Interior's standards for rehabilitation and guidelines for rehabilitating historic buildings such other matters criterion standards as may be adopted by the resolution of the cultural heritage board and ordinance that follows so that's what the board here does so next we're gonna ask for public comments any public comments that have nothing to do with what's on the board agenda does anyone have any public comments just for fun you can say anything you want nothing okay hearing no one having public comments we're gonna close the public comment section and and now we're going to ask for any statements of head sentience and so hopefully no one has to abstain or will be in trouble so and also of site visits so because I've been to the site before I only did a very abbreviated site visit this time and I was there back in February I think and looked at it so February and yesterday's good deal I went by last February but I did not get a chance to go by again and no abstentions thank goodness okay now no until our scheduled I so we're ready for staff presentation thank you madam chair to Shazam members of the cultural heritage board I'm Kristen a to me and senior planner with planning and economic development and the item before you should look very familiar since it's an addendum to a previously approved project back in February it is a single-family resident existing single family residents at 629 Monroe Street shown in the figure in outlined in yellow so it's not the quarter of Monroe and 12th the project parcel is located within PD 0226 which is a zoning district with a historic preservation overlay it's also known as a junior college plan development district and it's divided into various zoning sub districts this subject site is identified as R1 PD single-family residential and the parcel has been developed in a manner that's consistent with the R1 PD sub district of that plan development the original dwelling was constructed in 1924 and is a contributor to the McDonald preservation district that district was established by City Council in 1998 and recognizes the period of significance ranging from 1878 to 1940 the general characteristics are pretty extensive in the McDonald district it includes large medium small houses on medium to large lots with less than 40% lot coverage entry doors generally face the street and many lots have alley access as does this project most buildings are one and two-story and are clad in wood siding or stucco and the dominant architectural styles include bungalows colonial revival craftsmen English cottage Greek revival hip roof cottage Italian at Mediterranean revival prairie school provincial Queen Anne shingle stick Eastlake Tudor revival 1930s track type so a wide very wide range this is the dwelling as per the DPR it shows when it was built in 1924 and shows it as a contributor the project is immediately surrounded by single-family homes as you can see you can see the alley access to the rear this was the original elevation prior to any modifications to the residents this is the view from 12th Street and that is the garage in question this afternoon on February 21st 2018 the cultural heritage board approved a major landmark alteration permit found number LMA 17-025 to allow the construction of a 436 square foot addition and to interior remodel to the rear the main residence the demolition of the existing 563 square foot detached garage and the replacement of the demolished garage with a new 462 square foot detached garage with a 462 square foot accessory dwelling unit above so this is a rendering of what was originally approved back in February but some minor additions for lighting and other design details garage I think the garage door and on October 26th the applicant submitted an amendment to that major landmark alteration to modify the previously approved scope of work they decided not to include the accessory dwelling unit above the garage and the the applicant proposes to replace that existing 563 square foot garage with a new 416 square foot garage so slightly smaller with no accessory dwelling unit above no other changes are proposed from the original originally approved plans because the originally approved plans included some alterations to the primary dwelling here is a site plan you can see the very light purple shaded rectangle represents the garage that is the proposed change here is a first floor plan just shows the garage that would be 12th Street on the top of your screen here is an elevation of what is proposed so you can see to the right of that screen is where the proposed garage will be located here is a comparison of what was previously approved and the proposed revision the historic architecture report dated November 26 identifies the proposed changes as a mix of preservation and historic rehabilitation efforts as described by the secretary of interior standards of care according to that report most most of the 1924 craftsman is intact while the detached garage with the tar and gravel roof was likely added after 1950 the historic conditions will be matched as practical including doors windows trims and finishes the applicant is proposing board and baton siding up to the water table and then shingles above that to match the existing primary residence there's also a plan to superficially attach the garage to the main structure with a breezeway the applicant provided some proposed lighting fixtures there's one craftsman style lighting fixture proposed centered above the garage door and you should have those in your packets based on the information provided the planning and economic development department recommends that the cultural heritage board by resolution approve the addendum John and Andrea Hibbert addendum garage without a du for the property located at 69 Monroe Street staff is the staff and the applicant representative are here if you have any questions thank you good job so refresh our memories remember what we did the last time but anyone have any questions for staff maybe we just start with mark and work our way down I'll echo the comment made earlier thank you excellent presentation you answered a number of the questions that I had in your address I noticed that there was a cloud around the floor plan which indicates a change on the floor the garage area it was that changes limited to the removal of any stairways and making it a little bit smaller or are there any other changes associated with the footprint of the garage the applicant representative Mr. Perry could add a little bit more detail but my understanding is the the revision includes a slightly smaller garage so the original approval was for a 462 square foot garage so slightly larger footprint and now they're proposing at 416 square foot so they lost almost 50 square feet did you have anything to add to work okay second question I don't see any clouds around it but just want to just make sure the trellis area that connects the buildings are there any changes in that okay my understanding is there's no changes still be connected with the breezeway okay and let's see last couple it appears from the comparison drawing you head of the elevations that the roof line on the tower I'll call the tower section has been lowered I think that's the case I believe so the drawing you see at the bottom is part of the building permit package yes all right for the benefit of the other board members thank you since it's no longer ad you we're a little bit more free to comment on the garage that said I think it's fits better with the neighborhood in its current configuration that I did before that's a comment I'm supposed to be asking questions I know one last question is were any alternative locations considered for the light fixture on the garage I'll defer to the applicants architect okay put it over the garage door centered okay there is I'm not sure how we got the elevation incorrect the shingle comes down to the water table it looks as if this elevation has two materials above the water table and it the plan is not to do that it's but it is to have shingles directly from the water table up to the underside of the eaves the I have a set of the actual approves construction documents that's been reviewed by the building department and that's what we're gonna do so I'm not quite sure how we I think that's an older drawing or something or maybe I did that when I made the application and saw it as a drafting here and corrected it but above the water table which is just above light fixture is shingles not two bands so you have board and bat up to the water table and then shingles above the water table all right thank you for the clarification that's like do you mind passing around those construction drawings thank you all I was gonna say is I have no questions at this time just wait take a look at those two it's better to speak into the microphone so that we can record your conversation yeah this guy like I see there's school there are square ones rather the long okay and then we also just took a look at the the siding on the on the new yeah we looked at the horizontal siding I mean the board bat below the water table and then the single shingles above the water table that's right and we know the curb cut and the reason the door is off-center on the new garage is because of the curb cut right and the other original the original taking a look at the original garage that's my sorry John yeah I would that's funny mark marks playing musical chairs so did you was there an option I mean I'm sure you know this is just a question it's not a requirement or anything that I'm really looking at it but obviously we're all aesthetically looking at the way that that door it's no with the gable and the off-center in the light is was there any way to adjust the curb on the on the street well we dropped the ad you to say three hundred thousand dollars in the curb cuts would be another 10 so not really we just trying to maintain the sidewalks and curb cuts as they are and actually I value original curb cuts in historic districts and sidewalks and try to honor them when possible and the use that's original though because if that garage was put in the 50s it would be outside the period of significance so sure but there's a half to honor it but I completely agree with you but I think in this case you wouldn't wouldn't need to honor that curb cut I do find that new concrete is a much different coloration than aged concrete that's true so green green new concrete is more aesthetically disharmonious in my mind so I try to maintain any site work I can I hate to lose the ad you obviously I mean that's great I do like the the new drawing I like the lower I think it looks great I would concur with with board member debaker I I think it fits in really well and I like that you drop the roof line for the hip roof on the center portion lower than the the ridge line of the of the house I think it looks great was there was it just feasibility as far as financially that the ADU couldn't be part they started realizing they they didn't know that they wanted to be up there they up that high they were sure they're gonna be using it and it is $350,000 worth of construction that nobody was gonna offer them yeah they didn't they decided they didn't want to do it they wanted to have because we've had to re if you've been by the site you'll see that because we do have a building permit we have started construction and we have demolished everything that's going away and we did find which is the other reason we wanted to cut some construction costs we had to redo foundation work throughout the back of the house and make some some other changes yeah so they wanted to have some buffer in the budget and they were not that invested in the ADU but they loved everything else so it's all going just the way we planned thank you for your approvals oh I think you brought up the skylight did we I don't remember in our minutes from the last meeting if we how that was addressed or I don't know if there's anything we can pull up I don't believe it was addressed I was just simply confirming the reconfiguration of the skylights as a change to a longer unit to a square unit in the other direction yeah I don't like them either if I can get them to do flat ones I will yes right that a placeholder pure medical skylights I use on houses those I don't know that we do that there they may or may not even do the skylights they may just make it flat but if that's okay we'll do the lowest profile flat skylights we can find mark come on there we go couldn't see the light if I can one more question the garage door type that's in there yeah we we have an image of it at the end of the package it is it's a custom door but I like it and that's what we'll do I think it's very appropriate my question was is the intention to paint it or to do a natural metal I don't know I don't know I would prefer natural finish if I had my way most of the context there is painted in this case yeah I like it myself but I'm not sure that's the the norm for that neighborhood yeah okay thank you I do have a question if we're asking go ahead John yeah go ahead mark can you just talk quickly about the casing facing the north of that large window ensemble what is it made out of and how far does it project from the wall it's 11 inches off of the face it does not project beyond the eave line it's three and it's about three inches behind the eave line and it is a solid it's framed out of parallel lambs which are about three and a half inches thick all the way around it's basically a box window and here attached to the face of the building with finished wood all the way around painted the color of the trim so it's going to be a flush finish is it so at at the bottom I can't tell from that shadow it's a it's a bay it's basically a bay window that projects off the face of the building up to the eave line it's square at all edges there's no nothing correct okay thank you correct you might note that I actually managed to squeeze in square foundation dents those are kind of a fashion forward and unusual but I like them a lot thank you yeah I like those two do you have any other comments John questions all right I don't think we have any other questions of staff or does anyone have any any more questions the final final comment on the garage is the right side is also workshop so it makes the doors offset because a the curb cut that's the closest we could get to it and be the use of the space is for a shop to the right of the garage door so but then did we did we take a look at the light is the lighting fixture it's included right and yeah it is it's a it's a craftsman style it's kind of a contemporary craftsman style it is a led light so if you haven't seen them it looks rather cool because what you're looking at is a grid of bronze and then the light is up above it and just illuminates the grid of bronze and the and the ground below it so you don't see the light you just see the grid of bronze it's recessed in the top of the fixture and then it goes along the face of the building so it illuminates the building you don't see the light directly because it is a down light it doesn't you're not looking at it doesn't hold the bulb the bulb is above the fixture so it's just illuminating the craftsman grid and then the ground below it and a soft wash of light on the wall that's interesting yeah actually rather interesting yeah I like that too my favorite part of the square foundation that's fixture the rest of it's just it's the little things right well the rest of it's just appropriate architecture you know yeah that's right well I don't have any you know any other questions or comments um I you know let me just go back through the board and see if everybody's on the same page Mark you're I do have one comment for for discussion though there is precedent for having a roof line that much high higher on that garage rather than keeping down low close to the the garage door now there is precedent for that so I'm assuming that it's that high not necessarily because you need the space in the garage but so that the breezeway works both mainly the breezeway to receive cleanly the car the trellis area but also it's nice to have a space in the garage it's going to be a shop so do you think they might consider lowering the roof and the breezeway well the breezeway wouldn't we can't lower the breezeway roof because it the building the fact if you go back the grid the ground floor of the houses is higher so that breezeway is just at the head height as you come out the door to the residence so it won't work if we lower it okay thank you for clarifying that and it was already approved so I didn't want to change it do you know what architectural page number you're referring to so I apologize I don't have a slide for this page but it's a cross section of the elevation it's a longitudinal section of the entire residence and garage as proposed I think that shows well that how you would come out that door right in the height that is this is a head height that we want right so the room it is a cat too so it has to slope to drain so the lower this is a lower end of the back side you can see that right when you were looking come on uh yes I can see it I understand that I think for the disproportion wise the garage itself would be better if the roof was lower that said uh as I said before there is precedent for having the taller garage bases um and it's not a dew killer it just would improve the the proportions to be more like the house to have the roof line of the garage closer to the door height just sidebar we have lowered the roof nine feet from the last project that you approved that's right yeah it's funny how you notice different things the next time around right six feet it's six feet low it's sort of good to talk about so then when you're doing other projects later then you remember that that's something that sort of popped up right even though like Mark said it's he's not considering a deal breaker but it's those are uh good comments for sure just a friendly reminder to open the public comment if there is any I shall do that right now is there any public comment about this project come on no public comment all right someday we'll have a public comment right thank you closing for public comments about this project John you want to give us your overall sort of feeling of where you're at on on I like this project I like the October 18 2018 revisions I like that better than the uh what we originally approved on in February I think it it looks better it's more aesthetically pleasing to me and uh I appreciate that even though you're doing that because of budget considerations which you've done that very well and and I appreciate that so that's where I am I'm going to support it excellent John number two I would echo John number one's assessment I think the design has improved by unfortunately moving the removing the adu I think the proportion and scale and it's just fine and it kind of reminds me of the typical Thomas hubka big house little house something barn so there's a succession of roof lines descending in one direction I have no other comment or or concern I would move that we would approve this at the opportune point thank you well that sounds like a good suggestion so we need a motion do I have to read the whole thing you can wave the wave effects I would make the motion that we accept the revised drawing set that was developed in October 2018 and that we reviewed today I'll second that I'd like to offer a friendly amendment I agree I think so just one stipulation on it would be that that I'd like to see the garage door painted rather than natural finish on that just to be consistent staff also request clarification on the cladding of the garage what should be above the water table should it be as shown on the plans or should it be shingles sorry I don't understand the question so the plans that I have show horizontal siding above the water table followed by shingles and the architect is requesting a change to just shingles above the water table is that acceptable to the board and if so can we include that as a condition and this is related to the front elevation of the garage yes that's correct I make a friendly amendment that we accept that change so I think the painting is a suggestion but I think the wood is a condition if that should be correct because I don't think we can require the painting correct so so the person who made the motion can either accept or reject the friendly motion yeah just to clarify the statement we don't have authority to declare color we do have authority to direct finish and I do would like the painting to be conditioned I guess I would have to ask something of the applicant is these are custom doors right or have you purchased them yet okay we would still main you know we don't need to do solid wood doors right we would do the same design it's still have the diagonal and the lights okay if it's not diagonal it'd be a bead board style you know vertical board instead of the diagonal board amendment which are now conditioned and just to clarify the cut sheet on the door provided us indicates the doors available either in stain or paint finish okay I think we're ready to take a vote you accepted those friendly yes I did all right let's do it all right thank you congratulations great job thank you thank you Martin I'm going to pick up my plans and head out for happy holidays all right so we have a little board business um board member reports we really don't have any some of our board members aren't here I think that if we have any anything to offer any information in regards to areas that you're used to covering or um anything you think is important that we can we can go ahead and go through that report yes okay all I have this time I don't have any news from St. Rose I do have I did receive two complaints one from the mcdonald district on our last one and one from a previous project approved at Ridgeway where the public has complained that with about our approval of projects that they we should have been more stringent could you amplify on those complaints a little bit for my I'm sorry I can't could you amplify on those complaints for my edification thank you the complaints that you got the complaints yeah one was a former chp chair uh not chair but my member actually both were former chp board members one is a builder and one is just one of the founders of the cultural heritage board so the nature was we were too lenient or what was the correction okay we think they didn't think we were stringent enough okay so john number one any comments or anything you know that's going on that you would like to share in regard to heritage or preservation or in sections that you're mcdonald district or anything like that I have nothing to report I have no comment thank you so um I don't have much but I um did want to let you all know that we as part of my um uh class that I was teaching for grad students at Sonoma State for the National Register we did review part of what I tasked them to do for this semester was to review and rewrite the deter ground bar national register nomination so currently the national register nomination for the deter ground bar and only includes architectural which is unfortunate because the real history and the interest in that building is actually the events that surround it which are the trotter horses and deter himself so um it's sort of the growth and and what that happened so so um I have some fantastic history and and some other details and the students at Sonoma State in the grad course are asking me to continue the efforts to amend the national register nomination to be more accurate and fuller and and have more interest um and understanding with the city of what the impact of deterq was and and the horses and and nationally what that meant and locally what that meant and and all the history behind that and how significant that area is um so uh the information that I have is going to be repackaged and what I'm going to try to do is have once I get it sort of gathered up because the classes ended and there's just finals next week so what I'm going to do is um uh it's going to be resubmitted um as amendment to the national register nomination which will go the office of historic preservation but I believe is my understanding that the students or at least some of them also want to present either refor city council or refor this board with what they're doing and what this is um now this would be after the class is over so um but it is um similar to the uh the korea adobe where you know um it may you know be already listed somewhere which this one's already listed on the national register and is already locally listed but it is not listed it's not fully been vetted and understood so this is an opportunity to understand a better history of that area and a turk um there's information about um uh this sort of event that was a nationwide event that happened in the 1800s and why he had these horses and these horses he had and how the fairgrounds was involved and how the mayor and the people and things that happened in the city that surrounded this um there were a really important event to this city that is just sort of lost history right now so um so anyway we have that but I promised them that I may not be able to do it within six months it could be a year so you may not hear about this again I might get him an update um in the spring but it's a lot of work and um so we'll just see how that goes but thank you that's wonderful I do have a concern you know that uh I think the the history of the events and persons needs to be played up and improved absolutely but I don't think that the importance of the structure should be decreased and I thought I heard you would the structural stay the same we're adding to it so what what we're doing is a lot of times national register and denominations are done very easily with just the built environment so someone will do it they won't bother to do the event or the person or the data the the research so they'll just go straight to that because it's easy and get it on the register but you lose sort of that context so this is just um adding to it because without the building you don't have the context for the um for the event so it'll be it'll be fabulous and you'll love it yeah I was really impressed the students worked really hard um and they worked on it all semester so I think it was an effort worthwhile so I'm really super proud of them so as a city-owned property I know we're not a CLG should that come before us before it goes to the the state uh it can't they can present it but they you know it it the city has to agree to the amendment um because it's a city-owned property so but it still can go up for uh nomination it just can't be amended officially and re-listed differently unless the city approves right I guess that's what I'm saying if we're going to go through the process why can't we act as the board to review okay that's what that would be my recommendation I mean I don't know how that plays out but christenay maybe you could um you don't have to do it this next month or two months or so maybe down the road we'll bring it up again you can figure out where how how we do that but I think that's a great idea because then we are really participating in the things that we're supposed to be doing that are part of our um yeah mandate uh we're you know adding things to the register adding things localist and and and I think that thinking is for the ssu students so they were really fantastic so we'll do more hopefully so that's all I have um shall we move on to department reports thank thank you um there is you should have received a request from staff confirming your availability for january 17th um for a drb chp joint meeting it will be a combination of a meeting and a walking tour and the plan for the walking tour is to begin at 230 and it'll be a walking tour of the downtown station area specific plan area which will begin and end at city hall the tour which will highlight specific points of interest within the plan area and will take about an hour and a half and then the joint meeting would follow the tour um at around 430 with the drb and the cultural heritage board a study session that'll be held um during the drb meeting and what we're hoping is a study session will be a kickoff and scoping discussion of the downtown station area specific plan update project um so far we have confirmed um board member murphy and mckew but uh we need at least two more board members from the cultural heritage board to confirm so i i'd already said that i'll be i'll be in washington dc so i can't attend so i wish i could but may i ask who has the contract who's doing the the initial worker the hot tea or the adi and i forget the name of the environmental consultant um i saw them at their kickoff meeting last week and at the joint meeting will we be uh making decisions on anything or is it just a review of what they've discovered today it would be a study session and a scoping discussion so what they should pay attention to during their review okay uh chair i just wanted to express um at the current time i'm not able to stay on my feet for two and a half hours oh yeah um i'm scheduled to have a double knee replacement uh sep could be in january at this point oh yeah uh so i will be um i'll know more this friday okay um but my mobility may be limited during that time definitely so that's something i'd like to be rough but i'm sorry about that keep that in mind did you um so are we the only board christine that is not uh does not have the quorum for the 17th right now yeah what makes it difficult is it's a regular dr b meeting not a chp meeting yeah and right now we just have only we only have two uh confirmed cultural heritage board members we need two additional um to have a quorum i should have a better idea uh after this friday maybe what might be helpful is if they put what i would suggest is maybe they instead of just i don't know how you're gonna how you're gonna plan or how you would do it but it might be a good idea for them to actually during their prior to the meeting um on the 17th if that takes place in january that there's um some sort of questions or ideas or what they're looking for from the cultural heritage board so that that we can be prepared so even if it's just the friday before what have you that it's sort of you know these are the things that we're looking at these are the things we're needing to know what you know that can help us move forward my understanding is the focus will be on how to increase height and density in the downtown and from the culture heritage board perspective how that would interface in an existing existing fabric of a neighbourhood um also to answer um board member murphy the consultant name is diet and batia spelled d y e t t and b h a t i a urban and regional planners so i think one of the things you're going to run into is probably that um because there hasn't been an inventory down downtown so that's a problem so you can't really discuss height and density if you don't know what what resources that you have so um i mean i don't know how they could move forward with even that conversation without not knowing you know it's just going to be piecemeal so um and very little has been evaluated downtown so very little has had historic resource evaluation so i'm not even sure that folks on this board could address what you could do with height and uh density bonus in regards to placement downtown when there's no inventory i think our mandate would be limited to the areas of the stationary plan within historic districts for those overlap but i think you were specifically talking about downtown so be the downtown station area specific plan area and i think there's only a bit of um uh one of our maybe two of our local districts sort of bleed into maybe the top which is a very small portion but that whole area within the downtown um specific plan area that i think extends now to the east has not been evaluated nothing's been inventoried at all well i think we at least based on existing conditions we could talk about the historic urban form and how it's evolved without and i believe the downtown station area plan is the downtown station area plan and it includes most of railroad square and other areas we'll need to see them out i don't think it does i am confident that it does yeah quarter square it's meant to be dealing with mass transit and how the downtown responds to it and includes railroad just to be clear there are going to be areas within that that we don't know anything about right so i think that that needs to be understood like there are sections like you're saying that look so it does cover say railroad square and all that that's areas we could speak to but i think it would be um it wouldn't be within our purview to speak to things that are outside of those preservation districts beyond making sure that there's a requirement for professional survey correct that's right yeah so we want to make sure that we understand that there's an understanding of what our limits might be well the meeting won't occur if we don't have four of you so all right well i'm sorry i can't be here it's an important meeting to have very important and um you all are gonna you you both got the email and take a look at your calendars and respond if you can this week would be great oh um board member murphy and mckay you are confirmed so i'll check you're the two that are confirmed yes i will check with um the absent board members okay so we hit on we have a maybe we'll check i cannot and then you got three three others you're gonna check on okay great all right okay any more department reports no no thank you okay perfect okay well um uh i believe we can adjourn is there something else i need to read or do or is there anything i'm missing it's crazy month all right well let's let's call this meeting to an end then one more question it can go beyond ending the meeting is there a probability of a meeting on the 19th or do you not know yeah i don't think so i think the next meeting is january but i can confirm and then do we have just just a quick question do we have anything on our agenda for the beginning of january so far do you think something's coming around yeah potentially um okay yes one of my projects actually do you do you and i'll talk to you after because there's a project i'll have to recuse myself from so i want to double check when that might be coming up okay okay great all right i think we can adjourn


Hi, welcome back to analyzing software using deep learning. So this is a course at the University of Stuttgart. And in this part of the course, we will look into how to use convolutional neural networks for classifying programs, or more generally, for reasoning about programs. Convolutional neural networks have been pretty successful in other areas where you have to reason about some kind of hierarchical data. And because programs are naturally also represented as hierarchical data, it makes a lot of sense to also apply convolutional neural networks to programs. And in this lecture, we will see how to actually do this. As for most of these lectures, I'll start by giving an overview over the kind of neural network architecture that we want to talk about here. And these are, in this part of the lecture, convolutional networks. So I'll introduce what they actually are, why they make sense, and what their properties are. And then in the second part of this lecture, we look into one specific application of convolutional neural networks for program classification, which is based on a paper that was published a couple of years ago and has had some impact since then. As usual, if you want to know more about this specific approach, feel free to look into the paper because there are many details that we cannot cover here, but they are covered in the corresponding paper. Let's start with some historical background. So as many interesting results in neural networks, the idea of convolutional neural networks has been biologically inspired. And specifically, the inspiration here came from experiments done on animals to find out why and how animals are actually seeing and how their brain processes whatever they can see. So in this set of experiments which were done with cats and monkeys, the researchers found out that some neurons in the visual cortex of these animals, so in the part of the brain that is responsible for seeing, that some neurons in this part of the brain correspond to small regions of the visual field of what these animals are seeing. It specifically turned out that there are two kinds of visual cell types. On the one hand there, what they call simple cells that correspond to specific parts of the visual field. For example, they respond to straight edges that have particular orientations that appear in the visual field of these animals. And then on the other hand, they're complex cells that are not sensitive to these particular small features in the visual field, but that are sensitive to larger receptive fields and can then see larger objects or larger parts of what is in the visual field of these animals. And these complex cells, interestingly, are insensitive to the exact location of what they are seeing. So they can basically see an object that is here or there. And no matter where this object is, these complex cells are able to recognize that this is this kind of object. And these findings in neuroscience on animals have actually inspired these neural networks that do something similar, but in an artificial neural network. And initially, this work has been proposed in image recognition. So we'll first talk a little bit about how this generally works in image recognition and then see how this applies to software. So let's start by giving an intuition of how these convolutional networks work. So the basic idea is that you have some input data and that this input data is hierarchically organized. Now, what does this mean? Well, we'll see what it means. This could, for example, mean that on an image such as the one that you see here, the different objects and they may be nested into each other. So for example, on this image, you see a house. The house has windows. And maybe the windows have window shades. And these different objects are organized in some kind of hierarchy. The same may be true on source code, where, for example, you may have a project that consists of different packages. And each of these packages consists of different files. And the files then consist of some kind of code structure that may be represented as an AST. Now, before looking into code, let's have a look at how we can use this hierarchical organization on images. So essentially, what happens is that there are three steps. The first step is to look at the raw data and to identify some kind of primitive features. So these primitive features could, for example, be oriented edges in this image. So for our little example here, what this first step could, for example, recognize is that there are some edges here like this, which correspond to the roof of this house. And then there are some other edges here like this, which correspond to one of the windows. There may be some edges down here, which correspond to this car. And then inside this, or sort of overlapping with this, there may be other edges that may correspond to some of the wheels of this car. Now, given this, we can actually recognize some of these parts of the objects that you see on the picture. And this is the next step, where some model recognizes object parts. So for example, we may be able to recognize that on this picture, we have some wheels. And we will see that these are the wheels. Or for example, we may be able to recognize that there are some windows here. For example, this is a window. And as you see, this lifts the primitive features identified in the first step to a higher level of abstraction by combining different oriented edges into some parts of an object. And then the last and final step is to look at another level in this hierarchy where we can now identify entire objects based on the object parts identified in the previous step. So for example, for our little image here, we may be able to identify objects such as a car or maybe an entire house. So let's say what we'll see is that these two wheels and maybe some other object parts that I haven't detailed here on this slide will be part of the car. And that some other parts of this image correspond to this house that we see here. And this final task is one of the tasks that are typically done in image processing. And this is called object recognition. Now, one way to do this hierarchical reasoning, and this is the way we focus on here in the lecture, is to use a convolutional network. Similar to some other networks that we've seen in previous lectures, this is a feedforward neural network. So basically, information moves from the input towards the output without any kind of recursion or loops. But in contrast to some of the networks that we've seen previously, this network exploits the hierarchical structure of the input data by using specific connectivity patterns in this feedforward neural network architecture. And we'll see how exactly this works. What is important here is that a convolutional neural network is not a fully connected network. So not all neurons in one layer are connected to all neurons in the next layer, but instead only some neurons are connected with each other. And we'll see why this makes sense. In order to summarize specific parts of the input and in order to do this again and again in a hierarchical manner, there is something called a convolution function, which is essentially a mathematical approximation of the stimuli that we have seen in this experiments with animals within the receptive field. So basically, what the convolution function does is to take some part of the input, for example, some part of an image or some part of a program and summarizes it in a specific way. Now, these convolutional networks have a lot of applications. We look a little bit into image and video recognition because that's a very intuitive domain, and that's also the domain where these networks have been proposed in originally. They are also used in natural language processing because natural language also has a hierarchy. So a document may consist of sentences, which consists of words, which may be compound words, and so on. And then, of course, and that's the main focus of this course, they can be used for reasoning about programs. For example, to classify programs or to do some other kind of reasoning on top of programs. To understand why we do not want to have a fully connected neural network, but instead use this idea of convolution, let's start by reasoning about the complexity of a fully connected neural network. So this kind of reasoning that we'll do now is not specific to a specific input domain. So it's not specific to images, for example, but we'll use an image as the example. So for this example, suppose that we have some input of length n. And for our example, this could be an image. We also have just one single output. So for example, this could be some numeric value that predicts what is on this image, or maybe just a binary classification that tells us whether the image is, let's say, an image of a cat or not. And now, within the network, we have three hidden layers. So it's a moderately sized neural network. And these layers are fully connected. So basically, every neuron in one layer is fully connected to all the neurons in the next layer. Now, looking at this input, sorry, at this network, we'll see that it looks as follows. So somewhere we have our input neurons, which I just write down here at the bottom. They go from 1 to n. And then we have the first hidden layer. And here, let's just make the simplifying assumption that the hidden layers are just as large as the input. So it's, again, n neurons. So we have these next n neurons here for layer number one. And because it's fully connected, this neuron will be connected to all of those. And the same for this neuron, which is connected to all of those. And of course, this one is also connected to all of those. So basically, what we have here is the input. And then up here, we'll have some fully connected layers. And the one that I've just drawn is the first of them. So now we said we'll have three of those. So basically, that means there are two more that look very similar. And again, they are fully connected. So all of this goes here and here and here. I'm drawing a little faster now. Hopefully, it's still readable. And then there's the third layer, which, again, is fully connected like this. And then we said we have a single output, which basically means there's one neuron up here, which will serve as our output. And in order to receive information that we can output at the end, it needs to be connected to the final layer. And because it's a fully connected network, this means there is a connection from each neuron in the final layer to this output. Now the question is, how many weights does this network have? So that's a measure of how complex this network actually is. And here, let's make some more assumptions, namely about the size of n. And the question is, how many weights does the network have? Four. And now let's just make some assumptions. And let's say our input is an image. And we represent this image as pixels. And let's say it's a very small image, or maybe a downscaled image, of just 32 times 32. So 1,024 pixels. So this is a small image. Or it could be any other data that you can represent using 1,024 input values. Now, how many weights do we have in this network? Well, because it's fully connected. And because each of these connections is controlled by one weight, we'll have n times n weights that connect the input layer to the second layer, or to the first hidden layer. And then another n times n, so n squared, that connects the first hidden layer to the second hidden layer, yet another for the second hidden layer connected to the third hidden layer. And then finally, another plus n connections from the last hidden layer to the output. And now if you do the math, this is roughly 3.1 million weights. And as you can see, this is a surprisingly large number, given that the input is relatively small and that our output is just a single neuron, and also three hidden layers doesn't sound so much. But because this is a fully connected neural network, we have a lot of weights. And in practice, this means that each of these needs to be stored in memory. And also each of those weights needs to be optimized individually, because we want to train the neural network based on some data. And that essentially means that each of these weights needs to be optimized. So the takeaway of this is that having a fully connected neural network involves quite some complexity. And the question is whether we can do any better based on the knowledge that our input data is actually hierarchically structured. The answer is, of course, yes, because the idea of convolutional neural network is to reduce this complexity based on this idea of convolution. So what is the idea? The idea is to not have a fully connected computation graph as the one we've just seen. But to instead have, well, two properties. One is we want each input to only influence k other neurons. And this basically limits the number of connections that we need to have in our computation graph, because well, if every input influences at most k neurons, we only need k outgoing connections for each of these inputs. So just to illustrate this, let's assume that k is equal to 3 and that our network or part of our network looks like this. So we have five neurons in one layer and then five neurons in another layer. And now instead of having all neurons in the input layer, so this is the input here. And then this is what we'll call the convoluted input. So instead of having all of them connected to each of them, we basically just for every input neuron have three of the neurons in the next layer influenced by this input neuron. So for example, the input neuron here in the middle just influences the three neurons indicated up here and the same for all the others. So this one will just influence this and this and there's none on the right. So this is only two actually. This one will influence this and this and this. The one in the middle we've already covered and then on the right basically the same happens. And what you see is that we do not have all the connections. So for example, the input neuron on the right is not influencing the leftmost neuron on the convolution layer. So this is one of these ideas which is about how many neurons are influenced by a specific input neuron. And conversely what you also get from this idea of convolution is that each neuron in the convolution layer is activated by at most k neurons. So also from looking at it from the other side, there's less complexity. So let's again illustrate this with a little picture. So these are again the neurons in our convoluted layer. And down here we have our input neurons. And now just looking at the same connections that I've just drawn, but now basically from the other perspective. So how many neurons are responsible for activating a neuron in our convoluted layer? So let's have a look at this one here. Then you see that this is actually activated by just three neurons instead of all neurons from the input layer. And similarly for the others. So this one here up on the left is influenced or activated by these two. Similar here. And then the same on the other side. So now reducing the number of connections in our neural network is one part of the trick to reduce the complexity through convolution. The other part of the trick is to not learn how to control each of these connections separately, but to actually use a so-called kernel which corresponds to parameters or weights that control these individual summarizations from one layer to the next layer. So let's again look at our little example here where we have this input I. So I is this input vector. And then what we'll get at the end is the convolution S. And now the idea is that we want to control how for example these three neurons are influencing this one. And this step of mapping the inputs at the bottom to the one neuron at the top, this is what is called convolution. And the way this is controlled now is by something called a kernel which is essentially a function over in this case three inputs that will give us one value which is then stored in the upper pink neuron SD output. So this function is called a kernel. Let's call it K. And this works over a vector of length K. And in our example, K is three. So our entire input goes from J equals zero to J equals four but K in this case is only three. Now let me also add the connections here. It's the same as what we had before. So these three is basically what is controlled by the kernel and then by just moving the kernel around we can also do the same for the other parts of the input. And the trick is that we use exactly the same kernel for all the different parts of the input. So the convolution S is the product of some part of our input I and our kernel K. And when I say this is the product I'm ignoring the boundaries here. So of course at the beginning and at the end of the input we need to have some special handling. So let me just make a concrete example. So let's assume that our kernel is a vector two, five and three. And in practice these numbers will be learned because these are basically the parameters or the weights of this kernel. And let's assume that our input, so the full input vector is three, one, two, three and four. And then in order to compute the convolution at index two, so the one that is marked with pink color at the top we would basically compute one times two plus two times five plus three times three. So the kernel tells us how to summarize these three values in the window that we look at in this input vector into just one vector. And then we move the same kernel to the left and to the right to do the same for the other windows that we can put over this input. So now I've shown you how to do it in a single dimensional or one dimensional scenario where we have basically an input that is just a sequence of neurons. But now the motivation came from images. So let's also have a look at how to do this in a two dimensional scenario, which could for example be the pixels of an image. So let's suppose this image looks like this. So we have just a few pixels here for this example and I just call them ABC and so on in order to be able to refer to them. And now what we want to do is we want to summarize these pixels into a smaller dimension. So let's say we want to convolute it so that at the end here, we have a two times three matrix instead of this three times four matrix that we had before. Now in order to do this, we can have a kernel, which is basically a function that tells us how to take four pixels and summarize them into a single value. So just putting the same terms that we have used on the previous slide here again. So this down here would be our input I. What we want to get at the end is a convolution called S. And we do this using a kernel called K. And now in this kernel, we'll have some weights or some parameters. Let's call them W, X, Y and Z. And these parameters basically tell us how to convolute four pixels at a time out of the input image into our convolution matrix. So for example, we can take the kernel and basically put it on top of these four pixels here. And then it will summarize all of this into a single value at index 00 of our convolution matrix. And then we can move this window of pixels we look at, for example, to those four. And this will give us another value, which is then put here. And similarly, we basically do this for all possible windows of four pixels in this input matrix until at the end we also arrive here and then we filled all the six values in our convolution matrix. Now, how do we compute these values in the convolution matrix? Well, the idea is the same as before. We basically multiply the input values with the kernel just now in two dimensions. So in general, if you want to compute S of i and j, then we sum over one dimension and the other dimension. And for each of them, we look into what we have in the input and then multiply this with our kernel matrix. So for the concrete example, let's say we are interested in S at indices zero and zero, then we will basically take A times W plus B times X plus E times Y plus F times Z. And these four values that we then add together give us one value, which is S of zero and zero. And similarly for the others, so for example, S of zero and one would be B times W plus C times X plus F times Y plus G times Z. And finally, I'm going to give you and finally for S of one and two, we'll get G times W plus H times X plus K times Y plus L times Z. And the idea is that each of those computations is done using the same set of kernel parameters. So we do not have to learn that many parameters because we basically just need these four parameters to go over our entire image, one window at a time, and then summarize each of these window into a single value. So now you've seen this idea of using convolution in one dimension or in a two-dimensional scenario using these examples. Let's now reason a little bit more about the properties of this idea of convolution that help improve learning on hierarchical data. And there are three of these properties, namely sparse interactions, parameter sharing, and equivariant representations. And we'll go through each of these three now and see what they actually mean and why these properties are useful for learning about hierarchical data. So the first of these three properties is that we have sparse interactions. And what this essentially means is that we have fewer connections than we would have in a fully connected neural net. And specifically, what does this mean for our training process? Well, it means we have fewer weights that we need to store. And it not only saves memory, but it basically also saves us time because it means we have fewer weights that we need to optimize. Now the cool thing is that despite having these sparse interactions, if you have a deep network, so many layers, then the neurons in deeper layers may still be indirectly influenced by many of the inputs. And this is actually a nice property because otherwise you would have a very local reasoning and no reasoning across different parts of the inputs, which we actually want to have, but this is still possible if you have a deep enough network. So if we have such a deep network and again deep basically means many layers, then the neurons that you see in the deeper layers, so the layers that are further away from the input, these neurons may be indirectly influenced by many inputs so they may indirectly interact with many inputs. So let's just illustrate this with a simple picture. So let's say we have five input neurons and not just one hidden layer here, but another hidden layer. And let's say we have a convolution with K equals three. So basically three values of one layer are always summarized into a single value in the next layer. Then we would basically summarize these three neurons into this and then these three into that and these three into this neuron here. And then in the next layer, we would again have a convolution with K equals three and then would basically summarize all of this information into this one neuron in the middle at the top. And that means that all of these neurons down here in the input have an influence on this one neuron at the top because we have different layers and that means we can still get non-local reasoning that reasons maybe about the pixel in one corner of the image and the pixel in the other corner of the image despite the fact that we do not fully connect our network. The second interesting property of these convolutional neural networks is that we have parameter sharing. So what does this mean? So essentially it means that the same parameters are used for multiple connections in our network instead of having one parameter or one weight for each individual connection. So let's just contrast what we do in a convolutional setup with a fully connected network. So if you have a fully connected network, then the parameters that we need to optimize are all the weights in our weight matrix. And that typically means as many weights as we have connections in our network. Now for convolutional network, we have fewer parameters to optimize simply because we are reusing them. And specifically what we need to optimize here are just the weights in our kernel matrix. And because we are moving this window of attention over our input and reuse the same kernel matrix over and over again, there will be fewer parameters to optimize because we are essentially sharing them across different places in our input. So what this means is that in a fully connected network, each weight is just used for exactly one connection. And in contrast in a convolutional network, the same weights that we have in our kernel matrix are used for many parts of the input. So the big difference here is this one versus many. So let's just have a look at this using a little picture again. So if you have a few input neurons down here and then look at a layer that convolutes these inputs and have some kernel matrix, then what we'll see is that basically this connection will share the same parameters with that connection and this shares the same with that connection and with that connection and with that connection. And the same is of course true if we have K that is larger than one. So let's say K is three, then we will have also connections like this and also they share the same parameters and the same for the third connection that we have here for each of those. So they also share the same parameters. The third interesting property of convolutional neural networks is that we get equivariant representations. So what this essentially means is that if our input changes in some way, then the output changes also in some way and this is independent of where exactly this input is. So the change that we see in the output for given input change is the same no matter where this input is. So just illustrating this with a little example, let's suppose we have some inputs down here and then compute a convolution of them up here and let's say we again do this using our window or our kernel with K equals three. So we'll get something like this and now let's say we have some specific input down here. So these two pixels really matter and they influence this value of this neuron up here. Then this will be exactly the same kind of influence as what we'll get in a different scenario where we have the same number of inputs and neurons in the convolutional layer but where this input happens to be at a different location. So in this second scenario, let's assume our input is not in the second and third element of the input but it's actually here in the third and fourth element and because we use the same kernel to convolute this input, we'll get exactly the same output and the same influence on this neuron up here as we get on the left, simply because it's the same just at a different location. Now what does this mean for the different applications? So let's just look at two applications that are relevant here. One is images and for an image, this basically means that if an object is moved, then its representation will move by the same amount but the representation will basically stay the same. So what does it mean for source code? So the application that we are most interested in here in this course, well, it basically means that if you have, for example, a statement that has a particular property, let's say it's a statement that is buggy or let's say it's a statement that uses a particular API, then no matter where this statement occurs, its representation stays the same and the network can then reason about the statement, no matter whether it occurs, let's say at the beginning or at the end of a source code file. So if you have a statement with a particular property, then this stays the same or its representation stays the same no matter where exactly the statement occurs. All right, so now you know something about convolution. Let's now have a look at the bigger picture and let's have a look at how this idea of convolution is typically used in a more complex neural network in order to reason about some hierarchical input data, for example, in an image or a program. So typically this idea of convolution is used in combination with some other steps and one typical way of using convolution which we'll also see in the second part of this lecture where we look into using convolution for reasoning about programs is to combine it as follows. So we have our input somewhere and then this input is given to some convolution layer which works exactly the way we've just seen or maybe multiple convolution layers and then given the output of this convolution layer which has basically summarized different parts of the input into smaller entities, there's some kind of detection layer which for example identifies objects or specific statements in the input and then there's something called pooling which essentially focuses the attention on the most interesting of the features that have been detected by the previous layer and then there may be some more parts. So we look into pooling in a second just to give you an idea of this detection layer. So what typically happens here is that we have some nonlinear activation function which then summarizes whatever has been summarized by the convolution into some higher level piece of information, for example, the fact that there is a statement of a particular kind. So now I've just mentioned pooling, let's have a look at what pooling actually is. So it's essentially form of down sampling. So we have a vector representation of some size and would like to compress this into a smaller representation while focusing on the most interesting or most relevant bits of information that we see in the input. And the basic idea of different pooling approaches is that the output at a particular location is replaced by a summary of nearby outputs. So we're basically compressing things that are close to each other into just a smaller representation. And the intuition behind these pooling approaches is that it doesn't really matter exactly where a feature is, but it's more important that it is present and how this feature is located with respect to other features. So for programs, for example, this could mean it doesn't really matter exactly where a particular statement is, but what matters is that the statement is somewhere and that it is maybe before or after or maybe around another statement that is also present in the code. One way of implementing this idea of pooling is what is called max pooling. And this is the only one we look at here because it also happens to be used in the approach we'll see in the second part of this lecture. So the idea of max pooling is to essentially summarize the values we see in a specific region of our vector representation by looking at the maximum output value within this region. So let's have a look at the concrete example to make this more concrete. So let's say our input is a 2D matrix that looks like this and let's say we have some values computed as the result of our convolution that look like that. So the original input would have been an even larger 2D matrix, for example, but then we applied convolution maybe multiple times with multiple convolution layers and at the end we have summarized the input into what you see here. And then if you would now want to use max pooling in order to further down sample this information, then let's say we wanna down sample it into a two times two matrix, then we would basically look at the maximum value in different regions of the given inputs. So we would look at the maximum value in here and see that this is six. So we would put a six here. Then for this region, we would again look for the maximum value and see that this is eight and same here. So we would look at this region and see that this is three and the same for finally this region where we would find four. And the idea is that these maximum values represent the presence of some features and by still keeping the same arrangement of these different regions. So basically the blue region is still left of the pinkish region. We still see that these features exist and also how they are located with respect to each other but everything is summarized into a smaller scale vector. All right, so I hope this gave you some idea about convolutional neural networks and how they work and what the intuition behind them is. Now we've mentioned programs and source code a little bit already in this first part of this lecture but now in the second part that follows next, we look into how to actually use this idea of convolutional neural networks to reason about source code. So thank you very much for listening and see you in the next part.


What's up, guys? It's WillyBlaze here. I'm really excited today to talk to you about the Fez Dry Leaf Vaporizer. This is definitely one of the top of the line vaporizers out today. And I'm really excited to show you guys all the cool features about it. First of all, right in your hand, it feels good. It's got a great rubber matte texture. It just feels like a solid product. It's something you can hold on to and enjoy. To load the Fez Vaporizer, the first thing you're going to do is a quick twist to the left and pop that cap off. Super simple, super easy. Go ahead and fill this generously sized chamber with your desired dry leaf and enjoy. You can also add this cool little feature that I've never seen before. It's a little capsule that slides into the chamber where you can put concentrates. So you can actually use dry herbs or concentrates with this vaporizer.  So to power it on, you're going to hold this button down for two seconds and it's going to go and glow red. Right now, the Fez Vaporizer is heating up. It's going to take about 55 seconds to reach its full temperature, at which point you'll be ready to enjoy whatever contents you have inside your chamber. Fez is a very smart pen. When you're not using this, when it's on the table, it's not on your hand, it actually goes into low power mode, which is a really cool feature. You know, it's 2016. It's about time we have smart vapes. Other than that, the charge lasts for about 2,000 puffs, which is a great battery life. And you're gifted all of the necessities to go ahead and charge up your pen at any time. You've got a charger plug, which is very nice, along with the micro USB cord. Go ahead and keep this guy running conditions at all times. Other accessories include a concentrate case, a very nice double-sided dab tool, which is long enough for you to really use it for whatever you need. Several mouthpieces for the extra cleanness. And of course, a cleaning brush. This is very important when cleaning Fez Vaporizer. It really allows you to enjoy a good taste, a clean taste throughout the entire smoking process. So, if you have any questions, you'll be gifted with the user guide. There's a lot to go over with this high-tech device, but I hope you enjoy it. And if you're lucky enough to get one of these guys in your box, I think you'll have a great time with it.


Thank you very much and thank you to the Brain Foundation and all its donors for facilitating this research. It's a real honour to be here. So my research is in brain injury as well. I think there's probably very few people in this room who've escaped the current media attention around brain injury and its consequences. So that current media attention suggests that people that experience mild repetitive traumatic brain injury such as that that occurs when they have an impact with someone else in a sporting game or suffer a concussion has lifelong neurodegenerative consequences. In its mildest form they suggest that traumatic brain injury in this form can cause depression and anxiety and that it's worse can cause cognitive deficits that are indistinguishable from Alzheimer's disease and other dementias. The research has come largely from a research group in the states that have been studying American football players that have self-referred because they've been experiencing cognitive problems. And they find a high proportion of those individuals have a neuropathology in their brains that have cellular changes that are known as chronic traumatic encephalopathy. But the research is in its early stages. We don't know enough about whether chronic traumatic encephalopathy causes these clinical symptoms that people are describing. In fact people have reported that the incidence of depression and anxiety is similar in the general population and many of these symptoms are actually similar in the general population. So this grant will allow me to look at chronic traumatic encephalopathy in the large number of cases that I hold in the Sydney Brain Bank for which I'm director of neuroscience research Australia and the University of New South Wales. We want to examine the changes in these brains in these individuals that have been well characterised in life.  We can test their genetics and we can understand the association between TBI traumatic brain injury and chronic traumatic encephalopathy. So we can provide answers not only to the sporting community but at the grassroots level to parents that are worried about their children playing sports.


A flexible and stretchable sensor based on highly conductive ionic gel has been developed that can detect multiple mechanical deformation modes and is customizable in color and self-illumination, making it suitable for wearable sensing applications. This article was authored by Peng Wang, Guixian Li, Wei Yu, and others.


Hey everyone, this is Jack. I hope you're all doing well. A few things that I wanted to talk about today. First of all I just wanted to say thank you. We recently hit 90,000 subscribers and at the time of recording this we're coming close to 91,000. So thank you all so much for your support. I really don't get a chance to say it enough But it really means so much to me. I just wanted to say thank you There's gonna be a lot more coming in the future which I'm really excited to talk about and I guess that's where We're going to be going from here Before we do get there it did want to just quickly give a shout out to all of my patrons The content I've been making if you haven't been able to tell over the past couple months It's really going big on patreon or not going big But you know making the effort to make it the best that it can be my content has improved considerably the more time the extra Resources it's all adding to it and hopefully you've been able to tell a difference And if you would like to do your part and add into the patreon there's a link in the description Please don't feel forced to but the end of the day there's a lot of great rewards over there I say it every video, but um Yeah, if you're interested in supporting the channel that is definitely the best way to do so I recently got the adobe creative cloud suite, which is a little bit tricky to work with there are a lot of moving pieces but Really happy with that. I'm getting better at it So over the next month or so the continued evolution should be a lot better anyway What I really want to spend the majority of this video on is discussing the new situation with the channel and basically how I'm going to Be going forward So if you haven't been following the channel too closely and really Detailing everything and how I've been doing it essentially for the longest time I did one video a week and That just wasn't sustainable for where I'm at right now I've got a lot more going on outside of these YouTube videos Which I'm really happy with I'd much rather be doing something than doing nothing But of course means I have to significantly cut back on the time That I can devote to YouTube which is unfortunate because I really love doing this It's a passion of mine. It's been a passion of mine since 2014 is when I Became Jack's movie reviews and with an online blog, but anyway, that's for another day. It's a passion of mine I love doing this. I love talking movies. This is the perfect way to do so. So anyway total tangent there Basically, I had to cut back to two videos at the mid-June Basically had to cut back to two videos a month one every other week and I'd like to do more than that. So the I Won't be able to go back to once a week But what I will do a nice compromise I guess you could say is two big videos like nine to eleven minutes, which is the range I've been sticking with recently. So I'm going to be doing two of those videos every month Then it's also going to be a third video and that's going to be something Quite frankly something easier to produce So it might be a quick like three to four minute quick analysis scene analysis to discussion of one factor of a movie where it's the last Research base. It's more just my thoughts on a movie I'm excited to do that. I've done a few of those videos in the past And I think that can be a super interesting way to get more content out there and also have it be one good content But also easier to produce. I'm not looking to make bad content That's not something I want to everything I do I want to be the best that it can be but things that kind of are easier to produce not necessarily in a bad thing the other Option I was thinking of is more talking to the camera similar to this In which I'm discussing a film so last year I did the Oscars video Hopefully something like that and maybe four or five others throughout the year as well So basically two film dissections a month That's going to stay the same as it has been for half a year now But also going back and adding more to it So I'm going to be starting this in December and then hopefully continuing it through as much of 2019 as possible and into the future Like I said, I do these videos because I love doing them and I Want to do more quite frankly so Definitely looking forward to adding more to that. So anyway get excited I've been promising big things to come and I think I've scheduled out the rest of this year I'm looking to schedule start looking into the new year and It should be a very positive change to the channel There are a few things that I didn't want to promote in addition to the patreon I guess first of all I want to give a big shout out to Thomas Pollock he's did a bunch of These film video essays similar to what I've been doing a few years ago He started doing this before I didn't has made a ton of great videos, but Left YouTube behind to start making short films is made around a half dozen since quitting YouTube or putting you to it So it's taking him at his hiatus. I don't know the correct way to say it But he's done a ton of great work over there. He's gonna be a link to some of his work on screen We're in the top corner. I guess so if you're interested in that check it out He has done a ton of great stuff and it's definitely well worth watching. So definitely be sure to check out Thomas's stuff You're probably familiar with those videos. He's made some pretty great ones be sure to check it out And anyway, that's all that I wanted to say for now next week. We are going to be doing Traditional video one that I've literally been thinking of for a while So we can talk a little bit more about that in the patreon exclusive commentary, but Yeah, that's where I am right now. It's all good news from here Love and making these videos as much as I ever had as much as I ever have So anyway, just wanted to do this quick little update again. Thank you all so much I've said it a million times your continued support means so much to me and I'm really looking forward to making sure that The best videos out there get made. So anyway, here's my chance to you. I can promotions Anyway, be sure you hit subscribe again We're coming up on a hundred thousand subscribers, which is absolutely insane in case you missed my last video There's that right here. Hopefully it's probably gonna be a little bit off But anyway, and there's probably a patreon link somewhere over here as well So a lot of great content a lot of links to click and hopefully you click one and again Thanks for watching and I will see you next week


Deep inside of the back rooms lies a very dangerous level, shoved far into the cracks of reality. This level is caused of many to go missing, and many to have a fate even worse than that. And today I'll be explaining it. This is back rooms level 231, the slaughterhouse. Let's get into it, shall we? Viewer discretion is advised. Level 231 of the back rooms is one of those levels that's kind of a legend between wanderers. It's one that people still tell each other, not knowing if it really exists or if it's just a horror story, almost like a ghost story. This is supposed to be a place that is so dangerous that if you get sent there, you definitely won't survive.  Sounds kind of fun, right? The level is classified as a death zone, and that's mainly due to its environmental hazards and also the potential for anomalous creatures, which I will explain later, but these creatures in question are still up for debate. The layout and the size of this place is simple enough. It takes the appearance of a brick and concrete slaughterhouse of an unknown size. A slaughterhouse, of course, being where animals are processed for their meat. The slaughterhouse might be infinite, or it might just be too big to explore each part. Either way, it's very large.  So that is exactly what this slaughterhouse looks like, too. And when you begin your journey in this level, you'll start at the entrance front door, where you will see a hall in front of you comprised of grated metal walls as well as some platforms and stairs. That regardless of what you actually see, you'll notice something right off the bat. The metal ceiling above you is dripping blood, and it's always dripping no matter where you walk. Just a constant thing. Now this is the first of many red flags in this level that shows how dangerous it is, so buckle up and get ready. Disembodied screams and screeches and pleas for help echo throughout the entire level, as well as miscellaneous machinery sounds resonating, the sounds of buzzsaws and nozzles and engines and everything like that, and even strange robotic humanoid type noises, like vocalizations almost. More on that later. At this point in the level, you'll be walking down that first hallway, and you'll start to notice this stomach-churning grotesque smell wafting through the halls. That smell is rotting meat, and it just gets worse and worse the deeper you go. I'm sure you can tell what kind of meat it is, I can't mention it because YouTube will slap me in the face, but it's not animal meat. Those machinery noises you heard earlier are not just normal machines. They're meat processing machines that are cutting up meat and blood and flesh. That stuff is then dumped into funnels and then dumped into large pits where tons and tons and tons of flesh lay and rot. That's where the smell mainly comes from on the level. Obviously a bunch of rotting flesh has got to smell bad, but to make it worse, this level is constantly over 80 degrees Fahrenheit, which makes it just this hot, steamy, gross smell. If you take a closer look at the walls of this slaughterhouse, you'll notice cracks and pieces missing, and behind those cracks, you'll see a hidden tunnel system with these strange electronic components and machinery and railways in them. You'll also notice strange mechanical arms that can reach out of the cracks and attack you. These arms and machinery seem to all be a part of a carnivorous hive mind of this level, meaning that everything here works together in an attempt to attack and consume wanderers, and they all kind of work with the same brain, so to speak. Deeper into the level, you need to be careful where you step, because the floor itself might fall off from underneath you or give in, and it'll cause you to fall into a pit below where you'll be trapped and then disassembled and thrown into the flesh pit. Areas like this have been classified as traps, and these traps is the main way the level will get its victims.  Further on, at the level, you might run into a strange set of rooms known as the chopping rooms. Now these are the only other places besides the pits of flesh and the metal rooms and the trapped doors where wanderers can actually access, and these chopping rooms are where the bodies are processed before being sent into the pit of the level. These areas have concrete floors and walls, low buzzing lights in the ceiling, and are covered in strange flesh and liquids. The smell in these chopping rooms is awful, of course, and if you even make it to a chopping room without being trapped or attacked by one of those robotic hands, then you're probably about to die, because this is where the machinery becomes really carnivorous and aggressive.   The machines have that one goal, and that is to dismantle and attack wanderers. The only reason we even have information about how deep this level goes, or anything about this, is from an unnamed wanderer. This wanderer managed to escape a chopping room by removing his own hand when his hand was grabbed by a carnivorous machine. He cut it off and then ran out, and then while the machines looked around the hallways and rooms for him, he would hide under the piles of bodies to avoid being captured, which is gross.  That can't help it either. Leave a like for that guy.  I'm sorry, dude. To enter this slaughterhouse, you have to be on level two, and you have to open a set of gray double doors. Now, these doors are actually the front doors of this slaughterhouse.  So once you open them up and walk through, they close behind you, and that's where the level starts. And this exactly is why the level is horrific. I mean, you can literally just get sent to this level from level two, which is supposed to be one of the safer first levels of the back rooms, you know? So if you somehow come here, you have really bad luck. I'm going to be honest. Just don't open any gray doors on level two, and you should be OK, though. Level 231 has no recorded exits. And as of right now, being sent here will cause you to be trapped in the slaughterhouse forever with the smell of decay, the heat of death and the carnivorous robots stalking around you. And because of this, the level has been added to the do not explore list of levels and is completely banned from entering unless you just don't want to be alive or something. I don't know. The origin of these robots or why these things work together to attack wanderers is unknown. Some say that an entity of some sort built this slaughterhouse to harvest flesh and consume it. Some say that it's just another part of the back rooms. And some say that it's just random and we can't control it. Whatever the case may be, I would never go here. Hey, I hope you enjoyed this video. Thank you for watching. Let me know if you want to see other levels like this. This is horrifying and dangerous, and I have a feeling it's going to get demonetized. Hope not. And I really hope you enjoyed. Thanks for watching. I'll see you later. Goodbye.