various possible advantages of the system, what is the suitability of the system where it can be in comparison to the other methods which we have discussed so far, what are the conditions where it is having much better suitability and we had looked at some of these aspects, we had said that since in this method we have complete control on the operations, we can go to the extent of applying any level of irrigation and that gives us additional advantage. We had also mentioned that it can be used for very coarse soils as well as very fine soils because in this particular case the application is over the surface is sprinkled over the surface, the water is coming in the form of rainfall as the natural rainfall occurs. So from that angle the other difficulties which we have encountered so far because of the fact that the water moves from one end, the upstream end of the field to the downstream end which is typically the case of all the methods where the gravity is used for the flow of water, this method has that advantage, then we do not need any leveling, this can be used for the areas where the areas are undulated, all the areas which are having lot of undulations where the other method, conventional method cannot be used, you can make use of the sprinkler irrigation method, then the other areas where the slopes are very steep, again the advantage is because of the fact that here you can control the rate of application to an extent that there would not be any runoff generation, in all the previous conventional methods we could not irrigate those areas which were having slopes, excessive slopes because of the reason that we were trying to restrict the slopes to that level where the erosion problem should not be getting activated. So once the erosion problem gets activated because of the fact that the surface runoff gets generated or because of the fact that you are trying to move a sheet of water or some stream of water over the surface of the land, whereas in this case the application is over the land in the form of the something looking like naturally informed, the drops of water which are falling directly on the surface and that the rate of application is controllable, so you can always control the rate of application to such an extent that there is no runoff generation. That is the reason that steep slopes can be now, you can even have cultivation on those areas which are having steepest slopes. Again that is besides the point that what type of crops you can grow there, what type of whether you can have only orchards or there are some other crops that suitability has to be accounted for. The other advantages that even very small stream can be earlier we had films that of the stream size, the rate at which the water can be applied has to be constrained with respect to the type of soil where you are applying that water. So that now you do not have to be very much bothered about that. If you have a smaller stream size, if the rate at which the water is available that is low you can run the system in such a manner. You might be able to use the same availability on an area which is smaller because you are using the water through the pipe system. So now instead of having a bigger extent covered in that case you will need higher discharges. You can cover smaller areas where the discharge requirement will be lower and you can use that lower availability of discharges available in such systems. Besides these major advantages which are because of the fact that the way we are applying the irrigation, the way the whole thing is formulated and the way you are making the water released from the nozzle heads or what we call them as the nozzles, there are some additional advantages which accrue as byproduct. These advantages are there because of the fact that the way we are using this system, the way we have assembled the whole system. These advantages, let us have a look at some of these additional advantages which are there because of the way you are supplying water. These advantages are you can now control the frost to certain extent. There are some areas where the frost formation is quite active. Now if you use the sprinkled water, if you use the sprinkler system you can avoid the frost formulation because once the water is sprinkled over the area where the frost and the timing has to be suitable. In this case the timing can be within yours and you can irrigate the area whenever you want and most of the time the irrigation is done during the night times because of the fact that during the night times the wind effect is minimal. So that is the general tendency in the case of sprinkler irrigation systems wherever the winds are quite predominant during the day time. The choice of the night is quite an obvious one but for frost control you can fix your timing in such a manner that you irrigate the areas just during the time when the frost can be active and that frost formation can be stopped there. You can also use the system for control of temperature. This can be either the temperature of the soil or in some cases the temperature of the canopy also the plant itself. It has been seen that some crops they behave preferably to the controlled temperatures. Now when you are sprinkling the water over the plant the plant will get wet and it will have an enchanted temperature in comparison to the other system when you are supplying the water over the land surface the plant is not getting wet. So in those areas where the temperatures of the plants go very high the system can bring down the temperatures to a lower level. Even you can afford to depends if they are the temperature requirements are such that you can artificially also you can make the temperature of the water at the intended level and then you can supply the water. So that control is possible in these systems because of the fact that the water which you are sending is through a system. There are other units which are essential units which we will discuss later. For example the like filter unit is there because of the fact that you want the water to be quite free from most of the impurities which have a tendency to get clogged into the pipe system. Since this pipe system we are trying to use over a very long period the life of the pipe has to be looked into and you for that matter it becomes essential in all these sprinkler systems filter unit has become essential unit. Other additional advantages which you can think of is the application of fertilizer pesticides. Any such requirement whether it is pesticide or whether it is fertilizer or whether it is we decide all these things can be sent through the same system. You can mix the fertilizer with water and apply it onto the system without having any additional labour requirement and for that you can have additional unit which is called the fertilizer tank which can be attached to the system in the beginning of the uhh where the we will be having the pump where the pumping is being done. So close to that there is a fertilizer unit which can be made use of. In this fertilizer system that becomes additional advantage that now you can have a application of the fertilizer through the system along with the water. It will have a impact of having a uniform distribution of the fertilizer. In other case when you are doing it besides the activity of irrigation water then you will have to do with additional labour requirement and the uniformity of the application might not be as good as you can get with the along with the system. But only thing is that when you are using the fertilizer you have to be slightly careful that after the use of the fertilizer because these things can react with the material of the pipes and can damage the pipe system. So you have to ensure that after the application of the fertilizer is over you must run the system for quite sufficient time so that all the traces of the fertilizers or the pesticides can be removed from the network and it is not remaining inside so that it can have the detrimental effect. We have uhh already discussed this aspect that light and frequent irrigations are possible in this system and this in turn helps us in some situations. For example if you have some soil where the crusting is a problem and in that case if you use a conventional method you will find because the crust formation the germination might not be very good. So in that situation where the crust formation can be a problem this type of system will be very effective where it would not end up in having the soil crusted up and thereby it can help in proper germination of the plants. Now it is not true that the only advantages of the system there are some limitations also. Let us have a look at one of the possible limitations because everything has two sides of the story. There are some good points, there are some points which are which are restrictive points in the sense that they are either making the thing unaffordable there they can be economic reasons, they can be the reasons of distribution. Let us look at in this particular system when you are using a sprinkler system the basic limitation is that the system can be very expensive and when you say expensive there are many aspects one is the initial cost. Initial cost itself is quite excessive in comparison to the other methods you have to invest a lot. Then the operation cost also is more than what you incur in the other methods and maintenance also. The maintenance will be the wear and tear of the system, there will be some parts which will need replacement and since the initial cost itself is large anything which has a beggin initial cost and it has mechanical parts the maintenance cost will obviously be quite reasonable or reasonably excessive in comparison to what you do in the other conventional methods. So the expense is one of the major limitations but the same time let me say that in a country like ours where it has been felt that the system has a good potential in terms of saving the resource, the water source which is becoming more and more scarce, government is even helping the farmers by providing some subsidies and procuring the equipment. So it is a question of whether you feel sincerely that yes the thing has the potential then the resources can be catered to. The subsidies are being provided to all those farmers who feel that they are not in a position to invest that much money, they are being given loans which they can repay back.  Others limitations are that you need continuous power supply, since your whole system is dependent on the availability of water and the pressure you need to run a pump either you have to use the electric supply or you can use the diesel sets. So in any case some source of supply has to be, power has to be available which can be made use of and this looking at the availability of electric supply in our country again the efforts are on to make the electric power supply available all over the country. Within the next 5 years they are trying to do their best to get the required demand fulfilled, right now we are having a lot of deficit and even the privatisation is stepped towards that. So I think this situation, the power supply situation will improve and this will affect this sector also. Then under windy conditions that says conditions when the wind velocity is more than 15 kilometres per hour or the temperatures are high, you will find that when you use the system under these conditions the distribution efficiency as well as application efficiency they will drastically reduce because the fact that there will be losses on account of the wind conditions as well as account of the evaporation. When the temperatures are high the evaporation loss will increase. When the wind conditions are high the spray losses will increase. So if you have these type of conditions prevailing in the area of your interest then you might find that this method again the efficiencies will be low, the suitability of the method will still be there but the basic aim that with which you are trying to save the water if that is the basic aim is not worth it. But if the aim is to make use of the other conditions which are not suitable for the methods of surface regression, the methods which are using the gravity flow then this method might still be used. In case for you having saline water, if you are using saline water it has been found that the leaf perning can be excessive. When you spray the saline water over the surface of the canopy of the plants then the leaf perning can be a problem. So this method should be avoided if you have the available water saline in nature. The other limitation is that when you are using portable system, loss of water can be there. We have not yet discussed what type of systems can be possible and the sprinkling method. When you are using a sprinkler system there can be many different setups. You can have a fixed system, you can have a portable system, we will come to that. When you are using portable system what it means is that you will be using a very small number of pipes which are the lectures on which you have by installing your sprinkler heads. Those pipes will be removed from one place and they will be taken to the next place. They will be again attached to the main line or the sub main and the system will be done again. So when you are moving the detachable components from one place to another place, when you are ending one process and going to the next one there can be some loss of water because all those pipes which are fed with water that will go waste that water. There will be some other losses when you are opening and the system is not on at that time but still the loss of water will be there since you are if you are using this method to save the water any loss will be additional loss. This order of magnitude is not much because when you are closing the system you are not the transition is there for a small period when you will be having because it will take some time for the pressure to build up. Similarly when you are closing the system it will take some time when the pressure will fall down and come to the lowest level. So that will be the time where your water will be getting wasted. Okay with that we have had a look at what the sprinkler system has to offer. We will go on to the next system, the last one which we have in the method which we have discussed and this is known as drip irrigation system. In this particular system the characteristic of this system is that we are applying water directly near the root system. If we look at the system which we have been discussing so far that we are having a plant where it has its root system looked into that. Now this this plant we also know that it has some roots on depth in most of the methods, all the methods practically what we have discussed so far. We are wetting this total area, this total area is being wetted to cater to the the requirements of the plant. But we also know that the effective zone out of this total zone and one extent is the roots on depth that we have the vertical extent we have already discussed that no water is being used which is available in the soil beyond the root zone depth. So if there is some water availability here in this particular zone it is not going to be affecting the growth of this plant because its root system is not capable of extracting this moisture. But we have not looked at what is the extent of the moisture extraction in the other two dimensions in the horizontal dimension. So if we try to locate that we might find that this might be the the zone which is supplying moisture to the root system. Even if the water is available here it might not be made use of by the plant by the root system of this plant which means that there is no point in wetting this area if we can if we can avoid wetting this area the growth of this plant is not going to be affected at all. So with that with that knowledge and to implement that knowledge the drip irrigation system has been involved. The basic idea of the drip irrigation system is to cater to the requirement of this particular plant and provide the moisture in that zone only which is directly supplying the moisture to the plant. Now if this has to be achieved then what are the various requirements? The first requirement is that since this is a small area if you will if you will try to wet this area at a larger rate at a rate which is large enough if the moisture supply because the way you have to supply the moisture is when we have seen supply the moisture at this end from here the water is supplied at this level it moves on to this level and wets the whole area on the way. When it moves as a sheet there is some infiltration taking place and depending on how much time the water has stayed on the top of the surface it has wetted the area and you have tried to restrict that wetness up to a up to a just beyond the roots on depth that was what our surface irrigation method was. And this paint irrigation method what we have said is that instead of supplying the water from this end we will supply the water from top and let the waters be sprinkled over the whole area but in that method also we could not restrict the water only to come on to this this surface or this zone even the water was being sprinkled over to the other areas where the crop root system might not be making use of that water. So this will be effectively it will be going to the atmosphere through the operation it will be lost through to the atmosphere through operation. Now if we have to restrict the application only to this zone we have to have some device which will make the water available in this region so that has been done by having a pipeline going over the land the ground surface and then having a opening here somewhere a small opening. Now this opening is nothing but is a device it can be either opening in the pipe or it can be a small device which is taking some water from the pipe and then making it drip over to the surface. This device is called the different name given to it some people call it dripper some people call it emitter right here and be either emitter or it can be a dripper basically that is what has brought this name drip irrigation because this device is such that the water almost trickles down it drips as a leaking tap drips. So the rate of application is something of that order of magnitude because of the fact that if you will try to apply the water at a higher rate it might flow over the surface it might go to the other areas depending on how much is the infiltration rate if you apply the water at a rate which is higher than the infiltration rate it will have its intensity to flow over the surface. So with that reason the rate of application is controlled by providing these drippers and in case you feel that the requirement is more than one dripper is not sufficient to take care of this requirement what is being supplied or what is needed to be supplied to this zone you can use more than one drippers that is not a problem. So that process of irrigation when you are catering to the individual plant is the drip irrigation system though there are advanced versions where it is not necessary that you will be using the each individual plant only it can be a row of plants which you are catering to. So the mechanism but the philosophy remains same the mechanism can be different people are using this method for crops which are not only the orchard crops they are vegetable crops are being irrigated using the drip irrigation system. Let us try to look at having understood the arrangement. Now in this case also before I go to the other aspects the arrangement of layout is similar to the spring irrigation system you will have the only difference is that in this case you are using PVC pipes which are flexible pipes which you can take anywhere depending on what is the layout of the plants you can take them anywhere you can even bend them around the stem of the plant you can pierce them with the drippers you can have increased the number of drippers depending on your requirement from time to time in the beginning when the plant is small you might need only one dripper or it needs more water you might need two drippers for the same plants that can be that flexibility is available when you are using the late trills. In this case also the pipes which are PVC pipes in the previous case they were the metal pipes on which you are installing the sprinkler heads in this case they are the PVC pipes on which you are putting the drippers but in both the cases they are known as late trills. And beside beyond the late trills you will have either the sub-main just the main line that main line can be of any material that is not of PVC that is normally the mild steel which is used for that or it can be the plastic also these days plastic pipes are also being used but in this case also the water will be under pressure so you will have to have a pump unit you will have to have the filter unit because you want the water to be quite clean so that it should not clog the pipes in this case the clogging is a much bigger problem but the pressures the prevailing pressures in the drip irrigation system are much lower when the water comes out of the dripper is almost at atmospheric pressure because what you requirement is that the water should just drip the water should just trickle onto the surface. Now let us look at what are the advantages of such a system you can think of many advantages after we have discussed the way the sprinkler system is applying water onto the areas you can the application slow rates are possible since the pressures are very the pressures required are very low the rate at which you can supply the water can be very low so even if you have very small quantity of water still you can you can cater to the requirement of the plants you can run the system then we have seen that it does not wet the whole area small area is it does not wet the total area only portion of the area is wetted and that portion how much portion of the area should be wetted is under your control you have to decide that depending on what is the spacing between the plants so you will you are basically reaching each individual plant in terms of catering to its requirement when you do not wet the whole area the weed problem is the weeds are there because of the fact that the moisture is available on the soil so if the moisture is not there in the other area where you are not having any interest in that area the weed control is the weed growth is automatically reduced and in this case also the fertilizer application is possible as in the case of sprinkler irrigation system it has also been seen that using this system the plant growth now that is you know imagine what is the reason because in any conventional method even for that matter even the sprinkler irrigation method there you can have more intermittent applications the frequency of application can be higher but in this particular case you are almost supplying water almost every day the rates are so low that you are trying to replenish the the rate of requirement with the rate of application you are trying to replenish the moisture in the soil at the same rate at which the moisture is being lost so when you when you will try to do that what is happening in fact the stress level is much lower because in the conventional system the plant will be very happy when you are when you are brought the the moisture level at the field capacity level just after the irrigation now as the moisture level goes down the the amount of force which the root system will have to apply to extract the moisture from the soil will keep on increasing that will keep on increasing the stress level in the plant also when you are using the drip irrigation system since the moisture supply is almost continuous the stress level in the plant is very low and that is where the growth of the plant the yield ultimate yield is also going to be affected and it has been seen that in many cases the yields have become so much example in the case of coconut people have found that the yields have increased three times using the drip irrigation system the farmer once he has known that yes this can be achieved they have automatically gone for the drip irrigation system because that has that has proved as an incentive they will get the higher the yield more will be their benefit so even if there is some initial cost if it can be offset by the increased production the farmers given a help may be that he did not have the initial investment so if the help is given in the form of loans they will like to go in for such a system so there are some situations may be not all where you have lot of advantages using this system and that is where you have to decide what are the conditions under which you are irrigating and whether which system should be should be selected for applying irrigation water the suitability in terms of the crops as we have just mentioned the orchards is one all the orchards crop are the crops where this system can be very suitable then vegetable crops also it has been seen that the system can be quite successful you can also use for nursery plants all those crops which need nursery plants in that truss situation the system can be used people have also used for sugarcane and it has been seen that the sugarcane yield also increases by using the drip irrigation system let us have a look at the limitations of the system what are the possible the problems which you encounter most of the time when you use the system the major problem which is encountered is that of amateur clogging these emitters or drippers they are basically there are two ways one is that you can place the emitter on the top of the ground surface in that case the evaporation losses can be excessive because all the water since the water is being supplied at a very low rate majority of this water might get lost directly into the because the top surface will be wet all the time you will have lot of evaporation taking place. So to avoid that sometimes you try to have an out opening of the emitter slightly below the ground surface you might embed it into the ground at a very shallow depth may be around an inch or so or couple of inches when you do that then the clogging can be the major problem even otherwise the clogging is not only because of that clogging is also because of the impurities if they are there when they the water is so the pressure at which the water is coming out of the emitter is so small that over a period of time you will find that the opening and the openings are also very small and you will come to the design levels we will see that the openings are very small so even a small portion of impurities can clog the emitter sometimes the soil can go inside and clog it. Now at the time you might find that they can be depending on what type of these pipelines have been used they can be a possibility of algae formation the water is standing inside the pipe so that can also clog the time this basically the trouble is that you are having so many emitters spread all over the place it might be too late before you detect that the some emitter is clogged because the rate at which the water is being supplied is so small you would not even observe and if you have not observed for a for a period which is detrimental to the plant by the time you realize that the plant is wilting is too late this is one of the major problems of the clogging problem is one of the major problem of the drip irrigation systems then the other limitation is that it cannot be used for all the crops the crops which are having high plant density these cannot be used because then the requirement will be that you need to wet the whole soil so if that is the requirement then you do not the number of emitters which will be required will be so high that is not justified to use this system so in that case it is better to use a sprinkler system or any other surface irrigation system which uses the gravity otherwise for some of the crops it has been found to be very successful it saves lot of water you have the complete control on the way the water has to be supplied you also have the problem the advantages of using the fertilizer with that the suitability has to be looked at with respect to all the factors put together so with that we conclude this topic of irrigation methods we have looked at almost all the major irrigation methods which are being used in the present day context we have covered the conventional methods we have covered the method which are the recent methods and next we will go in for these individual methods we will look into the design aspects of these methods how we design the individual when we select the individual method we have looked at the selection process while looking at the suitability of these methods so having selected a particular method what are the other things which need to be sorted out whether it is in the form of laying the fields that is there is a topic which will cover after we cover all the methods that how we are going for the farming of the areas the land leveling and the land grading but before that we will look at what are the various design processes for each of these methods okay any question thank you.


It's a 10,000-square-foot floor in this building open to the unhoused and migrants. PR director Suzanne Morris of the Unitarian Universalist Association says, there's a number of commercial real estate properties that have vacant space currently, and we wanted to show that this can be done. But Cindy Fishman doesn't think it's a good idea. I personally look at it as a home for illegals, not migrants. A lot of our resources are going to them rather than Americans. BU professor Tom Byrne, who specializes in housing, says he understands why not everyone is on board. People who are in need of emergency shelter for whatever the reason are a highly stigmatized population and there's a perception that there's something bad and dangerous about having a shelter in one's neighborhood. The Unitarian Universalist Association says that the shelter will house up to 25 families with children. The governor's office says that the shelter will be open until the start of June and that they are working with United Way to fund the shelter. United Way and the governor's office did not respond to confirm the statement. This is Joanna Malvis reporting with BU TV 10.


Item No. SCP-303 Object Class, Euclid Special Containment Procedures As SCP-303 has not been known to travel beyond the boundaries of sight, the entire area of sight is currently considered SCP-303's containment area. All rooms in sight are to be altered where possible so as to have two entrances separated by a distance of 10 meters or a line of sight. Personnel are to be distributed evenly throughout the facility, with available radio or intercom contact, so that encounters may be resolved quickly. Personnel who witness SCP-303 are to be submitted for immediate psychiatric evaluation. All SCP objects housed at sight ██████ since before June 4, 2010 are to be transferred to Site ██████-B one at a time. Each SCP object will be transferred again to Site ██████-A once it can be verified that SCP-303 has not migrated from sight ██████ with it. Once SCP-303 either migrates to Site ██████-B or remains present at sight ██████, once all SCPs in question have been transferred to Site ████-A, containment procedures will be updated as appropriate. Description Witnesses describe SCP-303 as a nude, sexless, emaciated humanoid figure with reddish-brown skin.  It continually vocalizes a wheezing noise, loud enough to be heard from the other side of most solid doors. All individuals who have had encounters with SCP-303 are capable of describing it in full, including individuals who have not physically seen any part of it. SCP-303 will periodically materialize behind any closed door, hatch or other entryway barrier opposite of sentient observer, chosen by unknown means. SCP-303 will then remain behind the door for an indeterminate amount of time. Any individual attempting to open the door or barrier experiences intense, paralyzing fear that lasts until SCP-303 dematerializes, either on its own or to avoid being directly seen by another observer. The source of this fear is not clear, but it appears to be similar in nature to Erratnophobia or Aphidiaphobia, originating on a pre-conscious genetic level. Analysis indicates that SCP-303 is not, in fact, purposely inducing fear in the affected individuals. SCP-303 does not allow itself to come into direct visual contact with any observer, and has never allowed any one individual to view more than 10% of its form. When the door or other entryway barrier is partially or completely transparent, SCP-303 will materialize in an orientation that leaves 10% or less of its body visible, or cause effects of fog or frost on the transparent surface to achieve the same effect. If SCP-303 is approached from a direction in which there is not a solid object or door breaking line of sight, it will dematerialize before direct visual contact is made. Any electronic or complex mechanical devices that SCP-303 encountered are temporarily disabled. SCP-303 has made no recorded attempt to physically or verbally engage any observer. While SCP-303 arrived at sight ██ is not known at this time, SCP-303's first recorded appearance was on March 1st, 2010. It is suspected that SCP-303 was inadvertently transferred along with or manifested by another SCP on site. All SCPs on site ██ are being re-examined accordingly. Agent ██ was showering in her private quarters bathroom when she became aware of the presence of SCP-303 on the opposite side of the shower curtain. It was wheezing extremely loudly. Startled by the discovery, she accidentally struck the shower curtain, causing it to sway outwards. The curtain partially wrapped around SCP-303, revealing that it was less than 0.5 meters from the curtain, standing erect and facing the shower. Agent ██ reports spending approximately the next three hours sobbing in the shower quietly as not to disturb SCP-303. Agent ██ reported that the wheezing stopped very suddenly, at which point in time she was able to exit the shower. Incident ██ encountered SCP-303 inside the site ██'s second floor break room. He was attempting to obtain coffee creamer from the counter cabinet when he heard loud wheezing emanating from the cabinet and was overtaken by overwhelming fear. Agent ██ later reported that SCP-303 was huddled in the cabinet in the fetal position. Agent ██ claimed to be certain of the information, despite failing to open the cabinet door. Later, when the cabinet was examined, one container of powdered coffee creamer was missing. Note, this is the first recorded instance of SCP-303 removing an object from a scene. Incident ██-303-6, Dr. ██ was discovered dead from dehydration in the second floor storage room. It is estimated that Dr. ██ spent up to five days in the storage room before being discovered. A small 4 meter by 4 meter decompression chamber separated the storage room from the adjoining hallway. SCP-303 occupied the decompression chamber for the duration of Dr. ██'s isolation in the storage room, disallowing entry from either direction and making it impossible for Dr. ██ to leave. Test Log 303-A, a team consisting of Dr. ██, Researcher ██, four security personnel and four D-class personnel, were assigned to be dispatched to any reported incident of SCP-303's materialization in order to immediately perform on-site testing. These logs take place at the door to room ██ in the first floor hallway. SCP-303 was reported to be within room ██, Test 303-1. One male D-class personnel, D-303-1, was ordered to open the door and threatened that he would be transferred to SCP-███'s duty for non-compliance. He refused, citing extreme fear. Test 303-2, one male D-class personnel, D-303-1, was ordered to open the door and threatened that he would be terminated on the spot for non-compliance. He refused, claiming that if he were to do so that SCP-303 would ██, he was terminated on the spot. Test 303-3, one female D-class personnel, D-303-2, that had witnessed the termination of D-303-1, was ordered to open the door and threatened that she would be terminated on the spot for non-compliance. She refused, claiming that if she opened the door that SCP-303 would ██, researcher ██ was visibly shaken by this claim. D-303-2 was not terminated. Test 303-4, one female D-class personnel, D-303-2, was ordered to open the door. One male D-class personnel, D-303-3, was given one combat knife by security personnel in order to ██ until D-303-2 opened the door. After two hours of ██, D-303-2 died from blood loss, D-303-2 made no attempt to open the door. Addendum ██, May 1, 2010 SCP-303 appears to have claimed the second floor storage room as its own. It has so far disallowed any personnel entry to the room since April 5, 2010. It leads periodically to a choir foundation property, which is then moved to the second floor storage room. Today, the following list describes all non-classified items taken by SCP-303. One ██ cryotube, three sets of standard foundation surgical equipment, two D-class research cadavers, one gasoline-powered generator, a variety of chemicals including large quantities of tryptophan, phenylalanine, ██ and tyrosine, among others. One container of powdered coffee creamer. In addition to this, a number of classified materials have been obtained by SCP-303. Staff are still attempting to determine what specific purposes SCP-303 may have for the materials. SCP-303 is now available at SCP-303, May 1, 2010, SCP-303, May 1, 2010 SCP-303 is now available at SCP-303, May 1, 2010, SCP-303, May 1, 2010


Hello everyone I how you doing? Okay, great awesome. I hope you had a great pie day yesterday and today we have a quite interesting day still ahead of us promising eye-track And I would just like to do my part and talk a little bit about the machine learning landscape that we have today So as announced, my name is Ico. I work for a company called ThoughtWorks and And we are a consultancy which means we build individual software for different clients and in different domains using different Technologies and we try to follow what we would consider the best practices. So we try to be agile actually agile Try to follow things like test-driven development continuous integration continuous delivery and and pair programming and we are also a huge fan of open-source software and So there's this one statistic that I found so this is where the arrow. This is the company and apparently we are Number eight on that list or whatever. I mean the statistic being used here is the employees the number of employees Contributing to open-source software on git. I'm sure there are other statistics But I think it gives a gives a nice idea that there is that we do have some interest and for a company Around 5,000 people. I think that's quite impressive. Yeah, especially compared to some other lower on the list who are Significantly work power All right, we also have an office in Singapore so check it out. We do some meet-ups I think Martin Fowler is coming next month and Obviously as everyone in tech we hire as well Yeah, so working for a consultant he kind of makes me a consultant, but I would consider myself a developer and The my my passion is machine learning and it started when I was at university. I wrote my thesis about convolutional networks on Mobile devices and then later continued when I was working at clients at ThoughtWorks and one there's one particular client that I would like to tell you about that is That was the online retailer in Germany the the second largest behind Amazon and What we did there was we use neural networks to predict Orders Based on browser session data, so we looked at the browser session data of users that were coming in and then predicted how likely it is that That user will buy something within that session and if it would be unlikely we would Give offer some discounts or something like like that but another about me we're here to talk about machine learning and So as as heard before multiple times and as we all know AI machine learning all these terms are At least let's let's not say overhyped let's say popular nowadays And I think it would be good to get a common understanding of of what these term mean at least in my understanding and Artificial intelligence and I mean we hear AI everywhere Especially in the AI track Artificial intelligence, I I would consider artificial intelligence I like this definition that the box for dictionaries.com offers a lot And this is by the way also the the definition you find when you ask Google to define Artificial intelligence and it it basically is The theory and development of computer systems able to perform tasks normally requiring human intelligence So that doesn't sound too crazy does it and and one example that I that I like is is a chess program where if you think of a person playing chess you would you would assume that person has like some level of Intelligence to be able to play chess Now if you make a computer play chess Then that is the artificial intelligence But if you know how chess works There are certain moves you can make and then in the next round you can make certain certain other moves You could basically brute-force that right you could rate like write a couple of if-else Branches and then you can brute-force all the possible solutions and then find the one that gives That makes you win That that chess game But then again brute-forcing and diverse isn't isn't that crazy, right? so for me and for also dictionaries the the definition of AI is less a technical more like a Physiophic, you know what I mean Yeah, rather technical but but more physiologically shit, right? Yes, who was it? Thank you very much Yeah, but then so nothing nothing fancy so far but where the fancy part starts for me is machine learning where This defining your algorithm and that then follows step-by-step to achieve something isn't really the case anymore like in machine learning where The way of solving a problem the way the algorithm works is actually defined by the data we put into a machine And if you put in different data or we put in more data the way of of How the algorithm solves the problem changes And that's where I that's where I think that the fancy part starts and then there are neural networks, which is the subset of machine learning Modeled after the human brain where you have like Neurons and they are connected to each other and send signals and then deep learning which is probably the most Hyped one is basically just a neural network with lots of layers It's like a deep structure And then there's data science and we've Heard that term before as well And data science is basically extracting valuable information out of or insights out of data and it can use these methods, but it doesn't particularly have to So the question is why do we need machine learning? So there are there are a couple of use cases where Machine learning just simply outperforms other approaches and one thing that machine learning can do is it can find Non-obvious correlations in a set of data going back to the to the online retailer there was was working with We had like 700 data points of browser session data like finding manually finding correlations between 700 points and someone buys something would be infeasible where throwing the whole data at a neural network Was rather successful Right, so as I said my passion is machine learning and last year early last year I want to do something cool and machine learning and I Remember this this quote of by by Larry Allison who was the the founders and CEO of Oracle Apart from it being slightly sexist I think it still holds true and let me maybe prove that by asking a question Please raise your hand if you use a patch key a patchy calf guy at work Well, there's I expected more actually So I did this at a different conference and like 40 40% raised their arms and the thing is the follow-up question is How many people of you think a patchy Kafka is the perfect solution to the problem you're solving and then half the arms went down I'm not sure what whatever. Yeah, I see not nodding. So I guess I guess it still holds true even nowadays that in The computer industry we just go for the cool new kid on the block so I realized that and then I realized looking at the landscape of of Machine learning that's out there. There's so many things and it's so overwhelming that I need to do my own analysis of What tools are out there and what they're good for and this is basically the what I try to do with this talk is lift The fork around the machine learning landscape a little bit I'll be focusing on Machine learning and then later on its subset of deep learning So right when you try to analyze the landscape of something that can be used to Analyze data I mean what what possible approach could you do? Obviously you do that data driven So what I did was I compared a lot of tools based on certain features I looked at the the popularity of these tools where I went to get up I mean I wrote a Python script to get star the star count of github for each of these tools and The same thing with the questions that were asked With this specific tag of one of those tools on on stagover flow. I looked at some API dogs I played around with some tutorials and when possible included expert opinion and all that to to get a better understanding of What kind of tools are out there and what are they? capable of That means If one of you have worked with I don't know Pytorch for the last two and a half years. You're probably more an expert in Pytorch than I am I Have used some of these tools, but definitely not all of them and Right, but the thing is they are lots of them are right new rather new like two three Maybe three and a half years old I don't think anyone in this on this planet can be an expert in all of them so that saves me a little bit in that regard So your question now is I could you spend all that time analyzing that landscape So what is the best tool and I have to disappoint you a little bit because I have to give you the classical consultant answer It depends It depends on a couple of things that depends on The use case you're you're doing What do you do image classification be it for signature checking or for optical character recognition? Or you want to do order prediction like we did at the client in Germany that I mentioned before and I think in general a good advice would be if The tool you're picking doesn't solve the problem you're having Don't pick that tool That might sound obvious I've seen cases where that advice wasn't followed It also depends on your experience like a team's experience regarding the programming language that is used regarding the tool themselves Also regarding the the general general understanding of machine learning depends on the infrastructure whether Your model will be running on a powerful server or on a mobile device Or whether the training of that model will happen with unlimited resources in the cloud or whether it's on your laptop without a without a GPU and Depends on the structure needs of your data. So what kind of databases your data stored on is it in a graph database? Or is it in a on a Hadoop cluster? Depends on the feasibility and what I what I mean by that is Are you currently trying to figure out a general? general solution to the to the problem or are you like are you in like some prototyping stage or are you in Production and you want to fine-tune and get the tweak the level of the last little bit out of your model right and Obviously it depends on how much data you have like if you are Training a model with Amazon's clickstream over the last two years Or you train a model with your grandmother's cookie recipe collection the amount of data might Might be a different depending on your grandmother And depends on lots of other things Like how much time you have how much money you have basically how big the team is etc. right to give you like a Some insight on how I approach this whole thing I Let's talk about tensor flow a little bit everyone anyone has not heard about tensor flow by now Everyone it's by far the most popular deep learning framework with more than 100,000 stars on github and what I did is I had those for Features if you will or for categories where I ranked where I gave basically marks for for all these technologies Where ones being the lowest and fires the highest and I think the I mean the most obvious one is probably the ease of setup here with the two and Now the the question is why is that and what I did was I went to the Installation page of tensor flow how you install tensor flow on jubuntu and That's what I got In order to take this screenshot I had to install a browser plug-in that takes a screenshot scrolls down and like stitches all these pictures together and Now you're asking if one is the lowest mark that that Framework can get why is it still to I did the same thing with with the Microsoft cognitive toolkit and This is what I got then in in Microsoft's defense. This is installing it on Ubuntu Apparently installing the Microsoft cognitive toolkit on Windows is rather easy But maybe one quick question is anyone doing machine learning on Windows good to know So to be true to tell you the truth this slide was made about a year ago and The especially in in machine learning everything is changing so fast Tender flow has drastically Reduced the the steps you need to take to install it in fact right now. It's Installed tensor flow Then you have the CPU version So that is that but the side was too funny to skip it Right There A couple of other things So even even last year when when this was the case and you had to go through all this pain to install it Why was popular? Why was tensor flow so popular back then? And that's a couple of things right it has this thing called tensor board, which is basically Real-time visualization of your training process Other frameworks have picked that up as well But tens of we had that from I think the first version 0.6 on Which is kind of cool because you can in real time see how your how your model behaves or your accuracy is your Cost function And it also is able to visualize the computational graph that you bought There There is however There are a couple of things So two other features that I had look at this the degrees of freedom or the parameters exposed basically How many hyperparameters can you play with how many how much in detail can you go to to fine-tune your model and? tensorflow has a ton of options there But that also means So that's great for for this However, it also has like pure tensorflow has a pretty steep learning curve which benefits the ease of use a little bit right and I've already mentioned the the adoption in the community which Which itself? I mean maybe in high adoption rate doesn't particularly say something is good But what it what it can indicate is that if you run into problems and the community using that technology is rather rather large Then you have a high chance of getting actually some help from the community that's That's using it Right same thing I did for Keras where Keras is we've heard about it. Who does not know about Keras? All right, so I quickly talk about Keras is not actually deep learning framework It's something like a higher level interface that you can put on top of Deep learning frameworks and it offers a more intuitive set of Instructions, so you can easily build the neural networks and So for example, you can build a long short-term memory network in like ten lines of code Which is which is pretty crazy and it offers due to that it offers the fast prototyping and You can explore solutions and build PLC's and Keras currently supports a max net deep learning for J tensor flow Microsoft cognitive toolkit and piano and in fact If you install tensor flow, it's already built in and you can use a Keras even without separately installing it The installation is basically if you don't use tensor flow where it comes Default comes by default. You can do the pip install Hands the floor and then you get that however for the other cases you need to have a second separate what they call back-end the The deep learning framework where this Keras then works on top of right That was about the methodology one thing I did is I separated Classical machine learning Libraries or or toolkits from deep learning mainly I mean for several several reasons the resources the resource requirements are different All deep learning framework support GPUs and after all if you can find a linear model to find your To solve your machine learning problem. I would advise picking Is for straightforward model rather than going for deep learning approach Because this is a more complex system and in general If you can solve it the easy way easy way why why not? right This is where I plotted These machine learning libraries against each other where the adoption rate is on the x-axis and the degrees of freedom on the y-axis And the ease of use is displayed by the size and you on the on the on the very right of the chart You can see weaker pie brain and shogun these haven't been particularly popular in the in the last time In fact, pie brain. I don't I think the last change on the github repository was like two and a half years ago I mean, maybe there were five trains in the last two and a half years something like this So I would consider that being being discontinued Weakah offers a graphical user interface which also doesn't seem to be that popular It is however popular when it comes to data mining But I think the the what I would call community kings on the on the left side of the chart There's scikit-learn, which is again, I would assume most of you have heard of highly adopted in the community of us a powerful interface to To play with and you can treat your metal model with the many different parameters and then if you're If you're working with spark there is And there are these other three that that are interested that might you might be interested in ML lip which comes with spark by default and even has a higher level rapper spark ML And H2O which in some cases offers better results than spark ML or ML lip, but then Even if you start with ML liberal spark ML, you can just add H2O later on anyway another Perspective when choosing a machine learning technology, you might have is scalability and we have as I mentioned before scikit-learn is highly doubted and offers a Vast options for improving your model It is however limited to run on a single machine That doesn't particularly mean is it's one thread or one process because some algorithms can be implemented and to run a parallel and as far as I know scikit-learn has done that However, if you aren't if you can't like batch process your your training then you might have a problem because you're limited to the machines memory But if you don't need that's the amount of scalability and you don't need to get no need to go distribute it and you don't Have more memory than it fits more data than it fits in memory and you're comfortable with Python then scikit-learn is is a pretty safe bet There are however H2O ML lip Pachi Mahoud and Chogan, which if you are more comfortable with Java They would they would work quite well and in fact Pachi Mahoud ML Lib and H2O Can all run on top of an HDFS So I guess the the question you have to ask yourself is one of the questions that you want to ask yourself is whether you need distributed computing power and Well, the structure needs of your data being whether it is running on HDFS or not in In fact, we talk about scalability here when we talk about deep learning frameworks that I will come to later They are all highly scalable And by later I mean now And there are a lot of a lot of deep learning frameworks and as I As I said if you know, I didn't say that actually but all of these are capable of Mostly capable of of running the same things that you have seen that you could do with the non-deep learning Libraries that I've shown before It's just the case that in some areas deep learning is significantly more successful and Some examples would be I think computer vision is the prime example of Where deep learning shines a lot and out performs every other approach speech recognition is another example audio recognition and Machine translation as well, but there are there are disadvantages to to deep learning It's more complex The setup might be a little bit more difficult than for these for these frameworks In general you need a little bit more data So there are there's something called transfer learning where you can train your model Take And retrain it slightly Right, so I've only seen this this transfer learning being applied to thank you very Okay, I've only seen transfer learning being applied to Image recognition. So if you have structured data I'm not aware that there's any I don't know pre-trained structure data model that fits your financial analysis Another another drawback of deep learning is It's difficult to comprehend The so what I mean by that is that the the per feature importance It's difficult to figure out Given right it's difficult to figure out. So that means You put in some amount of data and then you get some output Figuring out which of these particularly data points are now responsible for creating that output is It's rather difficult And I I can't think of an example where that would that would make me personally uncomfortable This is great I said I go to a doctor and I Tell the symptoms and the doctor is typing the symptoms into the machine and into a deep learning model and a deep learning model test We have this condition. I Would like the doctor to be able to verify why the deep learning model would suggest I have this condition now and Yeah, the basically the simpler the model is the more easy is to Get a direct relationship between a variable and the outcome Right, so what do deep learning frameworks probably all of you have seen your network by now, so this is your network Artificial your network We have some input nodes and in this case they're all connected to the next layer to the nodes of the next layer And then there's another hidden layer then output layer which Gives you the That you want as Mentioned before deep learning would just have more of these hidden layers I mean This network is rather small. I'm not sure what it could do So if you have an actual model, it would also have more nodes not only more layers But then the question is why do we need deep learning frameworks? and all of these deep learning frameworks offer the basic building blocks of Deep neural networks, so you can just you can get you could just say give me a convolutional layer and then you get a convolutional layer and you can basically create all these layers You so they are I mean all these nodes to some kind of computation And all you can get all these these algorithms You can use different functions that the the deep learning model Provides and all of them are optimized I've said it before the all of these support GPUs, which is I mean The fact that we started putting all these All these computations are similar at the fact that we started putting them on on single shaders on GPUs It's one of the reasons why deep learning has been so successful over the years over the last couple of years and can in fact reduce Training time from days and weeks up to down to hours Right So these are these are the framers you've just seen again mapped against each other with the adoption rate is on the x-axis and the degrees of freedom on the y-axis And the ease of setup as the color according Accordingly to this thing here and the size represents the ease of use and We've I've already talked about tensorflow and Keras They are the most adopted Currently followed by by PyTorch and especially in combination where you have tensorflow back-end and have Keras on top of that They've been highly popular Keras as mentioned is good for this prototyping and you can like quickly define your your model Where tensorflow Absolute outchines every other framework when it comes to deployment whether you deploy it on a distributed system or on mobile devices And if you use both of them you get the both best of both worlds However, there are a couple of things so Debugging and all these fine-tuning and whether you want to run you can define to run on different GPUs all of that would still need to be in tensorflow directly and you can't do that through Keras So in the middle, there's this this Blob of frameworks, which I would consider they're all fighting for attention and and the reason for that is So there are quite a few big companies who have fast amount of data are behind some of those frameworks Data itself isn't Particularly valuable if you don't if you can't get insights out of that data and you can't analyze it correctly and therefore All these companies like Facebook, Microsoft Google Open sourcing these these frameworks Basically make it open for the community to to improve upon is brings value to at least to them All right, there is I think the so I call Keras and tensorflow the the community kings right now, but the the strongest contender Currently is pytorch pytorch is a re-implementation of of torch and pytorch is Being has been it's being developed at at Facebook PyTorch is especially popular amongst researchers pytorch seems to pick up recent academic research results significantly faster than all the other ones and And that seems to be quite important for research. It also has I don't I don't want to go into detail there But it has the dynamic Graph creation which tensorflow now also has I mentioned I mentioned earlier that when I made the first tensorflow slide and that is a little bit outdated because the Tenderflow change a little bit last week tensorflow 2.0 was announced where they have quite a couple of changes and quite a couple of implementations that actually take away the Advantage from from pytorch And then we have a couple of other ones Tiano and I indicated that by this arrow. Tiano was one of the first deep learning frameworks was developed by the University of Montreal, but last year they actually stopped that project because they thought that the the environment the landscape has already matured enough and Piano isn't necessary in that case anymore Then there is Kathy which has also been around for quite a while the It is there is Kathy too, which has also been developed by Facebook Kathy is still around because it seems to be quite popular for image recognition. However you For Kathy you define your model in YAML and that doesn't seem to be up to date anymore I mentioned pytorch towards the original has a very As a fairly small community Maybe because it's written in Lua and you have to write Lua to use it There's deep learning for J as the name suggests this I think the So that was that one over you there is a there is a different again a different point of view You might want to ask yourself when choosing a tool and that is which project life cycle Which part of the project life cycle you are in? There are some of them offer easy fast prototyping You can build your POC is fast Where others offer very fine-gain fine-grained control To basically treat your model and get the most out of that and Machine learning or if you try to solve a problem with machine learning the path to success It's not always that obvious like if you just I mean if you develop Number software and you get your user story You kind of know okay after this this this go to that class to this and then you do that and then your story is done with machine learning You have to experiment a lot. You have to figure out which is the right model I mean there are some there are some ideas if you If you do image recognition, you probably want some convolutional layers in there but in general there's a lot of exploration and and Try an error basically before you know which general model works good for your use case So in the beginning you need a you need the technology that supports this kind of fast We are in later stages of the project life cycle you want something where you can get the last bit the last 0.01% accuracy all of your model and now Mean I'm basically telling you you have to start writing everything like with the framework on this side And then six weeks later throw it away and they start writing the same thing with in the different framework more on the fine-tuning or control side Luckily, that's not that would be horrible. Luckily, that's not the case There is something there are Framework interoperability solutions being developed right now and one of them is on X Stands for opening your network exchange, which is basically a Common format of machine learning models that multiple of these frameworks use So currently there's cafe to Microsoft going to toolkit and makes net and and pytorch who store their model in this specific format so what you can do is and I Mentioned Facebook before because Facebook is Developing pytorch and cafe to and that's exactly what they're doing. They use Pytorch to to design their model Because it's easy you can fast prototype and then take that model The exact same model throw it on to cafe to and then let that run in production and there is Right and not only are these these four that I've mentioned supported but also Like tensor flow for example was not part of them. It's not storing Their model in this specific format. However, they are Converters that are able to convert Tender flow models to on X and I think it's bi-directional. I would guess so right Another perspective I'd want to have when choosing deep learning framework to Solve a problem is mainly a question. You need to ask yourself. Are you solving a core problem? or are you solving a peripheral problem of your domain and Let's say Let's say the you are online retailer and Product recommendation is probably one of the most important things of your business And if you can if you can make your product recommendation 0.1% better. That's I mean that's cash, right? That's probably depending on your I mean depending on the size that can be millions of dollars In that case you do want the the most fine-gain control you can get for for this particular model because it's It's cash, right However, if you and let me give you a different example Imagine you're not an online retailer, but you're a dating site and In order for users to sign up for this dating site you need to they need to upload an ID know for well for multiple reasons people don't like uploading their IDs to some website and We have like 50% of the pictures being uploaded is just not ID at all it's like pictures of I don't know flowers of the wall of something and The verification verification process for these IDs is currently being done manually. So you have like five people verifying these IDs and half of these pictures they look at rubbish Now having a machine model That can filter out 80% of these non ID pictures That would be great right that would reduce your labor and cost a lot but Do you really gain a lot from like if you spend like if you create a 50 people team trying to optimize This use case probably not So a question you have to ask yourself is are you solving a peripheral domain or a core domain problem? and So a couple of things I haven't talked about are these I mean I'm not actually I'm not actually talking about GCP and Azure and AWS but all of these cloud providers have Some machine learning as a service Solutions available, but they are very specific. So you can it usually is like an API where you upload something And then you get a JSON bag or like XML or whatever With the analysis of whatever you've uploaded you can upload pictures and then you can do something like a landmark Detection or you can do face detection or a speaker recognition and all these all these kind of things But you usually can't train The model yourself so you have to you can do some things But you have significantly less freedom as if you develop your machine learning or your deep learning solution yourself but then again if I can just upload All my pictures and Google tells me this is an ID or not my problem is solved right so I mean as long as I'm fine with uploading IDs to one of those services and paying money for that All right Let's skip that actually Right, I hope I could give you like some idea of what the machine learning landscape currently looks like and I hope I could make the point that you need to ask yourself a certain set of questions to make a reasonable decision on How to pick a machine learning tool? Thank you very much. Thank you very much Ico we have time for a couple of questions Hi, Ico. I'm a mode and first of all really thanks for a very nice presentation I really liked your slides and we are presenting is really awesome Regarding one of my question that since being a machine learning developer. I have a question that basically when we are Kind of working on GPUs, especially which library or which kind of API Which we should prefer because basically it should be like Python and kind of tensorflow or Python or open CV Or is any other GPU which we should kind of basically use which is freely available on the cloud? What's your thought on that? Well, I think again it depends on on what situation you are in whether Like I would even argue that not for all cases Especially in terms of prototyping you need a GPU But then as far as I know When when the let's talk about deep learning because then that's where GPUs become interesting As far as I know they all work on CUDA And therefore I I don't see a particularly performance difference in most of them If you I mean if you need The highest possible performance, I think AWS has a couple of of images where you can just like get some ml Instance and then that probably you can scale it up to Whatever your bank account can pay for Further questions if that is not the case, let's thank again Ico very nice presentation


Good god y'all are should I say complete global saturation That's a hot mug guys. Hey guys, this is my review for season 5 episode 2 of Supernatural Good god y'all This is the episode that the horsemen of war is introduced the brothers further Distance themselves due to the distrust between the two Bobby's paralysis is made more permanent We meet up with Joe and Ellen again, and we start to see the tides of the apocalypse Coming across the land the episode starts off with Castiel coming in basically saying I can't heal you Bobby and Bobby being real Frickin pissed off about it until Castiel says yeah, you see I You know betrayed my entire brethren my brothers and sisters that I have been loyal to for thousands of years for you Chuckle heads and you still failed. So yeah, I'm pissed more than you all the while The brothers are still having this big distrust especially Dean of Sam Dean can't trust Sam with anything and As they go out to the town that has Rufus Joe and Ellen in this battle with the entire town Sam can't Be trusted throughout the entire thing and it's getting on his nerves further and further as the battle continues in this town To the point where he's literally pushing Dean aside getting very angry with him and asking him Can we please do this like professionals Dean obviously having this distrust because of what has been caused at the same time I'm still being aware and wary for his brother, especially when his brother is left behind with the other Supposed demons now. This is actually probably one of the best concepts of the episode is that war is there and he's actually played By Bosch if that's the show name from the Amazon This is cool because he was on this Four years before that show started and he has a really freaking good monologue The Sam at one point just talking about how war happens and where he's been and what he's caused And he's just like, you know, it's not really hard to really push you guys And what's cool, too is that you never really know what he is You can't tell if he's a demon an angel a creature or whatever because all he is is a guy with a ring And he's just spinning really really badly obviously and that brings me to the very kind of odd crux I have with this episode. I have a lot of things I like about this episode But there's also a lot of parts that maybe go what the hell's going on particularly the acting. It's bad There's a lot of scenes especially between Dean and Sam that the dialogue just comes off as corny cheesy or very Unprepared and there might be a reason to this they shut down Matt squeeze Matt squeeze this tiny little town near Abbotsford mission that has essentially one main road through it And it's kind of funny that it's still there and it's kind of funny how the town is still so small considering It's right across the bridge from mission, but it is this tiny little quaint town They shut the whole frickin thing down when they're in there in front of that church That's literally downtown. That's me that gives you an idea that this town is actually small and it's funny enough It's not even far away, but it's a great setup for what they're doing in this episode But I have a feeling it's kind of like that situation that happened with Ladner and season 15 where things felt real fucking rushed and really badly shot and shit like that They had a choice of trying to do all the cool setups and middly some of them are interesting But some of them do kind of look like they're half baked and that's how the episode feels a lot And it's odd too because directed by Phyllis of Garia It's written by Sarah Gamble and just can't pinpoint it because some of the dialogue some of the interactions in this episode are Truly bad, but a lot of these are covered up by the good elements There's great action scenes in this episode the distrust when done well between the brothers is really well done Bobby and Castiel's Conflict at the beginning of the episode. It's really really good. That's even if cast says I'm gonna go find God which The God that opens up so many Bubbles now in terms of how the show proceeds, but we all know it's a fool's errand I'm kind of a little bit vague on how it goes. I just know that he fails and he's like Yeah, he's not here and then with the battle in the town and that everyone is seeing demons But really they're not it's just They're all being manipulated by war and I love that very simple Man-on-man concept and it just takes one little thing and even if a reason is being said to them They'll still want to fight to protect themselves and again I like these elements of this episode of love how Dean has to try and convince Rufus which by the way to I keep going back and forth between thinking it's possible that Dean could have figured this out that it's war because it all Happens in one conversation which I think that maybe if this episode script had a few more minutes in the oven It could have been a little bit more fleshed out It isn't exactly implausible that Dean couldn't figure this out But it's also kind of a stretch at the same time It adds up when war comes into the room and Dean kind of listening what he says is like Wait a minute this what's this Joker talking about obviously he then tries to manipulate him again But to try and prevent everyone from killing each other they go and cut The ring off of war's hand and he just disappears which again Who and what was he? I'm really curious as to what he was. It's cool I think we're gonna talk about him though later on in the season when he's talking about where The aim where the horsemen are and then the episode ends with Sam and Dean Splitting up.  I have a problem You might be wondering why I started off the review with that little quote Do you know who is in this episode? I first I thought hey, isn't that the Han Solo guy? No, I think that's the iron fist guy. No, it's Sean Roberts.  He's a Canadian actor. He's out.  I thought that was hilarious, but I don't care. I'm short too I just you know in the movies Wesker just looked so tall, but was he wearing like Seven-foot pump-ups or something either way the episode has a lot of issues Purely because I feel that they were rushing things The script was a little bit needing more baking at the same time the actions fun The concept is really cool. The idea of war his monologue is really well done The goods outweigh the bad still but there is a bit of a conflict between them Which is why I'm gonna give good god y'all a five out of seven. It's Yeah, one point it would dip down to a four for me And then it went back up to a six and then it came down to a five So yeah, it's a little bit of an up-and-down battle, but it's unfortunate But yeah, like I said, it just felt a little odd in certain places I don't know if anyone else noticed that but it was it was a little bit weird watching it Good gone y'all is a good episode with a nice reunion with Rufus Ellen Joe and with Sam and Dean My highlight for this episode is definitely Titus Wilver He's just perfect in his double role of Roger in the horseman war despite having a limited screen time What I love about his portrayal of war is that he just enjoys chaos He enjoys it so much that he practically gets off on it. His conversation with Sam was really good It was especially when he lets Sam guess who he truly is by saying I was in Germany then in Germany then in the Middle East I was in Darfur when my beeper went off. I was getting hooked up with siblings. I've got three We're gonna have so much fun together overall. I gave a good god y'all a five out of seven Yeah, no Titus is definitely the best part about this episode his Very limited role in this episode is very standout it's probably one of the saving graces of this episode from it being kind of mediocre and Thankfully the guy has had a really good career afterwards good god y'all is an interesting sociological episode But one thing I find particularly Lee I can't talk right now interesting is that Castiel is looking for his father Which is completely reminiscent of Sam and Dean looking for their father in season one It is also a good storyline plot point that Castiel's powers are not in full capacity because a lot of his power came From the angelic hierarchy. It's also refreshing to know that the storyline of the amulet that Dean wears is significant So I'm really curious to what Bobby thought it was when he was going to give it to John though And Sam gave it to Dean because all Bobby said was I was really special and other than that We don't know much about it until season five of till season five of what it really does Unfortunately, we don't see it really come into full effect till season 11 naturally for Supernatural They're going to have revelations as a blueprint in some point as some way to represent the end of time So it was nice to see how and why a town turns on itself Especially good to see Joe Ellen and Rufus and now we are introduced to the four horsemen first being more I meant it's a little on the nose to have a red Mustang as the red horse But with Supernatural it makes sense That's because Sam and Dean live out their lives on the road with their steel horse that the horsemen adapt to that era of time Yeah, I'll admit the the coat maybe was a bit much Also, I've always found mustangs really small so I can always I can I see where you're coming from I to this day always remember that little bit between Dean talking to the soldier about how Dean talked about literally serving in hell I also love the line where Rufus says at the end stop firing usually means stop firing I did feel like them stopping war was a little too easy by simply cutting off his finger and the ring was the source of power Later on we see deaths ring coming into play in season six And I wish all the rings had equal significance of some sort But we didn't really want to repeat storylines off together to make by making apocalypse 2.0 One thing that always was always interesting is that the riders interpreted the horsemen as bringing down one Societies and countries collapse There are a whole bonus feature on it called the ride of the horsemen on season 5 DVD And I highly recommend it Sam leaving Dean was a punch at the gut Given how that we loved and cherished the brother the brotherly relationship for years But thankfully when they patched out they patched up good is one of my least favorite episode of the season And it's still a great episode in itself, but we'll get to my least favorite soon Oh, I'm interested to see what is your least favorite much like season 4 season 5 Instantly throws you into plot and quality. I love good God y'all mainly because the horsemen war the horsemen before the apocalypse were honestly one of my one of my One of the most unique and creative enemies in the series each of them a different in their own powers and personality I love that the fact that Sam admits That he has a problem. I give them ball props for having the balls to do that This episode is probably my least favorite horsemen episode mainly because of that one thing And I don't like and that's Ellen. I'm sorry, but I never felt like this We're her Ripley want to be behavior. I never understood why people liked her And instead hated Joe to me was the opposite. I liked Joe and I always felt that Ellen hold Her back controversial opinion, I know but I'm honest I didn't get that with Ellen I I get where you're kind of coming from sometimes a dialogue kind of rolls off like it's on repeat But I did like her I think that they she was in the show for long enough that her shtick didn't get old Maybe if she had been around a lot longer. Yeah, I could see that and I also could see why they use them so so Very seldomly. Good. God y'all is another strong episode love the actor that played war his dialogue with Sam and Attempting to justify or excuse his own actions while throwing a guilt trip on Sam is a juicy bit of dialogue Lots of villains have tried to guilt the brothers to get into their heads But this is one of the better interactions Sam and Dean each conversation between them is wrong and tense Sam struggles to prove that He can handle himself without Dean putting training wheels on him reminds him of how why it was easy for him to turn Ruby When Dean was came back from hell and this is setting up for character growth for both of them in fallen idols nicely Again, I can I still sympathize with both perspectives. It's not simple. He's right. He's wrong attitude No, it's not you're very much true that they're both trying to do the right thing But they're in their own way That scene in the market where Sam kills the two men and strongly looks at the demon blood on the blade He's a very well shot choreographed necked out Sam looking at the blood is one of the moments that from the season That always stuck out to me and his comment later about missing being able to save people sums up his ability to do his job As a hunter exponentially better, but not just the addiction to power blurred the lines between good and evil for him As Chuck said last season. I don't envy the weight on your shoulders the twist in the of the plot is a good one I think their demons they think their demons were demons What if there are no demons is just people killing each other the fight between Rufus and Dean ending with you Figured that on your own genius. Hope you enjoyed it. I did I did enjoy those bits You you bring up actually some of the very good bits and like I said, I do like the idea of Them turning on each other. It's kind of a variation of croton, but it's still actually pretty well done Good god y'all is one of those episodes I always cherish along with croton and I believe the second episode of the season nine the one We're avid on and a bunch of demons corner Sam and Dean with a bunch of demons and Either an abandoned village or something like that are my favorites. I line big I like big scale Episodes that make it and feel more grand like the stakes that are more monster of the week and show the larger ramifications more manipulating a whole town and seeing that it is epics and End-of-day stuff neighbor against neighbor and all that their episodes I wish we saw more of because it was the brothers more mere Militaristic side which they've had from John since he was a marine. I remember correctly. That's with That's a sighted. I always love to see I get supernaturals a small suburban town Go bump in the night, but at the end that when Zach Transports Dean of 2012 is kind of what the thing you need with stories like Lucifer walks again Are the darkness walks again plus we're introduced to Rufus I can't remember if you mentioned him prior or not But I fucking love Rufus and I wish we got more of him now I actually kind of going on a tangent about what you're saying here now I do like these larger scale things too. It's definitely a lot more grandiose a lot more Weight on your shoulders a lot more to go on in the episode, however And this is purely from a story-writing perspective as well as a production perspective It's expensive. It's actually really expensive to do these things So that's why they don't do these big episodes as much highly underrated character in my opinion I thought him and Bobby were comedic gold together plus the search for God and Dean's pendant being a teacher to him himself Essentially which is paid off to perfection and don't call me surely This is where I think me and you disagree on the truck reveal in 14 Don't get me wrong I share your sentiments and like Chuck, but after thinking about it all it makes sense at least thematically The boys find out their lives are determined in the season 5 is Great because the whole fuck you free will aspect But it's also the show's downfall with its overuse of meta narrative because once you break the fourth wall You can go too far then all you're left with is epitomizing the rat trap the characters are ensnared in if the show ended here It wouldn't have been a problem, but I can't say I was given if I was given the reins I wouldn't have ended it differently in season 15, but Chuck at this area is a lie With all season with all the seasons accounted for that's the only way you can take it now I could have done being it could have been done better. I would have lying down a little more focus on Chuck's side we get more more We get the my creations disappointed me in season 11, but upon retrospect if I think the only thing I can understand It was a shifting characters that the characters caught onto his game and realized that they were all his play things They were never free will it was always Chuck's plan story and no longer play and we can't Honest and I love that honestly I mean I wish there was more setup, but I guess it's the hindsight God was never painted See I now I can see where you're coming from here, too And also I said that maybe if they had actually properly built this up in season 14 instead of just throwing it in in the last 20 minutes Maybe maybe I could have believed it, but they did a such a terrible job at building it up It just made no sense thematically. It was just so poorly put together. Oh I will make one comment though before we go if you guys couldn't make them a little shorter Just a little shorter because this was what four comments and a one ten minutes long almost So I want to make sure I don't bore you guys to death though If you guys can make them just a little bit more concise that'd be great And the next episode is free to be you and me so make sure to give me you guys as comments about that in the Comment section below and I'll read those off in the next review Otherwise guys, I hope you enjoyed it if you did leave a like and if you're interested in more subscribe Till then see you guys next week Thanks for watching the video. My name is Nits and you might remember me from the animated cult classic TV show Undergrads it's been a while But I'm happy to say the click is finally getting back together in an all-new movie Thanks to a successful Kickstarter campaign But we are still asking for your support to see any and all updates about the upcoming undergrads movie Be sure to check out and like the bring back undergrads Facebook page and with any luck. We'll see you guys soon