We are looking at advanced reaction engineering, practice problems in gas solid reactions. The specific applications you are looking at is Spongia technology. Of course the context is very simple steel is a very large commodity around the world and blast furnace is the technology which is commonly engaged and we all know that blast furnace is a very large capacity kind of technology and investments are very large, footprints are very large, environmental impacts are also significant and so on. And of course it requires movement of materials, availability of raw materials and so on. Now Spongia is technology which is quite suitable for very small scale therefore is able to use ores of local origin and so on. So it is something that is gaining ground in different parts of the world particularly in India. So we look at this very briefly you have an ore which is iron ore as you can see. It is reacted with a gas hydrogen to form iron and water. So this is the reaction the equilibrium constant at 25 C at 25 C is 0.1 showing that you know it is an endothermic reaction. So you need high temperatures to reach reasonable velocities, reasonably favorable equilibrium constants and so on. So but if you look at a reaction a particle I mean generally particles are of various sizes you know it is never spherical but you know from the point of view of doing analysis. We assume that it is a spherical particle and this particle will react there is an external diffusion as you can see there is a resistance in the external layer as the gas moves through ok. So there is an external diffusion resistance, there is an internal diffusion resistance that is the resistance to the flow of gas movement of gas through the product layer. As the reaction proceeds the product layer keeps on becoming bigger and bigger therefore this resistance becomes larger and larger as the reaction proceeds plus the reaction takes place at the interface here let us say and there is a resistance due to reaction. I mean this is a very simplistic model of what might be happening what happens is much more involved than what I am saying therefore there are three types of resistances that are important for a reaction of this nature. We have at an earlier stage looked at problems like this and we have formulated this a gas plus b b solid giving products a reaction of this form in this case for example our in our case you have a gas so this is gas. So this a b b solid let us say b b solid so giving you products the two products are given here. So a gas b b solids giving you product we are looking at and then idealized form of this is what we are looking at and we have at an earlier stage if you recall in the course we have shown that the time required for this reaction to undergo a certain extent of reaction let us say if x b is the extent of reaction which is r c by r then given x b you can find out the time that is required for this extent of reaction. So this we have already formulated and derived at an earlier stage we will not do it again. What is important to recognize here that there is a reaction time there is a external diffusion time and there is a internal diffusion time all three of them together determine the actual time that is taken for the reaction to occur. Now we also recognize that all these resistances all these we have shown this earlier in our classes and then we are just using those results at the moment. So you have the total reaction time which is some of the reaction time due to individual resistances where the reaction time for time for complete consumption if it is a reaction control film diffusion control or product layer diffusion control these are also been derived in class. Just to contextualize what we are saying rho b is the rho b is the particle density rho b is particle density particle density you have r is particle size then this c a g is concentration gas in contact with solid with solid. Now the formulation the way the formulation has been done is that the gas concentrations have been assumed to be so large that it does not change during the course of reaction that is an example that we have taken but we will try to relax all this as we go along. Now k s is reaction velocity k g is mass transfer coefficient and then d is diffusion coefficient or internal diffusion coefficient. So what we are saying is that what we are saying here is that the time required for consumption of the particle is determined by all three factors the reaction external diffusion resistance and internal diffusion resistance. So we can do a small calculation just to get a feel for the kind of numbers we will get in the case of what we call as sponge ion. So I have got some numbers here let me just put down those numbers you have k s equal to 1.93 10 risks for 5 exponential of minus 12000 by t this is in centimeters per second then diffusion coefficient d in product layer that means hydrogen diffusion coefficient hydrogen diffusion coefficient this is for hydrogen. This data is given as 0.2 percent of the 0.3 centimeter square per second this is given film diffusion coefficient film diffusion equal to 10 centimeters per second these are all data given to us.  So basically this data is given to us we have to do the calculation to find out what is the actual time that is required that is the point. So this is k g. So let us do this calculations and then find out what is the actual time that is required for this reaction. Let me just do a small calculation for you what is k p k p we know this l n at say 2 we will say at k p at 298 and we were our reactions are taking place our reactions are taking place at 600 c we want k p k p at 600 c. So we want this we know is delta h by r this this is Vantos equation. So we know all this. So you can put your numbers delta h is given as plus 7 kilo cal per mole this is given. So you put all these numbers. So k p at to sorry at 600 c sorry 600 c comes out to be 228. So please put the numbers and you will see for yourself this is 228 showing that you know at this temperature the reaction is quite favorable. Now our reaction is what Fe 2 O 3 plus 3 H 2 equal to twice Fe 2 O 3 plus 3 H 2 equal to twice Fe plus 3 H 2 O. This is gas this is also gas. So k p can be put as p H 2 O to the power of 3 divided by p H 2 to the power of 3. This is something that we know from our basic physical chemistry. So this is nothing new now let us go through the whole exercise one by one. Now k p equal to so if you look at this reaction if this reaction is a gas plus b solid giving you c solid plus d gas. So I can this there is a 3 here which you can divide. So it becomes 1 by 3 and then this is 2 by 3 and there is a 3 here by 3. So we can look at our stoichiometry F A becomes F A 0 times 1 minus of x we know this this is gas and then F D equal to F D 0 which is given it is not given. So we take it as 0 F A 0 x A I will put it x A. Therefore k p we write as C A 0 x A we can put an R T here divided by C A 0 times 1 minus of x A to an R T here to the power of 3 that is equal to x A to the power of 3 divided by 1 minus of x A to the power of 3. This is equal to k p. So we can solve this k p is given as 228. So we have this is equal to this 228 is given solve. How do you solve this? Take the cube root of 228 let me just see we have the calculator alright I do not have it here right now. So we can take cube root of 228 and then find x A to be solve and x A comes out to be 0.86. It is valid the elementary take cube root of 228 straight away you will get the answers alright.  Now clearly in practice we do not reach this we can only approach this. Let us go further you know x A star now we have gone through all that exercise and so on. So let us not spend too much time we have shown see the problem that we are trying to solve is you have 2 mm particle it says 2 mm particle and if there is a rotary kiln which is rotating solids are coming in solids coming down. So you want here 95 percent equilibrium conversion this is the problem statement 600 C is the temperature of the kiln now the hold up here epsilon r hold up hold up of solids is 0.1 that is also given. Now we have gone through all the exercise we have set up the equations before. So there is no need to do the exercise again. So we know that this is given as l n we know this. So I am just writing the final form l n of x A star by minus of x A we know this equal to k g epsilon r 3 times divided by r tau g. So this is a result that we have gotten much earlier. So I am just putting that result right now. So this is something that we know. Now all the numbers are given here. So you can put all the numbers and do not find out tau g. So when you putting numbers I am just putting numbers what are the numbers here x A star is given as 0.86 and x A is given as 0.95 of x A star. So you know x A star you know x A k g is given and then epsilon r is 0.1 size of the particle is 2 millimeter that is also given putting all the numbers I find that tau g comes out to be 0.2 seconds. So essentially what we are saying is that the residence time gas residence times are quite small. So gas residence times are quite small. Now what we want is solid residence time. So how do you relate gas residence times to solid residence time? Solid residence time residence time is what by definition it is volume of equipment divided by the solids flow. In this particular we mean from our stoichiometry we know it is tau g V g by V s volume of the equipment is gas residence time minus the gas flow divided by V s. Now that is equal to tau g and then sorry and V g what is V g? V g is F A 0 by C A 0 and then C B 0 by C B 0 is that clear? So what is given F A 0 is given as equal to F B 0 given this data is given. So that if you put all these numbers now putting numbers F A 0 is. So you get this F A 0 equal to F A cancels of C A 0 and C B 0 put all the numbers it turns out that tau s equal to 285 seconds. How do you get this? Tau g is given we have got in the previous case C A 0 C B 0 all the data is given. So what are we trying to say that if you have a rotary kiln context once again let us not forget context is that you have a rotary kiln particles are coming in hydrogen is fed like this hydrogen and this is the ion ore and then what you get here is F E and this moisture escapes is it ok. So we are able to tell what is the kind of size of equipment that is required for your application. We have we have only done we have not yet finished what is called what is the suppose we do the same process in the laboratory that part we have still not done let us do it quickly. So that we can find out what is the time required for.   Now this particular case what is given is that the size of the particle is given as what 5 millimeter size. So particle size r is given as 5 mm and then k b all this k s and all that is given putting numbers putting numbers putting numbers we get tau r equal to tau r turns out to be about 10700 seconds putting numbers. Similarly putting numbers here putting numbers we get tau f turns out to be 71 seconds and similarly tau d I will write here tau d tau d equal to rho b r square let me write here rho b r square divided by 6 b d C A g putting numbers it comes out to be how much 5 9 5 2 seconds. Now C A g I did calculate C A g is p by r t that turns out to be p by r t p is 1 atmosphere r is 0.082 and t 600 600 273 is about 873. So that turns out to be 0.014 10 minus 3 mole per per milliliter. So basically what I am trying to put across to use that we can now calculate since you know tau r tau f and tau d we can now calculate what is t. So what is the question that is asked is the question that is 95 percent of equilibrium conversion. So we have been asked to find x A which is equal to 0.95 times x A star we have got x A star already. Let us just do that calculate the important calculation let us just do that our reaction is F e 2 O 3 plus 3 hydrogen equal to twice F e plus 3 H 2 O. Now F A 0 x A by F B 0 equal to thrice x B. So x B equal to 2 by 3 x A that is 2 by 3 of x A what is x A? So x B equal to we have got x we have got x A as 0.86 times x A star 0.86 x A star. So we can put all these things x A star is we already derived that x A star is 0.95 x A star sorry and x A star is 0.86. So we can put all these things 0.95 multiplied by 0.86. So that comes x B comes out to be 0.55. Now 1 minus of x B by definition is r c cube by r cubed. So x B is known this implies r c by r equal to 0.7 double 6. Now once you know x r c by r now we can calculate once you know r c by r we go back now our expressions are that is all here. So r c by r is as you can r c by r for our application is I will just write here just for sake of simplicity r c by r becomes 0.7 double 6 once you put r c by r values here you should be able to find what is the time for company for a for the extent of reaction desired. So what in a sense we are saying is that the time required the time required for our case. So the time required if you put all these numbers the time required comes out to be something like this let me write down t equal to 10700 multiplied by 1 minus of 0.7 double 6 plus 71 1 minus of 0.7 double 6 whole cube is 0.45 plus 5952 1 minus 3 times 0.7 double 6 whole squared plus 2 times 0.7 double 6 whole cube. So when you do all this it comes out to be 3333 seconds. So close to approximately 1 r. So what we are saying is that see in this in this exercise what we have tried to do is the following that we have we have looked at a problem which is of commercial interest which is of of considerable interest because small scale industries are important and then it is where we make what we sponge iron see we are making sponge iron. So what we have tried to calculate here is that what is the time that might be required what is the time that might be required to get a certain extent of reaction. If you do an experiment in the in the batch equipment but what we also pointed out is that when we do this experiment or do this in a rotary kiln when it is in a rotary kiln what we find is that because of the rotation of the kiln the external surface gets sheared off the product layer gets sheared off and to an extent and more importantly a shearing off may not be all that good but more importantly because of the fact that lot of gases are coming out and then it is quite porous and as a result those resistances may not be significant. I mean in our that is the kind of assumption we have made in this calculation I mean in reality these mean may not be correct these resistances will have to be taken into account and so on. So what we have tried to do here is that if we can manufacture sponge iron using a technology like this in a rotary kiln where it gives you continuous productions and so on and then you have to of course ensure that the temperatures are managed and then heat losses are minimized and so on. So this example illustrates how we can use the procedures that we have developed during the course of last several months and how we can apply them to understand a fairly important commercial process. Now we will go on to another application we are now going to look at what we call as a practice looking at advanced reaction engineering once again. Now this is the application we are looking at is practice problems practice problem in residence time we call it RTD. You have to take an example of RTD let us see how we can make use of this. Now the example in front of us is the following it says I mean this problem reads like this there is an arbitrary vessel to which this is this is the reactor. Now in this reactor you have you are getting a conversion of x a equal to 0.88 it is given. So reaction is a goes to b this is the reaction it says the residence time this residence time here is 10 minutes that is also given. Now the rate constant this rate constant k is given as 0.3 per minute the rate constant is 0.3 per minute. So it is modeled as a tanks in series tanks in series 2 it is modeled as dispersion model. Third it is modeled as dispersion model as a recycle reactor model.  So the question that is in front of us is that there is an arbitrary vessel which gives you a conversion of 0.88 for a residence time of 10 minutes rate constant 0.3. Now if you model it as a tanks in series model how many tanks will be required this is the question. Now if you model it as a dispersion model what would be the Peclet number that would be able to describe this experimental data. Now third thing if you describe it as a recycle reactor what will be the recycle ratio that will be able to capture this experimental result. On other words you know what in essence we are trying to do here is that in an arbitrary equipment we may have designed it as a plug flow reactor and so on. But the fact is that it behaves in a certain way we do not know what the behavior is. We have models which is able to account for deviations from ideal behavior. This is what we have been doing when we talk about non-ideal reactors how does it deviate from ideal behavior for which we have these models. So let us do one by one let us do this one by one let us see how what kind of answers we can think of. Notice let us look at the simplest and then go on to the more complicated. First we will see how our system can be described by tanks in series model. So what is a tanks in series model let us just understand what is tanks in series model. What is tanks in series model tanks in series model essentially says that our arbitrary vessel can be represented as a number of stirred tanks number of stirred tanks. And therefore, whatever we get here at the end of these tanks here in this particular case this x a is 0.88. And the total residence time tau is how many minutes 10 minutes is 10 minutes and this rate constant is given as 0.3 per minute. So this reaction a goes to b is taking place in this equipment. Of course, it is broken up as three stirred tanks or four stirred tanks, but the fact remains is that it is a single equipment which is being model this way. Now we know if this is c a 0 if this is c a and let us say this as n tanks in between assuming that there are n tanks. Our result says that c a we know this from our experiment c a by c a 0 equal to 1 by 1 plus k tau by n to the power of n. This result we know this result that we know from our understanding of stirred tanks. Now what is c a by c a 0 it is 1 minus of 0.88 you know this 1 by 1 plus k is 0.3 tau is 10 it is given what is not known is this n solve for n. So when you solve you find n becomes 3.3. So I mean you might say how can you have 3.3 number of tanks I think that is not the issue the point is you have an arbitrary vessel. This arbitrary vessels performs in a certain way now how do you try to describe this arbitrary vessel in terms of models that you have. Therefore, whenever you looking at these models you should recognize that this n is only a parameter it is a model parameter it could take any value depending upon the situation. Therefore, n equal to 3.3 only means that here is an instance of an equipment which performs similar to number of tanks between 3 and 4 that is what is to be understood. Now let us take the next example which is instead of tanks in series model we have dispersion model what is dispersion model what we said is that suppose there is a fluid flowing through that is say there is a packing here. Now what we said is that fluids a plug flow means what every fluid moves together they move together they move together move together that means a fluid elements which is at this level and a fluid element at this level they move through without recognizing the existence of each other that is an instance of plug flow. But, when there is dispersion what happens is some fluid elements move faster than the other because of dispersion they are moving faster than the other. Therefore, the kind of time of residence that fluid elements spend inside the equipment is different from what we might think and the basis of the plug flow model in which all the fluid elements have the same amount of residence time. So, this effect we had taken into account and then we have derived in our equations a certain equation that tells us tells us that the constant suppose this reaction a goes to b and then the rate constant is k first order rate constant k we have shown this I am just writing the final result there is no point in deriving it here by 2 divided by 1 plus q square exponential of p q by 2 1 minus q whole square exponential of minus p q by 2. Where q equal to the time called a number equal to 1 plus 4 d a by p. Now, we have we have done this in an earlier class and if you have not done this we do not recall this you do not just look up some books on actual dispersion models it is all derived fairly simple things. Now, if you want to find out conversion at the exit of this equipment essentially what you need to know is that d a equal to what is d a d a is equal to k times tau now in this particular case k is given as point 3 per minute we know this and then your residence time is given as 10 minutes. So, in this for this equation the left hand side is 1 minus of point 8 8 equal to the right hand side the unknown quantity on the right hand side is Peclet number there is nothing else only Peclet number is unknown. Now, it might be more complicated to calculate left hand side from the right hand side is easier the reverse is more complicated. So, you have to do a trial and error you have to do one trial to find the answer. Now, when you solve. So, when you put your numbers and solve you will find so putting numbers you can put numbers here is no great problem in putting these numbers. So, Q putting numbers find Peclet number corresponding to the left hand side LHS equal to point 1 2. So, Peclet number corresponding to about 10 or so you will find roughly LHS equal to RHS I mean you have to or you can read out these numbers from charts which are available and so on. So, what have we done we what we have done is that we have recognized that you can describe the performance of this arbitrary vessel this arbitrary vessel conversion here it is point 8 8 it is given point and then rate constant is given residence time is given. So, what is the parameter Peclet number that will describe this data the answer is that if you put Peclet number around 10 you will be able to say that the left hand side is roughly equal to right hand side. So, what are we trying to say what we are trying to say is that for this particular instance for this particular instance the number of tanks equal to 3.3 corresponds to a Peclet number of 10 is what we are trying to say in this particular case. Now, there is another way by which we can describe this data there is another way by which we can describe this data there is another way by which you can describe this data. Now, if you look at a recycle this is the recycle reactor now we said if you recall at an earlier class we have said that if you have a recycle reactor this is the reactor.  So, when we went through this exercise at an earlier stage we said that r equal to 0 means plug flow PFR r equal to infinity means CSTR. So, we said at that stage that basically recycle ratio is a way of describing mixing within the equipment. So, no mixing is r equal to 0 very large amount of mixing is r equal to infinity and therefore, we can describe various kinds of reaction equipment between no mixing to infinity amount of mixing or PFR to CSTR by adjusting the recycle ratio. We said the same thing when we looked at number of tanks in series we said when there is one tank two tank and infinite number of tanks we said when there is a infinite number of tanks we are talking about a plug flow reactor. That means n very large means plug flow reactor n very small means we are having CSTR ok n equal to 1 is a CSTR n equal to infinity is PFR. So, on other words r equal to 0 that means PFR is equivalent to n equal to infinity you understand. So, this is what is the kind. So, just like n number of tanks is a way of quantitating the level of mixing just like Peclet number is a measure of mixing within a reactor recycle ratio is also a measure of mixing ok. So, it is a first order reaction first order reaction we can integrate this whole thing and the integrated form is available to us the integrated form we will just write down the integrated form. So, the integrated form for a recycle reaction.  Now, of course, we can put it in various forms and so on. But the important thing here is that C A by C A naught is given as 0.88 this is known ok. Now, k is given as 0.3 per minute and this is tau is given as 10 per minute. So, 10 minutes are 10 minutes. So, putting all these numbers we can find out value of r. So, solve putting numbers numbers solve and then solve and then we if I find that it is around 0.9. When you put the circle point this the it is sort of it satisfies. So, what are we saying let us just summarize this is an important point what we are saying is the following. We have looked at an arbitrary vessel we have looked at an arbitrary vessel. So, we have an arbitrary vessel here coming in and going out x A is 0.88 tau is 10 minutes k is 0.3 it is all given we find Peclet number is about 10 number of tanks is 3.3 recycle ratio is about 0.9. So, what does it mean what it means is that the we are able to see this suppose you make a plot of r versus x r versus n r versus p p. So, I mean we can plot this. So, we can sort of get an idea as we as r becomes larger and larger it is CSTR. So, we should expect curves like this when n becomes as x that means when r increases it is going towards when n is equal to 0 n increases. So, we can plot on other words what we are trying to say is that as r increases it is going towards like this n increases it is like this and then Peclet number what do we expect Peclet number becomes larger and larger it is sort of it is higher the Peclet number you will have higher is the it is approach towards a PFR correct. So, let us understand this one by one when r becomes large when r becomes let me draw this once again I am not satisfied with this let me just do this once again. We have a we have a stirred tank x is 0.88 then k is 0.3 per minute tau is 10 minutes our results are n equal to 3.3 Peclet number is approximately 10 recycle ratio is 0.9. So, all these numbers describes this machine level of mixing in this equipment. Now, we want to understand this in a slightly better way what we expect. So, suppose let us say we increase for the same volume if we increase the number of tanks we will expect that conversion would approach plug flow that means the suppose you have similarly Peclet number if you draw you will find that it will approach plug flow this is what we expect. Now, but if you increase r if you increase r we will find that you will do this. So, this is recycle reactor this is dispersion model this is tanks and series model. So, what we are saying is that these are all numbers which can be used to describe a certain level of conversion. Therefore, if r conversion is let us say is 0.88 see this is 0.88 we can see here these are the numbers we will get this is for dispersion model this is for tanks and series model this is for recycle reactor model. So, by from this you can tell what is the parameter values for different models cut this long story short what we are trying to put across in this question this question essentially says given the data determine this parameter values which you have done. Then it says sort of try and understand how these numbers relate to each other. So, this relationship comes out of this it tells us how conversion changes with the parameter values. So, it gives you qualitatively tells you how these parameters affect extent of reaction. So, this this approach to understanding real vessels is very useful when you have an operating process and if you quickly get some data and you can get from the data what is the kind of mixing that is appropriate to your process. So, it is very useful to get a feel for what is the real life problems you want to go on to the next exercise interesting exercise, but something that not very difficult to do. So, this exercise we want to do now is advanced reaction engineering this is what is called reaction network analysis. So, we want to take an example in this reaction network analysis to illustrate how our techniques become. So, useful in some in many instances here is an example of A going to B. Going to C going to D. So, it is a reaction network some data is given A goes to B goes to C goes to D A going to B this equilibrium constant is given as 1 B goes to C B goes to C where are we is given K 2 is given as 2 B goes to C goes to D K 3 is given as 3 delta H 1 delta H 2 delta H 3 all these are given delta H 1 is 10 20 and 30 all in units of kilo cal per mole. So, this is the reaction network and this reaction is taking place in a stirred tank the stirred tank feed is only pure A is coming going out and then it is a coil to which which is added. Now, the question that is ahead of us is how much heat is to be added or removed. So, no that is not the question question is this is operating at 25 C operating as 25 C all these reactions are instantaneous all these reactions are instantaneous all reactions are instantaneous that is 1 and we are maintaining the temperature at 25. What does it mean to be able to maintain temperature at 25 when there is endothermic reactions are taking place it implies that we are putting in heat through our heating system. So, here is an instance of continuous input of heat into a reaction system to ensure that these reactions are in the at equilibrium. So, the question in front of us is what is the extent of reaction. So, let me ask this question to you once again what is the extent of reaction that we should anticipate. So, what is the extent of reaction we should anticipate when we maintain a temperature of 25 where the reactions A B C D are instantaneous which means that at 25 C they all exist in their equilibrium is that clear. So, we have set up this stoichiometry long back. So, there is nothing new for all of you. So, but just 2 for the sake of completion I can say if I say this is x 1 this is x 2 this is x 3 and then our stoichiometry says that f A equal to f A 0 times 1 minus of x 1 correct f B equal to f A 0 times x 1 minus of x 2 correct f C equal to f A 0 times x 2 minus of x 3 and f D equal to f A 0 times x 3. So, no what are we saying what we are saying is that is the multiple reaction there are only 3 independent reactions is obvious I have not proven to you 3 independent reactions if x 1 x 2 x 3 are the extent of these reactions this is the stoichiometry. And it means what it means that since they are all in equilibrium you know that C B by C A is k 1 C C by C B is k 2 C D by C C is k 3 yes or no this is the definition of equilibrium we know this. Now, since you know these 3 now we can using this relationship since k 1 k 2 k 3 are given since k 1 k 2 are given we should be able to find out x 1 x 2 x 3 there are 3 equations 3 unknowns we should be able to find out. Let us do that. So, that we know what the numbers are. So, help me now what is C B C B by definition C B equal to f B divided by V what is f B which is f A 0 times x 1 minus of x 2 what is V V equal to V 0 which is C A 0 times x 1 minus of x 2. Similarly, C A which is f A divided by V which is f A divided by V 0 which is C A 0 times 1 minus of x 1. Similarly, C C which is f C divided by V which is f A 0 times what is f C notice here what is f C f C is x 2 minus of x 3. So, we can put it here which is x 2 minus of x 3 divided by V 0 that is equal to C A 0 times x 2 minus of x 3 and then C D is what f D divided by V which is which is f A 0 divided by V 0 into x 3 which is C A 0 times x 3 is that clear. Now, what it means now is that now we know from our problem statement they are all in equilibrium. Therefore, C B by C A is k 1 therefore, from each of these equations we can determine and solve let us do that quickly. So, what are we having C B C B by C A is k 1 k 1 is 1 equal to k 1 C B is what C A 0 times x 1 minus of x 2 C A is C A 0 times 1 minus of x 1 equal to 1. So, our x 1 minus of x 2 equal to 1 minus of x 1 that is equal to what you get x 2 equal to or x 1 we will say x 1 equal to sorry 2 x 1 equal to correct 2 x 1 is equal to 1 minus of x 2 or x 1 equal to 1 minus of x 2 by 2. Now, we can substitute this in the next equation then second now we have C C by C B equal to k 2 equal to 2 C C is x 2 minus of x 3 divided by x 1 minus of x 2 that is equal to 2. Similarly, C D C D is what C D by C C equal to k 3 equal to 3. So, that gives us C D is what x 3 divided by C C is x 2 minus of x 3 equal to 3. So, let us 3 has x 3 equal to 3 x 2 minus of 3 x 3 therefore, 4 x 3 equal to 3 x 2 so x 3 equal to 0.75 x 2 0.75 x 2 now we can we can use this result. So, we have x 2 minus of x 3 divided by x 1 minus of x 2 equal to 2 and then we have in the sense we have not do this now because it is very elementary. So, solving we get x 1 equal to 0.9 x 2 equal to 0.8 x 3 equal to 0.6 this is something that comes out of our. So, we will stop here and little bit more to be done we will have to take it up and we meet next time. Thank you very much.


Welcome back everyone to theCUBE's continuous coverage of AWS Reinvent 2021. I'm John Furrier, your host of theCUBE. We're here with James Waters, CTO of modern applications at VMware here to talk about the big Tansu cloud native application wave, the modernizations here. James, great to see you. Thanks for coming on. Hey John, great to be back on and really excited about Reinvent this year and been watching your coverage of it. There's lots of exciting stuff going on in this space. Awesome. Well, James, you've been riding the wave of cloud one.o, two.o, what do you want to call it? The initial wave of cloud where the advent of replatformings there you get all these benefits and things are moving fast. Things are being developed, a lot of endeavors, things are tracking, some are kicking, Kubernetes kicks in. And now the big story is over the past year and a half certainly the pandemic highlighted is this big wave that's hitting now which is the modernization of the enterprise, the modernization of software development and even Amazon was saying that in one of our talks the software development life cycle's over it should be completely put away to bed. And that DevOps is truly here and you add security you got DevSecOps, so an entirely new large scale heavy use of data, new methodologies are all hitting right now. And if you're not on that wave, you're driftwood. What's your take? Oh, I think you're dead, right John? And kind of the first 10 years of working on this were sort of proving that the microservices, the container, the declarative automation, the DevOps patterns were the future. And I think everyone's agreed now and I think DevSecOps and the trends around app modernization are really around bringing that to scale for enterprises. So the conversations I tend to be having are hey, you've done a little Kubernetes, you've done some modern apps and APIs but how do you really scale this across your enterprise? That's what I think is exciting today and that's what we're talking about some of the tools we're bringing to Amazon to help people achieve faster consumption, better scale, more security. You know, one of the things about VMware that's been impressive over the years is that on the wave of IT, they already had great operational install base. They did a deal with Amazon, Ragu did that in I think 2016, that kind of cleared the air, okay, they're not going to do their own cloud or they have cloud efforts, kind of solidifies that. And then in comes Kubernetes. And then you saw a completely different cloud native wave coming in with the Tanzu, the Heptio acquisition. And since then a lot's been done. Can you just take us through the Tanzu evolution because I think this is a cornerstone of what's happening right now. Yeah, that's a great question, John. I think that the emergence of Kubernetes as a common set of APIs that every cloud and almost every infrastructure agrees on was a huge one. And the way I talk to our clients about is that VMware is doing a couple of things in this space. The first is that we're recognizing that as an infrastructure we're baking Kubernetes into every vSphere be it vSphere on-prem, be it VMC on Amazon. You're just going to find Kubernetes as a big part of vSphere. So that's kind of a big step one, but it's in some ways the same way that Amazon is doing with EKS and Azure is doing with AKS. But like every infrastructure provider is bringing Kubernetes everywhere. And then that kind of unleashes this really exciting moment where you've now got this globally controllable, you've got this global control plane that you can program to be your DevSecOps platform. And Kubernetes has this incredible model of extensibility where you can add CRDs and program right against the Kubernetes APIs with your additional features and functions you want your DevSecOps pipeline. And so it's created this opportunity for Tanzu to kind of have then a global control plane which we call Tanzu Mission Control to bring all of those Kubernetes running in different clouds together. And then the last thing that we'll talk about a little bit more is this Tanzu application platform which is bringing a developer experience to Kubernetes so that you're not always starting with what I like to say like, oh, I have Kubernetes, am I done? There's a lot more to the story than that. Yeah, I want to get to this Tanzu application platform on EKS. So I think that's a big story at VMworld we've seen that. But before we do that for the folks out there watching who are like now seeing this whether they're young, new to the industry or enterprises who are from replatforming or refactoring trying to understand what is a modern application. So give us the definition in your words what is a modern application? You know, John, that's a great question. And I tend to start with why and like, hey, how did we get here? And you and I both I think used to work for the bigger iron vendors back in the day and we've seen the age of kind of like the big box Silicon Valley. I don't know. I worked at Sun just across the aisle here. And basically we'd sell you a big box and then once or twice a year you'd change the software on it. And so in a sense like there was no chance to do user oriented design or any of these things like you kind of got what you got and you hope to scale it. And then modern applications have been much more of the age of like what you might say like Instagram or some of these modern apps that are very user oriented and how you're changing that user interface and that user design might change every week based on user feedback and you're constantly using big data to adjust that modern app experience. And so modern apps to me are inherently iterative and inherently scalable and amenable to change. And that's where the 12 factor application manifesto was written, a blog was written a decade ago basically saying here's how you can start to design apps to be constantly upgradable. So to me modern apps, 12 of the factors one of them Kubernetes compatible but the real point is that they should be flexible to be constantly iterated on maybe at least once a week at a minimum and designed and engineered to do that. And that takes them away from the old vertically scaled apps that kind of ran on 172 processors that you would infrequently update in the past. Those are what you might call like cloud apps. Yeah, it's interesting. Is that helpful? Yeah, totally helpful. And by the way, those old iron vendors they're now called the on-premises vendors. And so, you know, HPE, Dell and whatnot, IBM. But the thing about the cloud is that you have the true infrastructure as code happening. It's happened. It's happening faster and better and greater the goodness there. So you got DevSecOps, which is just DevOps with security. So DevSecOps is the standard now that everyone's shooting for. So what that means is I'm a developer. I just want to write code. The infrastructure is going to work for me. So things like Lambda functions are all great things. So assuming that there's going to be this now programmable layer for developers just to do stuff. What is, in context to that need, what is the Tanzu application platform about and how does it work? Yeah, that's a great question, John. So once you have Kubernetes, you have this abundance of programmable infrastructure resources. You can do almost anything with it, right? Like you can run machine learning workflows. You can run microservices. You can build APIs. You can port legacy apps to it. But it doesn't come out of the box with a set of application patterns and a set of controllers that are built for just, you know, modern apps. It comes with sort of a lot of flexibility and it expects you to understand a pretty broad surface area of APIs. So what we're doing is we're following in the footsteps of companies like Netflix and Uber, et cetera, all of which built kind of a developer platform on top of their Kubernetes infrastructure to say, here's your more templatized path to production. So you don't have to configure everything. You're just changing the right parts of the application. And we kind of go through three steps. The first is an application template that says, here's how to build a streaming app on Kubernetes. Click here and you'll get in your version control and we'll build a Kubernetes manifest for it. Two is an automated containerization, which is we'll take your app and auto create a container for it so that we know it's secure and you can't make a mistake. And then three is that we'll auto detect your application and build a Kubernetes deployment for it so that you can deploy it to Kubernetes in a reliable way. We're basically trying to reduce the burden on the developer from having to understand everything about Kubernetes to really understanding their domain of the application. Does that make sense? Yeah, and this kind of is like you mentioned Netflix. Early on, they were one of the pioneers in inside AWS, but they had the full hyper scalar developers. They had those early hardcore devs that are like unicorns. No one see how do you hire these people? They're just not many enough in the world. So the world's becoming, I won't say democratization, it's an overused word, but what we're getting to is, if I get this right, you're saying you're going to eliminate the heavy lifting, the boring mundane stuff. Yeah, even at Netflix, as great of a developers they had, they still built kind of a microservices or an application platform on top of AWS. And I think that's true of Kubernetes today, which if you go to a Kubernetes conference, you'll often see don't expose Kubernetes to developer. So tons of application platform starts to really solve that question. What do you expose to a developer when they want to consume Kubernetes? So I have to ask you, I know you do a lot of customer visits. That's one of the jobs that make you go out in the field that you like doing and working back with some of the customers that's been in the DNA of VMware for years. What is the big narrative with the customers? What's their pain point? Obviously the pandemic's shown them projects that are working and not working. They want to come out of it with a growth strategy. VMware is now an independent company. You guys got the platform. What are the customers doing with it? Well, I'll give you one example. I went out and I was chatting with the retailer had seen their online sales goes from 1 billion to like 3 billion during the pandemic. And they had been using kind of packaged shopping cart software before, like a basic online store that they bought and configured. And they realized they needed to get great at modern apps to keep up with customer demand. And so I would say in general, we've seen the drive, the need for modern apps and digital transformation is just really skyrocketing and everyone's paying attention to it. And then I think they're looking for a trusted partner and they're debating, do we build it all in house or do we turn to a partner that can help us build this above the cloud? And I think for the people that want an enterprise trusted brand, they'll have a lot of engineering talent behind it. There's been strong interest in Tanzu. And I think the big message we're trying to get out is that Tanzu can not only help you in your on-prem infrastructure, but it can also really help you on public cloud. And I think people are surprised by just how much. It's just in the common thread I see that that point is right on is that these companies that don't digitize their business and build an application for their customer are going to get taken away from by a startup. I mean, we're seeing, so if you don't have an app for that, you're out of business. I mean, this is like now, no, it's not like, maybe we should do the cloud. Oh yeah, let's get proactive. Pretty much it's critical path now for companies. So I'm sure you agree with that, what's the progress of most of the enterprises today? What percentage do you think are having this realization? I would say at least 70, 80% if not more are there now. And 10 years ago, I used to kind of have to tell stories like, some startups going to come along and they might disrupt you. And people kind of give you that like, yeah, yeah, yeah. I get it. And now it's sort of like, hey, someone's already in our market with an API. Tell me how to build API first apps. We need to compete. And that's the difference in the strategic conversation kind of post pandemic and post, you know, the last 10 years. All right, final question for you because this is a great thread. Obviously the web, having a web interface is not good enough to your point. You got to have an application that they're engaging with, with all the modern capabilities because the needs there, the expectation for the customers there. What new things are you seeing beyond mobile that are coming around the pike for enterprises? Obviously, web to mobile, mobile to what? What's next? I think the thing that's interesting is there is a bigger push to say, more and more of what we do should be an API. Both internally, like, hey, other teams might want to consume some of these services as a well-formed API. I call it kind of like Stripe envy. Like you look at all these companies, they're like, hey, Stripe's worth $100 billion now because they built a great API. What about us? And so I've seen a lot of industries from automotive to course financial services and others are saying, what if we gave our developers internally great APIs and what if we also expose those APIs externally? We could get a lot, a more fast-moving business than the traditional model we might have had in the past. It's interesting, you know, commoditizing and automating away infrastructure or software or capable workflows is actually normal. And if you can unify that in a way that's just better. I mean, you have a lower cost structure, but the value doesn't go away, right? So a lot of, I think a lot of this comes down to, you know, beauty's in the eye of the beholder. I mean, that's how DevSecOps works. I mean, it's agile, it's faster, but you still have to achieve the value of the net is lower cost. What's your take on that? Well, I think you're dead right, John. And I think this is what was surprising about Stripe is it was possible before Stripe to go out as a developer and kind of pull together a backend that did payments. But boy, it was hard. And I think that's the same thing with kind of this tons of application platform and the developer experience focus is people are realizing they can't hire enough developers. So this is the other thing that's happened during the pandemic and the great resignation, if you will. The war for talent is on. And, you know, when I talk to a customer, like, we might be able to help you even 30% with your developer productivity. There's like one out of four developers you might not have to be able to have to recruit. They're all in. And so I think that API first model and the developer experience model are the same thing, which is like, it's not, doesn't have to just be possible, it should be excellent. Well, great insight, learning a lot. Of course, we should move to the CUBE API and we'll plug into your applications. We're here in the studio with our API James. Great to have you on. Final word, what's your take this big story for re-invent? If you had to summarize this year's re-invent going into 2022, what would you say is happening in this industry right now? You know, I'm just super excited about the EKS market and how fast it's growing. We're seeing EKS in a lot of places. But we're super excited about helping EKS customer scale. And I think it's great to see Amazon adopting that standard API from Kubernetes. And I think it's going to be, you know, just awesome to watch the creativity the industry is going to have around it. Well, great insight. Thanks for coming on. And again, we'll work on that CUBE API for you. Virtualization of the CUBE is here. We're virtual, which we could be in person and hope to see you in person soon. Thanks for coming on. You too, John. Thank you. Okay, CUBE's coverage of ADUS re-invent 2021. I'm John Furrier, your host. Thanks for watching.


Whenever scholars said, keep the company only of someone with four traits, generosity when he has little forgiveness when he is treated unfairly, patience when he is tried, and contentment with Allah's decree. People of course like this are very few, but these are the most basic elements of good character. So we should seek out people that have at least one or two of these traits, if not all four. Patience here indicates love and detachment from the goods of this world. Forgiveness indicates humbleness, and again also a detachment from the goods of this world. Patience indicates that a person recognizes the reality of this world being a test and a trial, and that the end of this world is somewhere else, not something in this world. And contentment, contentment is the character trait of the muttaqeen. And this is the defining trait of the believer.


one second share can you guys see my screen nope no okay well I am going to have a problem share my screen but I know Deb isn't so I'm just going to do my intro um and welcome everybody to executive directors chat today's topic is artificial intelligence the good the bad and the scary we started to say the ugly but I don't think anything is ugly about artificial intelligence because we've been using it for so long and didn't know it right so we're going to have this conversation um executive director chat and I know many of you have been here to this platform and somebody put um I think they put on the survey uh about this platform they want for me which platform so executive directors chat is one of the platforms that we use here at TechSoup where it's the zoom platform where we get to see your face because you always see our face during the webinars and we love seeing your face and trust me all executives they're watching they come back and watch and they love to see your face they love to hear what you are saying with your voice so we want you to be a part of this conversation so while we're having the conversation or while someone is speaking please remain on mute if you would like to um make a comment or a suggestion please use the raise your hand option at the bottom of your screen where you see the reaction button today I'm excited because we have a guest speaker and I noticed that someone has all already turned on the um cc button so I do want to mention that if you need the closed caption just type on that cc button this is being recorded so you're going to get the recording within 48 hours probably tomorrow um if it all goes well with technology we will get this tomorrow along with the slides from today this is a chat where we all chat together not just Aretha chat not just Debbie chatting but we are all chatting so we would love your participation again use the um the reaction button at the bottom use the raise your hand option or raise your hand I've seen people do this so feel free to to wave at me if um I can't see you so I'm going to turn this over to Deb she's going to tell you more about her she is our feature speaker today and she is one of the executive directors who was here last week so welcome Deb and I hope your your feature is working day when you share your screen if not we're going to keep rolling with it I'm going to turn it over to you Deb well let's see how it looks does anybody see my screen Aretha do you see it yes yes all right okay well Aretha thanks so much uh so yes I'm Deb Sula Gross and uh as Aretha said we're not doing uh Clint Eastwood the good the bad and the ugly we're doing the good the bad and the scary um but I do want to give fair treatment to all three um so a little about me to start um I have a background that's both in technology and with non-profit organizations um and I have spent time with a small small uh software company early in my career for about 15 years so I got to see what it was like to be uh where payroll had to be met based on the next sale which is not unlike a lot of non-profit organizations um and then I spent about 15 years in two major uh our organizations and I which I call going to the dark side if any of you are geeks you might get that um and then the last uh seven or eight years I've been working with non-profit organizations um I've been a volunteer actually since I was 16 so I've been a volunteer for a long time with a number of nonprofits but then about eight years ago I left my IT career and joined a social service non-profit in Pittsburgh Pennsylvania where I live um to become a program director there and then the last few years I've been on a couple of different non-profit boards and most recently I had a couple year stint as the board chair of one non-profit I just finished that uh last Thursday yay okay that's not board but I'm the incoming board chair to uh chair to another board that I serve on so I've had the um really blessed opportunity to see non-profit organizations both from the staff side which is where I actually came from more originally and then more recently from the board side um but really at heart I'm a geek and you can call me one it's a four-letter word but I don't treat it that way uh and I love I really love staying in touch with technology I want to stay young for one thing but also there's so much exciting uh that's happening in technology that's pretty much what I'm doing so now um I am associated with an organization in Pittsburgh still called the Forbes Funds I'm an executive in residence there is the job title but basically I'm a pro bono advisor to non-profit organizations mostly about technology um and with this AI stuff I have to tell you um I just am I'm not one of those who's rah rah let's do it okay the good the bad and the scary so I'm going to be as pragmatic as I can with you but I am somebody who driving for 30 some hours over the last week as I relocated from Pennsylvania to Alabama through Georgia and North Carolina long story um I listened to a lot of AI podcasts and some non-profit cut podcasts because I was in the car all by myself and I am kind of a geek at heart I really like that stuff there'll be more of that at the end because I'd love to connect with as many of you one-on-one as I can going forward okay so today um certainly I want to talk for a bit so I hope you learn some things from me um I really want to learn from you and I'm not kidding about that I really do and I hope that we all learn from each other Arifa has been really clear and TechSoup is great in setting up this forum so that those of you who are leaders in non-profit organizations can learn from each other and from some of us who come in to talk I'm not an expert I'm not here to give you a lot of advice some information some inspiration I hope um but in about 20 minutes or less I'd like to be done with me talking to slides and then allow you to ask questions and learn from each other as well so I'm going to be taking notes guys about some of the things that you worry about or that you've tried and failed or that you've done and have succeeded so if you were just planning to sit back for the next 50 minutes and not really be engaged just you know bag it watch the video I'm really hoping that you folks will speak up with some questions um probably at the end I'm going to try and run through my materials to kind of skim things and then see what's hot with you guys for dialogue so with that AI the good so the good the bad and the scary I'm going to do three slides the good I'm guessing most of you are familiar with a lot of this so I'm going quickly uh AI tools can help you do things faster and so you can do things more efficiently and your teams can be more productive that could be chat GPT that could be an AI tool that helps to develop grant applications there are all kinds of tools available that can make things go faster second is it can help you make better decisions and generate creative ideas I'm going to use an example of recipes it's a common example in AI you can basically go and say here's what I have that I want to include in a dinner tonight tell me what I can make and I've had some examples where I've picked something for my husband and I that I never would have thought of so you can also do it also certainly applies to what we do as non-profit leaders another good one is you can engage with your clients and your stakeholders better there's a lot that's also been written about this and I'd be interested to hear some good examples if some of you have tried to do things so that having these tools allows you to personalize the way that you do outreach now this is going to come back in the bad mascara as well but it there are some really good opportunities to use these tools so that you can be better connected with those you care about and who care about you and then the cool thing is like a lot of stuff in this internet age a lot of these tools are low cost or free and they are very easily accessible on computers and they're getting there on phones so for some of you for your clients if you're particularly in social service and your clients really only have phones there are ways that they can exploit these tools as well but I'm going to speak mostly to us who are in leadership in non-profit organization and then hey AI the good it's going to help us cure cancer faster it's going to solve climate change before we burn the world up and a whole bunch of other fantastic things just look at the New York Times or the Wall Street Journal or the London Times or wherever you are in the world and AI is going to change the oil in your car diaper your baby cure cancer it's fantastic but okay but ah you know there's some stuff there that's really not so good I'm going to call this the bad and then I'll get to the scary in a moment for you for us as non-profit leaders the really bad bad bad bad bad right now is what's called hallucinations and that is an actual technical term like AI articles written by researchers that are peer reviewed use the term hallucinations what that is is you ask the AI tool whether it's barred or chat GPT or whatever a question and it gives you back what looks like a really well packaged response some of it is true some of it is not now for me when I read hallucinations in these AI articles I hear lie basically but lie suggests intent so but basically the AI tool is coming back and sharing and saying something in its response that is just dead wrong and I don't even have to continue this is the one big takeaway I'm going to state about my experience with AI looking at leadership of an organization like a non-profit which is you just can't take what it gives you without verifying there's an analogy that a number of people have used about AI which is for non which is it's like your best intern ever okay so you've got this intern who's available 24-7 who gets things back to you fast who bugs the crap out of you with questions like a good intern should so you can have the AI prompt you with questions and you know and I really enjoy mentoring early career folks I really do I've had many interns who I've been responsible for over many years I have three right now in fact and one of the things I've learned about the stuff that comes back from my interns is it's usually well designed so I'll get something that's packaged up in a nice powerpoint with a good background a good template and a lot of it will be really good and sometimes creative many of my interns have come back with ideas to me that I would not have thought of and I love it it's fantastic and you know some other times when I look at a slide like they'll hit down and they'll look at a slide and I'll say oh my gosh where did they go off of the just dumb or AI tools can be just like that to treat whatever response you get from the software from the AI tool as inspiration and maybe some useful information but you've got to verify it you've got to verify it I'm going to say that if my intern came back to me she's on a plane yesterday and today coming back from Taiwan so if she gave me a slide deck about the the task I gave her I would not just take that deck and go to our CEO with it even though it's packaged up nice I would not because I need to go verify that what she thinks she was finding out for me is actually accurate okay I'm going to move more quickly through the rest of this but I really really want us to acknowledge that hallucinations are bad and you must be aware of the risks if you don't validate what you get back from the AI tools hey Deb um yeah the audio went out for a moment so would you turn off your video because I think we're having problems with the audio sometimes okay I'm sure that was might have or were you speaking like that on purpose because it was pretty funny the way no I'm sorry folks um it was it actually wasn't the video it was uh my phone received a phone call and I'm using a hot spot now so that's why uh so if it happens again Aretha flag me though okay sure thanks um in terms of bad things um there are ethical concerns and biases that exist uh with current AI particularly regarding image generation and recognition and there's a lot that I think we should be very sensitive in terms of what we are giving and getting with AI tools and then another one I want to mention is that um we could become lazy the better AI gets the more possible it is that we will become distant from our stakeholders become distant from our clients um become actually lose the value you get from just slodging through and doing something and being wrong um and so we we could become kind of lazy and that's something that over time it would be very interesting I think to see but it's something as leaders that we should be watching from in our staff so development is probably the front edge of using AI and if all of a sudden the annual report and the monthly newsletters and whatever it else is going out is just seeming like it's sort of bland um it could be that our development team is depending a little bit too much on what they're getting from the AI tools and they're really not putting their heart and their minds into creating the kind of content that will reach the people we need to reach with the information that's important so I'm calling that bad not scary yet I'm getting the scary in a moment and then there's all kinds of other bad stuff about AI you can read the news but I want to get the scary so the scary thing here's where you could actually lose your job here's where you could do something horrible horrible horrible or someone on your team could so hallucinations that's kind of bad right but the scary part is if you or someone on your team takes sensitive information that you have protected for all these years and your IT team has helped you to be protecting for all these years and then because it's kind of easy to upload a PDF file or a spreadsheet to the AI somebody on your team does that and then exposes protected information that's really bad and I am a geek I'm an IT guy so I worry about I'm the bearer of bad news you know we want to run fast and I'm one of those people who has to say oh wait wait how about HIPAA or how about you know the donor information that's in our donor database yeah you can dump it out to a spreadsheet on your computer that's within our IT policy but you can't upload it to some AI tool to analyze for you just because it's easy and free but it's really tempting I have this one in yellow because to me this is for nonprofit leaders probably the scariest thing the thing the topic that you should be most aware of informed about and working with your team to internalize in the organization because it's so doggone easy that example I just gave download a spreadsheet from your donor database upload it to some AI tool and ask the AI tool to recommend who you should go after for money next that is so compelling but you don't want to do that unless you fully fully understand what's going to happen with that data that you just uploaded to the internet okay I'm going to mention displacing jobs there's a lot that's being written about that that actually to me is a bit scary in terms of the future of our society but also it could be an opportunity so you might want to think about understanding more about what a I could do for your organization pragmatically as you build your future staffing and budget plans and then the last I'm going to mention is what's called the black box effect it's kind of the cousin of the slide earlier where I mentioned that we might lose touch we might become lazy well the black box effect is similar to that which is basically saying that even the advanced researchers who are creating the AI tools do not fully understand how the results are being generated and can't validate the sources of information and that's scary it's okay if I don't know how how Google serves up to me a bunch of results when I do a search but I'm counting on the guys at Google knowing how that works so there's a bit of trust there with AI it's not there yet and that comes into a lot of the regulations that are being looked at in the EU and the US that we really don't want to get into today and then just because it's a slide with the title AI the scary I have to acknowledge that there is the whole stuff that's in again the Wall Street Journal and the New York Times about AI robots may cause us all to be extinct there's a lot written about that but that's way out of our concern hopefully it won't happen if so I guess we won't remember much about this talk okay now last thing I want to talk about is how can you get started because I really wanted to get to dialogue where you can ask questions about topics I've thrown out here we can have a conversation among folks so I have three slides about what I recommend you do to get going if you if you're already there I want feedback and thoughts about who's doing what so first of all there's a topic that's very prominent for an organization and small businesses that is creating an AI acceptable use policy and I think that this is something that if you aren't already working on you should even if you don't want to start with AI or do anything with it for a while get the wheels rolling to create a policy and I have some recommendations here about doing it I think that leadership and boards both and being now kind of a board guy no longer being employed by a nonprofit I have a new lens but there could be value in having the right board members involved but this is something that needs to be coming from the top in the organization like a lot of good things must come from the top and yet not be a mandate it needs I really believe firmly this needs to be something that you and your leadership team work on together and that it's there's buy-in and so adoption will be really good throughout your organization hallucinations happen so you don't want your team going off and ignoring that and you don't want protected data hitting the internet where it shouldn't also I think AI literacy is something that should be embedded into your organization starting now starting as soon as you can you can if you're already on that path I'm interested for you to share your experiences that's what ED chats about I'll mention that something like a monthly AI lunch and learn could be easy to do and then that acceptable use policy if you have a monthly lunch and learn and somebody from finance says hey and by the way we did this then hopefully your leadership somebody at the lunch and learn could point out that maybe they skipped the guide rules and shouldn't have done it and that's okay that's how we learn but that policy is crucial and then find a way that that policy is not just off on the shelf but it's something that your organization lives and then the third I'm going to talk about a little bit more here is I recommend that you build and review an AI roadmap for your organization and you include progressor courts reports and make AI I'm sounding like such a geek and I am but the possibilities are really fantastic and the risks are really real are very real so of all the thousands of things that you ED CEOs could put onto your staff agenda or could worry about or look to exploit I really do think AI is a tech thing that is worth it so I suggest that you create an AI roadmap and for your AI roadmap I have an idea of how to approach it and I made this up if you google it and there's something out there they stole my idea but basically it's the ESOP spable of the tortoise and the hare but I'm expanding it a bit to say we've got the hare we've got the tortoise and we've got the ostrich and I am asserting today that all three perspectives could be valid at different times and it's not something that is good or bad it's what makes the most sense and it's a nice quick way for you and your leadership team to identify what you're going to do in your roadmap and I have another slide with a quick example but let me introduce the hare the tortoise and the ostrich so the hare and ESOP spable kind of falls apart here a little bit but I kind of like the imagery so the hare if you're ready in some area to move forward with AI and some of you are already hairs I'm sure in some areas then identify projects and take them forward responsibly so that means we're going to try to use AI to make things better in doing so we're going to be responsible and pragmatic we'll have objectives we'll have a timeline we'll be sure that we can resource it etc it's a project the tortoise on the other hand areas of your organization where you're not quite ready to leap like a hare but you want to move forward so pick a pilot project maybe do some research study talk about it be really deliberate before you even take a step with a mouse in hand and then the ostrich I think this also absolutely can apply to parts of your organization but again I'm going to suggest you be forthright about this with your leadership team to say hey in this area we're going to be auspices we're going to stick our heads in the sand and hope our butts don't get burned by what's going on with AI until it's really shaken out by the way these photos these images the first two I generated on Dolly which is one of these AI things I asked the tortoise I asked Dolly to do a image impressionist style of a tortoise in the model of the thinker statue and I really that was really good okay so just about to finish up here AI roadmap this is an example and so I just want to point out that the way I would approach this if I were running a nonprofit would be to start with something like this with my leadership team that says in different areas where we could explore or add AI to how we do things what are we going to do this quarter what are we going to do next quarter and then some comments on why and you could expand this a lot I tried to just make something for one slide but leadership you start for those of you who are at the top man start now you're the hair you're going to do this you're going to make it happen and you're going to embed it in your organization no excuses that that doesn't mean you're doing anything with AI it means you and your organization will understand the good the bad and the scary of AI but then in development maybe for events you are the hair this quarter you try an event or two where you use AI in a deliberate fashion but then next quarter you say you know what we're going to plan that the next quarter we're going to do things the old way and just really reflect on how things went but on the other hand communication maybe we're going to be the tortoise this quarter we're going to understand what these tools can do for us mess around with them some understand where we could screw up and then next quarter we're going for it we're going to be the hair so we're going to use AI tools to help us with our communication etc now actually I did want to mention finance if anybody has an organization where finance or anything in finance is going to be the hair I want to hear about it please because that's the one place that like I put ostrich here for the next two quarters basically being anything having to do with finance scares me and if there's still a scary stuff in AI I think I'd want to wait but I might be wrong I actually there might be good examples of where areas of finance could move forward so what I recommend is that you build this with your leadership team and update it regularly and it gives you a way to debate how much you want to go forward and how much of your resources you want to put into pursuing AI stuff okay discussion topics we could talk about what areas could be hairs you want to run for what successes have you guys already had or setbacks what concerns do you have what areas like finance I'm suggesting might be ostriches and why and then I want to mention something about adoption in your organization which is what can you do to engage your internal champions inside your organization including your board or other key stakeholders and what do you do about your internal blockers this is always a question that should be on the forefront when you're going to make some change in your organization so with that I want to make one final offer here's my email account but also I'm really trying to learn as much as I can about this I've talked with about a dozen eds or directors of operations a couple of other roles in some small nonprofit organizations I'm doing a couple of little projects pro bono projects for some some of my friends in nonprofits but I really personally want to learn more and so if you're interested in chatting with me more one on one later um book sometime with me on my calendar half an hour hour whenever you want it could be august it could be september it could be tomorrow um and with that though I'd like to uh do what we're supposed to do with ed chat which is to say okay guys what questions and topics and experiences do you have to share so you can all learn from each other so aretha I'm going to turn it back over to you awesome this was so awesome so many great points so many things that I took away from this I'm going to go to the q&a section I know you may have a question deb if you can um take your screen down so we can see the faces of people I'm going to q&a because they were the first ones to ask questions and then if you have a question use to raise your hand awesome I see some snaps I see some people clapping yes I'll give her a virtual clap and some snaps in the chat so awesome so here in the q&a I hope I'm pronouncing your name right delta says has anyone drafted a company policy on the use and restrictions of use of AI that they will be prepared to share so has anybody done that um used a raise hand option you can do that or deb um there was another question that kind of ties into what she said from faith it said could you please share an example of an AI acceptable use policy written by humor preferably hey I have a quick comment on that aretha which is I think that might be a great topic for an ed chat or another session so um there there was the class that TechSoup sponsored in May and June and I see a couple names here of folks who were there the AI use policy came up there and um at that in that group basically it was acknowledged that there are lots of examples if you just go out to the internet and and look um there there might be I haven't looked recently there might be some better ones on one of the like boardable or one of the nonprofit oriented organizations um so there are lots if you look uh and I think it's one that everybody should do and so basically my inspiration today is do one uh and then aretha if TechSoup if we want to help I think we could absolutely and um Joshua I see you in the chat you put a link in here to a google doc with the sample AI policy I would love I'm looking over here because I'm looking at the chat so I need to be looking at you I would love it you know you would like to come in and and kind of do an ed chat on that explain more what should be in the policy and why and who and so um send me an email at asymonds at techsoup.org and that's for anybody who would like to be a feature speaker I would love to put my email in the chat as well um you definitely want to come to next month's ed chat because um we have somebody from Granville that's going to be here but let's get back to the Q&A uh Tia says we will be forced to use AI regardless to how we feel about it this is a question she has a question mark on there uh so what do you think do you think we're going to be forced to use it regardless of how we feel about it what do you think that no but resistance is futile um I don't actually let me step back I think it's possible that one two three five years from now which is a really long time the way that AI the way this AI stuff is changing I think it's very possible that organization non-profit organizations that uh could still be thriving without using AI I really do um because there's so much need basically because there's so much need however um I think it would be crazy to be one of those uh the the AI lot I three five years from now I don't think that will be forced to use it though I was at a I was at a non-profit in Pittsburgh uh about a year ago and they didn't have Wi-Fi so let me give that as an example so they were getting cables and my laptop doesn't have an ethernet connector and so they had to go so basically this organization has existed for 27 years and over the last 10 or 15 when everybody I thought had Wi-Fi they don't have it um and they're thriving because they do good work for the community and the constituents they need to serve that's powerful that's I'm glad you shared that Steve and Pam Chapman will we get the presentation to share with our staff absolutely we're going to email you this video replay and the slides by tomorrow so you'll get that by tomorrow and Reverend Tracy yes if I could add one thing so the slide deck at the end of the deck there are two or three slides I put in with just some additional information I knew I wouldn't have time to talk to it uh and so please feel free to use that as you'd like as well okay yeah we'll definitely send all your your entire slides so thank you your slides were awesome Reverend Tracy asked does AI pick up information that is dominating the market in the moment the hallucination seems a little scary like misinformation I would have given us on that I think it does I like absolutely yes and there's a there's a lot about why how AI how these AI works how these AI tools work that explains why they quote unquote hallucinate um my advice I'll stick with is um be aware of it don't let it be an excuse for not use the tools in a responsible way um but I think for from from what I've read and from the research that's out there about these generative AI tools which is what we're talking about here um it's likely that those hallucinations or lies or incorrect information it's likely that will continue for a while um so be aware of it and then deal with it that's that I didn't mean to sound harsh there but um I've talked to a number of eds as I've been scanning folks over the last four months so recently but I talked to a number who said ah I don't even want to I don't even want to go there because it lies it's wrong I go there and I really am hoping that and overcome that fear okay Gail Samson our chief development officer if you're still here would you unmute yourself and while she's doing that Deb would you put your link into the chat that you wanted people to contact you um yeah sure I'd be happy to do that hi everybody I'm Gail Carpenter I've had the fun of working with Aretha for a long time and I've just celebrated more a 22nd year working with TechSoup as their chief business development officer so a pretty big chunk of the stuff you see in our marketplace or companies I've had the fun of working with and I'm going to put a link in here uh of our technology wish list but if what I'm looking for since this is a burgeoning area of our industry and I tend to agree with you Deb that maybe it's not going to be forced to us but who thought that we were going to need to use Google every day either you know so things change and what I'm looking for are what tools what resources would help you do your work better help your organization to be stronger that you're not currently finding on TechSoup so if you can help me know what's useful uh what's going to be helpful what have we not started bringing your way please let me know I'm also going to add my direct email here so if you guys just feel more comfortable writing me directly feel free to reach me there too so Aretha that's everything I've wanted to say anything else I can help with awesome that was great and and Gail means anything so I have no people use giving platforms that we don't have let Gail know and probably she can bring on to TechSoup platforms so you can get at a discount so yeah and then there are sometimes people I'm working with as you know strategic advisors and working with one company that is actually actively looking at how to use AI to help your grant proposals be stronger still early days but you know so those are things that you know and also if there are things that you think I should know about that maybe it's not uh solid enough to be part of the TechSoup marketplace but you think that it's really interesting and cool and I should be getting a chance to follow those people please uh send them my way as well also that really to me pop in take care thank you now that partner you were just talking about that's their name start with a G I'm going to give that top secret information away yet I believe it does come to think of it and I believe you would me have mentioned their name earlier but uh yeah all right guys talk to you later bye bye thank you well you definitely want to be at ED chat next month so there's some questions in the Q&A um dad I'm gonna keep tossing them to you um beginning experience says some of us are sole employees who are overwhelmed with the work of the mission as a non-geek how do I try to incorporate AI into my work to be more useful versus a time sink I guess you know sink in time into it yeah and um that's a really tough question um so the nonprofits that I have worked with and three of the board boards I'm on are all fewer than five staff so I kind of feel your pain I'm going to say hey does anybody else have a thought on that because I don't have a real great answer I have a long-winded one yeah anybody want to use the raise your hand option to unmute yourself to answer that question because I know a lot of people are new nonprofits and you may be in the same boat um as this organization anyone okay so we're going to move on for the sake of time um Tia says would there be a document oh Joshua has one I was hoping he might see it I want to finish this question would there be a document to show those of us who signed on late to answer the questions that were already answered live no document so that's why I read the questions out loud so that you will know why the person answered that way so we hope you can gain some more insight after watching the replay as well Josh you want to unmute yourself yeah sure and and so great to see you presenting on this topic that's fantastic um so uh on being productive even if you're just one person um there's there's something we wrote about a a while ago called the six square wheel paradox there's there's a article about it about it's like people with a cart of rocks and you're like trying to get work done and someone's holding round wheels and you've got square wheels and you're like I'm too busy I can't do it I encourage you to think of some level of AI learning is that yes it's it's overwhelming because you've got so much else on your plate but even just an hour of learning how to use basic like GPT to generate drafts of documents or if you want to go one step further um there's something called code interpreter now which can do really spectacular things with data analysis and basically if you're a single person gives you the ability to have like a fairly sophisticated data analyst to like make charts for you and you can ask plain English questions about data sets and you could learn really any person could learn this within an hour and so it's it's worth that little bit of investment even if you're a one person nonprofit so that's that's all I want to say thank you so much for that um someone from this organization I don't want to mispronounce it but the last name is Africa we're impacting communities in 10 countries that's awesome congratulations by the way where the internet is non-existence can we introduce AI there offline very good question I would respond with two comments and then I'm curious if anyone else has thoughts the first is that most of the discussion of using AI for non-profit organizations has been for staff and internal operations little has been for clients and I think that's an area that we in the nonprofit world can explore how can we help our help other humans whom we serve to live their lives better or whatever through using some of these technologies now if what you're talking about is you have staff in other countries where internet isn't available it's very difficult there are tools on cell phones so I know many I'm very familiar that in many parts of the world cell phones are available and data plans are less expensive than we're custom to here in the US um there are tools that are available through phones chat gpt for example there's an app for chat gpt and there are lots more apps um but uh if you are okay so you have 6 000 volunteers in Africa yeah so um I think that there's not a quick answer to that but if they have access to the internet through their phones uh then it's very possible that they could take advantage of these tools if you have a good plan for what we want them to use that's a good answer and maybe um Lashika can we connect them with someone from NGO source maybe they can answer that better uh Larry says what is the best way to feed quote unquote or train a chat bot to understand the voice so the language organization so that the content it generates is in alignment with our internal language great question yeah so first I'm going to say two things to that briefly one is um training a chat bot to do work for you is something I didn't go into in any detail but um it is a really cool tool and you can use it both internally and externally so you can basically feed a bunch of PDFs into a chat bot and then have the chat bot be able to answer questions and I mentioned training that's a great opportunity there um I'm not answering your question yet I just want to settle context for how chat bots could be useful because I didn't cover it so chat bot could be useful internally for people to particularly for training uh for people to understand what your policies are or understand how your organization works chat bots also can be added for example to your website so that you can have a more one of those typical customer service type of dialogues with folks now in answer to your question um there are a lot of there've been a there's been a lot of research and articles written in this area I don't know enough to uh synthesize it so I'm setting up that hey why would you care everybody and it's because hey these chat bots can be really cool like internally for training um but does anyone here know the answer to the actual question which is how could you train a chat bot to be um more in the voice of your organization because I don't know the answer to that yeah you'd have to use their application programming interface for that their API um I've seen folks do it with training um specific subject matter and they would set the sources so it takes say 100,000 websites and do it that way um but you need uh an AI ML machine learning person like my colleague awesome and Ali um maybe you can answer this part this part of Rachel asked this question is similar to what the last question was what are some of the tips and tricks for working with AI to produce responses close to what you need for example she said I asked chat GPT to highlight redundancies or I tell it to condense text into three sentences what are your thoughts on that certainly I like Grammar League go beta which uh has prompts that you could do stuff like that much better than the chat interface that chat GPT has uh GPT itself is a large language model and it's more so evolving into a reasoning system uh than a chat bot just one clarification there um so it could help you reason with like those redundancy things that you talked about things I use it for is research synthesis and uh things of that nature um it's it's great for things that need to be done over and over but also for seeing things that you may miss uh it's it's been super helpful and useful and nice awesome I saw a couple of hands up and then they disappeared so if you still wanted to make a comment feel free to use a razor hand option oh Josh and then Kristen after that yeah I just want to say very quick um and just like five days ago um for the paid chat GPT and the code interpreter tool I mentioned earlier is also on the paid 20 a month um chat GPT plus but they allow custom instructions which is a no code like just create instructions that are persistent with your chat GPT that can include your voice your audience what you want so that no longer has to be something so it's not quite the same as training your own with huge amounts of your organization's data but it's it like literally five ten minute way of having a personalized chat bot that could create content for you in the tone of your writing your organization so I just want to show that look oh okay um Kristen did you still want to make a comment hi thank you um yeah I wasn't sure because I don't have like applied you know experience but I've just been going down the rabbit hole in terms of a lot of um different conferences and people in business use cases and I think it was the response to um someone saying training in your voice so I watched like a full kind of one hour uh piece on someone doing this basically if you kind of googled in how to you know train jet chat GPT on your brand voice so basically it can't take large amounts of text but you can you can kind of chunk it out so you could take your website you could take your copy um anything that you've put out your newsletter social media and then start to train chat GPT on that and then keep it in one thread and just say you know you always talk talking to chat to be tain saying pretend you're you know you're this person pretend I'm the copywriter pretend I'm the author of or I'm the CEO of this organization and basically taking it through and all and one thing they notice always to remind it because chat to be taken forget so always refresh them go copy and paste on that thread go back and bring it back and you're basically training them in that voice you have one consistent thread so as you're asking it to create your copy over time it will you know develop your voice you just need to create give it more and more data as you go along and so yeah it's just an insight that I got from uh from a workshop I don't have any hands on yet but I just want to show that thanks that was that was great insight because I'm asking chat GPT from stuff about home depot and then I'll skip to something else and so I'm that is is chat me I'm not training it in my voice because it's all over the place so that was good hi Joel get on me yourself yes I uh I've written several articles and uh so I said well I will try chat gvt and have it do an a summary of one of the articles and it summarized it I said well that's nice but it does not it didn't quite to me give the personalized feeling that I had in that article it was an experience that I had uh and so I did something else and I took a email that I had written to uh we have a seminar coming up and several people had pre-registered for the seminar we had to move classroom speakers around so they were double booked now and so we had I wrote an email saying you we're going to cancel your your registration because you can't attend two classes at the same time so I said what what are you going to do and chat gvt came back and gave a long dissertation all about well thank you for being a member of the and your interest in this and and thank you for that and thank you for this and we appreciate and so forth and I said make it more less formal and more personal and it came up with a phenomenal thing in which it said whoopee we we found an error in our thing and you know it went on that type of approach to it so my point being is dig into it and just remember remember that chat g gvt is a a foreign professor that's helping you all the time well that's good and so you keep asking that you're this person how can you help me improve how can you do something for me and think of it that way I like that I like that thank you for sharing that yeah feel free to unmute yourself that was good hi uh there are a few tools unfortunately I wasn't prepared to I cannot find them right away but there are somewhere in my big collection of AI tools there are tools when you can use chat gvt for your own domain so you basically can add either the main address or URLs or you know customize chat gvt so it won't give you like wrong answers or go somewhere where you don't want it to go so I I'll send it to you I mean to either gale or to you in direct mail and actually I have a quite an experience of working with different AI tools various including like visualization tools and summarization and not only chat gvt but really various because I I'm a researcher for innovation teams so and a lot of customers just want us to use chat gvt for ideation like how we can use chat gvt to innovate like we we are working on drones and we want to enter a market of say food market so and one of the thing that we did was like we cross-pollinated several concepts and it always works so there is a recipe for that I put a link in the chat so I encourage everyone to watch this Judiciary Committee hearing about copyright and AI and it's really funny it's not boring so really take your time watch a little bit and it's it's plain English and you'll get an understanding of how harmful it could be if you let it do what basically it wants or use it without your control and I totally agree with Deb about AI policy it should be drafted it should be implemented and there should be a certain level of AI education in every organization so I again I'll write it to you directly but I think that I may well volunteer to start a little bit of AI newsletters because I mean it's a huge it's a huge environment and what actually what we are talking about today is not AI it's generative AI and mostly text LLMs except for Dali but mostly I see people in the chat they're talking about text generation and working with texts so and it's really hard to you know to navigate in this ocean of tools and some of the startups they are there today and they're running out of business tomorrow and some of them want you to pay money like upfront although there are free tools there are liable tools there I would encourage everyone to just use one tool like one function tool say if you if you like to get something summarized then say try being it's safe because sometimes I mean what's the biggest problem with charge gpt is we don't know where it gives where it gets the answers from so we don't we have no idea how it was trained we have no idea what documents were input besides charge gpt only provides us with information up to 2021 so and again so thank you thank you thank you see I told you this is what ed chat is about for all of us to chat and have conversations and have our voices heard that was awesome great information and I hope your newsletter is a success if you decide to do that so I'm going to go to the q amp a we're going to just take one more question I know a lot of people put them in the q amp a but this one's really important to me because I don't Katie I don't want you to leave and feel like I didn't get what I needed and I'm sure a lot of you may feel that way if we didn't get to your question but it wasn't Katie Katie I'm going to roll your question into teen's question as well Katie asks what AI tools are available to create charts and graphs based on the data you put so this question ties in the Tina Tina said would you be able to send the list of different AI platforms that were mentioned I cannot send the list because that would mean that TechSoup is endorsing them and we're not we're not I'm not here to say that we agree with any of them or we are against any of them so for all of you who are on here go ahead and type in the chat which AI tool that you use chat dbt bar whichever one you use and also the one for making graphs so that Tina can can look at that and see that I want to say thank you so much to Deb Deb that was an awesome presentation I mean I learned so much the tortoise the hare and the ostrich right you guys are going to be the watch that on the replay thank you for everybody who had input I wanted to leave some closing remarks for Deb if you want to say anything I don't see you on my screen anymore no no but thank you to Aretha for the opportunity and I can't wait to read all the stuff in the chat yes and when you step away from this webinar you're going to have a survey please fill out the survey Deb I want you to come back and do this again so be thinking of topics if anybody else would like to be a feature speaker please email me at a simons at TechSoup.org thank you Gail Carpentier for being in the chat and Lashika so much for your input in the chat this was great lots of thank yous to you Deb so thank you to all you nonprofits for all that you do have a great rest of your week and take care of yourself as you're taking care of others bye bye