So, in today's session we will continue our discussion on the theory of cost, we are towards the end of theory of cost which talks about and the economic of scale and the economic of scale. If you remember in the last class also we discussed the basis for that why there is a use of long run average cost curve. So, in the previous class we discussed about different cost analysis like short run and long run cost analysis. Then we discussed that why long run average cost curve is use of what is the evidence of economic of scale and this economic of scale and in the different kind of average cost. Then we talked about the contribution analysis specifically the PV ratio and then we talk about the learning curve if you look at that is the other way to analyze the long run average cost curve. So, in one case we say that when there is a economic of scale long run average cost curve is use of because there is a economic of scale and this economic of scale whereas, in case of learning curve the long run average cost goes on decreasing and there is no point where it increases again. So, in today's class we will focus more on the or mostly on the economic of scale different type of economic of scale how the large firms or how the large production amount brings some cost advantages and that is how the firm produce at a lower unit cost production that is how we are going to discuss today. So, the focus or the session coverage is application of the cost analysis will take some the cost function the empirical determination of it and then we will talk about the economic of scale. So, application of cost analysis if you look at this is the in the first case in case of optimum output level we generally use the cost analysis to identify what is the optimum output level. So, optimum output has the reference to that level of that level of size of output which minimizes the average cost of production and for which the average cost is equal to the marginal cost. We have discussed several time this optimal production or optimal output. Optimal output is one which can be produced at a lowest minimum cost that is a basic understanding of the optimal output and here if you look at optimal output is that level of output which minimizes the average cost of production because here we are trying to fit the optimal output taking the constant as the average cost of output. So, optimal level of output is one which produce a produce that level of output which minimizes the average cost of production or may be the another condition for is that where the average cost is equal to the marginal cost. So, we will just take an example that how to find out empirically taking a typical cost function how to find out the optimal output level which minimizes the average cost at that point of time. So, if we will take a cost function like that is total cost is equal to 128 plus 6 cube square. If this is the cost function then in order to find average cost as we know this is total cost divided by cube. So, this is 128 so generally when we find this average cost we generally divided by the unit of output. So, this is 128 by cube plus 6 cube cube plus 2 cube square cube. So, that comes to 128 cube plus 6 plus 2 cube so this is the average cost. This is the average cost for the cost function now to find out at which point average cost is minimum we need to take the first order derivation that is first order derivative of average cost with respect to q and if you equalize that equal to 0 then this comes as 128 cube square plus 2 which is equal to 0 and if you summarize or simplify it again we get q is equal to 8. Now, how do I interpret this q is equal to 8? So, when we know that average cost is minimum, average cost is minimum mathematically we can find out that by taking the first order derivative of average cost with respect to q. If you take this with respect to 120 cube plus 6 plus 2 cube in this case we get a q value of q which is equal to 8 and value of q if it is equal to 8 then this is the level of output where the average cost is minimum. Now, let us understand the same cost function taking in term of the marginal cost or when we find out the marginal cost and equalizing the marginal cost and average cost again whether we get the same level of output where the cost of production or the where the average cost is minimum. So, taking the same cost function again suppose this is total cost is equal to 128 plus 6 cube plus 2 cube square here we will find out the marginal cost and how to find out the marginal cost generally we will take the first order derivation of total cost with respect to d cube. So, in this case how do you find out this? So, this term becomes 0 d 128 plus 6 cube plus 2 cube square as compared to with respect to d cube. So, if you do this then this term is equal to 0 calculus. So, this is equal to 6 and this is equal to 4 cube. So, 6 plus 4 cube is our marginal cost we have average cost we have average we have marginal cost. Now, if you equalize this average cost is equal to the marginal cost then in this case what is the value we are going to get? As we know the average cost is 128 cube plus 6 plus 2 cube because this is nothing but T shift divided by cube which is equal to 6 plus 4 cube. So, if you if you make a equalization of average cost and marginal cost and simplify this then again we get a value of cube which is equal to 8. So, q is q 8 is what? q 8 is the optimum level of output q 8 is the optimum level of output where average cost is minimum and we can say that this is a short run cost because we have some amount of 128 over here. So, what is this 128? 128 is the total fixed cost and if you remember we get the amount of fixed cost only in case of short run. So, since it is a case of total fixed cost we can say this is a short run analysis in case of short run cost analysis q 8 is the level of output or 8 is the level of output where the average cost is minimum. So, the point here we want to focus is that what is the role of this average cost, marginal cost or total cost or how it helps in taking the economic decision, business decision or how it helps to generally using it for the different kind of economic analysis. So, for to summarize this we can say that optimum output level is 1 where the average cost where the cost to be minimum and that we can check from a cost function by taking the taking out the average cost marginal cost and solving the value for the q. So, next we will say this optimum cost analysis in case of the optimum inventory level. So, when this concept of inventory comes generally inventory comes if all the production are not immediately sold and if you look at whatever is produced and whatever is sold there is a gap in between and the gap whatever the amount that generally goes to the warehouse that generally go to the stock and in economics term we called is at inventory because that is the stock kept in the warehouse which is not coming to the open market for getting sold. Now, how to define the optimum inventory level? Optimum inventory level is defined as the size of stock for which the average cost of inventory is minimum. So, if you look at it is just generalized into the same concept of optimum output level is generalized into the optimum inventory level and what is in optimum inventory level the size of the stock or the size of the inventory where the average inventory cost is generally minimum. So, the way we analyze the optimum output level that is that is the level of output where average cost is minimum. Similarly, here it is the level of stock where the average inventory average cost of inventory is generally minimum. So, let us look at how to find out the optimum inventory level. So, there are two types of cost involved over here one that is carrying cost. Generally, it includes the storage cost, interest cost and borrowed capital to finance stock etcetera. And second type of cost is reorder cost which includes the bookkeeping cost, telephone charges and some variable cost. So, there are two kinds of costs are involved in case of a when we are finding out the optimum inventory level. One is the carrying cost and second one is the reorder cost. Both has to be the part of the cost function when we are finding out the optimum inventory level. So, how to find out the average cost of inventory? The average cost of inventory is K multiplied by D by 2 plus F plus V plus D S divided by T. So, the first part of this equation is carrying cost and the second part of this equation is the reorder cost. Now, we will see what is the terminology stands for. S is the expected cell, D and why we are taking the expected cell because that will tell us that which has which is the what are the amount is that is going to be sold and what are the amount that is going to be stock or that is going to be in the inventory. So, S is the expected cell, D is the order quantity to be delivered and D is the number of orders deliver, F is the average fixed cost of delivery, V is the coefficient of A B C of reorder and K is the average carrying cost. So, if you look at this average cost consists of both carrying cost and reorder cost and different types of cost either coming under carrying cost or reorder cost that is added in the equation. And what is D by 2 that is the average inventory held between initial and terminal period and it is assumed that the demand is spread generally evenly. Because this is since we need to find out what is the average inventory cost, we also need to find out the average inventory level and that is the reason we bring this D by 2 variable into our average cost function. And D by 2 is the nothing, but the average inventory held between the initial and terminal period and it is assumed that the demand is spread evenly. Now, to find out the optimal inventory level held at any point of time, generally we do the first order derivative that is for average cost has to be equal to 0. So, D A C with respect to D, so if you take a first order derivative of average cost with respect to capital D, then it comes to K by 2 minus f s D square which has to be equal to 0. If you simplify this for the capital D that is root 2 f s by K and this gives us the optimum size of stock or the economic order quantity. So, popularly this is known as the economic order quantity and this is the optimum size of stock. So, D how do you find it? Generally we take the average cost that is the first order derivative, we take the first order derivative of the average cost with respect to the capital D equal as it to 0 and then we solve it for D capital D and that gives us the optimum size of the stock or that gives us the economic order quantity. So, that D comes out to be root 2 f s by K. Now, we will look at the application of cost analysis in case of the optimum scale. In the first case we talk about the optimum output, how this cost analysis is used in the optimum output. Then we check that how this cost analysis is used in case of optimum inventory level. Then we will say to identify the plant size, to identify the scale of operation that is what should be the optimum scale, what should be the optimum size, what should be the optimum level where the firm should operate and we will see how the use of cost analysis is being there. So, the optimum scale is given by the value of K that is plant size at which the total cost is list. So, if you look at there is a significant difference here from the application of cost analysis here in the last two cases that is optimum output and optimum inventory because in previous both these cases we identify that level of output or the level of stock where the average cost was minimum. But, here we are saying the optimum scale is given by the value of K or the plant size at which the total cost is the list. Now, what is the necessary condition for this optimum scale? The necessary condition for this optimum scale is dc by dk has equal to 0 that is also known as the first order condition that is dc by dk has to be equal to 0. So, first order derivative essentially has to be equal to 0 and sufficient condition is d square c by dk square greater than 0 that is the second order condition where it has to be greater than 0. Now, we will take an example to understand that how to use this cost analysis in order to identify the optimum scale. So, let us take a cost function that is c minus c is equal to 0.04 qq minus 0.9 q square plus 11 minus k q plus 5 k square. Now, what is the first order condition? The first order condition is dc by dk has to be equal to 0. So, if you do that then dc by dk will come to the first term will be 0, the second term will be 0, the third term will be 0 and the fourth term will be 10 k this has to be equal to 0. So, 10 k is equal to 10 k is equal to 0 and k is equal to so if you look at k is equal to 0.1 q. So, first order condition from that we find out that k is equal to 0 and what is our first order condition? First order condition is dc by dk is equal to 0. Now, we will look at the second order condition and the second order condition says that d square c by dk k square has to be greater than 0. So, in this case if you take this then the first order derivative, the second order derivative then we get it equal to 10 which is greater than 0. So, if k is equal to 0.1 q that is from the first order condition this is from the second order condition. So, we can say that both the conditions are getting fulfilled that is the first order that is dc by dk has to be equal to 0, the first order derivative has to be equal to 0 and the second order derivative has to be greater than 0. So, in the in this example whatever we are looking at now we are getting both this condition get fulfilled and we can say that the k is equal to 0.1 q and how to interpret this? If the firm is to produce if the firm wish to produce 10 units optimum scale is 1 and if the firm wish to produce firms wish to produce 50 units the optimum scale has to be has to be 5 because k is equal to 0.1 q and what is k? k is the scale of the plant or the size of the plant. So, this is how the application of the cost analysis in case of the optimum level of scale, identifying the optimum scale. So, if you look at the cost analysis whether it is average cost whether it is marginal cost whether it is total cost in all these cases generally it is getting used when the business station has to be made irrespective of whether this is related to output whether this is related to inventory or whether this is related to the scale. Next we will move to the topic that is economics of scale and I think we have just introduced the concept of economics of scale in our previous session when we are curious enough to know that why the long run average cost curve initially decreases then reaches the minimum and beyond that if the still the scale of operation of the firm is going on or still the production is going on then the firm generally the average cost generally increases. So, let us understand this economics of scale in details what are the sources and what are the types of economics of scale and how generally the firm gets advantage out of it. So, how do you define the economics of scale? This is the advantage of the large scale production that results in lower unit cost or you can say that this is the advantage of large scale production that results in the lower average cost per unit and mathematically how do you find that the average cost is t c by q and economies of scale if you look at its spread total cost cost over a greater range of output because whatever the total cost that is initially decreasing then its reaches minimum and increases it means the total cost is not at any point of time generally it spreads over the greater range of output and that is why we experience decreasing cost, we experience cost and average cost and also we experience the increasing average cost. There are two type of economies of scale, one is pecuniary economies of scale and this type of economies generally realize from paying lower prices for the factor used in production and distribution of the product due to bulk buying by the firm as size increases. So, here it is about paying a lower input price whenever we pay a lower input price we generally experience the pecuniary economies of scale or we get advantage in term of pecuniary economies of scale. So, when the price is being paid less for the input using the inputs we generally experience the pecuniary economies of scale that we discuss in a later point of time after we discuss about the real economies of scale, but for the timing the understanding for pecuniary economies of scale is it is not about using less quantity of input rather it is the less price what we pay for the input. Then the second type of economies of scale is real economy. So, real economics is associated with a reduction in the physical quantity of inputs, raw materials various types of labor and various type of capital. So, pecuniary economy if you remember what we discussed just now they are not using less input rather they are paying a lower price for the inputs when the scale of operations increases and that is how they are getting the advantages or that is how they are generating the economies of scale. However, in case of real economies it is the reduction in the physical quantity of inputs raw materials various type of labor and various type of capital. So, there will be reduction in the quantity of input there will be reduction in the raw materials there will be reduction in the labor there will be reduction in the capital and if this happens when the scale of operation increases then only we can say that the firm is enjoying the economies of scale. So, we will discuss this in detail this real economies and there are generally four type of or four category of the real type of real economy or the real economies is divided into four category one is production economies, second one is the selling economy selling and marketing economies, third one is the managerial economic and fourth one is the transport and the storage economies. So, we will start our discussion with the production economies that is under real economics how generally there is a production economies of scale and production economies generally it may arise from the factor that is either from labor or from the fixed capital or from the inventory requirement of firm. So, the advantage under production economies of scale either it comes from labor or it comes from fixed capital or it comes from the inventory requirement of the firm. Now, when you say that production economics comes specifically from the labor let us identify the factor what happens with respect to the labor input that the generally the firm gets advantage out of it. So, when you categorize again the production economies into the labor economy these are the factor because of what generally the firm gets some cost advantage or firm generally may be use less of the labor in order to produce the same level of output when the scale of operations increases. So, the first factor here is specialization, second is time saving, third one is the automation of production process and fourth one is the cumulative volume economies. So, we will discuss the specialization first and if you look at the subcategory this is part of real economy which is again comes under the subcategory of production economy again comes in the subcategory of labor economic and then it comes to the specialization. So, large scale allow division of labor generally because the number of labors are more that allows the division of labor and that led to the specialization of labor force with the result of improvement of the skill and hence the productivity of the various type of labor which results the saving in the time usually lost in going from one type of work to another. So, if you look at in case of large scale the pool of labor force is more. So, each the pool of labor force is divided into the different group and they have assigned a specific task and that is how the division of labor on the basis of their skill on the basis of their expertise and in this case what is the advantage? The advantage is that if you the laborer skill is fit into the work they will do a better work or they will perform in a better way which result in the improvement of the skill result in the productivity of the labor because they are doing a work which is which is more suitable to them according to their skill and that leads to more productivity and the decrease in the time required to do the work and that is the reason if you look at in case of large scale change the division of labor is there that leads to specialization of the work of the labor force and finally, that leads to the efficiency of the or the productivity of the labor and there is a less of time and that is bring the cost differential to the firm and the average cost generally comes down. So, the first factor what we have analyzed is the production economies in case of labor economics specialization is the specialization of labor of the division of labor is one generally where we the firm or the producer in general they get some advantages. Then we will talk about the second factor under labor economies that is time saving. So, division labor apart from increasing the skill of the labor force results in saving of time usually lost in going from one time to one type of work to another type of work and that time again brings some productivity because there is no loss of time the same time is getting utilized for the productivity of the labor. So, if you look at division it is related the division of labor in time saving in case of small scale firm how it how they do not get an advantage of time saving because the same group of people either they have to work here or they have to work there because there is not a different group to specifically work on project one project two or project three because it is a small operation. But in case of large operation it is possible to divide them according to their skill according to their expertise and in that case they get the advantage in term of productivity and in term of the time saving which brings the economies of scale to the firm.


Russia changes tactics of mass strikes, energy infrastructure, not priority. Russia changes tactics of mass shelling of Ukraine with long-range weaponry. Apparently, the adversary has shifted its priority targets for attacks, reports the Ministry of Defence of Britain on X. British intelligence notes that since December 29, 2023, Russia has intensified its strikes against Ukraine, utilizing a significant portion of air-based missile reserves that were accumulated in recent months. The recent strikes likely primarily directed at Ukraine's defence industry. It contrasts with its major attacks last winter which prioritised striking Ukraine's energy infrastructure the summery states. Intelligence underscores that the Russian shelling of energy facilities earlier in December indicated Russia's intention to reinstate energy-related terror. However, the recent attacks indicate a change in Moscow's tactics. These new operations suggest at least a temporary shift of approach in Russia's use of long-range strikes. Russian planners almost certainly recognise the growing importance of relative defence industrial capacity. As they prepare for a long war, the intelligence adds, on December 29, Russians launched 158 missiles of various types and assault drones at Ukraine. On January 3, Russians repeated a massive shelling. Initially, Russians released 35 kamikaze drones, all of which were intercepted. Following that, 99 missiles of various types including aerobalistic kinsels were launched across Ukraine.


Good morning everyone. Thank you for the introduction. So I'm a senior reviewer in the division of molecular genetics and pathology in OIR at FDA. So normally I review genetic tests, such as the chromosomal microarrays for the postnatal diagnosis, companion diagnostics, and next generation sequencing based test, and also some of the non-invasive prenatal testing. I also serve on the steering committee for the precision medicine initiative at FDA, and I oversight the precision FDA projects. So this is my disclaimer slide. For today's talk, I'm going to briefly go over the basic components of IDE submission, and then I will focus on the analytical validation studies that are required to be provided in IDE submission. So Jonathan has gone over the both analytical and clinical part, but for my talk I will focus on the analytical part, and then I will use one example to demonstrate the analytical studies submitted to the IDE submission. So this slide lists basic components of IDE submission. So basically we like to see what your device is intended to be used, and then how your device looks, what components you have in your device. So Jonathan asked questions earlier how you define the device. So it's your choice based on your purpose in the clinical trial, so you can define what components you have. You just let us know clearly that how you intended to use it, and then what component it has, and also how it works to generate the test results. And then in the clinical protocol, we like to see what's the targeted population, how many clinical sites needs to be used to enroll patients, and then how many patients will be tested using the device. And then this, how the device is planned to be used in the clinical trial will help us to understand your device and then understand your risk of this, the uses of your device in the clinical trial. And then the other part is the abbreviated analytical data. I will show you, we don't ask much in the analytical validation in IDE, and then there are also other some administrative elements. You can find them in the IDE checklist. Those are the elements that we normally follow when we do the review. So for this talk, I will focus on the analytical part only. So when you consider how you designed the analytical validation study for your IDE submission, you first need to think how you can demonstrate that your device can accurately and reliably detect and analyze, so by the intended user in different situations. So it's exactly like what Jonathan just mentioned. So the accuracy, precision, and the sensitivity models. So in this case, when you conducted the analytical studies, the intended type of clinical specimens are preferred. So this will get into the pre-analytical studies. So you need to consider how the specimens are handled, because different specimen types will require different studies. For example, the serum plasma or urine, they will have different metrics comparison studies. And the preservatives or stabilizers in the collection devices may impact your device performance, so you need to consider those factors in your study design. And also, if archived samples are used for the clinical studies, then it's very important to study the sample stability. So these are the factors you need to consider when you design the analytical studies. So in IDE, we really don't ask a lot of analytical validation studies. We only ask for critical components, the analytical accuracy, sensitivity, or we also call it limit of detection study, and then the precision or reproducibility study, and then some analytical studies. And if applicable, analytical specificity study need to be conducted. So on the right here, I'm showing what the studies are required in other submissions, in 510K, in PMA submissions. You can see we ask a lot more studies in those submissions. But in IDE, we only ask these studies. And also, I like to point out that the purpose for the IDE submission is to demonstrate that your device is plausibly effective. So we don't ask a large study that we can draw the statistical inference from the study. So the sample size of the IDE study can be small. So both quality and quantity of the studies between the IDE and 510K PMA submissions are different. And of course, when you conduct the IDE studies, there will be a lot of challenges. Like what Jonathan mentioned, in the accuracy studies, there may be a lack of comparative method or reference method. But normally we accept analytically validated comparative method to be used in the accuracy study. So if you can justify why the reference method is not available and why you think this comparative method is a validated and appropriate comparative method, then you can come to us and discuss through the pre-submission process, then we will give you our comments and the suggestions. Then you can use those comparative methods in your study. And also I want to emphasize for the precision study, it's important to use samples around the cutoff because we want to see the performance for those difficult to test samples or samples near the cutoff regions. So those will be the important samples to be included. So next I'm going to use the NGS-based ANCLE panel as an example to show what analytical studies we ask for. So the NGS-based ANCLE panel is a single test that can detect multiple biomarkers. Sometimes it can be used for multiple indications. So this creates the challenges for the traditional regulatory paradigm, such as in terms of clinical validity and analytical validity and all those robustness across different tissue types. And also for the next generation sequencing technology itself, it has challenges and unique aspects, such as it has potential to detect novel and rare variants. So the technology is evolving rapidly. As Dr. Kingsmore just mentioned, there will be new aligner coming out, there will be new algorithm coming out, there will be new chemistry coming out. Then how do we deal with those changes? This challenge. And then in terms of the analytical validation, we are facing a lot of challenges, as Jonathan mentioned as well. So because the panel can detect millions of variants, then how do we validate those variants? We cannot validate them one by one. Then what's the unit of validation? Should we do it by variant types, by different genomic contexts? Or how do we identify different specimen sources? If the platform claims that they can detect both FFP samples and the blood, then how do we do the different specimen types? And also there's lack of comparative method and reference materials. So currently the only available reference material is the genome bottle project reference material, the A12878. And then there's a new reference material just released but not been widely used in the community. And also for some rare variants, it's hard to find clinical specimens. So it creates another challenge for the analytical validation. So with all those challenges and issues, we acknowledge all these challenges.  So we like to see the basic key components of the analytical performance of a device. So in terms of the analytical accuracy, when the sponsor or investigator is designing study to select a sample panel, we like to see a panel to represent the variants that your device is claimed to be able to detect. So for example, even though you have a whole axon sequencing platform, or you have a big target panel next generation sequencing platform, but in your clinical study that you are coming into FDA, you only detect let's say 10 variants in two genes, then you only need to first specify these are the variants and genes that you are going to use in this clinical trial. Then for the IDE submission, you only need to show the data for those variants. And then when you select the sample panels, the clinical samples are preferred. But for the rare variants, if you cannot find sufficient sample numbers, then cell lines may be acceptable with sufficient justifications. And then for the reference method, as I mentioned, we do accept analytically validated comparator method. So if you have the reference method for the results that as Jonathan showed, you will summarize your result as sensitivity, specificity. But if you don't have the reference method, and you use the comparator method, then you will show the result as the percent agreement. So it's showing the similar metrics, the terminologies are different. And then it's important to know that the reference methods need to be pre-specified with the exception criteria and how it's going to be used. And then the results need to be summarized by sample and by variant types in terms of the percent agreement, the overall percent agreement, negative agreement, and positive agreement. For the precision and reproducibility study, it's similar in the sample panel selection to cover the variants that you want to detect or you claim to be able to detect. And then for the specimens, it's better to include different tumor types. If you have pan-cancer claim, so for example, in the clinical trial, you will say you want to enroll different cancer patients, then you will need to have different sample tumor types included in your analytical study. This is to show that you can do what you claim you are able to do. And also different tumor contents, like if you say I can detect tumor content with 20 percent, 30 percent, or 50 percent, then you need to have representative coverage of these tumor contents in your sample panel for the analytical study. And then as far as you have sufficient number of samples to cover different variant types across different genomic regions, then it's acceptable. And of course, so these like a tumor, content tumor types, it seems like it's oncology-oriented, but it can be generalized to other applications. So if you have questions, you can talk to us through the pre-submission process. Then we can give you our comments or suggestions to your questions and proposals. And for the precision and repro studies, you want to include different runs or different instruments if applicable, and then same methods to summarize the results. Another perspective in terms of the analytical study for the NGS test in ID is that we want to see the limit of detection of your device. So you need to show what's the minimum DN input or genomic DN input you need to use for your device. And then for the tumor content, can you go down to 5 percent of tumor out of this sample? And also another one is the mutant allele frequency of variants. For example, whether your device can detect down to 1 percent of the SMVs or 5 percent of SMVs. So these need to be specified by different variant types. So I think these studies are the performance that you also, so you as an investigator also wants to see because you want to know whether your device is accurate, whether it's reliable by different users. And then another one is the pre-analytical study. So this will include the specimen types, how the specimens are collected, whether it's corneal biopsy or other biopsy procedures, and whether you can handle the FFP samples or fresh frozen samples or only blood. So it all depends on your device description. And one point I want to emphasize is that, I said earlier, cell line may be acceptable to be included in the analytical study. However, if your platform claims to detect FFP samples, then when you use cell lines as part of your analytical study, then you also need to treat the cell lines by the FFP procedure, formerly fixed and paraphernalia embedded, the cell line, and then look at the performance. So we acknowledge all the challenges that the analytical validation is facing, such as what I have mentioned, so specimens can be handled differently, and it's difficult to obtain some clinical samples for rare alleles. And also there's lack of a comparator method or reference method. And also for the, when we get to the whole genome technology, it increase another complicated area. But with what I have, hopefully with what I have showed you, I'm telling you that for the analytical validation in the IDE, you only need to show the performance for the variants that you claim to be used in the clinical trial. And also a representative of variants on samples can be used in the analytical studies. Also I listed the analytical studies that we will require in the IDE submission. And as Paula also emphasized, it's useful if you go through the pre-submission process with early conversation with us, then you can find more information or guidance in this link. Yeah, with that, thank you. Next we have Dr. Haja El Mubarak, who's a master reviewer within the Office of Invitro Diagnostics at FDA as well.


For those of you who are disappointed that you couldn't look at the Ramified Prime counts in the list of number of fields, this was fixed very quickly yesterday. Edgar posted an issue on our GitHub or somebody in the audience posted an issue on our GitHub repo and it was actually fixed before I got back to my hotel room. So if I go here I can look at the Ramified Prime counts and since I'm feeling brave I'm even going to sort in reverse order. So take a second. There are 21 million number fields but we can find out which number field in the LMFDB has the most ramified primes. And there it is, a number field of degree 43 and a deceptively simple equation but we can see all the primes that are ramified listed there. Okay. The other thing I wanted to mention, I guess apologize for is I know many of you ran into difficulties during yesterday's problem session. This was not necessarily due to a bug but it was really a battle to the death between two web servers Apache 2 and our Jupyter Hub web server that were happy to coexist when there are only a half dozen or a dozen people accessing them but when they got hit with 100 students at once they each started fighting over who should be taking charge. We chose our Jupyter Hub server so Apache is now gone from our server and everything seems to have been working smoothly throughout the day. So I invite you to, you're welcome to follow along in this lecture, my hope is that everything will work fine. The lecture notes, the link to the lecture notes is posted on the Zulip. One note also and this is relevant to the problem sessions, when you're opening things that are in the course folder, which all the links I posted to the Zulip will be and even in here if I go into links and pull up notebooks, you can look at them, you can even run code snippets and make changes but you can't save anything because you don't have right access to the course folder directory and that's a good thing because otherwise your changes would instantly be visible to everybody else and then you'd start competing with each other to save your changes. So when you're doing a problem in the problem session, the first thing you want to do with a notebook that's linked to in one of the problem notes is to when you go to save it you can also say I want to save this in a different place and then you should save it in your default directory, your home directory up top where it'll be private to you and you can make whatever changes you want and no one else will be able to see them. If instead you would like other people to see them or you would like to share your notebook with someone else, one thing you can do is make a copy of it. You shouldn't use this as sort of your primary editing spot but you can make a copy of it to the shared folder but the shared folder is like sort of a big table with random stuff on it in the middle of a huge room that people walk by every day. Anybody can put anything they want on the table and anybody can take anything and there's no safeguards in place to prevent people from doing the same thing at the same time so really the way to use the shared folder is really as put your file there with some distinctive name and then you could let people know it's there but try not to edit files that are there unless unless I guess if you're the one who put them there. For those of you say wait I want to do collaborative notebook editing I want to be able to have me and all my collaborators be you know type working away in the same notebook at the same time changing cells left and right there's a wonderful resource that will allow you to do that online it's called Co-Calc and I highly recommend it it's very well suited to this that kind of collaborative interaction our Jupyter Hub notebook is not. All right with that out of the way let's begin the goal for today is to sort of take an initially initial step dip our toes into the world of actual actually writing code in each of these different computer algebra systems before I do that one of the last announcement I wanted to make is I understand that not everybody was able to complete the scavenger hunt because of some of the technical issues we ran into I encourage you to do so whenever you have time it doesn't it doesn't take that long when the server is actually working correctly and you can access everything without a problem and as I said yesterday if I had one goal for the course it would be to get everybody through the scavenger hunt and if you're if you're needing any motivation or maybe even a hint let my t-shirt inspire you okay all right with that out of the way let's get started so when it comes to turning math into algorithms or you know turning something you might have some cool example or maybe some cool results or something you want to investigate that you read in a paper you want to open up your computer algebra system and start playing with it you're now in the position of trying to turn math into code and one thing to keep in mind for those of you may not have done this before is that what makes for a good mathematical definition doesn't necessarily make for a good algorithm or a good way to work with an object in a computer algebra system and so I thought I just start with a very simple example something familiar to everyone let's let's consider the Fibonacci numbers which have a very simple elegant beautiful mathematical definition moreover it's a definition that lends itself immediately to an algorithm so let's go ahead and write an algorithm to compute phenomena to Fibonacci numbers so I'm gonna start out with I'm just doing these in alphabetical order I'll start out with GP by the way today is the only day where I'm gonna take you through the ordeal of doing something in every single one of these systems after today the instructions will be just pick whichever one you like the best okay or whichever one you'd like to learn maybe not the one you like the best okay so in GP we want to define our Fibonacci function so and this is one exercise where I'd ask you don't follow along with me don't type this function in yourself and don't run it you'll see why okay you're you're welcome to type it in just don't run your function because remember I said this was an example of how definitions don't always make for good algorithms so what am I doing here so in I'm creating a function in GP that I'm going to call fib for short for Fibonacci fib of n and what is this function well the equal sign is telling you what it is it's a function that is if n is less than or equal to 2 I'm not going to worry about negative n if the user inputs in a negative number they get what they deserve which is 1 for positive numbers so they'll get if it's 1 or 2 that it should return 1 so that way and if then else construct works in GP is if looks like a function that takes three arguments the first thing is the condition to test the second thing is what to do when that conditions true in GP these are called closures these are you know any argument can actually be code that gets executed and then the third argument is what to do with the conditions not true so if n is less than or equal to 2 it should return 1 otherwise it should execute the Fibonacci recurrence and need to close the parentheses and that's it and just as good software engineers we will write a quick test just to test it on some small cases and make it sure it gives us the answer we think it should hopefully I got all the parentheses right I didn't yes I can is that good enough unexpected thank you yeah making it bigger helps me to great they printed out I think look like the first 10 Fibonacci numbers okay let's go ahead and compute the hundredth well I mean it's a hundred not ten right it'll take it should take a little longer maybe let's let's give it some time well maybe GP is just slowly computing for a notch number so let's let's switch over to magma maybe magma can do this better I know magma a little a little better so I'm less likely to make typos I'm as likely to make typos when I'm less likely to make language errors and I know I'm writing magma codes so I have to put a semicolon after everything got it in one okay let's see if magma can give us the hundredth Fibonacci number no joy let's let's try Oscar Oscar's newer so maybe Oscar knows how to compute and Julie is supposed to be really fast question mark if I can type it and this is yeah this is like programming in teams except I have a team of a hundred looking to catch my typos so please shout them out when you see them this is how you specify a range and Julie that's one colon ten I'm gonna remember to use printlin because if I use print it doesn't put a carriage return at the end and that's always annoying in every other in all the other systems print will add a carriage return it's working let's see if there's anything going on Edgar we'll go over to Sage now which I can do it this way and I remember in Sage because we're using Python 3 if I want to print something I have to put parentheses around it I can't like I said could in Python 2 where I could have just said print space for then okay let me go see if I restart my Julia colonel this is what you should do if you have a colonel it doesn't seem to be doing what you think it ought to be doing this is actually straight Julia code I didn't type using Oscar because I actually know that Oscar the Oscar hasn't had any added any functionality that I'm actually using here we'll see an example an example when we do the Sivware a little later when we do sums of cubes where I do need to do using Oscar okay so it looks like it was just my colonel was in a weird state in general it's you know just when anything goes wrong turn it off and turn it on is always a good the first step in any tech problem restarting your colonel is the equivalent of turning things on and off okay well maybe my now GP is finished how about magma nope nope nope and if you happen to I didn't try it here let's sorry let's do it and if you happen to be logged in you could even go to the console yeah let's let's show you how to do that I could go here and I could go to the terminal and I could do top and I could see that there hopefully there should be somebody working away a little confused as to why they're not but they are computing they are doing some oh yeah there they are I say sorry my eyes are a little so you probably can't see that because it's way too small but there are several jobs running at a hundred percent which makes me think that a few of you didn't follow my instructions and actually did type this in but that's okay this is a big server it can handle half a dozen of us doing the wrong thing but not a hundred okay so I'm gonna abandon this and give up I'm gonna stop the colonel on all of them some of them will actually give you information when you stop telling you something useful about what it was trying to do so they go into Sage trims the stack dump but in GP you can actually see that it was you know fairly deep but not really that deep in this recursion you might have wondered shouldn't you know maybe my should get a stack overflow because I'm running a recursive function maybe the stack recursion was too deep no this is actually very shallow recursion but it's very broad because it's branching at two by two at each level and at the bottom which is only a hundred levels deep it's like two to the hundred yeah okay so what if we actually wanted to compute Fibonacci numbers well there's a bonus problem on the problem set where you could find at least a dozen different ways to compute Fibonacci numbers I'll just try one of them I'm only my only purpose in doing this is to show you that I should grab the upper right corner show you that it's perfectly feasible to compute Fibonacci numbers even with values much greater than a hundred using any of these four systems so here I just did the standard trick of exponentiating this matrix and picking the right hand corner yeah maybe you don't believe me that's the hundredth let's just check that it gets off by one okay so I wanted the eleventh one yeah so I would want the hundred and one so there's the hundredth Fibonacci number I could get the thousandth I could get the ten thousandth I could get the hundred thousandth no problem okay okay so with that life lesson out of the way maybe I'll pause for questions yeah in the back excellent question so the question was if you saved all the things you've already computed so that it didn't have to keep computing the same thing over and over again would that make the function more efficient and the answer is yes as you'll see if you do the problem set I mentioned that's actually the very second step is analyze the computational complexity of the program we just ran and then the second part of the problem is analyze the computational complexity if you use what's known as memoization which is precisely what you suggested don't compute something you already know cash every return value and you'll see that that makes it dramatically faster but that's still even with that improvement it's still far from the best way to compute Fibonacci numbers they're much better okay any other questions alright so now I want to take the sort of the opposite perspective so that was an example of a great definition that didn't lend itself necessarily to a good algorithm and this is you know maybe I don't know four or five hundred years I'm not sure exactly when Fibonacci was around but I want to know go now go back much further in history and look at what I would say is still today very definitely should be on the top ten list of all algorithms in computational number theory it's right up there with Euclidean algorithm which came out around the same time but I think in some ways that you know this this the sieve of Eratosthenes is even cooler and even more impressive you probably don't think so because it's probably something you learned in grade school and they're like yeah yeah anybody can do the sieve I know there's lots more sophisticated things one can do with primes but I just want to use the sieve of both as I think a good first nonch less trivial coding example in the Fibonacci also to give credit to Eratosthenes or whoever it was that actually developed the sieve that bears Eratosthenes names because it's quite ingenious and it achieves what even today is an extremely impressive running time on average and that will be something that we'll come back to in some of the later lectures when we talk about computing for example traces of Frobenius or as David Harvey will be talking about in week three computing zeta function in functions and what's known as average polynomial time Eratosthenes beat us to it by 2,000 years by giving an amazingly powerful average polynomial time algorithm for enumerating primes 2,000 years ago okay so how does this algorithm work just to remind you this is one form there are other ways one could construct it but I wanted to just pick what I think is the simplest and would be easy to implement in all these languages but require us to know a little bit more than we needed to know to write the Fibonacci program so we're going to create a vector just think of this if you read a big grid with boxes on it and you're going to cross out primes well each of those boxes contains either or contains across or it doesn't so it's a Boolean value and so we're going to create an array or a vector of n Boolean values our goal is to enumerate the primes up to n and we're initially going to say well we don't know anything let's just assume everything is prime I mean two's primes three's prime everything's prime and back then they probably considered one prime two we don't one is no longer prime it got demoted just like more Pluto okay but so then how does the algorithm work well then we know the ones prime we have to actually put in a special case for that so one is really a special case so we're going to skip over one just leave it set to true because we already know it's prime it's not sorry all but the first yes sorry we set the one to false in the first step I forgot to read my own writing so we set one to false and then for every successive integer p I'm not calling p a prime yet because I don't know it's prime just calling it an integer p is going to go from two to the square root of n and I'm going to check whether s of p the pth element of our vector array is set to true and that means it hasn't been crossed off yet and two hasn't been crossed off yet because we haven't done any crossing off so two's prime and so we're now going to cross off all the multiple proper multiples of two by just you know we could do this by incrementally adding p to p and crossing off all the multiples m between two p and n in this algorithm we're going to cross off every multiple of p greater than p there are many optimizations one can do and they're going to be considered in one of the exercises but let's stick with this because this is the way I've got the coding examples set up okay yeah and in fact we don't need to go all the way up to n either there are many many much more clever things and in fact we could invert the sieve so that we only run over odd numbers or we could implement a wheel or we could implement a recursive wheel to improve the running time there are a million different things we could do I wanted to start with what I think is the absolute bog standard simplest one my guess is that Aristothanes implemented something close to this algorithm okay and then when we're done crossing off all the primes we are a little bit clever about going up to stopping at the square root of n but in fact if we replace the square root of n with n as our bound it wouldn't change the complexity of the algorithm at all it's only going to be a constant factor improvement and then at the end we're going to output everything that didn't get crossed off those are our listed primes and before I jump in to show you some of the code just let's consider appreciate the complexity of this algorithm so what are we doing in this algorithm well we're going to for each prime that we haven't for each number we haven't crossed off and all the ones we haven't crossed off for prime so for each prime we're going to cross off all the multiples of p up to n and we're going to do that by adding p successfully so we're going to do n over p additions and then a crossing off a crossing off actually takes less time than the addition so we could just focus on how long it takes to do an addition if we're adding two numbers that are less than or equal to n well they can all be represented in roughly log n bits a log 2 n bits and so the complexity of adding to n log n bit numbers is o of log n and so we're doing this for each prime p and the number of additions we do for each prime is n over p so we compute the sum of n over p o of log n we get o of n log n log log n and for those who aren't familiar I'll just remind you what the big o notation means it's denoting a set of functions there's a standard abuse of notation going on here that equality doesn't really mean inequality it's asserting something about functions represented by the left-hand side or contained in the set of functions represented on the right-hand side but I think it's fairly intuitive and well established notation so we'll stick with it but you're welcome to read more about it if you want and I put a cautionary note just to remind you that it is not strictly speaking in equality and in particular it is not symmetric so n is o of n squared and also o of n cubed and also o of x to the n these are just upper bounds and it also has an excellent well excellent on average space complexity which is one bit for n to direct to n but not so great complexity if you're thinking about it as a function of n so it's o of n bits as I mentioned in addition to the optimization that Hendrik referred to there are many optimizations one can do the most significant is to use what's known as a recursive wheel which will actually improve the asymptotic complexity by a log n squared factor the space the most important practical improvement in addition to using the recursive wheel is not to actually have your Boolean and rego have n elements but just do them in blocks of n squared at a time you can achieve the same time complexity with much less space complexity but I'll just note that even this bog simple algorithm what would be another algorithm? another algorithm would be just iterate the integers from one to n and just test whether they're prime I mean primality testing is very well studied subject there are lots of interesting and wonderful algorithms out there including a polynomial time algorithm so why wouldn't we just apply our polynomial time algorithm to each prime it's polynomial time in the logarithm of the prime the order polynomial and the number of bits well even if we did that the best the complexity we'd get would be something like n times log n to the sixth we could use a faster algorithm we could be a little bit more a little bit less insistent on a deterministic algorithm we could use instead a Las Vegas algorithm which would give us results that would give us a running time of something on the order of n log cubed n but that's still much much worse than something that is running if we think about the complexity of our sieve it's running time was n log n log log n we're asking what is the average time per prime well there's n over log n primes so if we divide by n over log n we get O of log squared P log log P per prime that's how much time it's spending and that's faster than any primality test if it's known okay alright there we could get close to it using a Monte Carlo test but this is a rigorous algorithm and it's a rigorous deterministic algorithm and if we apply the improvement using what I mentioned before the wheeled sieve we can actually achieve an average complexity that's better than any primality testing known even one that is allowed to use as much randomness as it wants and be wrong 51% of the time okay so this is, you know, so I hope you appreciate the power


Daily Tech News show is made possible by its listeners. Thanks to all of you, including Norm Fasikus, Chris Allen, and Chris Smith. Coming up on DTNSA, new chatbot portends the coming of OpenAI's GPT-4, and I'll break down everything you need to know about the streaming TV landscape, just in time to answer all your relative's questions at Christmas. Or whatever. This is the Daily Tech News for Thursday, December 1st, 2022, in Los Angeles, I'm Tom Merritt. Happy December, everybody. From Studio Redwood, I'm Sarah Lane. From Los Angeles, I'm Justin Robert. And on the show's producer, Roger Chang. Happy Canadian Day of podcasting, everyone. Oh. And a blessed podcast day to all of our Canadian listeners. Blessed day. Blessed day. May the podcast download. Let's start with the quick hits. Researchers at Disney published a paper called Production Ready Face Reaging for Visual Effects, which details its new face reaging network, also known as FRAN, F-R-A-N. That's a neural network which automates the process of digitally changing the age of an actor. Disney says it's practical, fully automatic, and production ready method. Disney researchers generated thousands of examples of synthetic faces aged between 18 to 85 years old using the StyleGAN2 network to train FRAN and then developed a user-friendly interface for artists to use the tool in a production environment. Yeah, no Disney actor need ever age, ever again. Last pass disclosed that an unauthorized party gained access to some third-party cloud storage that LastPass used and accessed, quote, elements of our customer's information. Now, the company clearly stated no passwords were compromised, but because it's called LastPass, I see a lot of people still reacting as if your password got compromised. This is not great, but we're talking things like email addresses and maybe physical addresses depending. We don't know. They haven't given us that information, but it's a third-party access thing. It's not the password servers. The attacker gained access using information obtained in August 2022 breach of its developer environment. So again, not great, but not passwords. Salesforce co-CEO Brett Taylor will step down from the role on January 31st, leaving co-founder Mark Benioff as the sole CEO again. Taylor joined Salesforce through its acquisition of his company Quip back in 2016 and was named co-CEO last year. He also served as chairman of Twitter's board until it went private. Benioff said that Taylor left to found a new company. We have lots of things happening in the brain-computer interface world. Neuralink made several announcements Wednesday. A Neuralink team demonstrated a surgical bot that could insert electrode threads into a dummy filled with brain-like material in order to capture outgoing signals. During the demo, the robot was able to plant 64 threads, each a few blood cells wide in just 15 minutes. That was an actual demo that they did. That kind of precision may not even be necessary, though. Neuralink is wireless, and that same robot can also implant electrodes from outside the skull by just making some...  Neuralink is among many companies working on brain-computer interfaces. Others are Science Corp, Precision Neuroscience, and Synchron. Neuralink's first two applications are intended to help those with paralysis and to restore vision. Now, we don't know because we talked about it, but after we discussed Hive on Wednesday's show, the company announced it was taking its servers offline for a couple days to address stability and security issues. Hive is currently hiring for two developer positions, which would increase its staff to five. They may need more. I don't know. Just a gas. All right, let's talk about some large language models, Justin. Indeed, Tom. Technology Review reports on a spinoff of OpenAI's GPT-3 large language model called chat of AI. The implementation of the LLM is tuned for answering questions in a back-and-forth dialogue. OpenAI says it can answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. Technology Reviews Will Douglas Haven tried it out and said that he still caught it making some stuff up. The difference is, if you ask chat GPT, are you sure it might say, OK, maybe not if it isn't? And it will decline to answer questions about topics it hasn't been trained on, including events that happened after 2021. It also will not answer questions about individual people. Yeah, so OpenAI asked people to give examples of good responses to various prompts and then use those responses to train chat GPT. Then humans scored how that initially trained model worked. Did it work? Did it not? Did we get good answers? Did we not? And then those scores were fed into a reinforcement model to further train the model. As a result, it's better than GPT-3, but it's not perfect. Yeah, one of the ways it's better is it can spot errors. Heaven notes in the Technology Review article, if you say to GPT-3, tell me about when Christopher Columbus came to the U.S. in 2015. GPT-3 will go, Christopher Columbus came to the U.S. in 2015 and was very excited to be here. But chat GPT will say, this question is a bit tricky because Christopher Columbus died in 1506. So it goes that extra step of seeing the mismatch. However, when Heaven tried to ask it about diffusion models, it took four refined questions to be like, nah, that's not what I mean by diffusion model. That doesn't sound like a diffusion model until it finally understood and properly responded. So it's not a model that you can use if you don't already know something about what you're asking, which does limit its usefulness. This shows how hard it is to train algorithms and to tell fact from fiction. OpenAI is also working on a model called WebGPT that can look up info on the Web and give answers with cited sources. They may also add that to chat GPT in the coming months. OpenAI has made a demo of chat GPT available at chat.openai.com where you can report flaws you find to help them improve the model. Yeah, and I don't think OpenAI was trying to pretend it was. This isn't the thing that solves it all and you can just never be fooled by this. It's still, like you said, Justin, it's still hard for an algorithm to be able to tell fact from fiction, but there's a lot more fail safes here. And if you're willing to say, well, I'm not going to trust everything, but I know enough about what I'm asking it that I can get some useful responses and give me context. One person from OpenAI was saying that they use it for coding to kind of help them review their code and help them spot errors. Seems like it's an improvement on GPT-3 in that respect and probably a pointer to one of the many improvements everyone expects when GPT-4 comes out. Yeah, 100%. I have great, great, great faith that GPT-4 is going to be something that is not only impressive, but possibly even game changing for how we understand AI. When we look at these applications, whether it be this or Dahli or any of the other things that OpenAI has done with GPT-3, I think we should really just think of them as applications that's based on the OS of what they have created here. So look at them not as gigantic step forwards in AI in general, but rather just this is how powerful this engine is. And if you point it in this direction, this is what it can do. Obviously it needs to be trained like any model needs to, but from what is being whispered about GPT-4, that engine might be getting exponentially more powerful. Yeah, I mean, I find it interesting that the way to train the model seems to be, at least from the reviews I've read, is try to trip it up, you know? See, Christopher Columbus came here in 2015, right? And it goes, hmm, well, hmm, maybe you're right, but I don't think you're right. That wouldn't make sense since he died so many years earlier. I mean, that is a great way to make a model smarter. But I mean, I kind of liken it to like the early days of Wikipedia when I didn't trust what was going on with moderators on Wikipedia as much as I do now because the system is sort of in place. This feels like the first steps of kind of the same idea where it's like, all right, if I ask a question and I get an answer, do I trust that answer? Or do I think that I've somehow confused the AI? Well, the fact that it could even get to the point of human moderators, whether or not they are right or wrong or wise or stupid, is really a gigantic step forward. The idea that AI can only succeed if it is omniscient, I think, is a little bit of a red herring for AI's real true purpose, which is just going to be sorting through a lot of information very fast and being able to give you a lot of ideas or a lot of decision making that would otherwise be either time-consuming or impossible in the way that we normally do it or very, very costly. And I think that's what GPT-4, that's really where the worth is going to be, not necessarily it being an answer machine that always gives you the right answer. Yeah, I think we need to set our expectations around these models. I think there's two extremes. One extreme is too optimistic that these algorithms can do everything when they can't. And then the other extreme is too pessimistic, which is to say that, well, because they're flawed, they shouldn't be used at all. And we're going to talk to Andrea Jones-Roy about this on Monday, but I think the answer is you should be very clear on how good the model you're using is and what it's good at and what it's not. And then, like any tool, it's useful. None of us don't use search just because search isn't perfect. We all know search isn't always going to return the thing we're looking for, but we kind of understand that and we understand the boundaries in which it is. And so we still use search and we use it with an expectation of how good it's going to be and maybe it's going to get better over time, but we kind of know where it is. And I think that's going to be true with all different kinds of these models. That's the other thing is there's not an AI. I think we need to get out of that way of thinking about it too. There's lots of different flavors of it. I think that's a really good comparison. I mean, when I search something that I don't know much about, it's kind of like, let's just get on the board here. Give me some results and then I can refine things from there. And I think this probably should be addressed the same way. Well, if you've got thoughts about this, maybe you are an AI and you'd like to chat in our Discord. You can do that by linking your Patreon account at patreon.com. Well, streaming TV services obviously pay a big part in the holidays.  When you gather together, you may even get them or the devices they run on as gifts.  How do I use it? With all that in mind, we decided to put together an overview of the current world of streaming TV for both the new and the experienced. Some of you are very experienced. Some of you are kind of just getting into it. So Tom started covering this at CNET way back when Netflix had just launched a streaming service and Voodoo was a set-top box. So, Tom, you know, the most obvious question that some people still have is, have a cable subscription? Like, is this going to be better? What are the advantages? Yeah, I still see this a lot. Some people going like, well, I might as well just go back to cable. Cable and even broadcast, remember, only give you access to hundreds of shows at a time. You can only see what is linearly being broadcast. Even with your DVR, which you can save for a while, but you got limited space, you still have to wait for the episodes to air to record. Now, cable does have some on-demand offerings and even access to shows on apps, but they are limited and they almost always contain ads. Many streaming services offer thousands of shows and every show they offer is available the minute you launch that app. A lot of them are ad-free and you won't fill up your DVR having to save them because they're just there in the service. You don't have to manage anything. Also, cable gives you limited options over what channels you get and how to pay for them. Usually it's a few tiers. Most streaming services offer all their content for a much lower price than cable. Each streaming service won't have the breadth of cable, right? So you'll need multiple streaming services, but you have more control over them because they are cheaper and you can subscribe to more than one at a time and increase your breadth and add and remove services as you need. You don't have to call the cable company. You don't have to return any equipment to do that. There are more kinds of services that people realize though, Tom. What are they? Glad you asked, Justin. I'm going to lump streaming services into a few categories here. The biggest and the one most people think about is called SVOD, a streaming video on demand. That's the Netflix category. Disney Plus, Hulu, HBO Max, Paramount Plus, Apple TV Plus, Amazon Prime, even Peacock. They're all parts of that. There's a subcategory of those that are more niche. Your Brit Box, which does British TV, Vicki, which does Asian dramas, Shudder, does horror. Then you've got your movie services, stars, epics, showtime. There's Fast, which stands for free ad-supported television, streaming television. Tubi, Pluto, Zumo, Free V, the Roku channel. Those are all fast services. Peacock even has a limited fast service that you can get without having to pay it. Those only require you to install the app and create an account. Some like Tubi are mostly on demand, so they work like Netflix. They just have ads. Others like Pluto have a grid, although they also have some on-demand services. But that's kind of, I think, when people think of streaming services, what people are mostly talking about. Yeah, so people say, wow, how great I have all of this content for free. However, if you want to see certain shows or you want to have a certain experience, add-ons are kind of the way to do that with a lot of these services. So what is the add-on landscape looking like these days? Yeah, it's kind of confusing because Roku calls it a channel store when they talk about their app store. But you'll hear it called channel stores sometimes. It's almost like cable. It's the ability to add one or more of those niche channels to a bigger one. So Amazon lets you add stars. Apple TV Plus lets you add Paramount Plus. Hulu will let you add Showtime. That way you don't need to use multiple apps to access content from multiple services. You could just use the one app. You just have the one bill. And it's easy to add and remove those services unlike cable. You can go channel by channel. Not all services are available this way on all platforms that have a channel store. So this isn't really a cable replacement, although I think a lot of these platforms would like it to become one. They would like to be the platform that handles all the other services for you. We're just not there yet. Now, Tom, I think I remember seeing some services that seem a lot like cable. What's up with those? There are streaming services that imitate cable, Justin. YouTube TV, Hulu Live, Sling TV, Direct TV Stream. Unlike most of the ones we've mentioned so far, these are mostly US services. There are some analogs overseas. They give you a grid of channels that you can watch live. The ability to record shows on a cloud DVR. That grid that you would get from your cable service. And they all have the same limitations of traditional cable. Except that they're easier to sign up for and cancel in most cases. And you use the equipment you have.  They also tend to have fewer channels than cable TV. They just don't have all the deals yet. And while they cost less than cable, the cost has been rising. And they often appear to be more expensive than cable because cable advertises low introductory prices. But in general, they're still cheaper. So there are definitely the folks who say, alright, streaming, I'm in. But other people say, well, I really just only care about certain shows, individual shows and movies. Like maybe DVD or Blu-ray library that I have in my house. So how do those people get service? Yeah, so those are the stores, right? Your Apple store, your Google store, YouTube sells things through the Google store on YouTube. We mentioned Voodoo before. Voodoo is a store where you buy movies and TV shows. Amazon has a store as part of their service as well. And then there's a service called Movies Anywhere that will sync your movie purchases across these services for almost all the studios. There is no such service for TV shows, though. You buy TV shows on Voodoo, you're going to have to watch them on Voodoo. You buy a movie on Voodoo, though. And if it's part of a studio that participates in movies anywhere and you've linked it to movies anywhere, then that movie would show up on your Apple movie app. It would show up on your Google movie app, et cetera. Well, I'm glad that we got to the end of all the services available on streaming. Unless there's more. Justin, do you like the sports? I do. Live streaming sports services are probably another example here. ESPN Plus, or ones from the leagues themselves like MLB. There's community-oriented stuff as well. YouTube, Weverse, live news streamers. Those are usually free with ads. So you can get CBS ABC and NBC News apps that are absolutely free. They're just ad-supported. And then there's paid news services like Fox Nation. And some of the news services give you more if you log in with a cable subscription, et cetera. All right. So if you're thinking, why do these Netflix-like services change content? Why does stuff move around so much? Is there a way to keep track of it? And is this still the Wild West? Or can I just pick one option and get all the things? No, you cannot pick one option to get all the things, sadly. I feel like that was a lie that Netflix didn't combat in the early days. People were like, oh, Netflix, someday it'll have all the things. Netflix never promised it would, but they kind of let people believe that. But yeah, it's a big question that people have. Why does stuff move around? This is a complicated one. But I'll try to give a simple answer. Netflix has its own studio, and it makes a lot of things there. But the vast majority of Netflix content, including a lot of Netflix originals, are made elsewhere, and Netflix pays for them to be made by somebody else. Some are purchased from production companies, and those production companies agree to give Netflix all the rights to a show. Those are indistinguishable from shows produced in-house, so there's really not a real difference. But sometimes those production houses will keep some of the rights, some of the worldwide rights maybe, so Netflix might not be able to show it in all markets. For instance, Star Trek Discovery used to show on Netflix outside the U.S. and Canada. And then CBS said, no, we want to put this on Paramount Plus overseas too, and they took the rights back. There's a bunch of Korean shows that show on cable and broadcast TV in Korea, but outside Korea you get them on Netflix. Some are just fully licensed, where Netflix just paying a temporary license. That's how they got Seinfeld. And this is because Netflix doesn't have the huge studio operation that the legacy companies have. If you're a large legacy media company, like a Disney or a Comcast, you have huge studio operations. You have movie studios, you have network studios, you've been filling your services with your own stuff. Disney Plus is probably the best example of that. Almost every Disney made property ever is available on Disney Plus, almost. Not all of them, though. Well, color me befuddled, Tom. Why don't everything that Disney or NBC makes end up on their own streaming services like Disney Plus and Peacock? Have you heard of money, Justin?  I've heard of money. Yeah. So subscription services are an improvement over sales of individual items like DVDs, because the revenue is recurring. So you sell a DVD, you get that money once. Sell a subscription, you get that money every month. That's why music has embraced streaming. However, once you have that money, it's hard to increase it for individual subscribers. You have to add more subscribers to get more money, unless you can add features like 4K or surround sound. But there's a limit to how much people will add. If you own the content, though, another way to make money is to license it to another streamer. So for example, you own the Harry Potter movies. You let them disappear from your service for like six months. It's a good bet that the subscribers will be able to find something else on your service that'll keep them from cancelling, at least if you're doing your job right. So you're not going to lose money because you're not going to lose many subscribers. And then you license out Harry Potter for six months to another service and they pay you. So you make more money than you otherwise would. When you bring Harry Potter back to your service, you can then make a big marketing push and possibly get some new subscribers. So you can't do this all the time with everything. But if you strike the right balance, you can bring in some extra money and extra subscribers and beef up the bottom line for everybody at the company. Well, so for anybody who's learned a little bit more about what's going on on the company side and how distribution happens, what devices should people use to stream to make it as easy as possible? Yeah, yeah, we don't have time to really cover that question entirely. But for most people, the smart TV is probably where they should start. If they don't have a smart TV or it's old and slow or the options are limited, Roku is probably the easiest. It has the widest variety of services. Chromecast is cheap. It can be a little fiddly for people if they're not as tech savvy. But if they use their phone a lot, it's a good affordable option because it casts shows from the phone to the TV. Amazon's Fire TV is great if the person lives in the Amazon universe. If they're already on Prime Video, if they're buying shows through Amazon, if they're adding those channels, Fire TV works great for that. Apple TV the same if you live in the Apple universe. If you like buying your stuff through iTunes, you got Apple Music, all of that. It's a little more expensive than the other options too, but Apple people are usually used to that, so I guess it's not that big of a deal. I'm an Apple TV person mostly because it's just where everything is. I've got the apps for the things that I care about the most, whether it's live TV or movies or a variety of shows that I care about, but Roku would work exactly the same way, at least for my purposes. Justin, I don't know if you have other options, but I feel like, as Tom said, if you've got a smart TV that can handle this, that's sort of the point of a smart TV. My smart TV is sort of smart, but it's also old enough that it doesn't really work that well, so I don't access content that way. Yeah, I have a few smart TVs. They're really slow comparatively to some of the apps including Roku and Apple TV, and also entering passwords and usernames via their interface is a little annoying, at least on some of them. But in general, I think it's a great way to enjoy content, and they make fantastic stocking stuffers. Well, I suppose we should check out the mailbag then. Let's do it. So this one came in from Comey. Comey had a recommendation and also a question related to Nicole Lee's smart home gift guide that we had on Tuesday's show. This is a good one. Comey says, I have a tablet in our bathroom wall that plays movies on repeat. I enjoy watching a few minutes of random segment content, random depending on when I enter, every time I am in the bathroom to do whatever. The key point is that the tablet keeps playing the movie 24-7. So I don't have to touch it with a dirty or wet hand, but the wired speaker is turned on only when someone enters the bathroom and then closes the door. Otherwise, the audio would be distracting when we're not actually in the bathroom. Comey says, I use the old power strip with a motion sensor, but I replace the motion sensor with a magnet switch that closes when the door is shut. AC power doesn't go through the sensor cable, so it was a simple and safe wiring between the power strips, RJ11 jack, and the magnet switch. Here is Comey's question. Is there a simpler product that would turn on the power only when the door is closed? I like my setup. I highly recommend it. But the hurdle would be to come up with that door triggered power outlet. I asked this question in office hours when I was just streaming to folks earlier today on our Twitch channel, and Chaos ASL suggested a smart outlet that triggers on an open-closed door sensor. So you'd put a sensor on the door that can tell your smart home system whether the door is open or closed, and then have a routine or something like that that would say if the door is closed, turn on the power outlet to the speakers. So you'd have just the speakers plugged into that because it sounds like he wants the TV to keep playing, so there's always a random point. So you'd have to plug the tablet into a separate outlet from the speakers.  I have to say, Comey, I love this idea. At first I was like the same movie playing over and over, but you're not in the bathroom all day. You kind of go in there and go, oh, look, it's the scene I haven't seen in a while. And maybe you have, I don't know, 10 movies loaded up in there. It's kind of fun. It's a fun way to enjoy a bath. It's the modern equivalent of leaving magazines in a wicker holder. Have I read this magazine before? Yeah, but I still like it, and I'm going to be here for a minute. Exactly. Well, thanks to everybody who writes in with the questions and comments. Do keep that feedback coming. Feedback at dailytechnewshow.com is where to send those emails. And thank you to Justin Robert Young for being with us today. Justin, where can people keep up with your work? Well, they can head on over to Where Not Wrong. It's a podcast featuring myself, Jen Briney and Andrew Heaton. And on this edition of the program that just uploaded today, an email from Tom Merritt. Wait, which Tom Merritt? You. Are you sure? Oh, I know. There are quite a few. Oh, I know. I double checked. I backtraced it. I just, I just, I just... It's on the blockchain. I was ripping you all apart of that email. You all have to listen to hear my ranting take. Amazing. Skating email. Like you've never heard them before. Yeah. Well, it's lovely that Tom is sending you emails to Where Not Wrong. Also lovely is getting brand new bosses. And we would like to thank Matthew for being our brand new boss. Today, Matthew just started backing us on Patreon. Thank you, Matthew. And welcome. I like to think of Matthew as a trendsetter, right? You know, like, you want to look to Matthew if you want to be following the cool stuff. Patreon.com. Speaking of patrons, stick around for our extended show, Good Day Internet. We affectionately call GDI, which rolls right in after DTNS wraps up. But just a reminder, DTNS is live. You can catch the show Monday through Friday at 4 p.m. Eastern, 2100 UTC. Find out more at DailyTechNewShow.com. Slash live. I will be out tomorrow, but we got a jam-packed show with Rob Dunwood and Len Peral to join us. See you then.


Just play. Okay. Thank you Well, hi our body morning I am a network engineer at United stack since we had Got some problem with his visa. So I replaced him to give this speech talk about our business and Well, I'll get a briefly the introduction In this speech, I will provide our well brief and our customer case and What's our features? Well, we drawn the first open-style published cloud in China and We are the leading managed private cloud service provider and the top contributor to open-stack and safe we have many engineers work in Neutron project and Well safe and the puppets we have co-developers in safe project and puppet open-stack our customers spread our well finance internet game and ISP IDC IDC is our well made customer including the 21st one inch CNITE and Senhua what can United stack do for you? We try to resolve some problems of IT resource Which is no real-time backup data long-term preparation and the high cost of managed servers We face many problems of virtualization. So we want to get some well ideas and some We want to do some things to help Do a locate resources properly according to the application's life cycle Since time is not very Well rich, so I will be quickly this is our story system we provide six six thousand IOPS and 170 megabytes for seconds We have second-level response you can get any your need a source you need in seconds and What we provide is our quick development. We can accomplish from our documents in seven days and Monitoring based on salometer, but more powerful What we provide to customer is not a distraught for software or a Distraught for open stack. We provide service including Upgrade integration installation and monitoring and SRA guarantee. This is what's different with other open stack provider But we still have a fully opened IPPI. We get our We open stack powered the certification which we the only one gets this certification in China Unified storage will save as our story system at bank and it can provide any storage you need will file or Object or clock Thanks to save will and thanks to copy on right. We can get anything like a VM in just seven days Or you can get a well volume or VM snapshot in seven in two seconds Our SDN is based on neutron purely neutron and the open-wave switch This is the interface so we can provide our three agent H a and Many other features like rotors floating IP port forwarding many VPN service and GRE tunnel Our load balancer is based on the communities version since communities version is a pretty well Proprietary well tough. So we do many work on that The communities version two may be well improved This is what we add to open stack instance and the volume snapshots sub-account system project management and accounts collaboration Routime charging This is our roadmap. We have done our cloud cloud service and basic service at 2014 I know what we do what we will do in 12 2015 is well file sharing joy tunnel is what Is has done and we want to provide objects the knowledge relational database and the security gateway CDN and well many many features in this year What is more we manage the private private cloud? The left side is what we do the right side is the customer's work We will do most of upgrading development and R&D The team of customer only need to well use and Have fun This is difference between us and the other well non open source or open source open-style based release I we think we have a low cost and remote online upgrading transport to customers the The main difference I think is our DevOps engineer team and our R&D team They can They will guarantee your SLA including the eye the storage systems IOPS and while what network is through foot and in line time Most of teams just well install open stack version to customer customer servers and then The customer can do what they need to upgrade their version or Operate operate their system, but it is very difficult to them cause open status is really complex so we provide the service and export them to customer they can just use and With the confidence of our engineers and gets the SLA guarantee Okay With no locking with low cost it's really a well nice open stack business The speech the time of speech is a pretty short so Any questions? Yeah, okay, let's collaborate to make the difference. Thank you