my pleasure to serve as a convener for the second panel on next practical steps to accelerate and broaden the use of machine learning in the geosciences. You know we're trying to discuss the opportunities and challenges a little bit more and touch upon some of the issues we already brought up in the first panel in particular. What's the science right? How can we use machine learning to sort of intend them with physical model to physical modeling to get to the core of severe system behavior that we'd like to understand. We have one presentation and then time for discussion and our first and only presentation in this panel is by King K. Kong of the University of California at Berkeley and he's going to be presenting remotely. I understand he's online and he's going to be talking to us about machine learning and seismology turning data into insights so thank you. Hello everyone can you hear me? Hello. Yes we can hear you King Kai. Okay good to know. Sorry yeah I can't be there in person I think this is a great workshop that generates a lot of discussions so I wish I could be there but due to the baby sitting responsibility so I have to stay and anyway so I'm King Kai Kong I'm an assistant researcher here at Berkeley's seismology lab so today I'm going to talk about like machine learning in seismology that turning data into insights. So this is actually a talk like actually based on the paper we actually published last year so this is actually a collective intelligence like efforts that all these different authors actually contributed a lot into this paper that we're trying to answer the question what's the stats of the machine learning insights and how we actually can like generate like motivations to turn your data into insights so this is actually me and also Daniel Trogman and also Zach Ross and Michael Bianco and Brandon Meats and Peter Gerstoff that we worked together and Peter is the leading actually efforts that put together this whole team to work on the on this paper so in this paper we actually reviewed like a different type of learning like a career already covered this I won't go into the details about this but like a lot of the learning machine learning problems fall into these supervised learning and unsupervised learning and also a lot of that recently like we see a lot of improvement or like advances in the supervised learning because there's an actual bit of information the labels that can give the machine learning more more chances to learn the problem right and so on so in this paper we also covered different areas instead of based on different methods so you can read the paper like in the agenda like after the agenda in the in the in the in the booklet that is sending out last week so we can see that we covered a lot of earthquake detections and also this morning Zach gave a really nice presentation how to use like a deep learning to do the earthquake detection and also we covered machine learning applications in earthquake warning so that's also like we use the smartphone data to do earthquake warning and also like other type of machine learning uh machine learning applications in this field for example man andrew's work from caltech that's to distinguish noise from the real thing you know and so on and also like we have the coverage machine learning in the ground motion prediction how we actually training the model to do a better job of predicting the ground motions and also of course like a tomography is a big area in in seismology as well so there's a lot of like applications or like uh uh efforts in this area as well last in the paper but not least is the earthquake geodesy and other applications so if you read that you can see like in our field people are really trying to apply different type of machine learning algorithms or other type of algorithms that's related to applying that on the data to our field which are really interesting so um since we had a lot of discussion this morning on specific applications so i won't go to the details about like this review paper you can read this review paper offline and also like try to read some of the papers we listed there but in this section we have these three different questions so which i want to cover these three different questions but based on some of my thoughts it may be not right because some of them may be like just premature but i just want to lay out here so that we can generate more discussions in the panel discussion so today i'm going to talk so first some limitations of the current machine learning algorithms i know that everyone is like really into the machine learning these days but i think understanding some of the limitations of the current machine learning algorithm actually gives us like a way to basically to examine the machine learning algorithm and also think about how that applied to to geosciences and how to make improvement on that so i treat this from a like an optimistic like perspective so then i will show some of the examples that the existing efforts combining machine learning with the physics model because we the the topic or the the theme of these workshop is basically how we can actually use machine learning to beyond the black box right like we want to as a scientist we want to combine in machine learning with the great power of the like the traditional physics modeling and so on and also i will finish like concluded with the moving forward but i think karen already covered most part of it i will just add in some of the parts in the last section so let's start with some of the limitations on the current machine learning algorithm like i mentioned that this is not saying like a machine learning has a lot of limitations we won't we won't use it actually it's in the in in in in updates that like machine learning has a lot of great power that we already saw that like a lot of applications this morning but also these limitations that we need to use it more carefully and also we need to understand that like machine learning are not magic like a lot of people in our field actually i talked to with some of the the the folks at various places some of them are think machine learning kind of like our magic that we can cover all the problems in our field and to solve all the existing problems but this is actually not true so if you think machine learning it's really good at like a pattern recognition it's just a small part of all the all the scientific problems we want to solve so that's why i just want to set the stage that saying machine learning is not magic and once you learn the skills and the two sets it's just like a two sets into our like a geophytist or geosinist like a toolbox that we can solve more difficult questions that can't be easily used in mathematical model or can't easily be be modeled and so on so one thing is that when you look at machine learning you already i think machine learning algorithms are narrow applications which means that when we train a model we use a specific data and we we target very specific problems and we can get a really nice performance like it's showing here is just a sketch of what i was thinking of machine learning versus other general purpose algorithms there are the algorithms that works on various type of problems but the performance may be just like around the average like that dotted line showing maybe the average performance but like machine learning once you train with large amount of data sets for one specific problem so it can it can have a very nice performance but but these performance also have some sacrifice as well which means that these machine learning algorithms usually are narrow very narrow applications that if you want to apply to some other area or some other data sets you may just need to retrain or like it's just not working exactly as we thought like it can be working on wide range of problems and also another limitation i think it's already covered by other like speakers this morning that we need a lot of these structured data so when we do machine learning we found out that most of the time we need to cut our data into like these metrics and we need to make sure all the data is like well organized into the format that generate puts into the machine learning algorithms and we need lots of them especially when deep learning becomes popular and then it just starts to it's a really data-grady algorithm that we need a lot of data to train the gigantic model well and also we need lots of good data as well like if we have bad data we usually you output will be bad as well machine learning has some capability of generalize the problem but it's just like a weak generalization instead of like a strong generalization i will talk a little bit more about that later but also like a lot of the advances in the supervised learning that we need really good labeled data and this is not true in a lot of our fields that some some of the fields that due to the constraint of the timescale or due to the constraint of rare events that we only have small data sites or we only have like a very rare limited labeled data so how we actually use machine learning in the limited data approach that's also a hot research topic that's in computer science if you look at like a field shot learning and also some other type of learning algorithms try to like a teach the machine learning algorithms using like a small data sets but also sometimes I was thinking that if their data sets is relatively small and can be can be like a approach it's more addressed using the traditional approach that's actually maybe a place that we we definitely need to be like should just try the traditional approach first instead of like relying everything on machine learning just to give one this is like one sort of experiment for example it's maybe needs a couple of apples to drop on the Newton's hat to basically have the these like these these newton's law come out but how much data do we really need from machine learning perspective that how many apples do we need to observe or how many other like a data we needed to collect to train machine learning algorithm to recognize or to to understand this law or to basically condense this log into this equation I think this is still very hard problems that people are starting to attacking these these limitations but still there's a long way to go so I will cover that in more details in the second part like the physics model parts and also in our field we always have very famous say in garbage in garbage out I think that's still applies that when we have garbage data as the training data your lead the results are also very bad so this is like another saying like I want to detect like a some signal but the signal is completely buried into noise that can you use machine learning to find it I think that's probably a very hard problem for machine learning as well like machine learning is based on patterns we need to see these patterns from the data that we can generalize and also another limitation is that sometimes there's always bias in the data that it's very hard to identify and also very hard for us sometimes we we we are easily too like to prone to this this kind of bias I use an example from like other fields instead of like from our fields I can mention a little bit more of our fields as well but you can see that so this is a machine learning algorithm trying to recognize whether it's a manual women to do what kind of cooking and so on but the training data science sometimes like you start to have a lot of these pictures that have more females that actually starts to generate these type of arrows that the the the algorithm starts to make this prediction based on these best sorry I think because of the heavy smoke here yeah and also in our field as well like I always most of my research is focused on detection as well but I think these best are also there for example we have a lot of noise we have a earthquake signal have noise but this noise data from many different sources some sources are actually not present in the training data or it's very rare that it may be just a best that we ignore the model will ignore some parts of the noise and then just predict the majority we may we may have a model that have a very high performance but it may be making some wrong decisions at a very critical situations especially like for real-time applications like a earthquake warning that if we have some certain type of rare signal just to come up and detect it as a magnitude nine earthquake it will sorry yeah it will basically cause panic and so on and also we always talk about like machine learning has the capability of generalization that we train on our training data and some of the new data and thin data will perform equally well or even better and so on but but this is actually assumption that machine learning actually have weak generalization which means that if the data distribution you can see the left figure here so basically I plotted the data distribution and the the the training data distribution is the right curve here and also the blue one is the new training data or is a new tested data or the target that you want to machine learning to work on you can see that when the when the distribution are very similar to each other that this algorithm actually will work well is even there's some like discrepancies between these like two different situations but it may not work well I think for this is strong a strong generalization which means that my training data distribution is totally different from my my new distribution and also there's only slightly like an over life and so on so this is like the strong generalization case that may not work well this is also like from our Franco's figure I actually get from one of his famous book deep learning with Python so you can see that basically like if these blue dots are the training data we have we can make some extrapolation or we can make some generalization when we see some new data like nearby the the the data points so this is basically called like a local local local generalization or weak generalization that's current machine learning algorithm has so in terms of like extreme generalization it may not work well for most of the applications for example if we train on algorithm to detect like for example earthquake signals on this region and we move to other region that have completely different type of the signal might not like from the real earthquake or quarry blast and so on it may not just work like as we expected so that's why we need to realize these like a generalization purpose generalization capability of the machine learning algorithms and also recently the most famous one technique war like a way to to do this like a transfer to do this like a transfer the learning from one data set to another is this transfer learning so basically like we can train a model on a very large data sets and then so you can directly apply to other region war other problem sets but it may not work well due to like a specific situations at other regions but then what you can do is that you cut the model basically basically just a cut a certain part of the model generate the features and then adding in some of the new models on the new data set to retrain it to fight tuning some of the the structure so basically you can see this pre-train train to layers won't change when you apply to a new small data set at some other regions for example and only one layer will be like a retrain based to accommodate these new situations I think this is actually looks really promising in different fields and also in our fields there's people applying this as well like this is basically we adding some new layers and then we retrain we retrain this new layer based on only small data sets to be to assume that these pre-train layers have already encoded some of the knowledge or some of the the experiences we have from the previous large data sets so this is actually make the generalization capability a little bit stronger but still I try so here we actually tried to detect like the damage of building from social media data one of the students working with me on detect that we tried to use the image net actually to transfer the features we found out that that's only improve a little bit but if we include a lot of like the the damaged building in the in the in the image net we actually have better results still this is showing the capability that like the generalization capability is relying on like the distribution of the training data and sometimes like it's more critical that we understand that we apply to the new things we should have the relatively similar distribution and also of course like I think the main topic today in this workshop is that a lot of people are criticized machine learning as a black box that we have input we have outputs happy outputs but we don't know what exactly going on inside these box I think I think that's one thing is that it's difficult to interpret how it works well after we train the model and we just see that it's satisfying our our task but also the other thing is that it may not bounded by the existing laws for example if we want to predict a temperature temperature within a region and no no like a like a region or maybe the temperature also follows some like existing laws that we need to bounded the algorithm so this is all the missing parts in in the current machine learning algorithms so luckily there are a lot of people actually try to tackle this problem for for more advanced techniques so also there are more limitations I don't want to like spend more time on that but I think people maybe just like understand or have a sense of these ones are it's better for like a building future models for example machine learning algorithms are actually very actually very vulnerable so there's a lot of study already show that like when they train a lot of recognition pictures and so on so people found that sometimes even you change one pixel it will confuse the deep learning basically make wrong decisions so this is a lot of this is actually more important in real time systems that more on the security side but this is just a showing case that it's more difficult for us to train a model that that like arrow free or like arrow proof and so on so sometimes we need to know this vulnerability and to test this vulnerability in the future and also it's not easy to learn in real time even though there's a lot of algorithm trying to learn online algorithms like you have batch of new data comes in you're reaching the model but it's actually very difficult to train a stable model and so on and also that this link also gives you at this year's triple as meeting there's a nice talk about misapplication misapplying or for machine learning actually in science may generate like because of science crisis I think it's also worse uh worse reading and so on so I talked about these limitations but it's not like I mentioned that we we want as a machine learning practitioner or like applying machine learning in our field we need to know these limitations and to keep that in mind that machine learning is not a general tool that applying for everything but also there's a lot of examples in combining the physics trying to answer the question like how we beyond this black box so I put together this one here is that like a yearly as a domain scientist we are very familiar with like this physics model numerical modeling uses a small amount of data but also these machine learning algorithms are started to to like a dominant like the the data data rich field and also especially deep learning that use a lot of data to automatically find features to make the task finish and there's a need that we need to some hybrid approach that to combining the powerful the two domain two domains that to like advance our science so like I mentioned there's a lot of researchers are actually working on these I think I will just give like some starts to generate more discussions how we actually apply these to different like to combine the physics and so on so one thing is that we know that like a career also mentioned that the different models have different interpretability based on how they design the algorithm we can see that so the horizontal axis here is how flexible the model is or how powerful the model is you can think that way and the more powerful algorithms deep neural network we have less interpretability because like it's so complicated that it's difficult to understand what's it's going on but also there's a simple algorithms that give us more like a insight if we want to interpret the results so I think one thing is that we shouldn't stop when we training a machine learning task and get a good model I think that's not the point we stop I think that's actually just a start that we want to understand what exactly the model learned and how to interpret the results there's a lot of applications or like a research on the computer science domain that after they train the model so they actually try to virtualize the kernels and try to do some statistic analysis on these kernels to understand each kernel and each layer what it does it's learned so for example in earthquake detection problems so the kernel we learned in the deep learning algorithms sometimes also give us like insights as well so one of the exercise we did here we found that different layers kind of filter the way forward into different frequency bend and also some of the layers or kernels try to compress the the noise some of them are actually try to amplify the signal and so on so this type of application are more I think it is like a we need more analysis after we're training the good model and put it into production and also another one is like using synthetic data I think Diego gave a very nice presentation how or why we need to synthesize the data if we don't have enough observations to tackle some specific problems also there's applications that use this approach to reduce the simulation computation cost for example this is actually a work by by Fabie from Harvard's and also Brandon Mitz's work so they basically use machine learning to train to train on the like the the viscoelastic deformation calculation and then they actually can improve the speed by 50 000 percent which actually is a very nice application that when you apply these large scale simulations if you have a machine learning algorithm learns these features and learns how to how to do these simulations you can do this much faster in this sense and also there's ways to encode the physics laws into the machine learning algorithms for example we all know that in machine learning it's an optimization algorithm that we already have an error of the estimation from the machine learning compared with the observations and then we try to minimize this error by changing the weights so Diego and I talked that it's actually a very nice or a smart way for great search of the parameters or like to reduce the the errors and then we can adding in terms that like whether the machine learning estimation is followed the physics law or not by basically encoding these physics into the into the machine learning algorithms so this is actually a link to a paper published last year basically trying to use this approach to estimate the lake temperature modeling so you can see this is the different estimate the features generated into or fitting into the machine learning algorithms and also but machine learning algorithms always like try to estimate the temperature in different range and also like a in in like a random way but these temperatures in the lake actually have followed some laws if we know the density and also if we know the depths you can see this is actually existing laws so basically what they did is that they added in this loss function that whether the estimation from the machine learning is aligned with these physics law or it's in consistency with this law so basically when we minimize this loss function this algorithm will also like try to try to train the machine learning algorithm that to follow this physics law so this is actually the results you can see using your network so this is a physics physical in in consistency you can get like a nice result so this is actually vertical axis is the arrow from the from the the tested data sets so you can see using a pure physical approach you you don't have the physics physical inconsistence and your arrow is relatively high but use a artificial neural network so you have a relatively high physical inconsistence but the arrow is relative below but when you're combining these two so you actually have a very nice results that's lower lower arrows with like a more of a line with the physical physical like a loss and also there's ways that you can respect in the laws of the physics by like encode the non-linear like a PDE partial differential equations into the machine learning algorithm as well so this is actually I think I may forgot to put the paper link here I will add the paper link to this one and let me also see what's the time just to make sure I have enough time so basically this is the the the burgers equation that like a using using used a lot in like a fluid fluid dynamics modeling and so on so this is actually a way that using the try to use the machine learning to to base to learn these partial partial differential equation so you can see that you we have these like a different the the the like the potential fields you here like to take the derivative respect to like the input x the position and also the time scale as well so this equation you can actually form a function that to use the machine learning algorithm to capture it so this is a actually how we can actually capture it by putting together like a this is like if we use tensor flow that we can see that the code basically here showing that this is like we generated the night work or the weights of the machine learning algorithms to to mimic or to try to learn the u field like the potential fields basically from the input of x and t but also we can form another the function basically taking the derivative of the different parameters that we want to we want to like a based on the differential equation and then we're adding them together and then put that into a loss function that can basically encode the arrow the arrow from the observation and the the the the estimation of of the machine learning algorithm and also encoding these differential equation into the machine learning algorithm basically you can see that when we train the algorithm the algorithm itself will take the weights from the machine learning algorithm and take the weights doing the differential like the derivative respect to different parameters which actually encoded the physics model into the the the physics like a governing equation into this training and also this is basically another way to think about like this is another paper discovering the physics concepts with neural network which is showing a lot of existing examples that we can use the machine learning to try to understand for example this is a simple like a very very simple like a damped like a damped spring system that you have this k the spring of the the spring coefficients and also like the the this b value here basically is the damper and its governing equation basically is this differential equation and when they train the algorithm for using an artificial neural network they learned that the the neurons actually in each after training are actually correlated with these different parameters that you can extract this equation after that. There's also some other methods that we talked before I think a lot of in our field we we already seen that Bayesian approach that actually can incorporate a lot of the prior information from physics for example there's a Bayesian inverse finite force inversion from Sarah Minchin's work and also there's a Bayesian like a based detection like from George Russell's like a net VISA and single VISA and you can search for the paper basically trying to use the existing prior information we know the seismicity we know where the errors quickly already occur we know the this type of information for like a constant the model learning and also like there's another like a approach basically recently try to combine Bayesian approach with deep learning try to give uncertainty error uncertainties to the kernels that we learned from the automatic like feature extraction and also the gam model is also very popular these days which means that you have two models competing with each other one model is generating the data points from like from from like a certain type of distribution and the other model is making the decision whether this model is wrong or bad or like a more more fake or like more trustable and so on how we can actually adding in the physics into the generator the generator actually can help us to combining the data combining the physics into machine learning algorithms there's a paper about this I won't go into details but you can explore more if you want so I will finish you here with some of looking forward to here I think Carrie Anne did a very nice job in her review paper laying out of these different areas I also just want to highlight or like emphasize the the the some aspects for example the benchmark data sets it's really driven the community computer science community to make better algorithms and to make this deep learning becomes really really powerful so basically they put these image nets like these large-scale benchmark data sets every year there's people like try to compare the new algorithms like how they actually arrow decay on these data sets so that people actually can can compare with each other and also there's a lot of new data sources as well like Carrie Anne mentioned but also we need a way to fusion the data together like a good quality bad quality and also like a different type and so on sometimes it's hard to use the it's easy to use the individual data but how you combine them is actually very interesting problem as well for example my shake actually we I worked on use the smartphone data to detect the earthquake to do earthquake warning so we actually launched a new version last last week we actually got like a half a million downloads last week which gave us a lot of data that but how we actually combine this data into the into other type of data is another type of like a area that we need to work on beyond that also some of the new things that we never thought of before maybe like a smart meat smart like sensors at home like in the cars and also like in the CCTV cameras or zooms and so on all these things are providing new data and new challenges for us for the for the whole community I think I will stop here and hope that I generate a lot of like a heat here to to encourage more discussion on the limitations on the physics machine learning approach thanks thanks a lot Shanghai this is great and we really very much appreciate you addressing these questions so well and I think there's lots of a lot of good starts for discussion I have a quick follow-up question myself what people collect their thoughts you showed this lake level temperature problem paper and the machine learning approach with the physics guidance produced the same kind of adherence to the physics at lower levels of misfit and I was just wondering how that's possible because there's seems to be like a missing piece in terms of different priors so that the physics only model and for smoothness when it shouldn't have or is there something like that going on I think yeah that's a very fair question and good point so also like that that work is not my work and I just like maybe try to summarize that my based on my thinking so so what they used for training is that they had a lot of these observations from the lake and lake temperature and different features for example the the the sun like the sun radiation and also like some other features so if you use a physics like a tool to model these so you actually captured the general you basically captured the general match of the data there's a lot of like there's these small scale variations I think it's why it works really well is that the machine learning actually helps to try to capture some of these small variations now that's not captured by the physics but also with this constraint of physics that in the cost of function it's actually make the make the the the training more reliable even you use a relatively smaller data sets instead of like you need a lot of these data sets or like much longer observations to make the correct decision so that's why what I think is that like combining these two actually are powerful thank you all right let's follow up with questions to chinkai first thank you chinkai um I have a question that clearly we cannot do everything in machine learning otherwise you have to buy a lot of computers in general a lot of green greenhouse gas so is there a simple guideline for geologists that what kind of applications we should definitely go for machine learning and is there any kind of example that we probably don't need that kind of tool thank you yeah that's a really good question to be honest I don't have that list what kind of so I usually only think in my field like in my in my uh domain um in the past but I think I think karen's review paper also like in our review paper in the seismological uh research letters I also mentioned that like what kind of problems that you already tackled using machine learning and also these ones like most of the problems that go to add to like if the problem has like a certain pattern that's hard to capture to buy equations or hard to capture to buy like intuitive like algorithms so it's probably like a very good fit for machine learning algorithms but but if you look at most of the applications right now we are doing is still within like automate a lot of these time tedious work I think that's really important to as a first step that we try to automate a lot of these but then later part it's more useful to the to the sciences that's how you use the new automated system to generate more insights from like the science domain for example that's work like finding a lot of these new smaller events that's what that tells us and so on yeah other questions otherwise we will open it up to you you know all speakers of this morning so I have a question to chinkai and also probably zack and maybe others as well and I you you discuss the um the transfer learning and I I guess one of the challenges that we have in terms of understanding process has to do with coupled problems and we you know briefly talked about the landslide or cascading hazards as well but if you if you turn to it you know just take a simple or simple complicated example like a volcanic arc above a subducting slab and want to build a database um it's like an earthquake catalog and you have two very distinct processes shallow and deep and the in the same data site it's it's it's a um we traditionally separate one or the other and and analyze the group separately as well but uh is there a potential then with the transfer learning to actually make some um progress at coupled complex processes like fluid transfer from one from the dangling slab to the overriding plate time and space um or are we always away from these kinds of problems I don't know how far you've experimented with with more complicated data sets and taking learning from one to another uh maybe yeah yeah go ahead yeah maybe I answer first so um uh Zach can add in later so basically my experience with the transfer learning is that usually you have like a large data set like you have the problem very well defined then you train the algorithm the algorithm will capture the different features for these data sets and then these features there's some general features there's some more specific features so the specific features is more tied to this one specific uh problem or task and the general features may apply to different like problems so that's why I think the the transfer learning in this sense actually works that when you have another problem for example that you the two problem you mentioned there's like a similarities between each other but there's also difference between each other so if you use one like one problem to design the to extract the general features and use the general features applying to the other one that similar processes but the more specific features that will be learned by the new layers you added it's actually can can success or like can improve the the results but it's not work always like uh I saw like there's like like a different type of applications like there's like even for example in image processing there's like you you learn from one type of dog or drag uh or cats like you train them algorithm on that but then you're applying on detecting cars they're still working the reason is that like some of these features is just like a try to find the boundary of the object and then this find find the boundary of the object in cars is also useful like you find the boundary of the cars and then you make the decision so that's why like this transfer learning you already in my opinion it's it's like more useful when you have like some similar features and that's a similar in certain degree I guess to briefly just follow up on this it seems like if you're trying to do things like solid fluid in the actions or multi-hazards as soon as that then we already know that at certain spatial temporal scales some of those interactions will matter and then others they won't so it seems like again this is like a real tough and tall order for machine learning system to give a general answer because we already know it's not going to work for certain spatial temporal scales and this sort of brings me to a question I asked Karian when she visited you take a few weeks back I guess do you get good information about the misfit and the reliability of the extrapolation out of these other these algorithms could you at least use them to say oh stop here don't trust that anymore I got a note here Zach wants to follow up um getting kind of confidence estimates is a hard problem um it's it's something that's actively being pursued by the computer scientists um to some degree it depends on um whether you can you can learn a distribution of the data and um so there's there's some potential from kind of what would be called generative models which is basically yeah you're learning a distribution rather than or the parameters of a distribution rather than um discriminative modeling which is kind of more functional based um and so because of that is a distribution you can kind of sample the space and and kind of quantify where you are um but it's it this is still not an easy easy thing to do yeah I would just emphasize with that because I think this is a very active area of research in the machine learning community so it's something where people are proposing solutions for that kind of problem but I think it's still something that um you know hasn't really fully been been solved but when you usually when you apply normal language you get sort of a point estimate out and so that's what you get and if you want to get more than that um it's going to take sort of new techniques and methods and and so that is an active area but I think right now it is hard to get a measure of the uncertainty so um this reminds me actually a question I wanted to ask Zach earlier and I didn't really get a chance so one of your comments Zach was um when you were you know training the algorithm to detect p waves and s waves that this was comes to this sort of you know transferability of the learning question and you were making the comment that it was um relatively straightforward to transfer the learning from detecting p waves and s waves in one region to another region and you made the comment that this was you could do this better or I figure exactly how you phrased it but that this was easier to do than using the traditional methods I mean you know the standard seismic phase detection kind of approaches um that we've been using for decades and I wondered can you amplify what you meant by that a little bit because obviously we can deploy a seismic network and use you know standard triggering type algorithms to detect seismic waves and locate earthquakes and then we could apply the kinds of approaches you were talking about this morning so can you explain a little more where the advantages are of using these new approaches over the older ones um well so just to be clear when we're talking about transfer learning it's a very specific thing so there's transfer learning and then there's generalization transfer learning specifically basically means taking a model that has already been trained on some data set right um and so that model is the parameters have been filled out they've been learned now you're going to kind of keep part of that model you're going to retrain the rest of it and you're going to basically fine tune what you trained before to a different data set so that that's specifically transfer learning so the idea is that um the the original model can be trained out in full to learn kind of core structure in the data um and that's because you have lots of data that might be from a model for example the simulations um and that we expect the simulations at a very core scale to look very similar to um the real conditions that you're interested in but then kind of the more nuanced structure of the data might be you know noise dependent or things like that that's where the transferring learn the transfer learning is done because you're fine tuning that now um to this so just to be clear generalization is different where you take the model as is and you apply it to a completely different domain so yes we see um pretty in this order we see significant generalization capabilities it's not perfect and there's still lots of stuff we don't understand at all but but i've been able to take you know these models are trained entirely on data from um southern california relatively shallows has misses obviously no subduction events there um it's it's able to still kind of generalize to other tectonic regimes and the reason is because i think very simply it's that you're looking at kind of what's common to all of these p waves and what's common to all these s waves and so is there some kind of relatively crude characteristics of them which we know is the particle motion and things like that um that are as generalizable um a big part of the neural net where they stop working is is they tend to be very fragile to kind of you know to weird um conditions that are they're changing as well and that's part of this kind of i mean we talked about a little bit today but this concept of adversarial examples where if you add kind of epsilon white noise to an image or something then you can kind of push the the decision completely over the boundary to the other side and you can kind of interpret things with high confidence um we're seeing some of that for sure but it's not exactly clear where those things so if i can push just a little more but i still don't understand the difference so you know traditional network detection kind of approaches of stal ta and think algorithms association algorithms like binds and things like that obviously you can go and apply those to any other region as well so so what is it specifically that the the machine learning algorithms bring i mean to the machine learning algorithms obviously the southern california example you're essentially extending the detection capability down by one one magnitude unit and so you would expect a similar sort of uh effect in some other region but i i was i was just trying to understand if there was something else that these algorithms are bringing that you weren't getting when you transfer these more traditional sort of you know association algorithms to other regions um okay so basically from my perspective um if you were to just take an arbitrary data set you don't know anything about it you don't know if there's earthquakes in it or not um trying to so there's there's a couple aspects about this the computational infrastructure you need in terms of the software is like these giant stacks of stuff right libraries manuals reading through all this learning how to do this you have to become an expert in how to run these these software platforms you have to then know how to tune the parameters of it such that the results of the catalog are appropriate and things like that and that's totally a non-trivial exercise yeah with the standard stuff stl ta binder all this kind of stuff um what's the advantage of the machine learning is that in theory you could have kind of more end-to-end solutions um that you encode kind of simple rules in there like our associators just giving it examples of of associated um events right and it's learning to do that right and that's once you it's like this much code to do that once i generate the training data set and so um it's not a very complicated exercise to learn how to run all this stuff and there's very few sensitive parameters because the system is kind of self-optimized to get the best performance so that's my take on it one one quick question which is a follow-up to that have you have you tried applying this to well there's not that many earthquakes in cascadia but there are some where you get deeper earthquakes have you tried it with completely where you don't just have shallow events i'm thinking in particular the associator the that that would be presumably where there would be more difference um so i haven't talked about our associators actually train entirely with synthetic data um so we generate the training data entirely from a model yeah we take it it basically learns to recognize wave fronts from this synthetically generated data and we directly apply it in situ to um to the real data and it works what i was thinking is that the wave front the way the wave front goes through the network from a slab event will look different than a shallow crystal event sure um so that would be the question is how much do you have to retrain or could or or it might actually be getting principles that would apply to both so we train it separately for every reason it would apply to but that's a one-time thing you have to do it's it's only you know it's like a few hours of time to get it there and obviously we don't acquire data that at that pace yeah and then one of the quick question on this sort of transfer problem which is a little bit more general um uh chinkai mentioned uh transfer some layers and retrain some other layers how do you decide which layers to transfer and which layers to retrain is that a trial and error process or is there something that actually comes out of the algorithm that you get some sense of where within these multiple layers is it actually extracting information yeah that's like yeah go ahead okay yeah that that's actually a very good question for for the deep learning approach like when you train on a specific problem you're the the first a few layers are general like features and when you go deeper and deeper layers it becomes like more specific features for example uh use the cat and dog example like for the first a few layers maybe just like the boundary edge detection of the the cat and dog and then for the the later layers deeper layers maybe just like the eye like a year or color like more more specific features two different type of uh uh objects so that's why like you already when you do this transfer learning you want to use the general uh features that uh similar to each other that you just cut like the first a few layers and then transfer it to the to another problem and adding in layer adding in new layers basically in the deeper layer to learn these like more specific ones and then that will basically capture like more specific or more task or new objects oriented features um can I ask another follow-up question so I remember recently I read this article maybe in science or nature about uh autopilot they were not able to recognize a stop sign because there's a sticker on the stop sign so they just read it as speed limit 45 mile per hour so I guess my question is so for those you train your seismic data and could that be always some kind of new condition that came out like clock air or just something in your data set that you you were not expecting that and then because of that new condition and then you just totally miss identify something and because you are generating millions of detection and you actually you actually have you have very limited chance to actually find that afterwards um yeah um yeah I think this gets back to what I was just talking about about these adversarial examples so this is kind of exposing this fragility of neural networks that that's very well known at this point um and so um to some degree I originally just started out about five years ago where people recognized this flaw in these systems and then um it led to kind of this interaction back and forth where people then came up with a way to avoid it and then people broke it again some hacker basically figured out how to exploit this again and to it looks like there's even theoretical arguments for why that you would never be able to um to fundamentally deal with these kinds of um these shortcomings so um yes this is this is a big security issue on a number of domains um like the self-driving car example is one of them because if you cover up a sticker in just the right way you can trick the system to thinking that it's a stop sign or instead of being a stop sign you think it's a whatever assigned it to keep going and so a green light so um but this is this is very serious um yeah so I've been actually working on adversarial examples in this domain um and it is I think a problem there it's something that you wouldn't necessarily like someone isn't necessarily going to attack these systems but it does show you that they are very fragile and what I'm finding is that they there are a lot of concerns as someone coming from a math background I think of a neural network as it's just a function it's some sort of a mapping from your data space to some sort of target space that you're interested in and these mappings with neural networks are very complicated all these different layers learning the features these are very complicated mappings and these are in high-dimensional space you're training with a lot of data but you're in very high-dimensional space you have a huge number of parameters and so these functions in a lot of ways they do well you can regularize them so they give you nice results but there are ways in which because you have a really complex function in a high-dimensional space they're not always well behaved and so these adversarial examples why I'm interested in it is to me this raises concerns um this is something that's been known in the community for a long time that these methods are so sensitive and Zach was mentioning that there there are methods to to try to work against this um but it's still something that it's very difficult to show or to train a network that doesn't have this property that you can perturb it or change it just a little bit um and have it change the result pretty dramatically um and a lot of the methods is that there are some people who are trying to make methods that won't necessarily hurt performance but in some cases it does um and a lot of these methods are also only able to show that for very restricted cases of the kinds of perturbations like adding Gaussian noise or for instance like you may be able to show that you can't add Gaussian noise that will change um the class and so it is sort of an ongoing problem and I think but it is something that people like me are starting to think about and work on um but it is somewhat a fundamental aspect of um deep neural networks some of the last complicated methods you can also attack but it's more difficult so one of the things kind of going back to um to Richard's question about you know whether or not you know what's the advantage of this over STLTA I think one of the advantages of methods like STLTA and traditional methods is that we do know um you know we have estimates we have ideas of when they're going to fail we have for a lot of numerical techniques we know um you know how accurate they are and I think this is still a disadvantage of machine learning methods that I think both this community needs to be aware of but also it's something that's an active area of research um and I think that that's something that um I think geosantists need to be more aware of and think about as we're applying these methods all right I think this is probably a great time to end and Maldis a little bit more over lunch I'd like to to thank Ching Hai for his great presentation in all this morning speakers I hope they're going to hang around and we're going to break for lunch for one hour and then we're going to reconvene at 145 thanks everybody


September is National Suicide Prevention Month. This year the Navy's theme is thrive in your community. Sailors all around the fleet are hosting walks and runs to raise awareness. You can volunteer to mentor at your local school or participate in a number of projects to serve others. But bottom line there's no real true measure of what they need to do if they go out and reach out to just one of their shipmates that might be enough. Send your thrive in your community pictures or footage to suicidepreventionatnavy.mil. For more resources on suicide prevention go to suicide.navy.mil. From the Defense Media Activity I'm Petty Officer Ian Cotter.


Bioactive glasses have been used for decades to treat bone defects but more recently they have become an emerging area of research for bone tissue engineering applications. This paper reviews the latest advances in bioactive glass and glass ceramic scaffold technology focusing on their fabrication methods, mechanical properties and potential for use in bone tissue engineering. The authors also discussed the role of bioactive glasses in stimulating cell growth and vascularization, as well as their ability to release calcium and phosphate ions which can promote bone formation. Finally, the authors identify areas of future research that could further advance the development of bioactive glass-based scaffolds for bone tissue engineering. This article was authored by Aldo Arbaccaccini and Lutz Christian Gerhardt.


If you've ever been driving by Bemidji's Paul Bunyan Park this evening, you may have noticed a giant tent being erected in the park. The Bemidji JC has spent the day setting up a massive 60 x 140 foot red, white and blue tent for their upcoming water carnival. About two dozen members use sludge hammers and other various tools to stake the tent down and pull the 30 foot poles into place. While the space will be used for water carnival entertainment, other major events are scheduled to be held under the big top. So today starts our water carnival with the tent set up and the epicenter of all activities from Best Town Party, Hockey Day Minnesota announcement and all things water carnival. The tent will also be used this weekend for the annual Fishing Has No Boundaries event. The Best Minnesota Town Party will be held next Wednesday followed by Water Carnival which begins on Friday with live entertainment through the 4th of July. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


Seven, six, five, four, three, two, one, go. You'll never have anything. What's up guys, welcome to today's vlog. Today, we're gonna do a men's cut and the inspiration for today's haircut came from men's spire salon on Instagram. They got a huge following. You guys are probably following them already. I loved this haircut and I also loved this haircut. You can see the hard fringe that they were creating on these haircuts. So that inspired me to create this video today. This is the mannequin that we did. We created that hard fringe. I love that it's like a nice heavy bang. It's getting that hard fringe in the front and then a ton of texture throughout the top.  So definitely hope you guys like this video. Let me know in the comments below. Here we go. All right guys, so we're gonna start off by sectioning off the mannequin. Now those of you that are loyal followers of this vlog you're gonna realize that this is the same haircut as yesterday to begin with and then we're gonna change it up when we get to the top of the head. So if you wanna fast forward to the top of the head go ahead, save yourself some time. If you've never watched this vlog before we're gonna start, I wanna show you guys what I did at the bottom of this haircut. Now I sectioned off a horseshoe section on top of the head following the bridal ridge. So that curve of the head and then down to mid crown. Just kinda scooped underneath high crown just to give a little bit of that kind of fall over that calic area that happens in the back.  What I did before I started cutting with my clippers is I dried the hair, you get a much better result. It's kind of the same reason why you don't mow your grass when it's wet. It just gets all clumped up. It doesn't cut as even. So I like to smooth it and blow dry it with a comb and get it all going in the right direction. And then I go through with my clipper and I just get a better result at the end. So using that clipper, scooping up, you can see that I leave a little bit more weight at the low crown area in the back. And then I scoop up to that bridal ridge and then scoop out when I get to the bridal ridge on the sides. Now I know a lot of you guys won't be making this big of a change. So it won't be going from super long hair to short hair. So it would be just cleaning up the sides, doing the normal fade that you want. Really go with what your guest is looking for. So don't go just with a three guard because I'm cutting with a three guard. I chose a three guard because there's not a lot of density in a mannequin. So I didn't want it to look bald at the end, even though it still looks a little bit bald. But on a human, it would definitely feel a little bit thicker. So I would probably go with more of a two guard depending on what they are looking for. So now we're going to go through scissor over comb. And the scissor over comb technique, I'm using my Mizetani Pen Slim Scissor. It's a brand new scissor from Mizetani. It is one of my favorites in my kit now. I love the DB20, but this one, just for getting in those tight spaces. Also scissor over comb, it's great because it gets nice and tight to the comb. This is a six inch blade and just a lot of power. It's also nano powder metal steel, which really grips onto the hair. So it's great for scissor over comb work. So we're just blending that line right along the pridal ridge, right into low crown. You can see that the angle of my comb is just slightly tilted back. So I work it back and then I come away from the head as I work my way up.   A blow dryer can manipulate the hair however you want it. So if you have somebody with extreme callix or anything like that, on a daily basis, they should be hitting their hair with a blow dryer just to help push the hair exactly where you want it to go. Now we're gonna work that crown area. And what I wanna do is come directly out vertical from the head shape and I'm gonna bring it straight out towards me. So that build up of weight's gonna happen right at the tip of my fingers, but the rest of it's gonna kinda collapse down. So just building a nice layered look, little bit of weight build up just to keep that crown area because a lot of people get those weird callix in the back. So if you have a little bit of that extra weight, it becomes a lot easier to push it with a blow dryer later to kind of complete the look. I don't really like rounding off the crown that often. So everything comes straight out to me. It builds up a little bit of weight in that corner right behind the ear. But I'm gonna do something with that weight later. So again, just working those sections, straight vertical, straight back towards me. Now I'm working on top of my fingers but the same thing. And because of that little bit of over direction, you can see that weight just jumps right to behind the ear. I don't really like, you don't need to round off the back. I like that kind of heaviness that happens in the crown. Now we're gonna continue that vertical section all the way down the head shape. So this is gonna go right down the center of the head towards the front fringe area. And what I'm gonna do is continue a straight line across using a point cutting technique because I want to break it up. I want to have that texture in there. So I work the point cutting technique all the way to the front fringe. Then what that's doing is pushing all the weight to the front. So you could see how the head shape kind of curves down around the bang area around the forehead. What that's doing is giving me a lot of extra length and then that's gonna help me with the end result of this haircut getting that hard fringe in there later on. So right now I'm creating that texture, that textured look on top.  So now I'm working my way across the head shape cutting everything, bringing it straight up from the head, creating that texture. Then what I'm gonna do is I'm gonna go into the sides and blend it. So I don't actually go all the way down with those sections and so now what I'm gonna do is create that kind of rounded shape right on the edge of the head, right at the parietal ridge. So I'll just keep working across the head shape and using that point cutting technique and blending it with what I cut at the center top and what I cut down at the bottom with my scissor over comb. So just blending those two points together using point cutting. And at the very end I overdirect everything back. I know that seems weird because now we're pushing a ton of weight towards the front. But what that's gonna do is give me that density. So when I go to cut that hard fringe it looks nice and thick. So everything in hair cutting has a purpose. When you pull something back you're releasing the hair to go forward. But what that's gonna do is give me hair to work with later on in the haircut. So you definitely don't wanna, a lot of people think of how can I cut it to make the result right now? But it's not all about that. It's about how can I cut it now to work with it later and finish out the look that I'm going for.   So I'm working through the 334 comb from YS Park just working that hair back and forth. It's gonna help get rid of any calyx in the hair. And I just work that fringe straight down and prep it to get the dry cut. Now I sectioned away with some clips basically took a U section across from each temple area.  But I hold it in my comb first and cut it. Then once I get the length that I want I use that steady blade to go across the forehead and I cut that fringe straight across the forehead to give it the most low elevation as possible with no tension whatsoever.  So now I'm just gonna go through, add that texture and blend the top a little bit. But you'll notice that my over direction goes way up in the air and that's to keep that length and the density at the bottom. So I don't wanna affect the bang whatsoever but I do wanna create that texture in the top. So same thing, a little bit of point cutting. I've done all of this with the Pen-Sum Scissor. That's what's great about it. It works with pretty much every scenario. So from point cutting to texturizing. Now we're doing a channeling technique which is half open and close on the top of the head. You just slide that steady blade across. It just gives you those short kind of divots within the haircut and a bunch of texture. It's a technique that a lot of guys like. So more deep point cutting. Can't really go wrong with any of this because all it is is texture at this point. So I'm not changing the shape of the haircut. Just adding in more texture.  Today I decided I wanted it a little bit shorter so I go in and cut even more of a blunt line once I finish up the top. So you can really see the look coming together. If you wanted this more straight across I like having a little angle to it. That's kind of my spin on it from the pictures that I saw. But you could have it straight across as well. It would look just as good. So that's the last little bit. I'm gonna add brocado carved to it. This is a very light hold wax. It's a cream wax. So it's really just gives you that bedhead look. It doesn't give you a ton of hold, which I like. It's just a soft feel to the hair. So if you have really thick hair it's a great product for just having a little bit of control but not too firm. So there's our end result. Hope you guys like it. Let me know in the comments below. Hey guys, like always. If you liked this haircut then hit the like button. Hit the share button. Make sure you subscribe below. We have a lot more videos to come. Thank you guys so much for watching and also make sure you check out FreeSalonEducation.com for all the tools and scissors and everything that you need and also all the other free videos that we have available and go to FSESocial.com which is our social media kind of website where you can talk to other hairdressers out there. Connect, build a profile, share your work. We'd love to see you on there. Thank you guys so much for watching. I'll see you on the next video. Thanks.


bit of disclaimer that we are going to be talking about something that is currently happening, something that's perhaps quite emotional for people, or perhaps a little bit too topical, if you can say that. But we're going to be talking about the Ukrainian war, but we're not going to be talking about it in general, or the way that most of the stories are covered. We're going to be talking about it specifically in the context of autism. So today I have my very lovely guest, Aaron, who's going to be giving us some updates on the work that he's been doing, as well as some of the cool projects that he's currently working on. How are you doing today, Aaron? I'm very good. How are you, Thomas? Thank you for having me. It's a very good day. No, of course. Of course.


Thank you very much. Thank you. We've got to stop meeting like this. Well, hello and welcome to all of you. It's wonderful to have you here and see you joining in this important step forward for America. Twenty-one months ago, this country was in the grip of not one but five severe problems. Runaway government spending, double-digit inflation, record high interest rates. They just hit 21.5 percent, the worst tax burden in peacetime history and high unemployment. With support from the American people, we put in place a program that was designed to cure this raging economic disease, which had been ignored for too many years. A little more than a year has passed since our program went on the books. But already we've made solid progress against four of the five problems that we inherited. We have cut the growth of spending somewhere around in half, and we've reduced high inflation, high interest rates, and taxation. But let me just say something here about interest rates, which I'll expand on in my speech to the nation tonight. The very sharp and heartening decline that we've seen in interest rates brings us one step closer to the dream every American shares, which is lasting recovery. But let's understand that what brought, for example, government-backed home mortgages down to 12.5 percent, the lowest level in 25 months, was not a quick fix, but our progress against inflation. The decline in inflation led directly to the fall in interest rates during the last several months. The Federal Reserve Bank announced last week that it was lowering its discount level to 9.5 percent. That's the first time this key interest rate has been a single digit since 1979, and the fifth reduction in the rate in four months. The Fed's lower rate shows its confidence that inflation and market interest rates are continuing to fall. The Federal Reserve has been pursuing a steady policy of trying to reduce inflation, and we've been supporting the Fed in that policy. The Fed will continue to aim its policy at bringing inflation down further in the future, and that will continue to mean lower and lower interest rates as well. As I said, we're solving four of the five worst problems that we inherited. But unemployment, always the lagging indicator, remains too high, and clearly our most urgent priority today is to create lasting private sector jobs. And I'm not going to rest until every American who wants a job can find a job. Unemployment is a tragedy for all Americans in every region, every city, and every line of work. We need the strength of every back and the power of every mind to lift our nation from the economic swamp to higher ground. We must not waste the energy of one citizen who wants to work. All of us are moved by the plight of millions of our people who can't find jobs or provide for their families. Perhaps the cruelest trap of all has caught the legions of our youth. As they stand in the threshold of the American dream, far too many find the door of opportunity slammed in their face. Two out of every five people out of work, two out of every five are between the ages of 16 and 24. We continue to push for enterprise zones in our inner cities, a program to attract new businesses and jobs to areas of highest unemployment. It's tragic that this enterprise zone proposal has been bottled up in committee by the leadership of the House. I remember what it was like to be 21 years old and looking for a job in 1932. And I know how it feels when your future has been mortgaged by the generation before you. And that's a tragic mistake we must never allow our leaders to make again. Today's young people must never be held hostage to the mistakes of the past. The only way to avoid making these mistakes again is to learn from them. It's estimated, for example, that at least 20 million American workers now rely on skills that won't be needed within 20 years. The government has trained thousands more in skills that already aren't needed in their communities. Still others have been steered in to make work government for our young people. And that's why I'm proud today to sign into law the Job Training Partnership Act, a program that looks to the future instead of the past. This is not another make work, dead end bureaucratic boondoggle. This program will train more than one million Americans every year in skills they can market where they live. It'll make a difference on Main Street. It'll provide help, bring hope and encourage self-reliance and personal initiative. And here's how it'll work. State and local government officials, business and labor leaders, and other members of the private sector will plan area programs in private industry councils. Local people will decide at the grassroots level what opportunities are available in their communities and then match real jobs with needed skills. And here's something else that makes this program different from many past failures. At least 70% of the program funds will go to actual job training. We're eliminating the bureaucratic and administrative waste that has marked so-called job bills in the past. There's another reason for confidence in this measure. It's based on a tried and tested concept. The young people that are with me here today have already benefited from local programs very similar to the one we're about to enact nationwide. And I'm going to give some examples. Immanuel Clark, you're a high school student from Richmond. He learned clerical skills and found a part-time job in a law firm because of New Horizons, a training program in this area. Bienvenido Martinez, who came to the United States from the Dominican Republic, received on-the-job training sponsored by the Washington, D.C. Private Industry Council and now works part-time as a computer operator while attending college. A little while ago I told him what a change this was between generations because I worked my way through college washing dishes in a girl's dormitory. And Paige Davis, who once supported three children on unemployment benefits, enrolled in the Urban League Word Processing Training Program. She graduated in 1979, began work for the Booker T. Washington Foundation and did so well that she told me a little earlier she's starting a business of her own as a consultant in word processing. Now, these are just three examples of the opportunity this approach holds for millions of Americans. Long-time workers who've lost their jobs because of new technology, women who can't find work because their home-making skills aren't marketable, young people who aren't hired because they have no experience, all will be helped by this program. And let me just add a special word of thanks to Senators Hatch, and Quail, and Kennedy, and Congressman Jeffords, Erlenborn, and Hawkins for their leadership in getting this legislation passed. And now, a few young people will just pay attention or gather around close. I'm going to sign the bill. That's the bill. That's the word. I'm going to sign the last page. That's the page. The Democrats are calling this blatant election year of politics with the president. That doesn't surprise me a bit. But it doesn't make it any more true. Is it true, Mr. President? What? Is it true? No. Why not? Because this is the type of program that we've been talking about between going to the Congress or something. That's the same. I'm trying to go out there. Sorry. Didn't mean... I'm enjoying it, you know. You're free to continue, sir. No, I can't do that. But I want you to know that, as I say, all these young people are an example of what these kind of programs can do. And out there, government has a legitimate function. But that legitimate function, I think, in this area means cooperating and working with the private sector where the jobs with the future really are. And thank you all for being here.


Hello and welcome to NewsClick. Today we are going to discuss the vaccine trials some of which are now supposed to start in India as well. Of course Serham Institute is one of the big players, generic vaccine players in the market and they have also lined up with Oxford University trials that if it succeeds then they will also produce the vaccines in India. Satyajit, a very peculiar clearance given by ICMR, your friends in Indian Council of Medical Research who seem to have said the vaccine should be ready by 15th of August this year. Now does it seem absurd that the Indian Council of Medical Research should not know there are three places of the trials and they are now asking the vaccine manufacturer who had said that they will run the trials phase 1, phase 2 and phase 3 trials. Over 15 months they should do it in the period of roughly six weeks. The first response that many people had to the letter from the Director General ICMR that you're talking about that emerged as a leak I think they did before yesterday evening was is this some sort of a troll or a scam or a joke because so I have had people asking me do I really think that is his signature and I asked you did they make a mistake about the year in the letter then one person asked that as well so it is it is that extraordinary peculiar a leak but in very brief terms the letter is deeply problematic in two different ways firstly the letter is written to the private sector partner developer biotech industry biotech and to a group of a dozen or so hospitals let me not use the term medical institutions since all of them I suspect cannot be dignified with that term. A dozen or so hospitals who are supposed to be participating in the clinical trial of a SARS-CoV-2 COVID-19 vaccine that ICMR and Bharat Biotech are developing together. Oh ICMR is a partner in this. Oh that is even more strange. I you know the the number of weird things about that letter simply multiply them more carefully you look at the letter so I'm still hoping to wake up and discover that it was some sort of a joke or a mistake and that it wasn't written by the DJ ICMR at all although I suspect that that's not going to turn out to be the case. Can we demand to see his degree is that a legitimate demand the whoever assigned the letter. So but but completely frivolous response is although they are the most appropriate assigned let me point out that there are two ways in which that letter is worrisome two different categories in which that letter is worrisome one category is this is a letter coming from a senior government official. This is not simply the director general of the Indian Council of Medical Research. It is also a full secretary of the government of India in the ministry of health and family welfare and the secretary in charge of the department of health research. So this is then effectively a government letter and basically this is a government letter that is written to a number of parties some in the private sector completely a few in the public sector basically saying to them that they need to fast track and expedite a vaccine clinical trial which is in regulatory terms and extremely carefully hedged about undertaking it is hedged about by legal requirements it is it is encompassed in regulatory stringency all of which is for good reason. I mean normally fast tracking is something others might want but a regulatory body asking the people to fast track development of a vaccine is actually the reverse of what a regulatory body should be doing. So keep in mind that this is not the regulatory body technically speaking because the regulatory body is the CDSCO or the drug control agent in India or people in that component of government if this had been the regulatory body then the conflicts would have been even more problematic. Plus as I said ICMR is a partner developer of the vaccine. Nonetheless to say that is what does the letter say that serious views will be taken of failure to expedite the process to introduce the matter by saying that this matter is being followed at the top most levels of government or words to that effect. All of this is classical bureaucraties to threaten people. So implied in this is a threat that you better do it in six weeks or else. So the inner of the letter is deeply coercive and to have a senior government functionary not just participate in but actually initiate such a coercive letter going out to a number of organizations is really does not bode well for the integrity of our regulatory processes as far as biomedical technology development and drug development and vaccine development are concerned. So that's one whole category and we can talk about all the many facets and dimensions of this difficulty with the tenor of the letter. The other component of that letter which is worrisome is of course what everybody has focused on which is this quite weird deadline in which apparently the director general of the Indian council of medical research thinks that phase one clinical trials can be initiated by about the 7th of July since the letter says you should start recruitment no later than the 7th of July and a phase one phase two phase three clinical trial process including data collection analysis evaluation and regulatory consideration of all the above can be completed in less than six weeks to enable a vaccine to be launched on the hallowed occasion of India's what is it 73rd independence day anniversary this creates worries not simply about coercive government but about the competence of government. I would go a little further Satish and say the sanity of the person signing the letter if he indeed has a degree in sciences and has been reading up the files which I presume he has been in the position he occupies so it's a I'm going to be in the sense you know not as just as restrained as you are okay I know there are circuit in which you must intersect with ICMR so after which this discussion that might not take place in the future but in this is and I we haven't had a response from ICMR after all this has come out well I have seen somewhere in media a response by ascribed to an ICMR official named Dr Sharma I think who appears to have said something to the effect that this is simply encouraging all parties to work as hard and as quickly as possible and that the date is simply an aspirational date and I can imagine that containment of the adverse fallout to this letter can best be done by that kind of rubber-walling. Yeah but you know even if it's an aspiration it has to be an aspiration which is achievable just like I said if it was something which the ministry of health and IS officer had written I would have said okay it's aspiration but this is the Indian council of medical research so they should know at least better than lay persons what this phase one phase two is not simply that if you actually look at the trial date information that is in the public domain this is the trial about which that letter is has been written the trial itself says that it will last for about a year and I think two months or three months 15 months is I think the trial period roughly so the trial is perfectly reasonably initiated the trial does not also say that it's a combined weird phase one phase two phase three trial the trial says it's a phase one phase two trial so there is nothing in the background of this letter to support the extraordinary claims of this letter nothing in the background from the council from Bharat biotech from any of the participating institutions to suggest that the weird claim of 15th of august 2020 is on any of their horizons so okay I think we've spent a lot on what could be called hot air of the bureaucratic handling of ICMR of a very serious medical technical research issue how should safety trials be conducted for an accident and what should the duration both of which are really important issues and troubling a lot of the people worldwide because of course if you could produce a vaccine very quickly that would be of great help but the same time you don't want to produce something which actually backfires because the wrong vaccine or the bad vaccine would have very adverse repercussions on the total scheme of vaccines itself it will create doubts and as you know there are anti-vaxxers all over the world saying vaccines are bad so you are also fighting that in fact one of the one of the statements I think by Fauci is that 50% of the US people may not vaccinate themselves because they don't believe in vaccines so the anti-vaxxer problem is quite acute so in this climate if you produce a bad vaccine it's really going to rebound on us in more ways than one throwing doubts on the Indian capability of producing vaccines manufacturing selling vaccines and of course of the vaccine process itself absolutely as a matter of fact I would worry quite a bit about this particular vaccine candidate as you know I tend to worry pedantically about terms we use and so long as a vaccine is actually not licensed I would much rather that we refer to it carefully as vaccine candidates rather than vaccines and this vaccine candidate is essentially simply the virus which is where ICMR's participation comes in because ICMR this is an isolate of the virus a strain of virus that ICMR has grown in Pune at the National Institute of Virology from infected Indian patients that it has isolated it from so and the vaccine is formulated as grow the virus kill the virus and inject the killed virus and this is an old in fact it's the oldest of the many vaccine technology categories that we've discussed in the recent past and it's not technically perhaps the most demanding vaccine formulation to make in some ways but in other ways and it has its own potential caveats and limitations so for example the vaccine candidates for the older SARS the 20-year-old SARS-CoV-1 disease for which people were making vaccines or the MERS disease virus MERS-CoV-1 for which people were making vaccines in both of those this kind of killed virus vaccine led to some difficulties in animal studies while they provided protection against the virus challenge they did lead to some more inflammation in lung tissues than would be desirable in the in the vaccinated animals now this is not at all to say that the present killed virus vaccine will be similar and in fact again if you remember the first vaccine that we discussed in these ongoing COVID-19 discussions from Sinovac the Chinese company which put out oh two months ago now data on Rhesus monkey experiments showing that there was some measure of protection those data looked at monkey lungs and did not find inflammation so it's not at all my suggestion that that's what's going to happen here I'm simply bringing it up as the as an example of the kind of worries that you point to about vaccines and their safety and efficacy related issues that we have all have to be careful any vaccine needs safety and that's why you have phase three trials that's essentially the part of the phase three trials absolutely large numbers there is already adverse reaction in certain sections and and and as we have discussed here is the paradox in the speed of SARS-CoV-2 COVID-19 vaccine development which is that the better we control the rate of spread of the pandemic the slower vaccine development is going to be fortunately the US Brazil and India is providing now a sufficient speed of the disease I would not have used the term fortunately but yes I was being sarcastic I have to agree with with that and as a matter of fact you will note that that's precisely why all the phase three clinical trials that I'm aware of that are that have been initiated have been in Brazil yes that's right if the Chinese one is in Brazil Chinese don't have patients clearly and so the Oxford trial is in Brazil Oxford trial is also in Brazil oh yes yes okay why aren't any of them happening in India except the this Bharat biotech one the proposed Bharat biotech one any idea oh I think that well honestly I don't know why none of them is happening in India I think that one possible explanation is that the growth of the epidemic the spread of the epidemic in India is very heterogeneous okay and because of that there's always going to be difficulty about setting up the trial where the two groups vaccinated and unvaccinated are going to be exposed to over succeeding time to an equivalent risk of virus exposure so that extent Brazil provides quote unquote a better place to trial to hold trials that appears I'm guessing that may be one possibility another possibility may be that the Brazilian regulatory establishment and landscape is somewhat more rapid in its responses to requests the other other point and this is really the last point that before in today's discussion because you know we would tap you again and again on the other discussions as well but the last discussion coming out of the vaccine trials that we are talking about the hospitals it's also interesting that because of the NHS hospitals in in England in the United Kingdom they have been able to conduct much larger drug trials and what other places have been able to and that's primarily because the public health system is still there in England that the you have the NHS in England and you don't have similar things for instance in India a large number of hospitals working under the same agency or in the United States which are the two other places where you could have you could have seen a large number of patients but the hospitals are completely fragmented unlike the unlike the case of UK that's absolutely true in in in terms of sheer magnitude yes you're absolutely correct even though I can imagine a very large number of as they're called insurance system hospital chains in the US being being quite able to undertake drug trials of this kind the fact remains that the NHS in the United Kingdom is as you say an absolutely stellar example of how a true public sector public health system can even in the midst of great existential difficulties let's make no bones about that still work together and effectively to conduct not simply patient care duties but also these kinds of pressing research undertakings such as drug and other therapeutic trials and it's also interesting that the results from this have been more conclusive because of the sheer numbers as you talked about recovery trials have actually done certain things identifying dexamethasone talking about hydrochloroquine and it's not being effective and also in this aware all of these seem to show that they have an ability which others do not seem to show and even WHO which has been talking about its solidarity trials and yet to get off the ground or you know at least this numbers are relatively much smaller even the scale of the solidarity effort is is somewhat different and therefore I can imagine that the difficulties are larger but but but let me bring this point that you raise back at the end to the Bharat biotech ICMR vaccine that we began conversation with because in this context too it's interesting that the dozen or so hospitals that the director general ICMR has apparently written to who are the participants in the clinical trial range over an extraordinary variety of medical centers there are apparently apparently there are private hospitals there are apparently there there are quasi complementary medicine Ayurvedic centers there's there's a whole diversity across the country of centers which have been pulled together to undertake such a vaccine trial and one wonders on what basis were these selected who selected them how well are they going to be able to work together are all of them on the same page in terms of experience as well as in terms of institutional mechanisms and structures to do clinical trials of vaccines of this kind all of these become issues and this in a country where we have one of the world's largest ostensible public health system that's quite true that this this should have seen public health systems more closely involved because hopefully there would have been a standardization and a minimum level of competence and we do not know some of this Ayurvedic centers you've talked about but they were involved with the coronal trials of Ramdev Patanjali so we don't know of course we hope not but that's a question that still can perhaps be explored but you're right that after all Ayurveda does not accept double blind trials randomized trials anyway well the ability to conduct I don't even want to get at this point into the issue of evidence-based medicine and what it encompasses and what it does not all I'm trying to say at this point is in a country of 125 crore people with the world's so-called largest public health network we cannot find a dozen reputable public sector institutions of high credibility to carry out clinical trials for a pressing need vaccine that's a big that says something deeply distressing about the state of our both public health system and about our biomedical research systems well on the rather somber note and from my side a question mark on the sanity of the person who signed the letter or drafted the letter we will conclude our today's discussion I hope that ICMR at least will do some clinical trials about the sanity of his officials before it proceeds further on doing clinical trials about the vaccine thank you very much Satith for being with us and with my rather inclementary remarks on your fellow professionals in medical research thank you this is all the time we have a news click today do keep watching this click and follow our other interviews as well


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