Okay, thank you. So Welcome from my side I'm the last one standing behind you and the lounge. I know But remember that it's not me to blame if we are late And I hope I still can keep you Awake and I will speak louder than your stomachs make sounds this presentation is Summarizes or present some insights in things we have done in working group free Which I was led by myself together with the Lenny Hadzi who cannot be here today. She sends her regards Some of the presentations also put together by Elizabeth Pismo to the PhD student of me She also cannot be here So people have some people have to work while we have fun here I Will stick to the to the structure that was given So go through the introduction And the aims what did we actually want with this working group originally, which was not so clear always at the beginning Then we look at some of the achievements and I was close with some lessons learned. I Don't think I have to repeat the motivation in general for VOI in analysis, I just want to point out the this last point here, which is the specific purpose of working group free or In the end we said we aim at providing an overview and to some degree also some some links and access to these Analysises and computations that are involved in a VOI analysis We want to give all the guidelines on how to implement those methods and tools well we've seen that your analysis consists of many different parts and Go a bit more into that in the next slide And in some of these different parts we have quite elaborate methods, but Very seldomly these have been put together to actually perform a VOI analysis. That's one of the challenges So we have been trying to Collect identify develop also further and overview these methods and tools So that people coming from different disciplines Can access the whole set of tools and methods This way we also realized that Not a small challenge lies actually in the Vocabulary the fact that people coming from different fields speak different speak different languages People from health monitoring don't use the same terms as people that are more from more coming from the decision analysis point of view and then again people that deal with Reliability of structures as their main focus and again, you say indicator It means something very different to those people At least it meant Something very different to those people and that's a challenge. We realized also during some of the workshops we did and we try to address Okay, now next achievements personally, I don't like the word achievements very much maybe because I'm a scientist and It's somehow I Prefer to look at what what are the challenges what were the challenges and to what degree we have worked on this We know that we That the work is not finished and the more you know the better you are the more you'd realize that you still have to do things but of course we have achieved a few things and this presentation Goes through some of the things we have achieved. By the way, I forgot to mention that Not only was a lady had see also Leading this group together with me. She also made the first draft of these presentations are actually She did her work even though she's not here So we have Like in looking at the pure Output in terms of hard and soft Outputs we had actually produced fact sheets That reflect the work that that was done in work in in conferences in The groups where we met and I'm going to look at a few of those a little bit one thing we have done in the working group is to to come up with a Slightly different scheme than the working group to as presented We work together quite a lot initially also because it was not always clear to us And it's still there's no hard border between what working group two and working group three have done So we work together and we realized that okay for our purpose looking at methods and tools It might be useful to structure the problem in terms of an influence diagram that follows The decisions and the uncertainties throughout the process the colors here are the same ones that you saw in the presentations before So we use the same structures Not exactly the same code the RGB code will be slightly different, but still green Red and so on so what we have here in this influence diagram is a structured way of formulating a decision problem And in this case the VOI problem for SHM those Green greenish colored Parts here are to say typically physics-based models describing performance of our structural systems and Associated what I would be here call indicators again. We had this discussion about this term It's not always used in exactly the same way basically things that you can observe or that one can observe and measure To learn something about the condition of the underlying system. We don't have here It's somewhat red orange color in my screen these are the things related to the modeling and Decisions on the health monitoring so based on so what you can observe Depends on on your your system indicators and on your technologies that you use to to make the observations And Then as we know also very There's no benefit of all these health monitoring if we don't do any actions based on the monitoring results That's what you see here And then the actions will then change your system Hopefully make it better reduce uncertainty reduce particularly reduce risk Then all of this comes at the cost or at the benefit also and In the end we try to optimize this there's a time thing here So this thing is done in this I come back to that it's done in multiple time steps So the challenge lies in the fact that we have actually a sequential decision problem. She's kind of Hidden here in this slide Now the main point of our working group was to look at these techniques here that are kind of have Seem to be less important But actually are crucial and these are Different methods and tools used in the different parts of the analysis and as I said before we have to bring very different methods together or not very different but different methods we need methods to assess The structure the duration models we need Uncertainty Quantification propagation methods we need structural ability methods. We need the social mention nowadays We have all these machine learning tools and all that might go here We have Bayesian analysis We also have economic assessments that it should go there So there's a lot of different type of analysis and Methods that are needed if you really want to make this this process and there's basically nobody here who is really an expert in all these fields it's not really Maybe it would be possible, but I think nobody can claim that he's really the In-depth experts on all these fields So the challenge was to bring this together and the fact sheets in a way helped that no, so just go through a Do very quickly summarize Mention and just mention a few of those fact sheets that that go here and and These are in the colors where they correspond to no, so here we speak of indicators. So in this case We have the the paper by actually By the opinion, why did you ask about this question before because you wrote a paper about this? No, I Didn't understand why you ask a question We wanted to know whether the people are aware of your own fact sheet the thing is your fact sheet is in working group free so Where it's this is basically the review of vibration-based the damage identification methods which is most common studied type of approach to SHM here It also has a survey of algorithms on the extraction of the condition indicators We also then then there's another fact sheet here that Let's say much more directly applied is this real case study where the The methods are applied to And also tested and particularly the the effect of environmental influences on the health monitoring is evaluated there also Here maybe to just to quickly mention we had the fact sheet on a large data set which available therefore Let's say that this can but that can be used to transfer the methods that we develop into into practice looking looking at data from bridges I think was from roofs from all type of structures and then you have also specific These are some of them of that she's dealing with very specific applications from foot bridges to structure ship structures We also then have fact sheets that looked at within more broader on really the methods as they were applied we mentioned already based analysis many times and I'll come back to that also here. You know, this is a challenging vision analysis is challenging in the context of real structural systems where you deal with heavy models where you deal with its completely complex systems And we also have a fact sheet on Robust identification and prediction so which was mentioned before by cost us. So there's a limit there. There are some Cavities to the Bayesian analysis and if you're not careful you might Depending on what you do and depending on how you choose your model you might end up with very different results and So there are also people try also to find alternatives to the Bayesian analysis and Ian Smith here from Lausanne Did the fact sheet on that one approach and then there is Into a very nice fact sheet also by Bernd Lehrer here who again uses these methods and applies them to two examples one is a Of or at the offshore riser response monitoring and if you remember the second one is the ice-induced Strains in a ship hull Okay, there's also some fact sheet here on relates directly to the Method for optimizing the decisions. This is a POM DP Method which can which is one approach of solving the sequential decision-making problem and I Guess it goes as Papacos that you know, it's probably the best expert in this area in our field So he together with the Lenny wrote the fact sheet All these fact sheets are available. They will be So I think that I don't want to go into the details particularly because I'm not the one who wrote most of them so It's not complete so as you see There are so many So many different methods that we need and for each of them you could write multiple fact sheets So for sure, we are not complete. It's also not possible to be complete without writing. Let's say a hundred ten thousand of pages But I think we have a good starting point for somebody who is new to the field and wants to learn about key methodologies and tools In this area so also of course there was dissemination efforts there were special Sessions in conferences people wrote the papers related to the cost action inspired by the cost action coming out of Scientific missions and this list is much much longer I realized that the list on the home page is actually not up to date So I know at least of many other papers that are not there yet, but we'll be there so that has been a lot of activities and Now thinking how to do this presentation. So I was thinking, okay, I Can just go on and give you an overview, but it's a little bit boring without getting into some depth So I decided to to look at two Just give a little insight into two of those Works that have been done here and you forgive me if those are two works that I've been involved myself because those are the ones I can present best So maybe I'll just give you five minutes Of insight into some of the things that that were developed during the cost action Corresponds it also by other sources, but but it's a lot actually inspired by discussions that we had here Okay, one of the things that is really central to the speak of methods and tools is that In the general case at least if you want to model the whole process relatively realistically you end up with an enormous Decision problem. Why? So think you start with the monitoring decisions. You have already many options but then If you in the previous theory decision analysis, you have to model the whole lifetime and in principle at least in theory all the possible options of How things could turn out in your system? So it starts with the deterioration process In let's say this is the discretized time steps in year one year two So you have some you might have some deterioration If you think of a structure that is not just Deterioration yes or no, but it could be different type of deteriorations at different locations in the structure. So this is a huge Potentially a highly already high-dimensional parameter vector here You have a system condition which maybe you can simplify to say for failed. Maybe not Then you have Inspections that you can do you can also do additional monitoring You could decide to do maybe put monitoring later You can do inspections. You have outcomes of those is it those monitoring inspections again. This is high Possibly infinite dimensional parameter vector. You can then do some decisions on repair Maybe you can also change the operation of the structure There's many decisions again that you can do and this is just for one year and then it continues so such a model Really has a problem of being but somewhat exponential You have an exponential growth or polynomial growth depending on what You're looking at and already a long time ago people realize that this is In a general case you cannot find an optimal strategy in such a situation, which means we cannot also We have some difficulties as identifying the the value of information Because it basically the assumption is that we do the optimal thing, but if you cannot actually identify the optimal thing That's a bit of a challenge Peace space complete Now so that what what do we do and well This is not nothing new. I will show you that is actually completely old But we can give it a smart name that comes from the artificial intelligence community So what we can do in order to approach the the problem of These polynomial or exponential complexity with respect to so many having so many possible decisions to do is we define So-called heuristic rules decision rules. They are called in classical decision analysis And so we parametrize the problem by a set of heuristics and some of you might know might be familiar with the inspection optimization of inspection where value of information has been used since the 1980s I would say or 1990s and Some of these rules for example would be for for inspecting components is is that you whenever your Component or your system probability of failure reaches a certain threshold you you decide to inspect Or more practically that you you decide to inspect every five years That's also what is implemented in many rules now that is not going to give an optimal solution Because most likely the optimal solution is not to inspect exactly every five years But it has shown quite nicely in some Publications This gives a very good solution for a simple problem. This is a simple problem because it's not a system is just a component and just inspection but We looked at now extending this heuristic approach or as I call it direct policy search Term coming from the artificial intelligence community to To also with more complex systems So we don't only have a fixed inspection interval or a threshold But we also have the number of components to inspect we have to have a heuristic for which components to inspect What we have to have a heuristic for for what how do we do repair? When do we inspect based on monitoring results? So? one can add these we can add that and The we have looked at a bit into how to optimize for example the locations of of inspection or monitoring by using a Heuristic that says okay, we basically we should inspect or monitor those components that give us the highest value of information now That is something We cannot directly compute but we can find an approximation of the value of information. It somehow depends on how Reliable as the component it depends on the the importance of the components in the structure And and we parameterize this By a parameter and we then try to find We then optimize the whole thing Okay, now. Well, how does that help this heuristic and here we come to the way also back to the challenge I showed with the decision tree so In the end we want to optimize life cycle costs. We want to find the strategies that minimize life cycle costs So so you are the operator You're not the operator but the operator comes to you and he says to you Tell me how I minimize my cost Hopefully And because this of course included the risk and the reliability they might have constraints, of course But you want to that so you have to calculate that the risk and the costs Which is the farm which is the sum of risk Inspection monitoring cost and repair cost now the risk is a you seems trivial again is the cost Time to probability of failure Everything here is conditioned on the strategy. So we fix the strategy is important We say that okay now for let's assume we have a fixed strategy and we evaluate the cost of that strategy But even that is the still that has to see the polynomial Exponential problem and that is the as a gem outcome So that is a high-dimensional infinite dimensional vector So what once we fix this however the good thing is that once we fix the both the heuristic and and what we will observe in the future are There's no more decision involved because the decision is basically fixed by the heuristic and by what we observe so When we simulate that we can we can just directly evaluate For given set as The risk because the disc this probability of failure here depends on your actions But the actions are prescribed by s and said And then here we still need methods here because we have a reliability problem Still you have a structural system. So you have to have my reliability methods that actually can deal with this and It's a vaginal analysis. So so this is a very challenging problem But at least this is a field where efforts exist and are are being developed for example by cost us and so we have You can you plug in those those methods by the way, you might it looks trivial But some things are not trivial at all and here you have a conditioning on set and here have a conditioning on observations up to time t Whereas here you have Depends on the observations to the future. So in principle, you should also depend on the future observations But we can show that it doesn't but it's there's some things that here behind this simple looking formulas that are not so simple actually But this is a correct. So in the end we end up with this expression And then the good the thing is that we then have to Somehow get rid of the this fact that we don't yet know what we actually observe because this is always a random High-dimensional but we can deal with this in a Monte Carlo's approach. Why because the variability of this function is not so large because we have already gotten rid of the Remember for property of failure calculations, we need a lot of samples remember remember because the variance in the Intent indicator function is very large, but In the case where we have already done the integration over the indicator function So we have already calculated the conditional probability of failure The variance of this is not so large with a few hundred samples You can run you can you can solve this high-dimensional problem. So there's no better method than Monte Carlo at this point And then you have to do an optimization and that Okay, I'm not going the detail but to optimize that it's a stochastic optimization or You have at least your objective function is evaluated on the color. So it's a you have noisy objective function so better to use stochastic optimization and We have been using is this cross entropy based approach that gives quite good results So see here and then you optimize all your parameters. So here's for example, you have Okay, I'm not going to explain in detail what this means because the number of inspections the time and then some other parameters here But you in the end you have your objective function and you can find an optimal strategy and that enters then the value of information analysis so this complexity of the decision process and Together with with the fact that we have also a complex physical system process to represent Is a challenge that this is an approach But it's also not a complete solution yet There are many things to to do and there are also other approaches as I said umdp is another approach that works better in some cases than in than this So that is one of the challenges Now I'm looking at the time Just very briefly here. I want to mention this because this is another challenge that that we see Is that we need I mean, we have seen many applications, but but often we are dealing with simplified problems The real problem is a structural system So typically we need a structural system Analysis reliability analysis because we need to know the probability of we need to calculate the probability of failure in order to estimate the consequences and in order to estimate the effect of the monitoring system and And They're actually not that many methods that can realistically calculate Rehabilites for for structures for real structural systems. So so there has been work That that was performed During his cost action, which was also reported in his cost action on on further Trying to calculate the system reliability conditional information always We have to do it with a Bayesian approach in addition and one of the approaches that was developed by by Ronald here in in in BAM is that it was where he looked at the system that Basically, this is the case where a system is represented by a set of components each component Can be in in in in in an okay condition or in a failed condition And you can see already you have two to the power of n possible system states And then trying to to to find efficient methods for for this type of of system representation Um to calculate conditional probability of system failure in time Here uses a Substant simulation based approach to deal with the high dimension again. This problem has a high dimension It has many many random variables so this Substant simulation works quite well works quite well. We will still have a lot of uncertainty in the This is computational uncertainty Not modeling uncertainty But so these are some approaches that were developed and discussed and and worked on during this cost action Well, there was a try to It's not complete yet. We still You can still give inputs from feedback, but there is a first attempt of a kind of a database on on software that is available for Uncertainty quantification and more specifically value of information analysis. Now, you will not find Steadicated software packages for voi analysis. What you find is software packages for uq uncertainty quantification for reliability But but of course those I have Those tools boxes have many of the methods that we need for the voi analysis not all but many so You can you can find this information And just to show one example here of one one of the tools that is described there And this is available to the community And Since we are going to finalize this In the next In this period Please have a look at it and also let us know if we have forgotten some things Okay, finally And this is mostly the work of eleni hattzi and her group was working on a benchmark that is a benchmark for the Not so much for the decision part yet, but for them for the SHM part and Some degree the structural part Um A lady is not here so I cannot really give you many insights into the detail. I think generally it's quite simple There is a relatively simple case here of a beam Free supports you can you have the possibility to introduce damage Um in these elements that are indicated here. So there are six possible damages one can even can indicate one can introduce and Then the structures analyzed using plane stress elements And there are many things that one can vary that are also in real life systems For example I'm skipping forward here first. You can choose here different deterioration models you can Choose different environmental conditions and these environmental conditions affect of course the properties of the model um, you can Change you can change material properties loads. It's all it's also open source. So you can modify whatever you like but there's also a python based in gui here, which Helps to for people who are not So I feel with working directly with the cold The points you see here are the points where you can really get measurement data So if you want so you can assume that there are there are sensors there You can also and you can generate hypothetical sensor Then the results and you can use to test your Urbation analysis your your your mshm algorithms and you could also extend that to And this is something trying to do is to extend that to test your whole VoI framework. So now let's assume you put there some sensors. Can you calculate the value of information of those sensors? As every benchmark it It can it only represents what you put inside So it represents the uncertainties and the environmental factors that are actually encoded here it might not Include it does not include of course what you what you find sometimes in real data things that you don't expect at all But it's I think a very nice tool to For the community since I can really feel the hunger piling up here, so I'm concluding And just but a few lessons learned. I want to mention And it's very personal. Of course if you ask people that participated in the working group, they might have also different They might have very different statements but I guess that at least some of those are also common agreement Not surprisingly, we knew already before that you need a multitude of methods that you need to do a VoI analysis and that is also one of the challenges that we face that At least if you if you want to do that you you actually need to spend quite a lot of time and sometimes One thing we didn't do in this cost action, but what I want Thinking what should do is the the VoI of a VoI analysis What is the value of information of doing a value of information analysis? And that might sometimes not it's not always a positive I would think Particularly of course if the If you look at the small small problem So there is a there is a challenge We have methods for basically almost all the individual tasks I showed you exist But they're not always known to everybody of course Um, there's a problem with terminology And it's also a problem that we cannot come up with to say, okay, if we could just say, okay from the best method in structural ability is this the best method for assessing for evaluating cost is this the best tool for For doing the preposterior analysis is this if we could just put it together it would be great, but the methods we need and the The tools we need that are best suited are very much application specific and That is a bit of A challenge so we cannot go and say, okay, that would be nice. So if you have this type of problem use this Yeah, this is a problem use this It depends a lot on the on the application and it depends on the modeling that we do and this brings me to to Maybe fourth point, which is that in my point of from my point of view the challenge in The real application typically lies in in the modeling How do we model the problem and that the because you can make a model that Then puts a lot of burden on the on the computations Or you can make a model that is much simpler in terms of computations and so the The real challenge and that is very difficult to to to put in a in a In a cookbook type of Documents say, okay, do this do this do this is the modeling here and we engineers always say, okay, this is what we can do No, we are good in modeling everybody We know which models to use and so But in vi this is really the challenge This was the break for the It's a lunch break. No, yes, okay, just My math teacher also always did five minutes longer than Than the gongs or I will do that same And or three minutes Okay, so okay, I spoke about his heuristics and skip that System mobility I also mentioned is a challenge And as we saw also that this is even this common vocal Vocabulary is a challenge So remember this workshop we had in munich and we speak about indicators and After five minutes we realized that if you say indicator, you mean something very different than if I say indicator and if somebody else said indicator, so But we didn't realize at the beginning we just And and and when we write papers also in vi we always have this problem that okay We start out and we call this is a a is an action decision and then this is And then we go on and we find that people in In the mechanical modeling use a for something very different with the crack It's the crack length and then you go further and you read a paper about The particular assessment of health modeling data and they use a for again another thing And then you have to put these things together you have to come up with With a different parameter But then the person who reads this from from from shm community will not understand because a means something else to him or her and This is of course, it's always a problem of interdisciplinary work But the issues that That that we have to somehow or We also try in this cost action To to to do that to improve that to find the common Common ground in which we can communicate Or at least to create this understanding that a is maybe something else than in my personal Narrow overfield All right, this was some of the lessons learned conclusions so Just a repetition again. We need to combine different models methods and tools It's this is the the interesting but also the challenging part. We have provided A collection of methods and tools that that can be used Um, that is now available or will be available also for the public To use and to start as a starting point for future work and for people who are interested in this field Um and participants of the working group have I think also at least in my case based on the discussions and we had during this cost action further develop methods and tools And including develop the benchmark That concludes this presentation And I'm sure we have one or two questions If you want to make yourself unpopular with everybody else then You can now ask one or two questions All right Yeah, you really like you really like to be unpopular Yeah, exactly. No, nothing to do with No, I in your Description of this heuristic approach You mentioned that the identification of which details to To monitor or to inspect That the selection would depend on a number of factors of which also the importance Of of these details, I assume for the overall integrity Was one of the points But in this context, I just wanted to To Mention that Is as you well know, but you didn't mention that so I will do it that in the dependency the dependency between The locations the phenomena the locations which you observe and And I would say the really critical Spots in in the structure Also plays a huge role and and it can in many So there is a strategy Where you actually you design parts of your structure to be really weak At a location where it doesn't matter at all if you have a super strong Dependency between deterioration at that point Which may be an easy to inspect or easy to monitor Location if there's a very strong dependency to the critical the really critical and maybe More Expensive Locations To to inspect so like these I can't remember what we call them Daniel, but like Yes But basically yes, so dummy points or something. Yeah like dummy components or yes Like like moves kind of testing it doesn't really matter, but it contains a lot of information about places where it does matter Yes So that yes, so in this context, maybe just to add to this is that what we This is the fact that I did mention is that the probability of Actually having some some defect matters. Yeah, the reason is that If you inspect something where it's quite likely that you have a defect I mean if the thing is that we see the structures and small structures that we're dealing with Are higher reliability. So 99% of the time you're looking for something you will not find anything because they are doing well so that means that It turns out that those components which actually are not maybe are weaker Can give you more information because it's more likely that you might find that and that even if they are not the critical ones They might serve as an indication for other components Which is and this through the dependence that you have so They are built by the same manufacturer. They are under the same environmental conditions And Yes, so this is one of the reasons actually for allowing me this clarification. So this is one of the reasons why there's having a higher failure probability Or less lower reliability gives potentially a higher value of information. Yeah To look at that part of the structure and I would also like like to address So you commented on jen smith's Approach for robust estimation right? Well, I didn't really comment on his his approach. I think I mentioned in the general problem that he is trying to address Yeah I must admit that I also don't feel too comfortable speaking about his approach because I don't Feel I have Understood it enough. So we will not go into a discussion of that without jen actually being here Uh, but uh, also there was a comment Before about the significance of choices on possible priors And how that can seriously affect let's say ranking of decision alternatives in in in the end I So in With these two comments I I just want to highlight that I I still think that this community is too damp determined to find one system and is seeing the The importance of realizing that different assumptions for instance different assumptions on priors that They actually constitute different possible universes Uh different possible systems and you need to pull the effect of those possible systems into the Uh into the decision analysis in in in order actually to appreciate Accounting for these possible systems consistently I mean there is in many many cases you can debate assumptions You can debate, uh, let's say model adequacy And in the end you will you will not come out with with one Absolute model which can explain it all right? And therefore you need to account for it and robustness has to be seen relative to the ranking of decision alternatives So, uh on this topic on this aspect I think we are failing we need to make a slight turn around And wake up to reality And just realize exactly as you also mentioned in the beginning the more we We learn the more we appreciate That we don't know and this this topic Is definitely one of the I open us For myself. Yeah But there's also an imbalance between the Yeah, let's say this is Nice theoretical framework, which is not just nice. I mean, I I believe in that framework very much um, but there's also the We have to make some amendments for the for the reality of of of real life that that we often are not Able to to to to consider all the models because we have just a limited amount of time And we have to do something very fast and or even if we had more time But given the fact that we're dealing with a problem in many different areas so that My point is that here, I think This this this concerns that that are raised in in in this area not just by e and smith but other people um In other communities. I came across this first in the hydrology calibration of hydrological models, um, but if that These address issues that observe observe in real life and There is also a Bayesian way to to to deal with this But uh, I think this is something but I think we should not We should not make this into a discussion among ourselves, which we can have in the dinner But maybe we should if there's another I don't want to Actually, I I took a course in how to how to address uh, nasty questioners Yeah, I mean I was a phd student. I remember but uh, I don't want to make this discussion short, but Yeah, just just one maybe one more comment. So what what do you see in in in much of data driven modeling right now is that that In in appreciation that you might have these different models popping out of your data Some schemes for model averaging Are also being applied but that that then Let's say averages the effect of potential different models At the interface between the modeling and the decision making and this is where we need to be very careful We need to make the model averaging or the decision making And and this is like a A new type of paradigm. This is something to pay attention to. Okay. Yeah, but okay, it's a formal question But if I just want to say one one sentence is that you the one issue with this is that we have to In fact, you're assuming that you know the utility function throughout your process And this is something you often don't But let's discuss this. Okay. One more question by someone. It's by York Very short question Or maybe actually two questions But the first one is like bridge design currently or structural design is based on the safety factor concept So it's not a full Bayesian approach now There's two questions the first part is if you were like the one that does this generalization standardization stuff and you're Would you move to a full probabilistic approach in terms of looking at what data that we actually have and how the design concept works And if you if you answer that question with yes, what is still missing to get to that point because you already said like modeling issues What is the prior distributions? So how would we if we want to do the full probabilistic approach? How should we go to that goal at some point from your point of view? Okay Well, first of all, I don't I mean I don't believe that in the near future we should go to a full probabilistic approach Maybe not even in the mid middle future Maybe in the long term future We I mean one of the I mean, I think there are also different steps. So one thing is and this is something would be I'm looking at Jochen Köhler here because he's the expert in this really but We look at the code development that is done now. So there's a there's a level behind the code which is Based on probabilistic analysis where we try to calibrate the code to that And in principle that basis Could be used For for that actually designing structures The reason why we don't do it is because it would mean that first of all everybody In the structural engineering community would have to be an expert in these methods or at least reasonably well Not to make mistakes And there's not a question of what is the benefit in this so what do you gain by doing everything probabilistically and and and There's a balance of this today No, we should today we we should stick to the the format as we have it now, but as we get maybe maybe in 20 years We don't have maybe the computer is doing everything by himself For the computer using at least is the standardized probabilistic models, which are not necessarily the We call nominal models Might actually be better to use than what we have Today, but what we realize is that the the current code and the current practice is Is it's a strange mixture of of of empirical of empirical Let's say knowledge and things that have gone into it by by by a huge community Where people have with a lot of experience and and and things have gone wrong So we have realized okay, there is a problem here. We have to do something here and The issue is that it's not very well documented in more in many cases So it we would like to think that okay the structure we have It's it's a semi probabilistic. It's a very short question, but the very long answer I realize So I'm I'm afraid I'm having I'm not yet reaching the point where I see the the closure to the question So I'm trying to make a very short quest answer and then we can continue afterwards. So the very short answer is I believe that the goal is good. We should try to go there. I don't believe we should implement That in the near future But we should try to make the current Kobe design Also more more clearly documented. We need to understand why certain factors are there. Why What is the assumption go away from this or try to get To the extent possible rid of these Empirically based factors that are there somehow that nobody knows where why they came from but so that So that we are able to to slowly move into the process of formalizing Of better understanding the process and then We're probably doing fully automated fully probabilistic design in 100 years from now Okay, thank you very much. Thank you very much for your presentation insights and discussion


Income Tax 2021-2022. Government Authority to Tax. Get ready to get refunds to the max. Dive into Income Tax 2021-2022. If you work in the field of taxation you will almost certainly be asked by a client at some point in time or you might ask yourself where does the government get the authority to tax. A natural question in the United States a place where we're historically quite skeptical of taxation tried to avoid the taxes for as long as possible and you might hear arguments saying that the tax itself is against basically natural law theory so you might want to have handy in that event basically it's going to be the 16th amendment to the Constitution that's just a response if you need to respond to that if you're in like the tax field because obviously the tax has been well established at this point in time that ship has already sailed at this point in time the argument is really over the extent of taxation and basically how the


When we talk about a salt, we don't just mean regular table salt. We are actually referring to a very wide spectrum of ionic compounds, or compounds made of negative and positive ions. Some of these salts, like table salt, which has sodium chloride, are soluble in water. Some others, such as barium sulfate, used as a contrast agent when forming an x-ray image of the gastrointestinal tract, are insoluble in water. In this lesson, we will learn two different methods to form and isolate soluble salts. You have likely encountered copper-2 sulfate either as crystals or in solution, but have you ever wondered how it is made? Did you know that you can make your very own copper-2 sulfate crystals? Add two scoopfuls of copper-2 oxide, which is a black powder, into a beaker. Then add sulfuric acid so that all of the copper-2 oxide powder is covered and a bit more. Mix with a glass rod. Not very exciting, is it? But if you heat this over a roaring Bunsen burner flame while stirring, it turns bright blue. This is how you know that the reaction is complete. Let's remove it from the heat and let cool while we discuss the chemistry of this reaction. Copper-2 oxide reacted with sulfuric acid to give copper-2 sulfate and water. As copper-2 sulfate is soluble in water, a solution is formed. Copper-2 oxide is a metal oxide, which is an insoluble base. And as you may already know, sulfuric acid is an acid. So what has happened here is an acid-base neutralization reaction where a salt and water has been formed. An acid-base neutralization reaction is a type of A-double displacement reaction. The copper and hydrogen ions have switched, much like how certain dancing partners can switch. Therefore, a soluble salt can be made by reacting an insoluble base with an acid. Referring back to your solution, you may find some unreacted copper-2 oxide powder that can be removed by filtration. Here is a mini-challenge. How can we remove water without removing copper-2 sulfate? Please pause to think about this and resume when ready. We can evaporate some water right over the Bunsen burner flame. Then we can place the solution in a crystallizing dish near a windowsill and find that copper-2 sulfate crystals have formed. These crystals are bright blue and you may notice that they have an irregular rhombus shape. We can also make soluble salts by reacting a soluble base or an alkali with an acid. We will make sodium chloride by titrating hydrochloric acid with sodium hydroxide. Remember to add phenolphthalein so we know the endpoint of the titration. And you remember the correct color change. Pause and resume when ready. Phenolphthalein goes from colorless to light pink at the endpoint. However, we don't want pink crystals, so we must make note of the amount of sodium hydroxide used in the initial titration and repeat the titration without phenolphthalein. Here is a challenge. Can you write the equation for this reaction just as we did in the first example? Use a piece of paper and a pencil, pause the lesson and resume when ready. The answer is this is a double displacement reaction whereby the hydrogen and sodium ions have switched. As sodium chloride is soluble in water, what we get is a solution of sodium chloride. This is also an acid-base neutralization reaction, yielding a salt and water. Now we can evaporate as much water as possible and let sodium chloride crystallize over the next few days in a crystallizing dish. Unlike the copper 2 sulfate crystals, sodium chloride crystals are white, smaller, and have a cubic-like structure. So there you have it. Two soluble salts have been made by acid-base neutralization and isolated. To recap, we can form soluble salts by the reaction of an insoluble base with an acid and the reaction of a soluble base or an alkali with an acid.


afternoon. I hope everyone has had a great day. We're getting towards the end of the day so I'm sure we're on information overload which is part of the reason why for this team for this particular session we decided to change it up a bit instead of talking at you, having more of a panel and a discussion so that way it could be a little bit more lively than a single person up here probably be talking. So I've got two of my my partners and colleagues that are going to come up and speak so I'll introduce Lindy first because she's ready. Lindy sprinted a hundred yards. Yes, yeah from the Edge booth all the way here. So Lindy would you like to introduce yourself to the group? Absolutely. Good afternoon everyone I'm Lindy Sabloff on the COO of guys AI. Guys is focused exclusively on the Far Edge and we're gonna get into it a little bit why we feel that that is so critical but we're solving real-world problems for enterprises and we're doing it in a way that is a more efficient system that is more effective and it's so beneficial for organizations. Excellent and then Darren, Darren would you like to introduce yourself to the group? Sure. Hi my name is Darren Blue I work in for the Intel Corporation and our Network and Edge Group specifically in the Federal Industrial Solutions Division. We focus on manufacturing and energy as well as some federal applications so what we'll be talking about today is when we focus on we talk about the Edge we talk about all the way down to the machine like Lindy's talked about here a little bit and where AI plays in the control and the data plane and how all that comes together and how that's actually gonna move towards the future with we've all heard of an industrial 4.0 and the digital transformation that's happening in manufacturing. Awesome so I'll start with you first Lindy with a couple of questions that we have so when you're working with manufacturing customers what do you see some of the biggest challenges that they have? There are a number of challenges with manufacturing and I think it's funny to think of a large manufacturing plant as being a resource constrained environment but it really is and it can be resource constrained in a number of ways one you can have bandwidth issues two you can have network outages or three you have to be much more cost efficient and you can't do that by sending all your data to the cloud the better way to do it is by doing inferencing at the edge where data is actually created. And then Darren I know that you've spent we've talked a lot about this you spent a number of years looking at the challenges that manufacturers have so when you think about kind of how they're using AI into their factories what problems do you see that solving? Yeah so specifically in North America and Europe there's a labor shortage that they have difficulty keeping workers so AI bringing AI in and trying to replicate some of the knowledge where they've had workers that have been in jobs for 30 plus years and they can't find someone to replace that trying to extract some of that knowledge into an AI model is very important trying to not even be able to hire someone to do say an inspection so Lindy has an inspection use case that she's showing over in her booth and some of those kind of things as well as just being able to make things run more efficiently and then with the pandemic that happened they put things together with bailing twine and duct tape and now they want to put some kind of enterprise solution in place because they you know they realize hey this was a better way to do it how now do I take that to the next level and make it more permanent. Yeah I couldn't agree more about you know the issue of attrition in the workforce and while AI isn't you know directly replacing people I think the way we think about it is we're empowering field engineers right and that AI can be the Iron Man exoskeleton that allows them to do five to ten jobs rather than just their one and then have a huge gap. We see a lot of that as well with a lot of the automation that we're starting to put in place and many of the manufacturers so it definitely gets to the skill challenges the talent shortage that they may be having. I'm really curious on how you see and I'll point this at you. Absolutely. So with the AI space I think in the past we've seen a lot of kind of bolt on solutions that are providing kind of data into manufacturing establishments but now we're starting to see where AI may be starting to interact with the control systems or in the actual loop and so I'm curious if you can kind of share what you're seeing from Intel's perspective on what the manufacturers are doing there. Well and let me tie this into the demo a little bit that we have and we're showing with Kelly and her team so the if you look at the control systems that have been in place for and worked very well for long term they weren't really necessarily built with the instantiation of AI in the future they weren't necessarily built as data systems so when you start thinking about data and the IT types of solutions you've got to come up with an infrastructure that can handle and flow that data and at the same time as you start adding these devices in and the factories get bigger there's more and more devices you've got to find a way to integrate those things and manage those things so that virtualization technology that data flow those containers being able to put all those together and then have more remote manageability more remote lifecycle management is what's coming into play as this whole transformation is beginning to happen where it's going to it's going to be a some time till this happens obviously because the manufacturers are not going to replace it till they you know see the benefit and need to do it but that's what's kind of happening that's what we're showcasing in the the demo and that's what's the value prop that's going to happen over time as people start making that transition they start seeing what the art of the possible is with AI and then want to feed that back into their control type solutions I think that's really interesting so I know like Lindy your team is predominantly focused in the computer vision space but I know that from discussions that we've had you're starting to see just like the natural interaction of just AI in connection with even computer vision and the data coming from control systems so how is guys kind of preparing for these changes that are coming to manufacturing that's a great question I think we need to take a little bit of a step back I think they're still unfortunately is this persistent notion that AI models are large that they're cumbersome and that they're expensive and I think really when we need to start talking about the edge there are a couple things we need to get across one you can have these incredibly powerful but lightweight algorithms right I like to joke around it's it's Robin Williams genie in the 90s version of Aladdin where he pops out of the lamp and he goes phenomenal cosmic power living space and that's really how we have to start thinking about you know edge enabled AI and that these models are available and they are pardon the overuse of the term transformational but they really are transformational and I think from an adoption standpoint it's going to help really start accelerating adoption to be able to have you know these different algorithms for specific use cases that can run on you know very lightweight but you know small compute it is absolutely critical and especially you know I warn the reasons we've partnered with Intel you know is the ability to optimize on open Vino right and that you know when you're doing quantization you can drastically increase the FPS and then you're lowering you know the memory consumption and so it makes it so these models can run can run on small compute and can run very efficiently but the other thing I think to think about an edge is not just how AI has shifted to fit manufacturing to you know to fit energy and to fit you know other industries it's also to say that you know it's about scalability right and by having lightweight models it is more scalable but lightweight models that are agnostic and can run on different devices and also take input from all you know the proliferation of IOT sensors that are out there I'll stop it yeah actually I would love Darren for you to kind of chime in on this because I know we've spent a lot of time talking about embedded system developers and how different they are there's actually some of them in the audience because I know some of them the way that they operate is a little bit different in the way they think about how to build the applications or even the AI models will be different so I'm really curious for you to kind of share what Intel is trying to do in this space with especially with the use of open Vino to help with some of that optimization yeah I mean I think that this this transformation this whole thing we've been talking about actually opens up space for the developers and one of the things I actually hear from the manufacturers is the systems of the past because they were pretty vertically integrated because that was necessary when you start opening it up to more of an IT type of environment you can get developers and so the complaint from the manufacturers I get innovation only from one company when I can open it up and create a more open system I get innovation from all the developer community I get I have my own people that can write I can buy stuff from the developer community I can hire contractors I can even sell my own back out to the developer community and so we as Intel try to encourage that as a solution space so that you know it advances everything much faster you get more eyes on the whole thing you get more innovation happening and then that drives the whole transformation forward and do you have I know that we may not be able to talk about specific logos like specific customers but do you have an example of how this has worked with in the past with you and the work that you've done with some of the manufacturers with the manufacturers well it it's still very early in this transformation so the developer community is still kind of spinning up but it you know like she mentioned open Vino has had a lot of success and so there are a lot of companies out there using like you know you've got I've got one sitting here like we would consider her as a developer right and so she's taking that open Vino space and moving it forward we've got something that expands upon that with a data bus called Edge Insights for Industrial that is also being used by some system integrators to deliver some video applications as well so those are actually continuing to move forward and and be evaluated and taking more so right now it is more in the video inspection space but I think you're going to see that expand into more and more use cases as it goes forward okay that's great and so Lindy on your side with your team as you're looking at some of these new use cases and the interaction with customers can you describe for the team like what that engagement model really starts to look like because I know that you have a starting point of a model and then that's where your team really kind of engages to the customer environment yeah it well there is some you know one size fits all in in terms of AI models you're always going to have you know a certain bit of specification for the customer right and especially to maintain accuracy you know AI is about iteration and so as you go in you know and you're working with the customers you know to understand you know their needs from a hardware perspective power consumption you know what is the bandwidth available and so you know going in and really being able to very rapidly it's not a completely bespoke solution but at least customize so that you can achieve high levels of accuracy depending on the customer okay so now I'm going to ask you the thing that because I work for Red Hat I should ask you this so let's talk about how we've been working together and so in particular let's talk about the work with Armada and the system that has been built and the role that you see some of the open source technologies that Red Hat provides brings to the AI space oh I am an unabashed fan of Red Hat you know I think you know prior to working with Red Hat we looked at some solutions that you know gum and shoelace right that we made it work but it wasn't it wasn't efficient and it certainly wasn't scalable right and at the end of the day the customer needs something that is as close to turnkey as possible they don't they need something that they don't have to add IT personnel and that doesn't take you know in tens of hours of training they need something that they can install turn on and use that is going to make a difference in what they're doing you know be it manufacturing be it oil and gas and so where Red Hat has been critical to us you know especially Red Hat device edge I think you know we had looked at you know different Kubernetes solutions but when you're talking about the far edge and you're talking about some very small compute Kubernetes is just a little too heavy and so we were introduced to Ansible a little over a year ago and it's made a significant difference and I think that opened our eyes to say we can actually create an entire edge ops platform around it so we've actually integrated Ansible inside you know doing the callbacks and orchestration inside our edge ops platform so that's one two you know especially rel 9 has been absolutely marvelous so using rel 9 and pod man and then you know to replace Docker and then also using quay as our image registry so we consume a incredible of Red Hat products but it really has made a significant difference and I think you know Kelly you and I've had a number of discussions you know as we go out you know getting back to that idea that you have to provide a turnkey solution to a customer you really need a best of breed partner ecosystem because I think as we all go out if we don't have unified messaging and we can we can't come in and say here is your solution and a solution isn't just the software the solution you know you know is the hardware it's the processors you know it is you know the ability to to optimize with with open vino to come in and say here you know xyz corp this can you know can go right into your manufacturing plant this is this is going to be your base for how we begin automation and it is critical to get those components right but to have you know a a glove in hand kind of go to market with all the partners so there isn't so much noise when you go in and so red hat not just from a product standpoint is has been fantastic and and Intel as well you know working with a number of different departments within the company but everybody goes in and says how are we gonna get this done how are we gonna create that turnkey solution and it is such a difference when we sit in front of the customers and we all say here's here's your sheet this is how we're gonna do it here's your solution you know let's begin and it's it's been incredible yeah well I think I love the power of the the user interface that you guys have built that allows for the seamless kind of not only cataloging of the devices and the locations that you're pointing specific models to in the cameras but how seamless it is for the deployment so I'll shamelessly plug red hat with that because the deployment of all the models within their platform is they use Ansible for that and then it obviously runs on on the podman container system with rel now with that because we've been doing a lot of this work what's great is that when I look at the work we've been doing with Intel and we look at the control systems which becomes like the happy marriage of bringing the control along with the data and analytics together we start to see a lot of commonalities in the architecture and so if we look at as an example Darren the the infamous water tank demo that is with Schneider electric in our edge booth I know that we see a lot of similarities in some of the technology that's used but do you want to share with share for the group just in case they haven't been to our booth how that's all working with the water tank demo absolutely so this is a journey we've been on for a little bit we piece some Frankenstein some stuff together in the lab initially and then over the last year or so we've worked with red hat and Schneider electric to put together commercial software actually to bring in more to reality so we have a simulating a process control demo on the edge area on the floor using general purpose compute so some edge devices they use to core l car lake and tied to a server that uses red hat edge linux along with ansible and a runtime from Schneider electric to control the level of two tanks with one of them draining into the other constantly and it controls a variable speed pump to keep a constant level on the top tank so the and it can do bumpless failover which is very important when you're a control environment so that you know nothing ever lose and then ansible detects that something failed spins a new one up and then you you get the redundancy back into place and so if you haven't seen it please go over and see my friends Rob and Jeremy they'll be happy to take you through all that but the other plus to that then is you've got general purpose compute right at the machine level right out on the floor that allows you to do other things that you might want to try so you can deploy a vision case or you can deploy a machine learning that takes time series data and does something you know if you're if you're pulling information off of a welder that's just with arc and electricity etc to do different things so that allows you to do a lot of different things you can and so you know that general purpose compute on the floor allows you remote manageability allows you that to do those other things as well right there on the floor and then you know the things that we still have to to do is you know make sure that all of that creates the determinism that creates the reliability as we move this forward and there are some deployments being tested actually in real factories in real areas that we'll get some feedback on for that so it whenever that whenever that moves forward and it gives then that gives the sort of the the sandbox for people to explore and what they can really do when you get that kind of opportunity right at the machine I think that's a great point so what we're starting to see in the factories is people putting more general compute in their factories some would call it like a mini data center but the intent is then for it to be able to take a lot of the data that may be coming off of the control systems place it within their historian which is basically a time series database and be able to do advanced analytics on it but in addition that because they have the general compute there you can run additional inferencing applications if you need something that's a larger compute or potentially you can get away with not having it directly connected to the camera on that exact same box so we start to get into more general purpose where in manufacturing today I think many of them are are much more accustomed to everything has one specific purpose right lots of small computers and so that you've got a ton of compute that requires a ton of hardware refresh a ton of software updates when they can get to more of this general compute and start some of the I don't really like the word but it's used all the time the workload consolidation they're not really workloads to me if they're running your business because they are literally deciding whether you make money but I know that workload is a pretty common key term and it you know obviously that's very important for us at Intel and to move towards that general purpose compute but I'll steal a phrase from I work with Dr. Henning loser from Audi and been working for a while he was on stage at an ARC conference and his point was today I do have to buy a dedicated box in the future I just want to buy an application I can load on then I can even try it without having to you know go buy a whole box install all this stuff and if it doesn't work I just you know take the application back out and I can try something else and I can try three from different places and decide which one I like the best so not only does it give him opportunity to try to manage his whole factory a lot better but it gives him the opportunity to try different options from different places as simple as maybe not quite as simple because it is a manufacturer environment is downloading an application on your phone that is very interesting we've had a lot of discussion actually in the edge booth around can we just put QR codes on everything and then people can easily scan the the hardware as it comes in with all the software they need and just plug it in so there is definitely the concept of making things simple especially manufacturing which I know that you brought up as well Lindy and so when we think about how we may build AI systems for manufacturing in the future like if you were going to give the audience a little bit of advice what would be kind of the three things that you would tell them to think about that they have to keep in mind when they're going to do AI in the factory it's a great question I think there are a couple a couple things there you know one you know how are you going to scale right you know especially when you're thinking about a factory or not just one factory but multiple factories across multiple geographies so how are you going to scale and you touched on that earlier it's kind of why we built our Armada edge ops platform is to make you know scaling simple you know across so that you can either run you know in the cloud you know or actually on the edge server itself you know I think the other thing you know there as well is how are you going to keep your data secure and so we've added an encryption layer as well that you need to encrypt at the device level and you need to make sure that those divide you know edge is much much more difficult than cloud when it comes to security so you have to make sure that you have the right system in place so that somebody can't just come up and walk away with that intel nut right that if that happens then you don't have any compromises and you know you need to think about you know what are the parameters in the manufacturing plant itself you know is energy consumption a critical factor for you is cost you know a critical factor for you I think the power of the edge is that you can have hundreds if not thousands of heterogeneous devices but you know I think this is why we've partnered with Intel is that Intel really gets that in a way that other companies that we work with have not and that you know you really can run you know on small compute and the edge is a volume game and you need the right partners who understand that who can build you know the right hardware and the right infrastructure along with you as you go out and as you start building you know the different ai use cases Darren same question for you well I'm going to just build on what she said and expand on it so absolutely the security the intrinsic security I think is comes across is very important the other thing I'm going to say about the scalability is what you see is people will put in they'll test something over here and they'll test something over there and they'll test a third thing and only start putting a fourth thing and then the plant manager the owner or the engineering manager come in and go like well we need a data platform and none of these things connect to it now I've got all these disparate systems that I want to connect I've got to start all over and so they get upset so you have to think about as you start growing you need to think about your data platform and how that goes and then the other piece of when you start installing ai is are you going to close the loop so what I find is okay well I know what's wrong now but I how might what am I gonna do about it and so because in some cases we've we've done some you know POCs and stuff with vision and if and we can tell you whether something's going good or bad in milliseconds but all we can do right now because the systems aren't quite there yet is stop the machine and have somebody come over and go oh okay let's do what we can't so the next question is well how do we connect all these things together and close that loop how do I make it so that it's becomes more and more autonomous which we see as moving forward I'm smiling because I feel like you set that up for me perfectly with our edge ops platform or Armada does you know especially with with IoT devices and cameras you know there's a whole layer in there that actually allows you to start stop processing you know look at camera angle you know set your ROI so you know if you're doing optical character recognition you can say you know focus in on on this component of the so you can there's some amount of self-correction built into it again making it more simple for the customer to use excellent so I want to thank both of you for coming and talking about the work that you're doing it for AI and manufacturing I think that we've got a little bit of time so we'll definitely like let's stick around see what questions that the group may have because I have a feeling there may be some like this gentleman right here seems to be a very interesting question yes it's much more about physical security right because if you're moving data to the cloud then you only have one issue when you're talking about the edge you're talking about tens if not thousands of devices that somebody and these are small devices that somebody could literally come unplug put in their pocket and walk away with and if you're talking about computer vision depending on the use case and depending on the privacy level if you're oil and gas you have some very proprietary information on that device if you haven't encrypted at the device level and then attached that you know kind of glove in hand to the network properly you big problem and so it's not like somebody's coming in and you know lugging you know some big server out of there that somebody's going to notice when you're talking when you're talking about a you know an Intel knock or an SDM or Raspberry Pi right these are things that are very easy to tuck you know tuck into a pocket and walk out with but I think there's also a piece in the the cyber piece like if you're talking about the control system that is very true um when I talk to manufacturers the intrinsic security I'm mentioning is their security today is an air gap like if you would if you wanted to mess with a refinery you'd have to run through the door and turn a handle on a valve right if it now becomes connected everyone outside the cement box that the control system resides in today everyone kind of has the somebody hacked into target through the air conditioner or something if if somebody does that to our refinery it's a bad day for the neighborhood right I mean in something in that case or and so they're obviously very sensitive to making sure that that something like that cannot happen right right those are very important things and they want to make sure that as the system is built security is thought and built in as it's created so I think you have to think about what maybe is taking place within a factory so if you think about whether it's a refinery or it's an actual you know physical factory like making automobiles there is human life that is at risk most of the time when you're thinking about what happens in these facilities whereas when you're thinking about kind of endpoints off of the the ingress or egress from the cloud that's just application and data while privacy and data security are still really important there's a slightly different dynamic when you're talking about the risk of human life and it's not just the the risk of human life of the people that are working in the factory but as I kind of joked with some people when they are looking at the demo around the water tank what happens if this you know the water tank overflows well in real life things could go boom and that also means that you know neighborhoods and you know people outside of the working condition could be at risk so many of these systems have to be designed very differently from a security standpoint because while they may not be a government entity I think many may consider them to be like national infrastructure on thinking about how these particular industries may run right so in it's more than securing the actual hardware yes yeah yes Hello Steve it's nice to see you again industrial-centric context or you know they have all lived very very uh temperature environments humidity environments, power constrained environments and so like in this the whole thing about the way we are but if you have a question I can forward a lot of the answers I wanted to give from the table or give the your context to this so broad once you put a device in manufacturing compliance or in a bus stop that device will be missing from what walks out of the plant over there how critical is the data? GDPR compliance yep yeah the GDPR compliance is is critical if the second you step foot in Europe but I want to piggyback off something you said as well that I think what's critical to think about in terms of the edge is what you don't want to do when when you're talking about security is create you know a very tedious environment as well so you need kind of a good soft handshake between the edge device and and the edge server that you're running so that you can make it so that these things you know are are are automated they are repeatable you don't want something having to go and you know you don't want to unlock the door every single time you have to run an application if you take the device and leave I can decrypt everything and you add on the thing I can try to pull your language model links off of the device and have it reference something in the server for security department and at that point this is securing the zero of the edge but then I think of the edge you can do it right when the zero is just architecture and you segment your network correctly and you have the correct thing to place that the edge would probably be the place you could start carving the most that's one aspect of it you know but you know let's take what they're doing with with Schneider Electric right in terms of that demo like when you start automating you know those systems you have to make sure that somebody can't take them over externally as well and I think it's there are a lot of different things you know going on with that you know especially also not from the security aspect you know but the other thing you know and I'll get back to an earlier point I made AI is about iteration right you can't just have a set and forget for a model otherwise your accuracy is going to drop over time and so you do have to have that you know a good easy to use management layer so you can push updates so you can manage you know one of the things we've added to it recently and I'm excited to demo this for you soon is we've actually added a data layer to it so we've created a data store so now you can pull you know either the inferencing the JSON data or the image data pull it back to the edge server and so you can actually do retraining at the edge so you've made the system actually a little bit more secure because you don't have to bring any data off of the edge in order to do retraining so slightly different aspect of security but critical nonetheless any other questions I'm we're what's stopping people from being able to have a drink so yeah yeah I fully appreciate that so thank you all very much for coming really appreciate your time and if you have any other questions for us you will find us in in the edge booth in the the exhibit hall if you haven't seen it we have lots of really cool stuff that you actually get to physically see it's not just a display screen so that's booth yeah so come on down thank you thank you everyone thanks


Hi friends, welcome back to my channel and a very special welcome if you're new here. My name is Jen. I'm a certified weight loss and nutrition coach and I'm on WWE personal points. Happy Friday. It is Friday, so it is post-Hawaii vacation way in day. It is also WW workshop topic recap. I'm going to share with you some unfortunate news as well as what my plans are moving into this next week.  Subscribe if you're not because I do a weigh in video every single Friday and I upload five videos per week. Check out the description box down below where you will find nutrition coaching. I offer personalized to you macros and calories. I cannot recommend this enough. This is how I have been successful with over 125 pounds of weight loss and I also offer one-on-one coaching if you would like to chat with me directly. Links, discounts to my favorite things and last but not least come join our Facebook group. We would love to have you. So let's jump into my post vacation way in the WW workshop topic, the unfortunate news and let's set some goals moving into the new week. I hope you had an amazing week. Thank you to everybody who reached out to me via Instagram and Facebook direct message and commented on all of the pictures from my trip to Hawaii. My plan is to do an entire vacation recap with lots of photos in this next Wednesday's What I Eat in a Day. So make sure you're subscribed and your bells turned on so you don't miss that video. But if you didn't know, if you don't follow me over on Instagram or you haven't watched my most recent videos, I just returned from an almost week long trip to Hawaii. I took a little girls trip with my best friend and my mother-in-law. We went to my mother-in-law's granddaughter's wedding on the Black Sand Beach in Hawaii. It was beautiful and then we decided to make a vacation out of it, a little girls trip out of it. It was an absolutely incredible, incredible time. So definitely stay tuned for Wednesdays. What I eat in a day, I'm gonna share with you my entire trip and we took a ton, and I mean a ton of photos. Also quickly, I did not track on my vacation. Not one day did I track my food. I ate mindfully. I enjoyed everything. I ate anything I wanted on my trip. I just did my best to make the healthiest choices, which in my opinion is the best way to vacation and to make your vacation not focused on food. I did not go to Hawaii thinking about food. I went to Hawaii thinking about all of the experiences that I was going to have and then food just fit in where food fit in. And I feel like I did a really good job navigating vacation. The unfortunate news is on the final day of my trip in Hawaii, my best friend and I took a nighttime Manta Ray snorkeling cruise where we got to get in the water with the Manta rays and when we first boarded the boat they asked us to move to the front of the boat, the bow of the boat to do a safety presentation before the boat moved to our destination for snorkeling. So I was walking through the cab of the boat up into where the captain is and I stepped out of the captain area of the boat. And I don't know if I stepped wrong if it was my flip-flop that I was wearing but I fell sort of. I mean, I didn't fall completely to the ground but I fell and had to catch myself on the side of the boat. And in the process, just twisted my right foot. I don't know if I sprained my ankle, twisted my foot, sprained my foot. I have no idea but there was immediate pain. I was able to walk on my foot. I actually did the excursion and just took it really easy climbing back into the boat. And by the time we got back to shore and I was walking up the really steep hill to get to our car, my foot was just throbbing and on fire. So my best friend as sweet as she is put together a little ice bag for me and got me cozy in bed and covered my foot with ice and I was able to ice and elevate my foot the night before we flew home. It was hard walking through the airport. I think that I sprained my ankle because that's where a lot of the pain and pressure is. It's a little bit swollen. So since I've been home, I've been keeping my feet up and icing it at night. In fact, my feet are up right now on a chip box and a pillow under my desk just to help elevate my foot. I'm grateful that it happened on the last day but it's really putting a damper on my workouts because I can barely walk around and my foot gets really throbby and sore and inflamed and swollen when I walk a lot. So since I've returned home from my trip which was just a couple of days ago, obviously my workouts are out for a little while. I actually checked in with my fitness coach this morning, let her know what happened. I'm not sure what this is going to look like for my workouts, how long it's going to take my foot to heal. I also asked her if I was able to maybe go to the gym and do upper body if as long as I wasn't putting any pressure on my foot. So I'll let you guys know in Wednesday's video as well what the update is on my workouts. So I am really, really food focused right now because I'm unable to go to the gym, which really bums me out. To be completely honest with you, the minute that it happened, I said to Rachel, my best friend, I'm like, what does this mean for my workouts? That is the first thing that came to my mind. It wasn't, oh my gosh, my foot hurts or what did I do or how am I gonna finish this tour? It was, oh my goodness, my workouts. That's how important my fitness is to me and that's the part that's the hardest for this. I mean, besides the pain is knowing that I'm not going to be able to work out for I don't know how long, a week to maybe even longer. So overall, obviously my trip was absolutely amazing and well needed. I have been working a lot. So I needed that vacation and that little bit of relaxation to really just lift my spirits and get me remotivated and I'm there. I am motivated and I'm excited now that I'm home from my trip to get back to normal. I have been tracking my food since the day I flew back from Hawaii. So I started tracking the morning I was dropped off at the airport and have been tracking this entire time. And like I said, I'm just focused on food because you can't out exercise a bad diet anyways and really the only way to lose weight is to eat the right foods and be in a calorie deficit. So that is my focus until I can get back into the gym with my normal workout routine. So before I jump into the post vacation weigh-in, let's talk about this week's WW Workshop topic which I really like and this is how to simplify tracking. One of the most effective tools for weight loss is tracking your food. Not to mention weighing and measuring your food. These are essential for losing weight. How else do you know how many calories or points you're eating if you're not tracking your food? Learn to track smarter, not harder. Tracking is cumbersome. Tracking can be annoying and tracking can really be something that a lot of people struggle with. So learn to do it just a little bit smarter so that it makes it a little bit easier. There are some things you can do right inside the WW app to make tracking easier. Number one is you can use the WW apps search tools. Both the barcode scanner and the microphone on your phone's keyboard save typing time. So voice put in the foods that you're searching for rather than having to type them in. You can also snap a picture of your meal and then track it later. At least that way you can remember everything that you ate and you can even post that photo to connect for extra accountability. Save your go-to meals and recipes. Tap the search bar then create to group foods or ingredients or ingredients. Next time you'll track only one thing. It'll be saved in the app for you. Track similar restaurant dishes. If you can't find your meal in the WW database, no need to try to log every ingredient. Just find something similar. This is not an exact science. So just find something similar. You can also guesstimate your points at events. Instead of tracking a lot of foods, tap quick, add an input, some of the foods that you ate at the event and their total points. Again, the guesstimated total points. You can also pre-track a higher point meal. When you have a special event coming up, log it and then plan your week around it. Try a shortcut then reflect. Did it save time and effort? If yes, cool, keep it up. If not, then go ahead and test drive another strategy.  That is the one meal every day that I know that I'm going to be eating. As you know, I meal prep. So I eat a lot of the same breakfast and lunch as well. So sometimes I'll even pre-track those. That way I know what's left, what money is left in my bank account for the rest of the day. For snacks, for desserts, even for breakfast. So I always pre-track what I can pre-track. And that's been really important in my weight loss journey. Also remember, this isn't an exact science. You don't have to track everything to the calorie or to the point. It's okay to guesstimate. There's a little bit of flexibility in both points and calories for those times when you just don't know exactly the points or calories in the foods that you're eating. Remember, the point of tracking is awareness. It's not to keep score. It's not for it to be an exact science. It's for you to be aware of what you're eating. And this is why consistency will always beat out perfection. Guestimating your points and calories is just fine. As long as you're consistent in doing that, you're consistent in tracking your food, you're consistent in staying in your points or calories, that's where you're going to see rewards. It's not about perfection. This makes your journey easier, saves you time, less daunting, and it's something that you'll stick with. When you make tracking your food easy, that's when you're going to stick with it. If it's something that's daunting, annoying, and frustrating, you're gonna quit. So make sure that tracking your food is a top priority, but you take all of these little shortcuts, tips and tricks to make it a little bit easier and you're working smarter, not harder when it comes to tracking your food. So I really like this workshop topic. I know for me that the thought of tracking can sometimes still overwhelming. That's why I pre-track as much as I possibly can. And I've also been known to track everything at the end of the night when I'm relaxing in bed, watching TV, before I go to sleep. That's another thing that I've done as well. And that's where sometimes taking pictures of your food can come in handy so that you remember what you ate throughout the day so that you can track it as accurately as possible. Remember consistency, not perfection when it comes to tracking your food. So now, now let's jump in to my post-vacation weigh-in. Again, I was gone almost an entire week in Hawaii. I did not track my food. Actually, for a solid week, I did not track my food the day that I flew into Hawaii either, and again, I didn't pick up tracking again until flying home. So almost an entire week. I ate what I wanted.  I tried all the Hawaii foods, all the different fruits. We went to a luau. I ate a little bit of everything. We ate out for every meal except for breakfast. We actually cooked breakfast in our hotel room, which was great because I was able to really be in control over what I was choosing for breakfast, kind of setting my day off on the right foot, but every other meal was eaten out. And when I stepped on the scale, I actually lost weight. Who am I? Who am I losing weight on vacation? I am down 0.8 pounds. That's almost an entire pound post vacation. And I weighed in one day after returning from Hawaii. So it is proof. I am proof that by being mindful and remember consistency, outweighs perfection, I was consistent in making good food choices, watching my portions.  I was able to lose 0.8 pounds on vacation. So if I can do it, you can do it. Tracking, no tracking, being mindful, watching your portions, making sure that you're still in some type of calorie deficit can lead to weight loss even after vacation. So I will go ahead and pop up here on the screen what my total weight loss is so far on my journey. And like I always say, I'm inching closer and closer to my goal weight. I'm actually heading out for another little mini vacation here this next week. I actually fly to California on the 29th, which is a Thursday and I am gone through that Sunday for a nutrition conference, which I'm very, very excited about. It's kind of like continuing education for me for my nutrition certification. So I'm very excited for that and it's another time of travel. And we'll see how my weight loss does after a second trip one week later from my big trip to Hawaii. And on that note, a reminder that there will not be a meal prep next Monday either. So I will have one here in a couple of days. So definitely stay tuned for that, but there will not be a meal prep the following Monday because I will just be returning from my trip to California. But all of my other content will be per usual up on my channel. So now I wanna hear from you guys. Let me know how your week was if you followed me on Instagram or in my Facebook group, if you enjoyed seeing all the photos that I posted from my trip. And of course, make sure you're subscribed and your bells turned on so you don't miss Wednesdays, what I eat in a day where we're gonna go through my entire trip. I'm gonna share my experiences with you, my reviews of the hotel that we stayed at, the tours that we took, and tons and tons of pictures. So let me know down in the comments how things have been for you. How was your weigh-in? What do you think about this working smarter not harder when it comes to tracking your food and is tracking an issue for you? Let me know down in the comments.  Check out the description box for nutrition coaching, links and discounts to my favorite things. And of course, don't forget to come join my Facebook group. I'd love to have you. Happy Friday, friends. Happy weekend. And I'll see you in tomorrow's massive. And I mean massive stock up grocery haul.