Everyone, welcome to the AWS startup showcase presented by theCUBE. I'm John Furrier, host of theCUBE. We are here talking about the next big thing in cloud featuring Explorium for the AI track. We got AI, cybersecurity and life sciences. Obviously AI is hot, machine learning, powering that. Today we're joined by Zach Booth, Director of Global Partnerships and Channels at Explorium. Zach, thank you for joining me today remotely. Soon we'll be in person, but thanks for coming on. We're going to talk about rethinking external data. Thanks for coming on theCUBE. Absolutely. Thanks so much for having us, John.  Congratulations. We just wrote about on SiliconANGLE, you're a new $75 million of fresh funding. Obviously you're part of the Amazon partner network. You're growing like crazy. You guys have a unique value proposition looking at external data and that having a platform for advanced analytics and machine learning. Can you take a minute to explain what you guys do? What is this platform? What's the value proposition and why do you exist? Bottom line, we're bringing context to decision-making. The premise of Explorium and kind of, this is consistent with the framework of advanced analytics is we're helping customers to reach better, more relevant external data to feed into their predictive and analytical models. It's quite a challenge to actually integrate and effectively leverage data that's coming from beyond your organization's walls. It's manual, it's tedious, it's extremely time consuming and that's a problem. And it's really a problem that Explorium was built to solve and our philosophy is it shouldn't take so long. It shouldn't be such an arduous process, but it is. So we built a company, a technology that's capable for any given analytical process of connecting a customer to relevant sources that are kind of beyond their organization's walls. And this really impacts decision-making by bringing variety and context into their analytical processes. You know, one of the things I see a lot in my interviews with theCUBE and talking to people in the industry is that everyone talks a big game about having some machine learning and AI. They're like, okay, I got all this cool stuff. But at the end of the day, people are still using spreadsheets, they're wrangling data and a lot of it's dominated by these fenced off data warehousing. And you start to see the emergence of really companies built on the cloud. I saw the Snowflake IPO, you're seeing a whole new shift of new brands emerging that are doing things differently, right? And because there's such a need for just move out of the archaic spreadsheet and data presentation layers, it's a slower, antiquated, outdated. How do you guys solve that problem? You guys are on the other side of that equation. You're on the new wave of analytics. What are you guys solving? How do you make that work? How do you get on that wave? So, yeah, so basically the way X4M sees the world and I think that most analytical practitioners these days see it in a similar way, but the key to any analytical problem is having the right data. And the challenge that we've talked about and that we're really focused on is helping companies reach that right data. Our focus is on the data part of data science. The science part is the algorithmic side. It's interesting, it was kind of the first frontier of machine learning as practitioners and experts were focused on it and cloud and compute really enabled that. The challenge today isn't so much what's the right model for my problem, but it's what's the right data. And that's the premise of what we do. Your model's only as strong as the data that it trains on and going back to that concept of just bringing context to decision-making within that framework that we talked about, the key is bringing comprehensive, accurate and highly varied data into my model. But if my model is only being informed with internal data, which is wonderful data but only internal, then it's missing context. And we're helping companies to reach that external variety through a pretty elegant platform that can connect the right data for my analytical process. And this really has implications across several different industries and a multitude of use cases. We're working with companies across consumer package goods, insurance, financial services, retail, e-commerce, even software as a service. And the use cases can range between fraud and risk to marketing and lifetime value. Now, why is this such a challenge today with maybe some antiquated or analog means? With a spreadsheet or with a rule-based approach, we're pretty limited. It was an effective means of decision-making to generate and create actions, but it's highly limited in its ability to change, to be dynamic, to be flexible. And with modeling and using data, it's really a huge arsenal that we have at our fingertips. The trick is extracting value from within it. There's obviously latent value from within our org, but every day there's more and more data that's being created outside of our org. And that is a challenge to go out and get to effectively filter and navigate and connect to. So we've basically built that tech to help us navigate and query for any given analytical question, find me the right data. Rather than starting with what's the problem I'm looking for, now let me think about the right data, which is kind of akin to going into a library and searching for a specific book. You know which book you're looking for. Instead of saying, there's a world, a universe of data outside there. I want to access it, I want to tap into what's right. Can I use a tool that can effectively query all that data, find what's relevant for me, connect it and match it with my own and distill signals or features from that data to provide more variety into my modeling efforts, yielding a robust decision as an output. I love that paradigm of just having that searchable kind of paradigm. I got to ask you, one of the big things that I've heard people talk about and I want to get your thoughts on this is that, how do I know if I even have the right data? So is the data addressable? Can I find it? Is it even, can I even be queried? How do you solve that problem for customers when they say, you know, I really want the best analytics, but do I even have the data or is it the right data? How do you, how do you guys look at that? So the way our technology was built is that it's quite relevant for a few different profile types of customers. Some of these customers, really the genesis of the company started with those cloud-based model-driven since day one organizations and they're working with machine learning and they have models in production. They're quite mature in fact. And the problem that they've been facing is, again, our models are only as strong as the data that they're training on. The only data that they're training on is internal data and we're seeing diminishing returns from those decisions. So now suddenly we're looking for outside data and we're finding that to effectively use outside data, we have to spend a lot of time, 60% of our time spent thinking of data, going out and getting it, cleaning it, validating it and only then can we actually train a model and assess if there's an ROI. That takes months and if it doesn't push the needle from an ROI standpoint, then it's an enormous opportunity cost which is very, very painful, which goes back to their decision-making, is it even worth it if it doesn't push the needle? That's why there had to be a better way. And what we built is relevant for that audience as well as companies that are in the midst of their digital transformation. We're data rich but data science poor. We have lots of data, a latent value to extract from within our own data. And at the same time, tons of valuable data outside of our org. Instead of waiting 18, 36 months to transform ourselves, get our infrastructure in place, our data collection in place and really start having models in production based on our own data, you can now do this in tandem. And that's what we're seeing with a lot of our enterprise customers by using their analysts, their data engineers, some of them in their innovation or kind of center of excellence is have a data science group as well. And they're using the platform to inform a lot of their different models across lines of businesses. I love that expression data rich.  They have a data problem, they have a lot of it. I think I want to get your thoughts because I think that connects to my next question which is as people look at the cloud, for instance, and again, all these old methods were internal, internal to the company. But now that you have this idea of cloud more integration is happening. More people are connecting with APIs. There's more access to potentially more signals and more data. How does a company go that next level to connect in and acquire the data and make it faster? Because I can almost imagine that the signals that come from that context of merging external data, and that's the topic of this theme, reimagining external data is extremely valuable signaling capability. And so it sounds like you guys make it go faster. So how does it work? Is it the cloud? Take us through that value proposition. Well, so it's a real, it's amazing how fast the rate of change organizations have been moving on to the cloud over the past year during COVID. And the fact that alternative or external data depending on how you refer to it has really, really blown up. And it's really exciting. This is coming in the form of data providers and data marketplaces. And everybody's kind of more and more organizations are moving from rule-based decision-making to predictive decision-making. And that's exciting. Now, what's interesting about this company, Explorium, we're working with a lot of different types of customers but our long game has a real high upside. There's more and more companies that are starting to use data and are transformed or already in the midst of their transformation. So they need outside data. And that challenge that I described is exists for all of them. So how does it really work? Today, if I don't have data outside, I have to think, it's based on a hypothesis and it all starts with that hypothesis which is already prone to error from the get-go. You and I might be domain experts for a given use case. Let's say we're focusing on fraud. We might think about a dozen different types of data sources but going out and getting it, like I said, it takes a lot of time, harmonizing it, cleaning it and being able to use it takes even more time and that's just for each one. So if we have to do that across dozens of data sources it's gonna take far too much time and the juice isn't worth the squeeze. And so I'm gonna forgo using that. And a metaphor that I like to use when I try to describe what Explorium does to my mom. I basically use this connection to buying your first home. It's a very, very important financial decision. You would, when you're buying this home you're thinking of all the different inputs in your decision-making. It's not just about the blueprint of the house and how many rooms and the criteria you're looking for. You're also thinking about external variables. You're thinking about the school zone, the construction, the property value, alternative or similar neighborhoods. That's probably your most important financial decision or one of the largest at least. A machine learning model in production is an extremely important and expensive investment for an organization. Now the problem is, as a consumer buying a home we have all this data at our fingertips to find out all of those external based inputs. Organizations don't, which is kind of crazy when I first kind of got into this world. And so they're making decisions with their first party data only. First party data is wonderful data. It's the best, it's representative, it's high quality, it's high value for their specific decision-making and use cases, but it lacks context. And there's so much context in the form of like location-based data and business information that can inform decision-making that isn't being used. It translates to suboptimal decision-making, let's say. Yeah, and I think one of the insights around looking at signal data in context is if by merging it with the first party it creates a huge value window. It gives you observational data, maybe potentially insights into customer behavior. So totally agree, I think that's a huge observation. You guys are definitely on the right side of history here. I want to get into how it plays out for the customer. You mentioned the different industries, obviously data is in every vertical and vertical specialization with the data it has to be, it's very metadata driven. I mean, metadata in oil and gas is different than FinTech. I mean, some overlap, but for the most part you got to have that context, acute context, each one. How are you guys working? Take us through an example of someone getting it right, getting that right set up, taking us through the use case of how someone on boards, Explorium, how they put it to use and what are some of the benefits? So let's break it down into kind of a three-step phase and let's use that example of fraud earlier. An organization would have basically past historical data of how many customers were actually fraudulent in the end of the day. So this use case, and it's a core business problem is with an intention to reduce that fraud. So they would basically provide, going with your description earlier, something similar to an Excel file. This can be pulled from any database out there where we're working with loads of them and they would provide this, what's called training data. This training data is their historical data and would have as an output, the outcome, the conclusion, was this business fraudulent or not? Yes or no? Finally, the platform would understand that data itself to train a model with external context in the form of enrichments. These data enrichments at the end of the day are important, they're relevant, but their purpose is to generate signals. So to your point, signals is the bottom line what everyone's trying to achieve and identify and discover and even engineer by using data that they have and data that they yet to integrate with. So the platform would connect to your data, infer and understand the meaning of that data and based on this matching of internal plus external context, the platform automates the process of distilling signals or in machine learning, this is called referred to as features. And these features are really the bread and butter of your modeling efforts. If you can leverage features that are coming from data that's outside of your org and they're quantifiably valuable, which the platform measures, then you're putting yourself in a position to generate an edge in your modeling efforts.  So your customers get a much better, more compelling offer or service or price point. It impacts your business in a lot of ways. What Explorium is bringing to the table in terms of value is a single access point to a huge, a universe of external data. It expedites your time to value. So rather than data analysts, data engineers, data scientists spending a significant amount of time on data preparation, they can now spend most of their time on feature or signal engineering. That's the more fun and interesting part, less so the boring part, but they can scale their modeling efforts. So time to value, access to a huge universe of external context and scale. So I see two things. You just make sure I get this right because it sounds awesome. So one, the core assets of the engineering side of it, whether it's the platform engineer or data engineering, they're more optimized for getting more signaling, which is more impactful for the context acquisition, looking at context that might have a business outcome versus wrangling and doing like mundane, heavy lifting. And the second one is, you create a democratization for analysts or business people who just are used to dealing with spreadsheets, who just want to kind of play and play with data and get a feel for it or experiment, do querying, try to match planning with policy. Yeah, so the way I like to kind of communicate this is Explorium's this one-two punch. It's got this technology layer that provides entity resolution. So matching with external data, which otherwise is a manual endeavor. Explorium's automated that piece. The second is a huge universe of outside data. So this circumvents procurement. You don't have to go out and spend all these one-off efforts on time finding data, organizing it, cleaning it, et cetera, et cetera. You can use Explorium as your single access point two and gateway two external data and match it. So this will accelerate your time to value and ultimately the amount of valuable signals that you can discover and leverage through the platform and feed this into your own pipelines or whatever system or analytical need you have. Is that great stuff? I love talking with you and I love the hot startup action here because you're again, you're on the net new wave here. Like anything new, I always, I was just talking to a colleague here, it's looking on the queue about, when you have something new, it's like driving a car for the first time. You need someone to give you some driving lessons or figure out how to operationalize it or take advantage of the one-two punch as you pointed out, how do you guys get someone up and running? Because let's just say I'm like, okay, I'm bought into this. It's a no-brainer. You got my attention. I still don't understand. Do you provide a marketplace of data? Do I need to get my own data? Do I bring my own data to the party? Do you guys provide relationships with other data providers? How do I get going? How do I drive this car? How do you answer that? So first, Explorium.ai is a free trial and it's World Product Focus Company. So a practitioner, maybe a data analyst, the data engineer or data scientist would use this platform to enrich their analytical, so BI decision-making or any models that they're working on, either in production or being trained. Now, oftentimes models that are being trained don't actually make it to production because they don't meet a minimum threshold, meaning they're not gonna have a positive business outcome if they're deployed. With Explorium, you can now bring variety into that and increase your chances that your model that's being trained will actually be deployed because it's being fed with the right data. The data that you need, that's not just the data that you have. So how a business would start working with us would typically be with a use case that has a high business value. Maybe this could be a fraud use case or a risk use case and B2B or even B2S and B context. This might be a marketing use case. So we're talking about LTV modeling, look-like modeling, lead acquisition generation for CPGs and field sales optimization. Explorium would understand your data. It would enrich that data automatically. It would generate and discover new signals from external data plus from your own and feed this into either a model that you have in-house or end-to-end in the platform itself. We provide customer success to generate, kind of help you build out your first model perhaps and hold your hands through that process. But typically most of our customers are after a few months time, having run and building models, multiple models in production on their own. And that's really exciting because we're helping organizations move from more kind of rule-based decision-making and being their bridge to data science. Awesome, I noticed that in your title, you handle global partnerships and channels, which I'm assuming is you guys have a network, an ecosystem you're working with. What are some of the partnerships and channel relationships that you have that you bring to bear in the marketplace? So data and analytics, this space is very much an ecosystem. Our customers are working across different clouds, working with all sorts of vendors, technologies. Basically they have a pretty big stack. We're a part of that stack and we want to symbiotically play within our customer stack so that we can contribute value whether they sit here, there or in another place. Our partners range from consulting and system integration firms. Those that perhaps are building out the blueprint for a digital transformation are actually implementing that digital transformation and we contribute value in both of these cases as a technology innovation layer in our product. And a customer would then consume Explorium afterwards after that transformation is complete as a part of their stack. We're also working with a lot of the different cloud vendors. Our customers are all cloud based and data enrichment is becoming more and more relevant with some wonderful machine learning tools be they auto ML or even some data marketplaces are popping up and very exciting. What we're bringing to the table as an edge is accelerating the connection between the data that I think I want as a company and how to actually extract value from that data. And being part of this ecosystem means that we can be working with and should be working with a lot of different partners to contribute incremental value to our end customers. Final question I want to ask you is if I'm in a conference room with my team and someone says, hey, we should be rethinking our external data. What would I say? How would I pound my fist on the table or raise my hand and saying, hey, I have an idea, we should be thinking this way. What would be my argument to the team to reimagine how we deal with external data? So it might be a scenario that rather than banging your hands on the table, you might be banging your heads on the table because it's such a challenging endeavor today. Companies have to think about what's the right data for my specific use cases. I need to validate that data. Is it relevant? Is it real? Is it representative? Does it have good coverage, good depth and good quality? Then I need to procure that data. And this is about getting a license from it. I need to integrate that data with my own. That means I need to have some in-house expertise to do so. And then of course I need to monitor and maintain that data on an ongoing basis. All of this is a pretty big thing to undertake and undergo. And having a partner to facilitate that external data integration and ongoing refresh and monitoring and being able to trust that this is all harmonized, high quality and I can find the valuable ones without having to manually pick and choose and try to discover it myself is a huge value at, particularly the larger the organization or partner because there's so much data out there and there's a lot of noise out there too. And so if I can through a single partner or access point tap into that data and quantify what's relevant for my specific problem, then I'm putting myself in a really good position and optimizing the allocation of my very expensive and valuable data analysts and engineering resources. Yeah, I think one of the things you mentioned earlier, I thought it's a huge point was good call out was it goes beyond the first party data because even just first party, if you just in an internal view, some of the best, most successful innovators that we've been covering with cloud scale is they're extending their first party data to external providers. So they're in the value chains of solutions that share their first party data with other suppliers. And so that's just again, more of an extension of the first party data. You're kind of taking it to a whole nother level of there's another external external set of data beyond it that even more important. I think this is a fascinating growth area and I think you guys are onto it. So great stuff. Thank you so much, John. Well, I really appreciate you coming on Zach. Final word, give a quick plug for the company. What are you up to and what's going on? What's going on with Explorium? We are growing very fast. We're a very exciting company. I've been here since the very early days and I can tell you that we have a stellar working environment, very, very strong down to earth, high work ethic culture. We're growing in the sense of our office in San Mateo, New York and Tel Aviv are growing rapidly. As you mentioned earlier, we raised our series C. So that totals Explorium to raising, I think 127 million over the past two years and some change. And whether you wanna partner with Explorium, work with us as a customer or join us as an employee, we welcome that. And I'd encourage everybody to go to explorium.ai. Check us out, read some of the interesting content there around data science, around the processes, around the business outcomes that a lot of our customers are seeing as well as joining a free trial. So you can check out the platform and everything that has to offer from machine learning engine to a signal studio, as well as what type of information might be relevant for your specific use case. All right, Zach, thanks for coming on. Zach Booth, Directive of Global Partnerships and Channels at explorium.ai. The next big thing in cloud featuring Explorium and on a part of our AI track, I'm John Furrier, host of theCUBE. Thanks for watching.


Okay, so now that we've seen how to solve an MDP with all its parameters known meaning that specifically the transition model P and the reward function R are known And we've seen methods for solving that using value iteration and policy iteration We are finally ready now to get into our very first reinforcement learning algorithms And remember we said at the outset that reinforcement learning dealt with MDPs where P and R were not given to you in advance So one really key Concept in many reinforcement learning algorithms is this idea of temporal differencing And to introduce that idea of temporal differencing Let's look at the policy evaluation algorithm that we wrote out earlier in the case of the fully known MDPs And we said to evaluate any policy meaning to assign states Assign values to each state We would start by setting all the values to be equal to zero and then iterate this update until convergence Right, so you should remember this is basically just the Bellman update The Bellman equation but written down as an update, right? And so we keep iterating on this until we eventually converge Now you'll notice that in this equation there is the known transition probability P and There is the reward function R and these are known and that's the reason that we were able to do policy evaluation in this way earlier Now how would we extend? policy evaluation to a setting where The transition probability and the rewards were not known now remember We don't actually have access to the transition probabilities and the rewards in the reinforcement learning setting But every time you perform an action in the environment You're going to get a sample from each of these distributions in particular the state that you end up at is observable to you Right, and so that gives you some information about P of s prime given s comma a and Similarly, you do get a reward back from the environment And this is what we said was the the reinforcement learning setting every time you emit an action You get to observe the next state and you also get to observe the reward So you get samples from both of these functions You don't get the entire function you don't get a formula for the function for example But you get samples for of each of those functions And so by acting in the environment and by slowly taking actions in the environment You get more and more information about these two unknown quantities Right, so just to just to restate that once more every time you take an action a from state s You get a sample from the unknown transition probability and the corresponding reward So this is going to be important to this idea of temporal differencing In particular if you stare at this equation That we are using as our Bellman update for policy evaluation The quantity on the right is an expectation over this probability distribution Right, it's an expectation over this probability distribution of this quantity because that's exactly what An expectation is right, so if you recall the the Expression for an expectation. We're taking the expectation of this quantity inside the square brackets under this probability. That's what's happening here Now we've just finished saying that every time we perform an action in the environment we get to see One sample of this quantity we get to see one sample in particular we get to see one sample of s prime right from drawn from this distribution and we get the associated reward and We of course already have access to the current estimate of the value function so what we're going to do is treat that single sample that we get as a proxy for the entire distribution as a proxy for the expectation over the distribution and Because it's obviously a very noisy estimate to just treat a single sample as the the expectation of the distribution So we don't go the whole distance and iterate until convergence, but instead just instead move the value function To be a little bit closer to the quantity on the right So let's see exactly how that works So again after having executed the action pi of s From the policy pi that you're currently trying to find the value function for because that's what policy evaluation does remember and After having executed that we get to see s prime and we get to see the reward function associated with s prime And so we can now take the quantity that we're trying to compute the expectation of we have one sample from that quantity because we also have access to the previous estimate of of The value function and So this is now going to be our proxy for this expectation and Instead of setting vi plus one Pi k of s to be equal to this quantity Instead what we're going to do is just move it to be closer to this quantity and how that works is By applying this update rule where we take the current v pi of s And then we apply an incremental update to it and the incremental update is the difference between the right-hand side and the left-hand side Where the right-hand side is replaced by the single sample like we said So think about this a little bit all that we're doing is so in particular if for example you had set alpha to be equal to exactly one then You can work out that this would This would reduce to exactly this expression here if you'd said alpha to exactly one then v pi of s would be set to the sample which is which is this expression over here and that sample we said is going to be a proxy for this entire expression But we don't usually set alpha to one instead we set alpha to a small value so that we only move a small positive value So that we only move by a small step towards this value and The intuition here is that as you do this more and more For all the samples that you encounter eventually You have kind of computed the running average over the samples So even though each sample by itself is a noisy estimate of the the expectation here Still by computing this kind of running average over the samples by using this update rule you have effectively actually Reproduced something like this this expression So that's an example of what temporal differencing is it basically reduces to this idea of treating every single sample that you encounter as representative of the distribution and Therefore what you get to do is you don't have to wait for a large number of interactions in the environment to improve your policy or to improve your Policy evaluation instead you get to learn from every single Step in the environment from every single action that you eminent in the environment and the feedback that you get from that So you are learning much more quickly Because you're making use of every single action in the environment every single interaction in the environment to update your Your policy or your value function or whatever you're trying to Compute so up until now we've seen temporal differencing applied to this policy evaluation problem But the next thing we're going to see is how can we apply this temporal differencing trick to Learning the optimal Q function and we've already discussed that learning the optimal Q function Amounts to being able to also perform optimal actions in the environment because all that you have to do is Pick the arg max of the Q function So to do that remember the Bellman equation for optimal Q star. We wrote this down earlier All that saying is we're taking this expectation again over the transition probability of this quantity now that includes this maximum over the Q star right so this should remind you of what we did on the previous slide and Let's also think actually about what the the Q value iteration equation Is so we've already seen the standard state value iteration equation But now we're going to look at the state action value iteration or the Q value iteration Which is quite analogous to how we derive the state value iteration all that you do is you treat the Bellman equation as an update rule and so your your new estimate of the the optimal Q is Just going to be The right hand side of the Bellman equation Where you plug in your previous estimate of the optimal Q in place of Q star All right, so this is exactly how we treated value iteration in the state value iteration case if you recall from a few slides ago Now again, we're taking an expectation of this quantity under the transition probability and we know now all that all about how we can use temporal differencing to to handle update rules like this Even when you don't have access to the the transition probability or to the true reward function We know how to how to do this now, right? So even though this expression right now includes it requires access to the transition probability and to the reward function We are going to apply now the TD trick to this the temporal differencing trick to this and So again, here's the Q value iteration Update rule and what we're going to do just like we did for the policy evaluation case is we're going to treat the single sample that you get as Representative of the distribution from emitting a single from performing a single action in the environment and getting the feedback from that you get one single sample from the transition probability you get one single corresponding reward and You can now treat this expression computed for that particular transition and that particular reward as Proxy for this overall expectation and instead of outright setting the The left hand side of this equation to the right hand side instead You just move the left hand side a little bit closer to the right hand side by using this incremental update All of the same stuff that we saw in the policy evaluation case So you execute a single action a from state s you observe s prime and the reward are that sample From the distribution is just going to be this expression here computed for this one one single transition and so this is going to be our proxy for this expectation and So we're going to now create an incremental TD update rule with this that looks like this So now you're gonna have Q of SA being replaced by Q of SA plus Alpha times the right hand side minus the current value of Q of SA and again If you set alpha to be exactly equal to one then you end up with the same update rule over here Except that you're replacing the entire expectation with just a single sample and typically that would be a bad idea because you are Updating it based on a very noisy estimate and instead of doing that completely noisy update you instead kind of smooth it out over time by Relying a little bit more on your previous Q of SA and that means that you set alpha to be a small value and so you don't outright update Q of SA to be Based purely on this one single sample, but instead you also bring in knowledge from your past interactions, which have shaped your previous Version of Q of SA and again, this quantity is often called the Bellman error It's simply the difference between the right-hand side of this equation and the left-hand side of this equation in this case In the context of just a single sample on the right-hand side instead of an expectation over a full distribution and This algorithm that we've just seen that replaces the Q value iteration with this incremental TD update That algorithm is exactly what Q learning is So here is an example of a Q function and what a Q function might look like remember a Q function is a function of both the state and the action and Because it's a function of both the state and the action in every cell in that grid world example that we saw We're going to have one value for Q of s comma a associated with every possible action a and so you can see for example here that from this state to perform an action going north the Q function for that is 0.54 and Given a Q function given the learned optimal Q function You can easily find a policy by simply following the action. That is that has the highest Q value So particular particularly in this cell The highest Q value is for the north action and so you would go north here in this case The highest Q value is for the north action again And so you would go north here in this case once again Well, actually in this case, it's the east action So you would go east here the east action the east action and then you end up at the the correct square Let's look at another example. So let's let's say you started over here the optimal Q is The highest Q is over here. So you would follow this action and Then once you reach this square, you would once again follow this action and then you would reach this square Now remember that we have stochastic transitions. So we aren't I mean at the moment We're kind of ignoring that when we say that if you just perform the west action you end up at this square But you actually have stochastic actions in this environment But if you end up at this square, then you would take this Action and so on and so forth and then you end up again at the starting square And so you follow the same route that we saw earlier Right. So this is This is that an actual example of a learned Q function after 1000 iterations that was learned Without having access to the true transition probability or to the true reward function Right just based on samples like we saw on the previous slide


Welcome to this self-training video for AutodevKit, a new development approach and toolset for automotive applications. Let's demonstrate AutodevKit's key advantages by implementing an embedded system dedicated to the Blind Spot Warning System. Here you can see the vehicle's blind spots when stopped at an intersection. Blind Spot Detection is a new standard function implemented in today's vehicles to prevent accidents. This is especially useful when one vehicle is overtaking another one. As shown there is an area that cannot be directly observed by the driver. We call this area a blind spot. The warning system detects vehicles in a blind spot and informs the driver. The demo includes a plexiglass panel with a set of boards to drive the application to simulate the loads. In the panel we have a microcontroller discovery board, hosting ST's SPC58EC 32-bit Automative microcontroller, a VI power actuator board with a high-side driver used to power the conveyor belt's geared DC motor, and an LED driver board with four channels at which we use one for our purposes. The demo uses two cars. One is in a fixed position while the other is placed on a conveyor belt driven by a geared DC motor. The LED is placed next to the fixed car and it represents the blind spot warning signal. Replacing the radars of a real detection system, we use a hall effect sensor able to measure the magnetic field of the car with magnets placed on the conveyor belt. Looking at the demo in action, we see that the truck on the conveyor belt approaches the range of the hall effect sensor that informs the microcontroller of the variation in the magnetic field, thus triggering the blind spot warning LED. This flow chart shows the steps required to create the blind spot detection application using AutodevKit. The first step consists of creating a new project and selecting the MCU platform to use. As soon as the project is created, we request SPC-5 Studio to generate the standard project files. The second step consists of adding basic components for initialization and MCU peripheral low-level drivers. The AutodevKit init component installs the board view editor that we will use later. The SPC-5 init component installs the basic hardware management files, including clock configurator, interrupt management, and an operating system abstraction layer, together with bootstrap files. It is now time to select the specific AutodevKit components for the blind spot warning system we intend to build. Therefore, we select the components managing the warning LED, conveyor belt, and hall effect sensor. Once we have added the AutodevKit components, we must configure them. The LED board requires a DSPI interface with two channels, represented by the chip select pins. Once configured, we proceed with the automatic pin allocation and peripheral configuration for the LED board. Now let's configure the VI power board for the conveyor belt's DC motor. For our demonstrator, we will only use the high side output and disable the current sense function. We complete the steps with the pin allocation. The last component is the hall effect sensor. In this case, it is much simpler, so we proceed directly with the pin allocation. We now generate the code for all the components that have been added and configured. In the SPC5 Studio's pin map editor, we can verify the allocated MCU pins, based on the requirements of the added components. Thanks to the board view editor provided by the AutodevKit library plugin, we can proceed with wiring the SPC58EC MCU discovery board to the others. We now see in detail the connections between the hall effect sensor and the MCU board. We now put everything together by writing our main function. To simplify this task, AutodevKit provides high-level APIs dedicated to controlling all the added components. A specific Doxygen documentation is available for each component. Here we present the APIs dedicated to the VI power board and the hall effect sensor. While here we present the APIs dedicated to the LED driver board and the low-level driver function required. The APIs hide all the implementation details, allowing board usage without having to read the related documentation. We paste the C code previously written using the AutodevKit components and we compile the code. All code sections are now highlighted, giving details on the implementation we put in place. In completed and compiled to the code, the next step is to download the firmware to the MCU and proceed with debugging. From the runtime setting, we can select if we want the code to be run from RAM or flash memory. The next step is to select the compiler options. The download and debug are done with the Universal Debug Engine, UDE, by PLS. Since the symbols file is loaded, we program the RAM and then we can start debugging. We set a breakpoint to enable the view to check the values of the local variables. We can now run the code and verify the changes in the values of the local variables. This sequence shows how we use UDE to program the flash memory with the code by our final application. After testing the code on the real system, we realized that there was some noise on the ADC reading of the Hall Sensor and we implemented a simple averaging filter. Thank you for watching this AutodevKit self-training video. Please join our online community and visit www.st.com.autodevkit for more information.


The Chinese remainder problem requires us to solve problems of the form, find a multiple of n that leaves a remainder of 1 when divided by p. We've been solving this by checking out multiples of n, but can we solve these problems directly? And in fact we can. Chen Zhixiao gives the following procedure, which we'll illustrate with the following example. Suppose we want to find a multiple of 5, that is 1 more than a multiple of 13. To do this, we'll set up a table. So our table will have two columns. The right side will be our multiples 5 and 13, and the left side will be the numbers 1 and 0. And we'll proceed as follows. We'll work the right column, and we'll subtract the smaller number from the larger number as many times as we can, keeping track of the number of times you've subtracted. So 13 minus 5 is 8, that's one time. 8 minus 5 is 3, that's a second time. And we can't subtract any more. So now we'll change our table. We'll multiply these two opposite quarter numbers 1 and 2, and add the result to the third quarter number 0. So 1 times 2 is 2, plus 0 is 2, and our table now becomes this. And lather, rinse, repeat. We'll subtract the smaller number from the larger number and keep track of how many times we can do that. So 5 minus 3 is 2, that's one subtraction. We can't subtract 3 from 2. The product of our quarter numbers 2 times 1 will be added to our third quarter 1, so that's 2 times 1 is 2, plus the 1 that's already there makes 3. And that's our second table. And again, smaller number from larger number and keep track. So 3 minus 2 is 1, one time. Multiply the corners and add smaller number from larger number. Now our end goal is to have the second column be all ones. And so we arrive at that end goal, multiply the quarters. And the way we can read this is that 8 fives, that's 40, is one more than a multiple of 13. So why does this work? Well, let's consider our tables. In our first table, we can read the two lines as follows. The first line says that 1 is 5 more than a multiple of 13. Namely, it's 5 more than 0 times 13. Our second line can be read as 0 fives is 13 less than a multiple of 13. Namely, it's 13 less than 1 times 13. And what this means is that for every 5 we add, we reduce a deficit by 5. And that's because our fives are 5 more than a multiple of 13. So whatever our deficit was, it's now 5 less. And so if we add two fives, we reduce our deficit by 10 and our deficit is now 3. And that gives us the bottom line of the table. 2 fives is 3 less than a multiple of 13. And what that means is that if we add 2 fives, we'll reduce an excess by 3. So 1 fives gives us an excess of 5. 3 fives will give us an excess of 2. And so our top row now indicates that 3 fives is 2 more than a multiple of 13. So if we add 3 fives, we'll reduce our deficit by 2. Our bottom row now tells us that 5 fives is 1 less than a multiple of 13. So if we add 5 fives, we'll reduce the excess by 1. And that gets us to this final table. 8 fives is 1 more than a multiple of 13. And 5 fives is 1 less than a multiple of 13. So let's look for a multiple of 8 that's 1 more than a multiple of 21. So we begin by noting that 1 8 is 8 more than a multiple of 21, namely 21 times 0. And 0 8 is 21 less than a multiple of 21, namely 21 times 1. And so we'll set up our table showing this excess and deficit. Now since 21 is greater than 8, we'll repeatedly subtract 8 and keep track of how many times we've done that subtraction. So we'll subtract once, twice, three times. Oh wait, we can't do that. Now we'll multiply our quarter numbers 1 times 2 and add our other quarter numbers 0. And this result will replace the quarter number 0. And this tells us that 2 8 is 5 less than a multiple of 21. And now we can continue the process. We'll subtract 5 from 8 and we can't subtract another 5. So multiply our quarter numbers and add and replace. And again we can read this 3 8 is 3 more than a multiple of 21. And we can continue. Subtract 3 from 5, multiply the corners and add, replace. And so we can read our new bottom line. 5 8 is 2 less than a multiple of 21. Now subtract 2 from 3, multiply the corners and add, and that tells us that 8 8, 64, is 1 more than a multiple of 21. Now while we have answered the actual question, let's go ahead and finish out the process. So 2 minus 1, multiply and add, and that tells us that 13 8, 104, is 1 less than a multiple of 21.


So the Jet Beast is a new vehicle to the game and today I will guide you to all of its upgradeable unlocks. How's it going guys? My name is DPJ and today I bring you another BL3 video. If you do enjoy it, leaving a like really helps out and subscribe if you do want to see more. Also guys, if you have any questions about the game and you'd like to ask me about any of them, join my Discord linked within the video description with dedicated trading sections for all platforms the game is played on and much, much more in association with Borderlands 3. Again linked within the video description. So the Jet Beast has a few decent unlocks I think you might want so today I will guide you to every single one. Time stamps for every part you will find in the video description and linked at the top of the comments section too if you're only missing a few things. So let's get into it people and we will start with weapons. So there are two unlockable weapons. The first one to cover is the Mortar Launcher. This weapon I absolutely love for sure and the distance you can reach on this thing is unreal. Now this Mortar is an exclusive reward from a side mission called Moneyback Guarantee. Now the mission is upon the blast planes in which you can see on the screen now. The mission itself is quite a funny one so go do this thing people and get that Jet Beast weapon. Nah you wouldn't be interested unless you want to get your hands on some real firepower. So the second unlockable weapon is called a Heavy Cannon. This weapon is unlockable via you hijacking enemy Jet Beasts. On screen now you can see where I found the vehicle in which it had this weapon on it. Now it can be completely random but for me this is a very popular place for this specific weapon. Ok so we're going to move on to skins. Now besides the default skin there are five in total to unlock. The first one we will take a look at is Beefy. So this skin beefy is unlocked via completing a side mission called Swordoo Valley which is located within the blast planes. As you can see on the screen now. Complete this mission rewards you this beefy skin. Next up we have the Devil Rider. This is tied to a mission called the Dandy and Damsel. And this mission can be found within Bloodstone Canyon which you'll see on screen now. So complete this mission people to get the Devil Rider skin. So the next three skins are of company car Reptile and Woody are all unlocked via you hijacking random Jet Beasts from enemies. On screen now you can see the locations of where I found the Jet Beasts which unlocked these skins. Ok so next up we have armour. Now the only armour you can unlock is Heavy Cases. Now the only armour you can unlock is Heavy Cases. And this is also found on random Jet Beasts and you can find it via the location on screen now. Within this area these are quite popular. Ok so next up we have Heads. So first up we have the Lucavore Blood Hauler. And this is unlocked via completing a side mission called Devil Rustlers which is found upon the blast planes as you can see on screen now. The second head is called the Helian Quickmoor. This is the head you get random for hijacking an enemy Jet Beast. I got my way you can see on screen now within this area. And finally guys we have Muds and they're only being one available and it's called the Thrust Turbine. And this is another random item you can find on those enemy Jet Beasts which you have to hijack. Mine was found on an enemy Jet Beast in the area you can see on screen now. And that's it guys. All spots and means of earning all of the vehicle upgrades. And I hope this helps you out. And on that note we have come to the end. If you guys enjoyed the video leaving a like really helps me out. If you're new around here and want to see more bodylines be sure to subscribe. And if you never want to miss a video I upload you can turn notifications on by hitting that bell button. But guys thanks as always for stopping by. Hopefully you enjoyed the video and hopefully I will see you on that next one.


Good morning everybody I'm Tommy O'Brien coming to you live from TFNN 9 a.m. Eastern time Monday morning shot shortened holiday week for trading days closed on Friday for July 4th right now we have markets in the green NASDAQ actually in the red NASDAQ negative by 16 points trading at 9848 NASDAQ futures you get the S&P positive by seven trading at 3,014 Dow futures positive by 126 trading at 25,076 Dow futures getting a lift from Boeing especially today Boeing going to begin three days of testing for their 737 max as they look to get out of the pain from that problem that they've faced start things off with the S&Ps quite a day on Friday we trade south and the week at about just over 3,000 open the futures last night 2983 we're about 20 points above that level right now but a little bit of a sell-off in the last hour we're as high as 3026 we're currently trading 3014 on the S&Ps crude contract up 37 cents just catching a little bit of a bid since about 4 a.m. trading from 3760 up to around 39 gold contract flat at 1780 quite a day for gold on Friday quite a week for gold last week 1796 the high silver continuing its volatility at 1804 and notes and bonds pretty muted action right now the 10 year minus three ticks at 130904 the third year minus 10 ticks at 17825 and we're sitting with a 10 year yield of 0.653% we'll start things off we'll jump over to the VIX this morning 3491 as we start the four day trading week and as I mentioned Boeing so Boeing catching a lift check out that acceleration from 170 to almost 185 we're trading at 17950 right now Boeing gonna start testing the 737 max three days of flights as they test their plane and hopefully get it back in the air but nonetheless folks airlines Boeing max pain for those companies recently as COVID persists with that in mind couple headlines secretary health and human services out there over the weekend pleading the window is closing to halt the coronavirus spike we've got the tools to do this he said but the window is closing we have to act and people as individuals have to act responsibly we need to social distance we need to wear our face coverings where we can't social distance particularly in these hot zones and folks you talk about a hot zone we got quite a hot song going on in Florida where's my chart where is it get it up it's a tough one folks there it is there's the acceleration folks we're dealing with big numbers across the country and there's your curve so stay safe wear those masks and you know what else you gotta do listen to Larry Pezzvento know what's going on the market coming up next we'll be right back


Global Just Recovery Gathering Welcome everyone, my name is Mahir Ghaz, I'm based in Turkey. I'm also one of the resident climate science and policy wonks at 350. We do have a few of those. One aspect of closely following climate science and policy is often the anxiety that brings. It's not uncommon to see scientists, activists or even people only casually following the developments in those two spaces lose their grip and burn out and drop out of the movement. Today we're going to delve into these issues in our panel called Grounding Ourselves Science Updates and Our Mental Health. We have four illustrious panelists to help us navigate through this conversation. And I'd like to briefly present our panel members to you. This is in no particular order, by the way. Ko Barrett is the Deputy Assistant Administrator for Research at the National Oceanic and Atmospheric Administration, NOAA. You might have also seen her name in relation with IPCC because in 2015 she became one of the first women elected to serve as a vice-chair of IPCC. George Marshall has in excess of 30 years of experience at all levels of communications and advocacy. He had senior positions in many international NGOs, active advisory roles for governments, businesses and international agencies. And his book from 2014 was called Don't Even Think About It Why Our Brains Are Wired to Ignore Climate Change. Louisa Neubauer is a German climate activist. She is one of the main organizers of the school strikes for the climate movement in Germany among her many engagements with multiple NGOs and many accomplishments. And Rene Lersmann is a pioneer and leader at the intersection of psychology, climate and environment. She translates psychology and social science best practices into tools that unleash the potential for creativity and courage. Rene is also regularly commissioned to teach, present and produce research and produce research for a range of institutions and agencies. So thank you so much for agreeing to be here today on this panel. We are going through difficult times. Our climate anxiety, which had been climbing for many years now has been compounded by the pandemic last year. We lost many of our usual coping mechanisms. And in this panel, we're going to be talking about what's the latest both in terms of science and just curry and how well and also how we can take all of that in stride and stay active in the climate struggle. So welcome once again. I do have a warm up question though before we jump in. It's a very short one and I can start by answering mine. It's a, is there a piece of music that boasters your courage? I'm a huge, you know, listener. Mine is is a not very well known Bob Dylan song. It's called let me die in my footsteps, which I like. George, do you have one yourself?  Good choice. And Renee. Gosh, that's George. I can, I think we just got a preview of you on Desert Island is so good warm up for that. I've been preparing for it for years. I can tell you've got it down. Gosh, you know, for me it's, I turned to Kirtan actually and I really love putting on like never ever a permal or some Krishna das when the going gets tough and it brings some wind into my sales. What are you cool? Oh boy, you're diving right into the differences between us in a very interesting way. All right, true confessions. I'm a big jam band band like Grateful Dead music and there's a lot of bands that kind of have followed in those same footsteps. And what I love about it is the true improvisational nature of the way that they approach either some, some jam in the middle of a song, but often the jam between songs and how as a participant and listening to that music, you're just taking along. Where are we going with this and where is it going to pick up? I just find that to be a fascinating way to engage with music as a listener. Perfect. And Louisa. And talking of differences in those moments, I make music, I guess. So that's when I get out my ukulele or my guitar or when I have a piano nearby, I play that. And that is so softened everything in me. And I start relaxing this really, you know, this thing here that you usually cramp so much. And I realize how much there was on my face and so on. And that's one thing. And for that, it's mostly classical music. And besides the couple of my friends in these, just to talk about some stereotypes here, a couple of my friends are playing in some indie pop rock bands. And I love listening to their music because I see their smiles when I want to listen to their songs. And sometimes I move them when they make music. And I remember what they were thinking about it. And I think it's a beautiful idea of people to produce something that is existing only in a moment, always, inherently. So yeah. Well put. Well put. All right.  We're done. And we can end the panel here. That's a joke. So, but let's start with Yuko because you are also holding that side of the things with your work, both in NOAA and IPCC. So what's your take on the role of science in just recovery? What are some, can it be considered one of the tools to, in our quest for just recovery, maybe? Yeah. Thanks for that question. I guess it makes sense for me to feel the question like that. As a scientist among the others on this panel. I mean, obviously science is has been a key to this whole issue of climate change. You know, over 30 years ago, climate scientists put the issue, you know, in the forefront of our minds.  Yes. And now to, what does this mean for me in my backyard? You know, in my neighborhood. And so, so science is obviously a big part of the picture. But recently, as I've been kind of thinking about it, especially in terms of what we were going to talk about today, I realized that it's just a piece of the puzzle. Right. It's an important piece and groups like the one that Luisa is working with and the youth movement have really taken up the issue of science and they lead with science, but they add something really, really important to the conversation. And that is kind of the human aspect of it. Sometimes the science can be very logical and kind of a thinking approach to the problem. But what I find with the youth movement and with other approaches to solving our climate challenges is that it brings us into the feeling world. It brings us into kind of, you know, needing to work in solidarity with one another. And I recently saw, I guess, I guess it was the secretary general of the UN talking about the response to COVID and what do we need? And he was saying, we need science and we need solidarity. And I thought we need that same exact approach in a climate sense. We need the science, but we also need to be working kind of empathetically together on solutions. We need to be putting the human face on this issue and realizing that we kind of all need to go together if we're going to get as far as we need to, you know, on solutions. So, you know, and it's an interesting thing to think about because, you know, often there's a little bit of a, we know the science, you know, kind of, there's a little bit of friction between folks who are not actively working hard on climate and those who are, and almost a sense sometime that, that if only you believed in the science, you would know how challenging, you know, how important it is to act. But in fact, if you look at the research, some of the people who are not actively working on climate solutions, it's not because they're not, that they're ignorant of the issue. In fact, some of them are very well versed in the climate science. It's more that people act on these issues from a value place, a place in the community. And they have their own interests that they bring to the table that they need addressed. And so, you know, I think it's important for us to be compassionate and understanding and to be working, not kind of in an antagonistic way, but how do we bring people together to work on parts of this that actually are solving the problem from the perspective that each sees it. And I think, you know, a focus on solidarity and really how we need to work together is kind of a part of that. So maybe that's where I'll stop for now. I hope we get to, you know, have some conversation on these types of topics. Thanks, Mahir. Speaking of science, what do you think about our tendency to ignore that science which you talked about in your book that came out in 2014? Why are we, why are our brains wired to ignore that information? And how can we change that? Or indeed in the seven years since your book came out, did your take on this evolve, maybe based on some of the latest happenings as Ko also mentioned? I really, I really liked what you were saying, Ko. And I'm drawing out these ideas of approaches which are based in the social and the cultural, as well as for kind of as the, you know, the data-driven science. I mean, we need the science, but I guess the point which I made in my book, and I think still holds very strongly, is that actually people's primary source of information is for social realm. And that you can say that they're scientific facts, but there's also social facts. And I think the primary reasons why climate, well, climate change is inherently hard to accept. I mean, we're actually very good at ignoring things as humans. We try and ignore things which are challenging to us, which creates a sense of internal conflict. What brings them into play is that they become more important for our social identity. So if something is part and parcel of who we are and the world around us and the society and the identity that we have when we take them on board, if they're presented to us as simply facts, we tend to ignore them. I mean, the classic case of this is as a lifelong on and off cigarette smoker and nicotine addict, thankfully not at the moment, is I know very well how it is possible to completely ignore all of the data about the dangers of smoking if you are in with a group of friends who smoke. And so we can do that very well with climate change. We actively seek to avoid it and it's challenging and uncomfortable to us. And I think it's important, therefore, to recognize these social facts, but there is a wider sense of belonging and identity which is what drives us. And therefore, one of the reasons why I think climate change feels so uncomfortable to us is that it has a negative or disturbing or a challenging social narrative. And similarly, the solutions for making it feel positive and something we can engage with lie in having a positive and involving sense of identity. I'll come back to that. But I think the first thing for us in terms of dealing with it is to recognize the mechanisms by which we distance ourselves from it. And it's interesting, Coa, I'd be very interested to hear what you think about this. My observation of professional scientists is that they often have a lot of internal conflicts and challenges of dealing with this emotionally and that many of the ways they behave are about distancing it. And I've sat through these long presentations by scientists where they present the most appalling and challenging material with its extraordinary degree of distancing where it's presented in these very dry abstract terms, data graphs without exploring what it feels like or what it's going to be or the impacts of it. I think professional science has a distance in culture. And also, I know very well that scientists, when you speak to them, are often very reluctant to talk about how they feel about it. That's a generalization I know that many often do. So I think we need to recognize that we have a tendency to distance. We have a tendency to say this isn't now, this isn't here, this is something in the future, this is something we don't need to think about. And we still see from the data, although many people accept climate change and we tend not to talk about it. Again, this is changing since I wrote my book, but it's still disturbing to me how there are large sections of a population who really don't talk about or engage with this issue from year to year. And of course, that means if you don't talk about it and it's not out there in the social sphere, it doesn't have a social identity, therefore it becomes, again, this frightening thing down the line, a bit like our own deaths, like something we do not think about down the line. And I guess the solution to all involve engaging with that and accepting it and bringing it back into the social sphere. So saying this is something we need to talk about. This is something that is important to me, sharing it with your friends and your family. I mean, I often say that a single most important climate action that anybody can have is to talk about it. Far more than, indeed, I think that your own emissions and your own behaviors is talk about it with the people around you, especially for people who are not themselves activists, family, friends, neighbors, and get out there and talk about it. And I guess as I kind of like put a whole lot of stuff out on the table, I guess I'd like to say and I'd like to invite the other people on the panel to share your own experiences of dealing with this internal conflict and pain that this issue causes us. My own way of dealing with it is actually through a sense of joy in the social aspects of my work. So I love the people I work with. I love my work. I feel empowered and excited through both my activism but also having a sense of purpose in my life. And that's what gives me the drive to keep going with something that could otherwise feel overwhelming. And again, it's that positive social narrative of coming together and feeling a sense of collective purpose is ultimately the only way of overcoming the sense of despair of the overwhelmingness of it. And again, I welcome you all to share your things on this but last thought I'd leave you with is this sense that we have a responsibility as change activists for how we present ourselves to the outside world. So I mean, we talk about poster children. Well, in a way we are the poster children. We are the face of this issue for most people. And so if we present a face of despair or despair or grief to the outside world, people are going to look at us and they're going to say, well, whatever it is, whether what they're saying is correct or not, I don't want to part of it. But if we can present a face to the world which is one of positivity and hope and drive and excitement, people say, yeah, I want to be with them. I want to be part of what they are doing. And I think ultimately it's that lesson of how we project ourselves to the outside world, I think is what makes all of the difference for building our movement. Rene, you have a new project actually which launched in 2020, in late 2020, if I remember correctly, which ties into what George was talking about. Do you mind speaking about that briefly? Sure. Thank you. Thanks for having me. There's so much to respond to and what's been shared already that build on what Co kicked us off with and what George has built on, which is just picking up on, we have a responsibility as change agents to be exceptionally mindful and intentional about how we show up and how we communicate. And I'm 100% in alignment with George that the single most powerful thing we can do is to talk, to engage. I would actually reframe it slightly less about how we communicate and message and talk to engagement, which is much more about the relationship and the dynamics and the interactivity that we as, no matter who you are, as a change agent, whether you're a scientist or a communicator or a student activist, a board member, CEO, that if you are actually seeking to be driving change that we now have a remit to level ourselves up and to skill ourselves up so that we can actually truly be effective at bringing people into this conversation. And I don't say that lightly because it's actually a very high, it's a high bar. It's almost like the ultimate human challenge, which is how do you, how do you navigate extraordinarily high stakes that raise our temperature in many ways? How do you navigate the high stakes and remain present and integrated and as a neuroscientist may say, regulated. How do we keep ourselves in regulation? Meaning that we're not taking our anxiety, our fear and just simply triggering and spreading that around so that we're unintentionally having an impact. It's like, I heard Rosemary Randall say years ago, the founder of Carbon Conversations, a pioneer in this work, she talked about the hot potato where our emotional relationship with these issues can be almost like a hot potato where it's like, I'm so, I'm having trouble tolerating what I'm seeing happening that I can't help but just sort of spread it around. And that makes total sense, but it actually undermines our efforts. And so the project that you mentioned, Project Inside Out, which is, you can find it at projectinsideout.net is a very new initiative that's an experiment funded by the KR Foundation to see if we can turn a lot of these best practices that Coe is referenced, that George has mentioned into tools and frameworks that anyone, especially activists, can use. And the essence of the Project Inside Out leveling up that I'm talking about is how do we be more guides and think of ourselves as guides versus trying to make people change and trying to motivate people. That's an old way. It's old. It's old way of thinking about it. I have to motivate someone. I have to get them to care. They don't care enough. How do I make them care? So I'm going to have to be either inspiring and hopeful or I'm going to have to be really, really clarify how real this is and break through the trance. And a guide is based on the work in the public health sector that George just referenced with, you know, whether it's smoking or diet or whatever where people have to make hard changes. How do clinicians navigate that and how do we take that and apply it to the climate context? That is, I'm with you. I'm a partner with you. I empathize. I get this is hard. I'm in this with you together. I'm feeling scared, you know, but I'm also feeling hopeful. How are you feeling? And what that means behaviorally is there's a lot more listening. There's a lot more asking questions there's a lot more. Would you like to know more, you know, as an expert or a scientist? Because the thing is, there's a lot of people working in the space know a lot. We have a lot of information that we're holding. And it's a lot to hold that. It's a lot. And so we want to figure out how do we skillfully share what we know rather than kind of, you know, not the opposite of mindful approach to that, which is to give a PowerPoint, to give a talk. It doesn't reference the emotional dimensions of this work. So it's really exciting right now. Very exciting moment we're in right now, which I just. I never thought I'd see this happening as quickly as it is. Is that we're starting to recognize that there's no separation between the content and the emotional. Tenor that that's that's also old, old. You know, that's like so 10, 15, 20 years ago. You know, I'm looking at all my books here on the neuroscience of learning and how we process information. You can't, you know, it's never neutral. It's always affectively charged. And so how do we just kind of accept that and then think about not, well, do we tell people how bad it is? Do we tell people how hard it is? Like how hard this is going to be. And that's not the right question. The right question is how do we do it? Not if we do it, but how do we do it? In a way that actually lowers our. Our make, deliver response that keeps us in that window of tolerance. And that has a lot to do with what both co and George have already mentioned, which is, you know, feeling like you're part of something bigger, feeling like you're not alone. But the key thing here to just end with is. It's really important that we normalize whatever experiences we may be having. And that we normalize it. It makes sense to feel anxious and it makes sense to feel scared. And that doesn't mean you're not hopeful or optimistic or, you know, that it's no longer about hope or despair. It's actually, you know what? We're in a human moment right now at existential change management moment. And that's my nickname for it. It's, it's changed at an existential level.  Like, okay, it's overwhelming. But it's also a huge opportunity for us as humans to level up and do things, you know, in ways we never thought possible before. So it's a moment of more skillful nuanced ways of working with ourselves and our own experience with our colleagues and our partners and with broader communities and groups. That was great. And I'm going to turn to Louisa and ask if you've had the opportunity to use some of those tools, some of those approaches and better underground, you have time to think that in depth and how do you deal also with the frustration of what George said, but people still not talking about it as much as they should maybe. Yeah, thank you so much. And I'm really moved by your wonderful words. And it's obviously talking now as a young, girish person. And it's obviously a very difficult situation to be there. I started, I started climate activism essentially because I looked at my life and I couldn't bear the idea that for my entire life I would have to deal with that catastrophe. And I just, and for me, you know, I'm 24. So, you know, just thinking like five years ahead for me, it seems like a lifetime almost. And so I found it so deeply, I found it unfair. I found it too much.   For us, the Fridays make sense because on Fridays we behave like we're in a crisis in a sense. But usually whatever we do, it doesn't add up. And that is a very, very difficult situation to be in. And I think, you know, I do believe that has something to do with age as well. That we think about the impossible much more. We think about it in the good ways, but we also think about it in the bad ways. And we don't have to, you know, we don't think about the 21st century in relation to the 20th century. It's just there and we have to make the most out of it. And we don't have any boundaries except that we see catastrophes everywhere. And I feel, and I think that's why many people turn to be climate activists that obviously the most helpful thing to do for me at least was to get active about this and don't take this as something that I don't matter. So I chose and what decided that I would make a difference and that I would make a difference, whether I wouldn't do anything or whether I would do something. And that's a very important part to understand because staying silent in this also makes a difference. And the more people do that, the easier it is to keep peeling and doing all this stuff that fuels a crisis. So, and that is, and that again brings you to the spot that you as a young person, you know, once you decide that you matter, that you make a difference, that you are important in this, that, you know, can be very freeing, but it can be incredibly scary as well.   And no one tells you that you might matter in this, in this bigger human moment. And so we're not trained to think about it that way and not trained to think about us this way, that we matter in the broader context. And I feel we heard so much about, you know, getting around this and this are acknowledging this, acknowledging us in this, without being buried by the thoughts of, you know, what might happen and what might not happen. And I feel though, maybe just to boil it down to three aspects which I find really important in this. One is we talk so much about natural sciences and I feel, you know, we often say we have to talk about science and then, you know, you talked about science solidarity, which I find beautiful. I do believe as well though that it has to do with understanding that science isn't just natural science, it's social science as well. And we are being taught so much about every single aspect of the, you know, molecules somewhere and people don't teach us what this means for society, is how to stay human in this, how to stay together and strong and then solidarity in this. And that is often, you know, overseen and when we teach people about the public crisis, we teach them about stuff that for us matters the least. And because obviously it kind of starts with the human connection in this. So that is one thing I think which I am really, which I try to mainstream, which I try to make more famous in my work and try to understand more about how do we cope with this as societies. Secondly, I think the most important conversation about the climate crisis starts with us and our feelings. In Madrid at the climate conference last year, where we met as well, and Johan Rockström was giving a talk at the summer assembly and he said loads of things, really important things about all the incredible things that he finds out. And the one sentence that I remember from his talk was him saying, hello, I'm Johan Rockström, I'm a father and I'm scared. And then, you know, it's care, it's all out there. What do we even talk about? And I feel, you know, teaching ourselves and to talk about the climate crisis in that way, starting with an eye, starting with ourselves, what do we feel? What do we wish? What do we hope? What do we fear? Those things, finding those spaces, creating those spaces where these messages have, you know, can be and can be acknowledged in those things. I feel that as part of the climate movement, we're much too rare, but hugely important. And then I feel finally, this is maybe some lessons, some lessons I take away from the corona pandemic is given that we have brought the planet to the state it is. Every moment in history, every moment in the future will be some sort of, you know, crisis moment.  So I feel this is, understanding this, acknowledging this, and I try to think about this in the corona context as well. You know, what do I do? What, who am I in this pandemic?  And we always said it would be hard. And, you know, two years ago, when I see my speeches now, I would always tell the people like, oh, it's going to be hard and it's going to be tough. I would have never thought that we get a tiny virus to pass on an infectious disease. And that the hardship that I was talking about would at some point be a pandemic. I couldn't imagine that. And I now feel how foolish it was to talk about this hardship in that kind of easy way. And now we learn differently. And I feel, you know, understanding ourselves as, or understanding our humanity or human side as who we are in a crisis is, I feel that's a really helpful mechanism to apply. Well, that was deep. And also as an activist touched me. Could I, sorry, could I just add one more thing? Because I feel that is one thing that the three of you mentioned. And I feel that is, for me, it defines all of my work is the idea that maybe it just doesn't work out. Maybe, maybe we will just mess up hugely and things will just be bad. What life, what time will we look back at? And I want to look back at a time where we fought for everything we had and everything we had lost already and everything we didn't want to lose in solidarity and love and kindness and, you know, being there for each other. And, you know, understanding or acknowledging that maybe it just doesn't work out. I feel that allows us and ideally drives us to a point that how we fight becomes as important as that we fight. Can I just respond a little bit? I just value your perspective so much. And I thought, I think you've kind of put a nice fine point on so many of the things that we've started to talk about here. I just wanted to pick up on your point about the challenge and the opportunity that the coronavirus is kind of putting in front of us, right? I mean, there's so many people who have been devastated by this and just the way the entire globe has had to change our way of being, our way of interacting with one another.  But it's also, I'm finding, and I don't have small children running around my house. So I don't bear that stress and my job continues so I don't also have that financial stress that so many carry. But I also, for the first time in 30 years, have not gotten on an airplane in a year. And I work on climate. I've spent my entire life on climate. And the year before this, I flew over 100,000 miles. And that's provided a respite from my greenhouse gas, my carbon footprint for sure. But it's also provided a respite for me in terms of grounding myself, which is for the message we were kind of going to talk about here, grounding myself in a different way in the work. And I found that because we're all working remotely and we're all interacting through Zoom calls, et cetera, I found, especially with the colleagues who I interact with very frequently, that the line between our work lives and our home lives is completely blurry. I love when someone's dog jumps on their lap in the middle of a meeting. I love when a toddler walks in and all of a sudden, my colleagues are not just people I work with on issues. They are human beings who I care about. And the blurring of those lines is so helpful to the way that we come together to work on solutions. So I kind of find that it's added to the humanity of working together. And I think, obviously, we've had a reduction of around 7% of our greenhouse gas emissions during this time. This is not the way we would choose to solve the problem. We would go about it in a more purposeful way. But I think it does open up the ability for us to kind of imagine or reimagine a just future because we don't have to go back to working in the same old way. Now, we're considering it as just a part of our working arrangements, more remote work, more work-life balance. In the context of IPCC, when we have our virtual meetings, we are challenged because not everyone has the same level of technological ability in their countries. Developing countries are often really challenged. And we have to deal with that. But we also get to include many more people. It's not just the two people who come to the IPCC meeting. We can have many more people included in the conversation, which is a hugely great thing. So I just want to say there are opportunities that this presents to us that I hope we grab on to as we reimagine our future. Go ahead, George. Well, I think it's I'm going to quickly follow up on a few points if I can, but I'm sure it's going to be a good lead-in for you, René. I know that I'm here. I know that you were interested in asking about 1.5 degrees. And following up on all of us, I wanted to say this thing about it's okay to feel scared. You said this, René. And, Louise, you were talking about this, too. I think I just wanted to say, I think there's a lot of wider evidence to say that the first step towards overcoming or certainly coping with any kind of emotional challenges is to recognize it and to have it there and to look it firmly in the face and to say, this is hard. This is scary. This is difficult. Maybe we have lost a lot and this is hard and this is how I feel about it. But together we can form a community of care. Together we can look after each other. I think from what you're saying, Co, that's also something coming strongly through COVID. I think COVID has produced a step change in public attitudes towards collective solidarity and collective grief, which I think is very, very positive for this. So I wanted to put that out there and say that part of the communications, the communication I have, I know communications engagement, but I have intuitively just years is the very positive, bright side of saying, hey, it's all going to be fine because we're smart and we have immense capacity for enterprise and for initiative and technology. And I don't think that works. And we know it doesn't work because within my organization, Clive and Outreach, we've gone out and done a lot of testing with very different groups and we know that message doesn't work. What works is to say, look, this is hard. This is painful. This is going to be difficult, but together we can do it. And just to leave you with a thought that the reason we know about works is because we've tried out in many different places and we have tried and tested message in focus groups around this in very, very different contexts. We've tried it across Northern India. We've done projects across North Africa. I helped to lead a project in Alberta in Canada, probably one of the most divided places on Earth and in every single place this messaging worked and it initiated a conversation between people about how they could work together about how it's hard, about how it's painful, but how we could get through. So I think that's the kind of key to the answer. So thank you, George, for that response. Now, let's do a... We are actually nearing the end of our panel, believe it or not. So let's do a quick lightning round maybe. I'll ask and feel free to jump in at any point during this round. So one thing that frustrates me and based on what you have been saying, it's okay to feel scared, but also we have a responsibility in terms of how we give our messages in order to not, you know, paralyze ourselves or others in the movement, so on and so forth. One thing that frustrates me is the the whole discussion around 1.5 and whether that's possible, that's not possible, whether we're already at it or over it and what it means for us. And why are we so obsessed with that decimal and can't we just focus on doing the best we can and ensuring that we have as much of a just recovery as we can in whatever we end up with, be it 1.5, 1.61 or whatever? Why that obsession with that decimal? There's a lot to say. I would just point out again that I'm representing the psychological lens. We all got our lenses. So through the psychological lens, we want to just keep in mind what works, what we already know works with human beings and how we process information and how we avoid information and what we get fixated on. And so we want to be thoughtful about what are we getting attached to and fixated on that maybe helps us avoid the more messy, complicated, nuanced, ambiguous, uncertain aspects of reality. So when we see an attachment to that, that should be a clue that hopefully makes us curious. Like what is actually going on here? Why are we so fixated on this? And usually what we'll find is that we're wanting to hold on to something. We're wanting to hold on to something that gives us more sense of stability and certainty and that we all know in this conversation that's kind of elusive, right? So to me, the reframe is to bring curiosity and compassion and empathy to, wow, this is, again, keep going back every single day. Like, wow, this is really intense. And look at us. Look at how we're trying to get a handle on what's happening. How do we shift that into a conversation and into an inquiry that moves us in the direction that I feel Louisa really articulated so eloquently and so beautifully, which actually takes us to a whole different altitude and how we're relating with these issues. One thing I'll just say about 1.5 is, the genesis for 1.5 is that some of the most vulnerable countries in the world were saying in the climate negotiations, two degrees is not low enough for us. We're already at threat. So we've got to be striving for 1.5. When you start talking about 1.5 degrees of warming, you are talking very soon. And all of a sudden this distant problem is right in your lap. And that's powerful. And I think that is potentially the most powerful thing about taking a look at the kind of scientific piece of what goes into 1.5. It is now. It was an absolute honor and a pleasure for me to be with these illustrious panelists here. And it has been an interesting inspiring conversation. I hope it's useful to everyone as well. And I wish everyone health and courage for the coming times. And love to everyone at the Global Just Recovery Gathering and everyone watching. Thank you. Global Just Recovery Gathering.


The government, or St. Lucia in assessing state assets, is examining the current condition of the building that housed the George Charles Secondary School. Prime Minister, the Honourable Philip J. Pierre, headed a delegation to a site visit. Harmony Mark reports. The government of St. Lucia has announced plans to rehabilitate the structure which once housed the George Charles Secondary School. Prime Minister of St. Lucia Honourable Philip J. Pierre made the announcement during an assessment tour of the building. The Prime Minister was accompanied by Minister for Education, Sustainable Development, Innovation, Science, Technology and Vocational Training Honourable Sean Edward, Parliamentary Secretary, Honourable Dr. Pauline Antoine Prosper and other government officials. Minister for Education Honourable Sean Edward indicated initial plans to clean up the premises and spoke to the possibilities for use of the building. There are a number of alternatives or a number of possibilities that can be explored. In the remit of education, we have given thought to using this facility as a national vocational training institute. There is also the possibility of allowing certain departments of the SALCC to operate from here. We are also looking at a facility that can focus in terms of certain skills that you want to teach in the wider community. There is also the potential for it being an after-school facility. So there are so many possibilities that can be explored but as I said we will sit down as a ministry with the policy makers and if needs be we may have to rope in other stakeholders in the education fraternity and even other agencies of government to see how best this facility can serve not just the immediate castry south community but St. Lucia as well. Honourable Philip J. Pierre highlighted the importance of building maintenance to avoid depletion of important structures. When it comes to the tightness of the fiscal space, if you notice there is one building that is well kept. This is the building where the Ministry of Infrastructure is. That building there is a contract, a maintenance contract. And I think going forward we have to ensure that any building that we build there is a maintenance contract so that the building cannot reach the state that some of our buildings are. But I think what's important is we need to have maintenance contracts tied to the construction contract. In repurposing the building the government of St. Lucia will retain its name as the government continues to honour and protect the legacy of Sir George Charles from the Government Information Service, the Mighty Mark reporting.