If I have one goal today, it's by the end of this, you all will understand what my title means. First to motivate this topic, I'm going to tell you a story. The story is a tale of two batteries, and the two batteries in question are lithium-ion batteries and flow batteries. Lithium-ion batteries, we all have lithium-ion batteries in our pockets right now in our cell phones. We all know what they are. They're a dominant force in a lot of industries today. Flow batteries, I'm curious, how many people know what flow batteries are? Raise your hands. We're a self-selected group of energy nerds, so number of hands were raised, but a number of hands weren't raised, and actually, when I talk to people in everyday life, pretty much nobody knows what a flow battery is. So that's pretty interesting, but let's go back to the beginning. Lithium-ion batteries were invented in the 1970s at Exxon Research Laboratories by M.S. Whittingham and colleagues. Flow batteries were also invented in the 1970s with a lot of work being done at NASA. So both of these batteries were invented around the same time. And then what happened? Well, lithium-ion batteries in the 1980s saw R&D. In the 1990s, Sony introduced the first consumer electronic with lithium-ion batteries, the CAM quarter. This led to a revolution in the consumer electronics. Phones, cameras, laptops, all used lithium-ion batteries. And then as time went on, they started increasingly being used in electric vehicles, starting to transform that market. And they're also already being used as grid-scale energy storage. They're the dominant technology for new installations in grid-scale storage and also into the future. How about flow batteries? Flow batteries in the 1980s saw R&D. In the 1990s, R&D, some niche applications. In the 2000s, R&D and some niche applications. And again, some R&D and some niche applications. So a very different trajectory for these two batteries that were both invented around the same time period. Although, flow batteries, there is a lot of excitement, including in our group, for the use of flow batteries in the future for grid-scale energy storage. But before we get to the future applications, I think it's worth reflecting on these two different battery systems invented around the same time with widely different trajectories. So why is that? Now, you can't pin it down to any one reason, but if I had to choose one reason, it would be this, it would be energy density. Lithium-ion batteries have about five to 10 times the energy density of flow batteries. That's why they're chosen for portable applications. And all of that use of portable applications increased their market share, it increased the manufacturing capability and the investment and it lowered the price and that led to it increasingly being used in grid-scale storage and other applications. So flow batteries don't have those same applications. And so they haven't received that same sort of cost reduction and scale up. So why is this and why are we still excited about flow batteries? To learn about that, we have to look at the architecture, the basic architecture of the two batteries. Lithium-ion batteries have solid compounds that store the charge and they're connected by a liquid that's ionically conductive. Flow batteries, on the other hand, have liquids that store charge. So the charge molecules are dissolved in these liquids and there's a solid ionically conducting membrane in between them. And the fact that these are liquids means that there's a lot of advantages to flow batteries. You can decouple the power and the energy, which means you can create a long-duration battery. You have the possibility for low-cost reactants because you have a much bigger space to play from. You don't have to always use cobalt, for example. And you could have a very long cycle life because you don't have a solid microstructure that's gonna degrade. On the other hand, the solids give you a very high energy density. So a few years ago, our group was thinking, what if we could make a flow battery that could have an energy density near that of lithium-ion batteries and somehow get the best of both worlds? The problem is that's actually really, really hard. And to see that, I wanna look at two different metrics that are critical to energy density of batteries. The first is the standard reduction potential and the second is the capacity. So what you want is a standard reduction potential of your two sides to be very far apart because that means that there's a very big energy difference between your two sides and you store a lot of energy for every charge you pass in between the two sides. And you want the capacity of both sides to be very high so that you can store a lot of charge in both sides. And lithium-ion batteries have both of these. They have a very far apart standard reduction potential so they have a very high voltage and they have a high capacity and that's because you're storing these lithium ions in these solids, the lithium would be green in these crystal structures. So you can fit a lot of lithium-ion batteries into these layered compounds. On the other hand, flow batteries and the conventional flow batteries use water as the solvent to dissolve the compounds that store the charge. They're constrained on both of these axes because you can't have the voltage be too far apart with a flow battery because if you go too far in either direction you either start making hydrogen or making oxygen out of the water. You start water splitting. You also can't have too high of a capacity because you need all of those water molecules around to create the liquid. So you, for just one charge storing compound you have all of these water molecules around rather than being able to fit a lot of lithium ions into this solid framework. So this suggests that in order to create a high energy density flow battery we somehow have to get rid of this water, get rid of the solvent. So how could we do that? How could we create a solvent-free flow battery? There's a number of different knobs you can turn to make a solid into a liquid. You can dissolve it in a solvent. You can heat it up, use a high temperature battery. You can modify a compound. Organic chemists can take compounds and put asymmetric groups, bulky groups, very long chains that are floppy and thereby lower the melting points of compounds. And you can also use eutectic mixing. And that's what I'm gonna talk about. Eutectic mixing is not new science. It's actually very old science. But it's something that we think is not paid attention to really at all in the context of flow batteries. And we think that it's something that actually might hold some promise. So what is eutectic mixing? What is the eutectic system? It's a system where the mixture of two compounds has a lower melting point than either individual compound. And you can see that in this eutectic phase diagram. So we have temperature here. So these lines show the temperature of phase transitions. And then you have composition on this axis. So going from pure A to pure B. So the melting point of pure A is here. The melting point of pure B is here. And the melting point of the mixture is lower than either of them with the lowest point being the eutectic point. So this is called a eutectic phase diagram. The most well-known eutectic is that of water and sodium chloride. And that's why we spread salt onto roads in the winter to reduce the melting point of the ice. So we started working on a eutectic between sodium and potassium. It's literally the combination of pure sodium with the melting point of 98 and pure potassium with the melting point of 64 to see what this looks like. Here's this video. This is a piece of potassium. This is a piece of sodium. Now these are very air and water sensitive. So we do this in an argon filled glove box.  It's a room temperature liquid metal that's being created, which is actually the stable phase at room temperature in this eutectic phase diagram. So we did some research to demonstrate that you could use this in a flow battery architecture if you use the ceramic membrane and we published that in Joule last year. And we're excited by this because if you use NAC, as it's called, sodium potassium, in a flow battery, you would have one side that has a very high capacity and a very low reduction potential. So that would be a good thing for energy density. But we still need something on the other side of the battery. So what will we put there? We were thinking of using quinones, which are organic redox active molecules. They're studied for flow batteries, both aqueous and non-aqueous systems. And they're studied because you can reduce them. You can actually put two electrons on each molecule, either with alkaline metal salt or with the proton to create a hydroquinone. And you can go back and forth. You can store charge this way. Now these compounds are solids at room temperatures with increasing melting points, as you can see. So we had an idea, what if we could mix two of them together and we can create a liquid that had a lower melting point than either one on its own? But the eutectic phase, this phase diagram is not in the literature. We couldn't find it at all. So we'd have to make it ourselves. We'd have to study it ourselves. But if it did happen to be true that we could do this, then a mixture of benzoquinone derivatives would have a pretty high energy density and be up here in this diagram. So it would be great as the other side of a high energy density flow battery. So we studied this with differential scanning calorimetry. That gives you peaks whenever you have a phase transition. And you can see these peaks show that we had a melting point that was lower than either of the two on its own, both through the charge and the discharge states, which means that we do have eutectic mixing going on in these compounds, which was exciting, but we also didn't have as much of a melting point decrease as we wanted. We're still around 100 degrees C for these compounds. So we still wanted to lower the melting point even further. So how could we do that? Actually, using eutectic mixing, if you start adding in more compounds and increasing the number of components in your system, you increase the entropic driving force to make the liquid. And so you can conceivably lower this melting point even further. But the problem with that is as you add more components, you now increase the phase space dramatically. So let's say you wanted to know the exact eutectic composition in this phase diagram by measuring every 3%. So you'd have to make 33 measurements. But let's say we had seven different compounds that we were mixing together and we wanted to know the exact composition of the eutectic by a 3% error, we'd have to mix roughly a million compounds. So that's not gonna happen. We need some model that could help us predict what that composition would be. So we used machine learning. No, actually we used a thermodynamic model. It is actually really a simple thermodynamic model that we applied here. I'm not gonna go over it, but it's a regular solution model assuming immiscible solids and thermodynamics everyone should study. I heartily approve. And the way we apply this is we took seven different compounds with melting points between 44 and 120 degrees Celsius and then we made every binary mixture of them and measured the eutectic melting point from the DSC and we plugged that into the model to get an interaction parameter in the model. And then we can plug that back into the model to get a prediction for an end component mixture. And to see how that looked in the end, if we took a mixture of all seven of these quinones, the model prediction is that the eutectic composition would be this different proportions and the melting point would be minus two degrees Celsius, which would represent a 70 degrees Celsius decrease in melting point from the weighted average melting points. When we measured the equimolar, so an equal proportion of each in the differential scanning calorimetry, we did see a peak at around minus four degrees Celsius. So that shows that the eutectic phase is there at about the temperature we predicted. But we saw a lot of other peaks, which meant that we still had a bunch of solids in the system that this equimolar composition wasn't the eutectic point exactly. But when we went to the predicted composition, we actually saw mainly one peak. There's a bit of a shoulder, so it's not exactly the eutectic composition yet, but it is a lot closer. And that's exciting because both it demonstrates that we can decrease the melting point dramatically by going to a higher number of components, going down to minus four degrees C here, and also that we can get pretty close to predicting what that composition will be just by this simple thermodynamic model with the number of measurements that we made. And that, and then eventually we might be able to use this in batteries. So to summarize, flow batteries have many advantages, but their big disadvantage is their low energy density, and that's because of all the solvent molecules that you have floating around that you don't wanna react in that take-up weight in space. And so what we've been working on is using eutectic mixing as another knob to engineer liquids that are solvent-free. So you just have the pure charge-carrying molecules as liquids by lowering the melting points via eutectic mixing. So I wanna give a few acknowledgments, is that as I do that, I'm gonna show you a video of the quinones being mixed. So these are three different quinone powders that have a eutectic point that's a little bit lower than room temperature. I wanna acknowledge Professor William Chu, he's the PI of all of this research in the material science and engineering department at Stanford, a number of people who have contributed to this research in various forms, formally and presently. ExxonMobil is our current funding source, which I wanna acknowledge and also our previous funding sources. And you can see this liquid being formed. Thank you very much. Thank you, Antonio. Thank you, Antonio. We'll have questions at the end, I think. Okay, all right. So our next speaker is Aisulu Aitbekaba. Aisulu did her undergraduate degree in Kazakhstan, received her master's degree from MIT, and now she's a third year PhD student in chemical engineering. Her research is with the Conelo Group, Mateo Conelo, and focuses on the design synthesis and application of novel materials for catalytic applications. And so today, she'll be talking on nanoparticle-based catalyst for CO2 hydrogenation to hydrocarbons, Aisulu. Good afternoon, everyone. Today I'd like to tell you about my research on nanoparticle-based catalyst for CO2 reduction to hydrocarbons. As you all know, CO2 emissions have been increasing since the Industrial Revolution, and in 2017, more than 40 gigatons of CO2 were emitted. One way to reduce the net CO2 emissions would be to react it with renewable hydrogen to produce high-value products, such as fuels and chemicals. This reaction at atmospheric pressure mainly yields two products, methane through the macination pathway and carbon monoxide through the reverse water gas shift. And among these two products, CO is more valuable because it can be used as a feedstock for other processes. My previous work on CO2 reduction was done at atmospheric pressure using ruthenium nanoparticles supported on Syria. And when we perform this reaction, the major product is methane with selectivity of more than 95%. And you can see it from the chart on the right because CO and methane were the only products formed. What we found is that by oxidizing these nanoparticles at very low temperatures, lower than 200 degrees Celsius, we can re-dispers nanoparticles into highly dispersed species. And these species no longer make methane. Instead, if we perform CO2 reduction over the single site, we make CO with selectivity of more than 90%. So what we found is that by simply changing catalyst pretreatment, we can change structure of our materials and thus get different catalytic activity. But as I mentioned, we are interested in making products beyond methane in CO. We want to make hydrocarbons. I just want to tell you that CO2 hydrogenation to hydrocarbons is usually referred to as modified fishtrop synthesis. Fishtrop synthesis is a process which converts carbon monoxide and hydrogen into hydrocarbons. And this is a well-established process that has been studied very extensively. And iron is one of the catalysts that is used for this reaction. What is interesting is that when CO2 is substituted, is used as a feedstock. Iron can also make hydrocarbons due to its water gas shift properties. Iron also has cheap price, which makes it appealing for this reaction. So it's not surprising that people have been interested in studying iron for this reaction. There has always been interest in how we can improve activity of iron-based materials by addition of various dopants. And specific attention has been paid to ruthenium. Because ruthenium is one of the most active fishtrop metals, its addition to iron is expected to increase conversion of catalyst. And by alloying iron with ruthenium, one can potentially induce different electronic properties which would result in different product selectivity. So if we check the literature and try to understand where the ruthenium promotion effect has been studied before, we can find studies that show indeed by adding ruthenium to iron, one gets higher activity and different product selectivity. We can also find studies showing that addition of ruthenium doesn't change activity or selectivity. And if we try to understand why people come up with different results, one potential reason is how these materials are made. Traditionally, such materials made through conventional methods, which can result in different structures of materials. For instance, conventional methods could result in ruthenium iron alloys being present together with segregated iron oxide nanoboticles, together with isolated ruthenium metals. So it makes sense that if one comes up with a different catalyst structure, one can get different activity. Another challenge with iron-based materials is difficulty to understand this catalyst using exitoo characterization. What I mean by exitoo characterization? It's when we perform a reaction, take catalyst from reactor, bring to a microscope and look at it to try to understand the state of the catalyst and try to relate it to its activity. The challenge with iron-based materials is that iron gets oxidized by oxygen, even in ambient air, so which makes it extremely difficult to relate property of a material to its activity. So a goal of our project was to use colloidal synthesis to synthesize well-defined ruthenium iron heterostructures and use exitoo characterization, basically characterizing materials under reaction conditions to understand whether ruthenium has promotion effect or not. So we made this so-called heterodimers, which you can see on figure A with smaller ruthenium nanoparticles attached to larger iron oxide, and you can see from EDS maps that the presence of two components was confirmed. So once we made these materials, we loaded them on alumina support and started for CO2 hydrogenation at six bar together with other control samples. Before performing the reaction, we reduced catalysts in pure hydrogen at 300 degrees Celsius, and what we found is that when we tested pure iron oxide, the catalyst made only two products, methane and CO, and you can see from CO selectivity axis that this catalyst had CO selectivity close to 100%. When we tested pure ruthenium, it also made only two products with significantly lower CO selectivity, consistent with ruthenium nanoparticles being very good at making methane from CO2 and hydrogen. When we tested the physical mixture, we also got only two products with selectivity somewhere in between, but when we tested heterodimers under the same reaction conditions, we formed hydrocarbons. So we got curious and we wondered, wanted to understand in greater detail what causes heterodimers active for hydrocarbon formation. And this result also elicited importance of proximity between ruthenium and iron oxide. So we brought our materials to SLAC, National Lab, and we were basically tracing oxidation state of iron in our materials during pretreatment in pure hydrogen. What we found is that, as I mentioned earlier, we performed our pretreatment at 300 degrees Celsius. We found that at this temperature, iron in pure iron oxide was mostly present as iron to oxide, and metallic iron can only be produced if we reduce catalyst at temperatures higher than 500 degrees Celsius. If we check the literature, iron to oxide is known to make CO2 from CO2 and hydrogen. So this oxidation state actually explains our catalytic results. But when we did the same with our heterodimers, we found that iron is present as metallic at 300 degrees Celsius in pure hydrogen. So what happens is that when we, what this result means is that by adding ruthenium to iron oxide, we promoted reduction of iron oxide. And this happens where a phenomenon called hydrogen spillover effect where hydrogen gets dissociated into atomic hydrogen by ruthenium spills over iron oxide and reduces it. So this difference in oxidation state of iron at 300 degrees Celsius explains our results. When we took our materials from reactor, we found that our heterodimers no longer looked like heterodimers. Instead, there were causal structures with, as you can see on the slide, with ruthenium being in the core and iron being outside. So what we found then is that when we start with heterodimer and perform reduction in hydrogen, hydrogen gets activated by ruthenium. And as a result of iron oxide reduction, ruthenium gets encapsulated by iron and we end up with these causal structures which are active for hydrocarbon formation. And this phenomenon is consistent with strong metal support interaction effect which was found in 1987 by Tauster. And this phenomenon says that when reducible oxide gets reduced by hydrogen at high temperatures, it partially reduces and covers metal surface. Since we knew that these structures are active, these causal structures are active for hydrocarbon formation, we wanted to know if we can change or increase hydrocarbon formation by changing the thickness of the shell. So what we did, we made these causal structures which was much thinner shell made of iron oxide. And when we performed catalytic measurements, we indeed observed fourfold increase in hydrocarbon yield compared to the original heterodimers. So conclusions from this work is that ruthenium promotes reduction of iron oxide via hydrogen spillover effect. Heterodimers upon reduction transform into causal structures which are active for hydrocarbon formation. And by tuning thickness of the shell, we can change hydrocarbon yield. Our future directions since we found this interesting synergistic effect when metal nanoparticles get encapsulated by metal oxide, we are now interested in encapsulating nanoparticles inside porous metal oxide. And schematically what this means is that we'd like to start with well-defined nanoparticles, deposit them on polymer, make a second layer of polymer on top of the first, basically have a sandwich structure with nanoparticles encapsulated in polymer, and then use this technique called nanocusting to convert this material into nanoparticles encapsulated in metal oxide, such as alumina. So nanocusting is a procedure where a template gets infiltrated with metal precursor and after certain thermal treatment, the original template is removed and its negative replica is made of metal oxide. With this, I'd like to conclude my presentation. I'd like to thank my PI, Matteo Cagniella, Professor, sorry, Simon Berry Group at Slack, our collaborators at Thermo Fisher Scientific and last but not the least, Precourt Institute for Energy for Funding our Project. Thank you. Thank you, Asulev, wake you up. Okay, and we have one more speaker for this afternoon, and this is Mike Howland. Mike is a PhD candidate in the Department of Mechanical Engineering. Under the direction of Professors John DeBerry and Sanjeeva Lele, his research focuses on the optimal design and control and predictive forecasting of wind farms. Mike will now talk on wind farm power optimization through wake steering. Great, thank you so much and thanks everybody for coming. So as was just mentioned, I'm a PhD student in the Mechanical Engineering Department here at Stanford under the direction of both Sanjeeva Lele and also John DeBerry, and today I'm going to be talking about wind farm power optimization through wake steering. And at this point, I'd like to acknowledge funding support both from Stanford University and also the National Science Foundation as well as a special thanks to Transalpha Corporation. So I just want to start with a quick motivation for the continued research and development into renewable energy technologies, and in particular in this talk, wind energy. We already know that wind energy has been deployed at scale and it's been done so effectively and in a low-cost fashion, but a recent estimate stated that just a 4% increase in the efficiency of wind farms in the United States alone will generate $1 billion in annual savings to the United States energy grid. And so as we transition to a low-carbon energy grid, we need to rely on technologies which are both efficient and low-cost and continuing to improve the efficiency of these technologies will help contribute to those goals. And so wind turbines are really efficient devices. When you take a single wind turbine and put them in a field or in the ocean and if it was completely isolated, that device is very efficient in an engineering sense. However, wind turbines are often placed close together in wind farms in order to save on land cost and also in order to share transmission lines and other energy grid infrastructures such as transformers. However, wind turbines are always controlled as if they're completely isolated. So they're not controlled considering that they're in a wind farm environment, but they're controlled as if they're just standalone in a field. And I've sketched what this looks like in a small wind farm where we have six turbines here and I'm showing a top view where the flow is from left to right. And you could see, as I've colored the velocity magnitude in this plot, as the flow is from left to right and every wind turbine is generating what's called a wake region immediately downstream, so immediately to the right of the turbine. And that wake region is predominantly characterized by reduced velocity, reduced momentum, and therefore reduced energy in that wind. And so you could think about it in conservation of energy. Essentially, there's some amount of energy coming into this wind farm and each turbine is extracting some amount of that energy and then there's less available for any potential downwind turbines. And then you could see that each wake is then hitting every successive downwind turbine and thereby reducing the power production that's possible at each downstream turbine. And so in the worst case scenarios of real wind farms that are already operational in the world today, this spacing where you have three to four turbine diameters apart between each other, as we show here, results in an efficiency loss of approximately 40% for that system. And so here at Stanford and in the Lele and Diberi lab group, we've been thinking about ways to actually mitigate these wake losses through operational changes at wind farms. So we're not interested in changing the layout where you would move the turbines away from each other because that would be prohibitively expensive and challenging to do, but instead we're thinking about control. And so there are only two ways that you could do control on modern-day wind turbines to potentially alleviate these losses without doing significant hardware changes. And that's induction control and yaw control. So today we're gonna focus on yaw control. And so as I mentioned, wind turbines are always controlled as if they're completely isolated. And actually the main feature of that is that the wind turbine is constantly turning to face the incoming wind direction. The wind direction is constantly changing in the atmosphere. And what we're interested in doing instead is something somewhat counterintuitive. It's instead to intentionally misalign certain turbines with respect to that incoming wind. And so I've sketched what this looks like in a two turbine scenario where we're again viewing it from the top and the flow is from left to right. And I've actually intentionally yaw misaligned this first turbine by the angle gamma with respect to that incoming wind. And what that actually does is it takes that wake region that I mentioned on the previous slide and it deflects it laterally along this center line, this dashed line. And you can see in this case, this center line is partially deflected away from that downstream turbine. And thereby increasing the downstream turbine's power production potential. And so what we're really interested in is if there's a case where now that we've yaw misaligned this first turbine, its power goes down as a result of the yaw misalignment but the downstream turbine's power may increase and thereby increasing the sum of the two turbine power collectively. And so it's thinking about the wind farm as a system rather than individual turbine. And so to think about how we might actually realize these gains at a real wind farm, we need to talk a little bit about how we model wind farms in this community. And so here I'm showing the most famous wind farm. You almost can't give a talk in wind energy without showing this wind farm. This is the Horns River Wind Farm off the coast of Denmark. And you can see very interesting physics here. The wakes of each turbine are very nicely shown by interesting humidity changes. But essentially we have complicated mesoscale structures in the atmosphere. We have every turbine generating a wake. The wakes are then merging and it's sort of complicated what's happening here. And so to actually resolve all of these physics in this wind farm, we would need to do a large-duty simulation it's called where we resolve all of these scales on a supercomputer simulation which would take on the order of days to months depending on the wind farm. And so if we're thinking about doing operational changes you'd have to then run a new simulation for every time you've changed your operation or your layout or whatever you're interested in studying. And it becomes very quickly computationally intractable. So instead what we do is we parametrize the key physics of that wind farm. So we take those complicated physics and we parametrize it in a so-called engineering wake model where instead of capturing all of the detailed dynamics of that wind farm, we're interested in capturing the main trends of power production at that wind farm. And so this is a sketch of what our specific wake model of interest looks like here. The two key features of this wake model that we've selected is that it's able to capture the velocity deficit behind that turbine, that wake region, but also that wake deflection as a function of the YAMAS alignment. And so here I'm sketching what the wake looks like behind this turbine which is YAMAS aligned by gamma again. So the wake of a turbine which is not being YAMAS aligned would follow this dash blue line. Whereas the wake of this YAMAS aligned turbine follows this solid red line. And then in terms of actually controlling this wind farm, now that we have this wake model, this is the protocol we take. So we take this wake model and then we rely exclusively on the historical data which is in the form of SCADA data. If you're familiar with energy grid systems, SCADA data is used for control by the energy grid operators and it stands for supervisory control and data acquisition, kind of a long name. So we use that SCADA data which is already recorded at every wind farm in the United States and we calibrate our wake model to ensure that we're capturing the trends of the power production at the wind farm of interest. Once we've calibrated our wake model we would then do a control optimization where we pick the optimal YAMAS limit angles for every turbine of interest at this wind farm. Then we would take that to the field, implement that in the wind farm, observe new SCADA data and then feed it back to the wake model in a closed loop system. And so fortunately we had the opportunity to partner with TransAulta Corporation in Canada and study one of their wind farms which is the summer view one wind farm in Alberta. And so I'm showing here on this left plot what this looks like if you look it up on Google Maps. And then here on this right plot I'm showing what this wind farm looks like when you normalize the coordinates by the turbine's diameter which is the relevant length scale of interest. And in particular in this study we're gonna focus just on these six top left turbines in the northwest corner called column B. And so they graciously provided five years of historical SCADA data for every wind turbine at the site in one minute average form. So quite a lot of data. And then we're able to take that data and input it all into what's called a wind rose which I'm showing here. And if you've never seen a wind rose before they're really helpful plots because they tell us two things. One they tell us the dominant wind directions at the site and so that is showing this view on the compass and it also shows us what the wind speed is. And those are the two main factors in determining where we expect wake losses and how bad we expect the wake losses to be. And so you can see from this wind rose that the predominant winds for this site are coming from the southwest. And if you could imagine the flow coming from the southwest you can see that the spacing between let's say this B column and the A column would be quite generous something like 10 or 15 turbine diameters. And we actually wouldn't expect significant wake losses. However you could see on this wind rose that there's a little bump coming from the northwest. And from the northwest the spacing of these turbines when it flows in this direction is actually more like three or four turbine diameters. And as I mentioned in the introduction at that spacing we would expect something like 40% efficiency loss for those six turbines. And so as I mentioned in that control loop the first thing we needed to do is fit our wake model to the historical data. So I'm showing what that looks like here where the blue is the historical SCADA data and the error bars denote standard deviation in that data for that inflow direction, that northwest inflow direction in five to six meters per second. And then the red is the model fit to that data. So it's doing fairly well at capturing the trends of the power production. And again at higher wind speed we can see we do even better quantitatively at capturing the power production of these six wind turbines of interest. So now that we've actually calibrated our model and we can see that we can capture the trends of power production quite well then we need to select the optimal YAMAS alignment angles. And that's actually a fairly challenging thing to do because it's hard to know what would be the exact best YAH angles to pick and if you have a large wind farm it grows unboundedly very quickly about how many different options you have. And so in particular we developed a new gradient-based YAH optimizer which actually utilizes tools from machine learning community. And so it uses the model form of the equations which we've developed and then comes up with a very efficient optimizer to select the optimal YAH angles. And so you could see for this inflow direction for these six turbines of interest with the different colors the optimal YAH misalignment angles being selected for each turbine. And what's interesting about this case is we can see that the power production normalized by the baseline power production for this wind direction has increased in this model prediction by over 20% which is very exciting. And so qualitatively what that looks like returning to this figure where we have that inflow from the Northwest direction and significant wake losses if we YAH misalign the turbines in an optimal way then we could see the wake of each turbine being partially deflected from every successive downwind turbine. And so we actually had the opportunity to test these YAH misalignment angles on the wind turbines of interest in Alberta, Canada. So these are the utility scale wind turbines. So each of one of those has a diameter of 80 meters so quite large. And we have YAH misaligned here each of the first five turbines by 20 degrees clockwise with respect to that incoming wind direction from the Northwest. And then the last turbine is not YAH misaligned since its wake deflection wouldn't benefit any downstream turbines. And so here I'm showing just a few of the field experiment results. And again, I'm showing the baseline SCADA data, so the historical data and the model prediction in this case as well as the experimental data that we observed in this field campaign. And the main features that are interesting qualitatively from this is that we can see that the first turbines power production has decreased as we expect but the downstream five turbines power production has increased quite significantly in this case. And then at higher wind speed for the same wind direction we see the same qualitative trends. And now quantitatively we can see that we've realized quite significant power production increases over the baseline in this experiment where we increase in the higher wind speed case by 13% power production and the lower wind speed case by 28% power production for these actual utility scale wind turbines. And so those are just a few of the highlights from this paper which summarizes the main results recently published on the cover of the proceedings of the National Academy of Science. And the main results I only mentioned the statistically significant power production increase today but we also noticed that through the mitigation of wake losses we reduced the variability in the power production so we reduced the intermittency of the wind farm and that's because wake losses also contribute to unsteadiness in the power production as a function of time. And of course there's always uncertainties in future work and research so we're trying to improve our yaw misalignment controller and we're also interested in knowing more about the fatigue loading so the mechanical loading on the wind turbines when you change the yaw misalignment angle to a non-zero value. And so with that I will take any question I guess we're doing questions together. Oh. Thank you very much Mike. Thank you. That's great. Antonio and Aisler do you want to come up? Three excellent presentations. We have about two or three minutes for questions maybe we can stretch that a little bit longer in the tradition that we have here at the energy seminar maybe students first if they could if they want to ask some questions. Yes. I have a question for Michael. Does your yaw optimization model inform us how to build new wind farms in addition to optimizing current one? Yeah absolutely so with that tool with the engineering wake model you can also think about layout optimization given as a specific to the model. Okay. Okay I have a question for Antonio. I was curious whether you saw whether you see flow batteries being used high energy density flow batteries being used only for great storage or whether those would also be potentially usable in electric vehicles or consumer electronics? Yeah great question. Thank you very much. Hello. Okay. Try to keep my answer short. Maybe. You know I think consumer electronics are a stretch but you know heavy duty transportation you know personally I think it could be possible you know there's cargo ships in China that are now being battery operator although they're carrying coal from one port to another but maybe it could be a flow battery. I have a question for Antonio. So if you were able to find that eutectic point with all of your charge carriers what do you think that the energy density of that would be at the beginning of comparing the looking I am versus the flow batteries? How much would you think that would improve? Yeah yeah great question. So basically if on that plot if we could have a NAC versus eutectic benzoquinone flow battery that's could get you maybe into the 500 watt hours per kilogram range which is actually double what current lithium ion batteries are but that's all theoretical so then you'd have to slap a percentage off of it for all the stuff that the battery is composed of so maybe all in you could get near current generation lithium ion batteries. Now lithium ion batteries are still improving so that I think you know 10 years later I don't think we could outdo lithium ion batteries with the liquid but if you can at least get into the ballpark then I think maybe it opens up these other applications too. Last question I think. Thank you. I have a question to the lady from Kazakhstan. I forgot your name, sorry. If I understand correctly you're reducing the process steps it would take to produce e-fuel or any other kind of migrants and how long will it take to commercialize that kind of technology? That's an excellent question. Well, as I said Fischer Tropsch is a commercialized process and it is used industrially on some scale. For CO2 hydrogenation to become commercialized I think we're still far from making this process feasible in the nearest future because there are many challenges that have to be overcome and the problem not on the inertness of CO2 but also the different difficulty in controlling product selectivities. So in our work we indeed showed that we can make hydrocarbons by reducing CO2 with hydrogen but ideally one would want to make a specific type of hydrocarbon for instance to force all of these and this selectivity challenge has to be solved first in my opinion. All right, well thank you very much. I think we have to wrap up now. So maybe join me one more time in thanking all three speakers and thank you.


The EMCSR 2012 is a unique opportunity to meet the greatest minds in the world from science, economics and arts. It is that place where the avant-garde of Europe meets but also internationally. It is very inspiring here, everyone is very nice and it doesn't matter how many PhD degrees somebody has. So what I like most about the EMCSR 2012 is that this is a conference and a meeting of people from very different areas of expertise. If you normally go to the congress they are all of the same. If you go to the energy congress all are energy men but here they are from different disciplines. I like a lot the diversity of the people coming here. Since everybody comes from different fields, I mean if you are open you can learn a lot from different people. Yeah, now I know a little bit more about planning and systems and city planning and it's very interesting.


Hello everyone, this is Rossett Teacher Toolkit. This is one idea in a series of 30 for teachers to kind of help you get through the summer term to get you to reflect on the things that you do. So tip number one. Questioning. Very powerful tool. We cannot ask lots of questions in that class. Sometimes lots of questions that encourage calling out or don't tend to give time for us to process. Information. So a very quick fix is a technique called Fermi Questioning, which is based on the principle by Enrico Fermi, who was a statistician who encouraged the process or a creative process that might not necessarily get the answer immediately, but it gets people thinking. So try and start your questions off with how many. How many balloons could you fit in this classroom? How many footsteps? And then if you can link that to your subject, so for example English, how many adjectives in the first paragraph of Macbeth, that gets kids thinking straight away. Obviously you would need to facilitate students to think first and to not call out.  You can then ask students to discuss on their tables and then seek the responses or not. It's essentially a good technique for kids to get thinking. So try it out. There's more on Teacher Toolkit from Fermi Questions, F-E-R-M-I, and thanks for watching and tuning in for another 29 ideas.


I'm Ravi Agrawal, the Editor-in-Chief of Foreign Policy Magazine. I'm also the host of F.P. Live, our video channel and podcast. We will be live streaming this discussion on F.P.'s site and our podcast, so I welcome our viewers and listeners from around the world and also those of you who are here in this room. Thank you all for being with us. Our session today is titled Protecting Democracy from Bots and Plots. And it's a fitting topic because 2024 is a really, really important year for democracy. This is the year that more people than in any other year in the history of the world were head to elections. You have five of the world's six biggest democracies heading to the polls. So that's India, the United States, Bangladesh, Pakistan, Indonesia. The only one that's missing from the top six is Nigeria. More people than in the history of the world will use their adult franchise. All of this, by the way, is the subject of F.P.'s latest print issue, the year the world votes. Now, elections are usually about hope and there's a lot of hope involved with four billion people heading to the polls. But there's also a lot of fear this year. And I think there are fears about the health of democracy globally. We can be honest about that. There are fears about the rise of nationalism. There are fears about what that rise of nationalism means for liberal values. There are also fears about myths and disinformation, which are ancient age-old problems, but they've kind of been turbocharged by technology, specifically now with the rise of artificial intelligence. So we'll be talking about that, too. I think the question I want to sort of pose to our group today, and I want to bring all of you in on this as well in the audience, is how can we make sure that AI and technology are net forces for good rather than chaos, especially in a year like this one? That is so important for democracy. And how do we do this globally, not just in rich Western democracies? So let me introduce our panel. I have to my left, Jan Nipawski. He is the Foreign Minister of the Czech Republic. To my right, I have Matthew Fritz, the CEO of Cloudflare. Also on my left, I have Alexandra Reeve-Givens. She's the CEO of the Center for Democracy and Technology. And then to her left, I have Andrei Kudelsky, the CEO of the Kudelsky Group. We will also be joined momentarily by Smriti Zubinarani. She's the Minister for Women and Child Development in India. I believe she's just stuck outside, but she'll be with us in another minute or two. So let her sneak in when she makes it. Alexandra, I'm going to begin with you. So you run the Center for Democracy and Technology. How worried are you about the role that technology will play in elections and in preserving or hurting democracy this year? So I think there's cause for concern. And there's a reason why it's been a dominant theme here at Davos this week and why you covered it so well in the release edition of the magazine. We already live in a fragmented information ecosystem where there are echo chambers, where there are many different sources of information hitting your average voter, your average citizen at any given moment. And now we have to think about how AI is layered into that. I think about the risks in a couple different categories. One is just general miss or disinformation, dis where it's intentional, about the state of the political environment, about the state of the world, misstatements about candidates. We've seen this already in the case of fake audio or fake video about candidates. We'll probably talk about some of those examples as you move in, whether it's about Slovakia or even recently, you know, former President Trump himself as being the victim of fake images of him on the plane with Jeffrey Epstein's private jet. So there's that kind of concern about what is the truth, what is the ground truth in the information environment today. Then we also have to think about targeted messages to voters. So already in previous election cycles, we've seen robocalls, we've seen automated text message campaigns that are sending incorrect information to voters about their voting location, about whether or not their poll is open or targeted manipulated messages that are designed to influence their behavior. Generative AI makes it easier to target that and personalize it than ever. We know that the threats through privacy, because of privacy leakage, it's easier than ever to come up with those tailored personalized messages. So we have to think about that too. And then the final thing I'll touch on is the threats that are facing our election officials who are underpaid, significantly overworked. Just to bring it back to the U.S. for a moment, we have, you know, over 8,000 jurisdictions through which elections are conducted and often those are conducted on a volunteer basis or with a very light staff. And what we have is a world where it's going to be easier than ever for them to be the victims of either phishing schemes, doxing where their private information is revealed. And so we have to think about the infrastructure that's supporting them as well. So of course that's a parade of horrible.  But these are threats we have to be really conscious of as we go into this year. There are threats indeed. Matthew, let me bring you in. AI often gets cited in this way where we think of it as a threat multiplier. But whenever I speak to tech CEOs, the comeback I often get is that well, tech could also fix these problems. So do you see AI as, you know, a whack-a-mole problem or something that can get ahead of malactors? So I think, you know, from a good-nosed perspective, companies like Cloudflare have fundamentally been AI companies from the beginning. We sit in front of somewhere between 20 and 25% of the web and the theory of the company has always been if we could see enough of what was going on, we could use machine learning and artificial intelligence systems in order to be able to predict new threats before our clients were attacked by them or vulnerable to them. And it's been amazing to watch the same way that I think we as society have watched open AI and other generative AI systems really almost emerge on the scene over the last 12 to 24 months. Internally at Cloudflare, we've watched as our own AI systems are finding new threats, finding new vulnerabilities that no human has ever identified before and surfacing those in a way that we can protect them. So I think that the good news here is that AI systems are fundamentally driven and are most successful by whoever has the most data and the good guys do have more access to data than the bad guys, but it does mean that we have to work together and coordinate. Can I push you on that very quickly? Because when you say the good guys have more access to AI, but the good guys where? So the good guys in America, sure, I buy that. What about governments, for example, countries that don't have access to the chips, don't have access to the know-how, might be newbies when it comes to AI and can't catch up? Sure, I think that that's where companies like Cloudflare believe that it's incredibly important to make our technologies as available as possible. And so we have a number of different initiatives. In the United States, we do something called the Athenian Project. We've worked with NGOs around the world to take that same project and make it global where we can say, even if you don't have the resources, we want to make sure that you have the infrastructure to make sure that you can have a trusted, reliable, believable election infrastructure. And I think that's incumbent on technology companies. It's not just us. Microsoft has done the same. Google has done the same. And I think we all benefit from having a stable governmental infrastructure and it's then incumbent on us around the world to make sure that we're making our technologies as accessible as possible to protect democracy wherever it's going on. I'm going to come back to you on that. Minister Lipavsky, your country had elections a couple of years ago. You're looking ahead to European Parliament elections this year. What are you worried about from a public sector, government perspective when it comes to protecting and safeguarding democracy from all of these threats that we're describing? Thank you for your introduction when you pointed that four billion people will go to election this year. And we are living in a world where society communicates through different kinds of internet platforms. Most of them are global ones and therefore what is happening in one country today might happen in another country tomorrow. So we need a really global solution and to have a global discussion about the way how we communicate as people, which content and how are we presented. And I think the society moved on quite, I think the discussion moved quite along, moved in a good direction, in a description of the issue. But now we need to be looking for solutions. And different actors are looking for different ways how to solve it. I honestly like the European way, the European regulation on NIF, for example, which will tackle with some of those issues. And definitely we will see more and more false content being used as something which will disturb the election process, which will disturb the way how the society makes decisions. And just very quickly, do you think governments are prepared to deal with that? I think governments need to globally agree on solutions to that. So it was, for example, my country which proposed and consponsored a resolution in the UN in this regard. And we should be more thinking about the right not to be manipulated in a sense that human-centric approach in AI and also in this way of communication, it's totally must. So I need to know if the photo or video of something happening is true or if it was created artificially. 20 years ago when there was a movie, Jurassic Park, out, I as a child went into cinema watching Tyrannosaurus Rex killing someone and I knew that this was content created for fun. I was sitting in a cinema, it was obvious. But today these things can be produced quite easily and it made disturb our society quite a lot. So I think we need to rethink also our focus not to try to put all the heavy burden on companies. We need to also be giving some guiding principles. Andre Kudelski. You run the Kudelski group. Do you think from your perspective that we have a sufficient framework of global laws to deal with cybercrime and if the answer is no, how do we go about trying to create that? I think one of the key issues that I see is that if you have an election process is in one country. As for cybersecurity, people that are trying to be the bad guys can come from any place and from any different jurisdiction. And take an example of regulation. So regulation is something that can be pretty useful and efficient if you have the same territory for the ones that you have to protect and where the bad guys may be coming from. And if you have this asymmetry, it's important to come with some technologies that allow to fight against this asymmetry. And as an example, you should have more contentress ability. You should have to use solution to trace and to identify if a content is fake or not. But even if you find out that the content is fake, you have to know if people are ready to hear this criticism or sometimes people that are interested to get the content, to get the information, it will not be really interesting to know if it's true or not. So assuming I had the curiosity to know whether a video was a real one or a deep fake one. So let's say I have that question. Is it even possible to verify? Yes, it's possible. But it requires to use the technology also in the full process. I'll give you an example in AI. Today, you just have some rules to say you should not manipulate some element and you should say where it's coming from. You can have some elements introducing traceability of contents that even going through AI you can find where the content is coming from. Set in a different way, it's like for food. To be able to know what are the components in the food that you are eating. And for element of video, that is something that you can achieve through combination of watermarking, element of blockchain. So fundamentally give more traceability here. But that is helping, but that is not solving everything. Matthew, is that possible from where you sit? I think for sure. This has been framed in this discussion very much as a technology question. You have two technologists, a technology policy person, two people from government. I actually think that one group that's not actually on stage answering questions which I think actually plays a big role in this is actually the group you at some level represent which is the media. And so if I were running a media company today I would be thinking about how can we using the role that we have traditionally played as reporters of what is going on as the truth tellers in society at some level how can we be working with or developing ourselves the technology to be able to say this is something that someone actually said is something that was actually generated and I'm going to help you distinguish between one another. That to me feels very much like it is a natural role for media companies to play. And I think we as technologists would be happy to help facilitate that. So I'd be curious what the FP is thinking about in terms of your role about disinformation and finding automatically generated content. I mean so we're less of a newspaper, more of a magazine. The amount of news that we do is fairly limited. We do a lot of news analysis, a lot of arguments and essays where our audiences in most cases read the news they come to us for a lot more. But putting on other hats I've worn in the past working for CNN or elsewhere I can speak for those places and saying that they'd love the technology you're describing. I mean if the AP or Reuters had access to the ability to fact check and verify in the way you're describing they would take it as long as it's affordable and sort of a customizable enterprise solution they would run with it. And I think that's the partnership because I think trying to say that Andre or my company is the one that says what is right or wrong, what is truth or fiction that actually isn't the place that we're really good at. We're good at saying was this automatically generated or not and I think that we're saying is this real information or disinformation that's actually been a role that goes way beyond AI and that's a role that traditionally the media has stepped up to play and I think that's the right place for a lot of these questions around this to be centered with the support of technologists to help us help you better tell those stories and differentiate truth from fiction.  I just wanted to add, however, not all tech-seers admit that they're not good at every single thing so I thank you for that. At least one tech-seer hasn't been invited here who likes to think he's very good at media as well. Jump in and then I'll come to Alex. Just as a key element, one of the most important things is to allow the viewer or the reader to be able to make his own opinion by himself. So fundamentally, to get some data where people can judge by themselves and not just believing someone or not believing because if not, it's more a religion just fact-checking. I agree. People want to be able to make their own choices. Alexandra, I was going to come to you with a question and really in the spirit of trying to push us towards solutions, in this big election year, what kinds of safeguards and measures are you thinking that companies and countries should be trying to implement as they get ready for elections? Sure. Well, I'll pick up on a theme that I think you were just getting to, which is one of the most crucial intervention points is how we surface authentic, trusted sources of information. And you're absolutely right that the media has a critical role in this. Some of the tech companies do. If you're a search engine or a social media platform, I think it is your duty to help surface the trusted sources of information. But we spend a lot of time with election officials helping them understand how to navigate this new normal and how to boost the trusted place to go to for your polling information. How do they rapidly respond when there are mis or disinformation campaigns going on in their jurisdiction? And really that is a crucial element. And there's some low-hanging fruit there. Again, forgive the U.S. focus and the study I'm about to cite, but one piece of research that my organization did looked at the domains that election officials were using. Were they using a trusted.gov domain or something like springfieldvotes.com? And only one in four election officials in the United States was using a trusted.gov domain. Wow. That is low-hanging fruit. And thankfully the Biden administration is focused on this as one of their areas to prioritize. How do we shift election officials over to trusted domains, those trusted pathways so that they can be putting out this authoritative information and have an anchor point for reporters to turn to and the tech companies to turn to to make sure that good information is getting out there? So when we say what are the interventions, there are some places like that that really feel like we can be moving on them quite quickly. And then I would say when we look at the other points of influence in the ecosystem, legislation and regulation is certainly important as the minister was touching on, but given the time sensitivity right now, I will say as my theory of change, I'm very focused on what the companies can be doing. I think there's a conversation to be had around the generative AI companies, what their products are able to generate, whether they're automatically labeling as you were surfacing, whether they have usage and content policies that stop people using them for mass political targeting campaigns, for example. Do they detect that type of behavior and have a policy against it? OpenAI has announced such a policy, for example. But then there's also the social media platforms. It doesn't really matter if someone creates a manipulated image if it's not being distributed in a way that's actually going to shape the election. And I think one of the pieces that is underreported and so crucial to focus on right now is even as we're living in this heightened threat environment, a number of the social media companies have actually been scaling back their investments in trust and safety, in particular around elections. And those that are still keeping up the work are facing more political scrutiny and pressure to disband those efforts than ever before. So in the United States, for example, right now we have congressional investigations and lawsuits against people that study mis and disinformation about elections on social media platforms. There is currently an injunction in place stopping the Biden administration from communicating with social media platforms about interference threats on the topics of elections. That's actually going before the United States Supreme Court this year. So we're in this bizarre environment where right as the threats are ticking up, the investments in actually doing the day-to-day work of online trust and safety for our information environment is being scaled back and is under attack. And those are all things we need to recalibrate right now to try and address the threats this year. And just quickly, how? Yeah, so we have to have the social media companies keep up the work. There are really important lessons that... Is there a way to force them to do that, push them to do that? You get them to places like Davos and you have them talk about the work. You know, right now, because a lot of it sadly is in the staffing and decisions of companies, making sure they're putting in those investments, making sure that they're sharing information, that they're doing it not just for the U.S. election but for the other elections around the world. You know, that has to stay a key focus even if there is political pressure. There are important lessons that we learned after 2016, right? Social media companies learned about how you track mis-and-disinformation campaigns, what coordinated inauthentic activity looks like on a network, how you put breaks in, when a rumor is flying, you get people to check whether or not have you read this article before you forward it. Fact-checking programs. That architecture, it hasn't been a silver bullet by a long shot, but at least that architecture has been in place and there's an entire academic field now that studies this and analyzes what interventions might look like. We have to make sure that those interventions are still in place this year as a bare minimum for us to be able to navigate this landscape. Just to move us into a related theme to what Alexandra was describing, how worried are you about the rise of nationalism and populism in Europe but also around the world? And the reason why I bring this up is that, you know, a lot of that is often related to freedom of the press being curtailed. A lot of that is related to group think related to election security as well, related to democracy itself being weakened, various pillars of democracy being weakened. How are you thinking about that? So, those are not new phenomena to our world. But they are accelerating trends. That's what I wanted to point. And definitely internet platforms, not only multimedia platforms if they are focused on putting people in different groups which radicalise, which is a mechanism which was recently quite very well described in behaviour in international communities. This can be accelerated and typically Russia information war plays with supporting both left or right extremists just for the sake of splitting up societies. So, it's obvious that any kind of lie is very well working to support these malign processes and therefore we need to be looking for solutions. How the freedom of speech free journalism needs to be supported but in the same way it should not endanger our democratic societies. So we need more resilient societies. We need companies to understand that the corporate social responsibility doesn't only mean to do something nice in a local municipality but that their tools are not misused for the sake of this. But also there needs to be some kind of accountability on the other side which is not easy to define. Very often this development is faster than the regulations goes. So for all of these proposals you're describing is there any sort of concrete measure that your government has been able to take to enact them? I think the EU it's quite on a good path with AI Act with different legislation on this matter. So I think it is not the best perfect solution but definitely I think this is one of the things where the EU delivers quite global in a global way I think we excel in that. Matthew I want to pick up on a strand of something I brought up earlier and that sort of about big and small. So you work a lot in cybersecurity and cloud computing but if you think about it the biggest players in the cloud computing space it's basically three companies which are among the three biggest companies in the world. You think of the most cutting edge fabs it's either Nvidia or you have TSMC in Taiwan that dominates the bulk market. When you have monopolies of that size and scale isn't that in some sense dangerous when it comes to democratizing what they do when it comes to how smaller countries, smaller companies and people at the bottom end of the supply chain can plug and play what they do. Aren't they at a disadvantage? Maybe this is more therapy session on describing things I'm not qualified to talk about but I'm not sure I'm qualified to talk about that. I will say that Nvidia actually uses TSMC so they are not themselves a fab. I think that though what we have seen through history is that even what look like very stable companies get disrupted by technology all the time I am tangentially related if you had asked me three years ago who was going to win in the AI race I would have said China is going to be first by far because they have invested significantly in it, they have got the technology and they have the best access to data as a local company and whoever has the most access to data tends to win in this space. Second would be the United States and then Europe would be a distant third I thought in part because of some of the privacy regulations around Europe which maybe just find trade off but that is a trade off that Europe has made. That's not turned out to be how the AI race has played out at this point in time and we are in pick your sports but the early innings, the very beginning of this so things can change dramatically but what I think is actually interesting is I was super focused on what the inputs into the AI system were and the fact that China had incredible access to this, the US had somewhat more limited but still access to it and Europe was more restricted I thought would really benefit China and yet China has actually been slow especially in the space around generative AI and the question is why and I think that the answer is at some level we have all seen examples where a reporter can trick a Microsoft AI bot into saying something incredibly racist or horrible and that's very embarrassing to Microsoft but nobody goes to jail, nobody disappears whereas imagine if you're trying to build a similar bot in China and someone can get it to pretend it's a student in 1989 in Tiananmen Square all of a sudden, effectively the regulatory apparatus that is regulating the outputs makes it very difficult to create those systems and so as there is regulation in this space I think we have to be really careful about what part of the value chain we're regulating and what's hard about AI systems is they are very different than any other technology that we typically interact with most of the time you know if you have X plus 1 equals Y, for any given X you're going to get the same Y, that's not how AI works, it is a non-deterministic system, the exact same input can result in different outputs over time and that makes it very difficult from a regulatory perspective if you say please guarantee you will never have this output to make sure that that's the case and so I do believe that there is a role for governments and regulators in this space but I think looking at what has taken China which was way ahead and held them back is actually come out of a cautionary tale as we think about regulation in the rest of the space and I think this is developing very very quickly and so we need to look at how these systems are being regulated but we have to be cautious about making sure that we don't shut down innovation as we do that and are you suggesting that in the West we're over-regulating I don't think yet but I think that we should be very very careful about regulating things that can actually be controlled, you can control what the inputs into an AI system are but inherent to these systems is that they're non-deterministic and so if you say never give this particular output I'm not sure how you can guarantee that that will always be the case and again we've seen example after example after example of even well designed, well restricted systems where people have been able to trick them, you know, pretend you're my grandmother and you're and I'm dying of a horrible disease and you know tell me what the secret is and the systems will work around some of the limitations that are in place that means that as we regulate these systems we just have to be very cautious to not again ask to do something that is impossible for a non-deterministic system to do. Alexandra just picking off when we think about tech you know in forums like this we immediately bring up AI but going back about a decade ago and you think of the rise of the smartphone in say places like Africa or India where essentially you had hundreds of millions of people come online in the space of a decade and without the smartphone they never would have come online because PCs and the internet were growing much more slowly and yet literacy rates in many of these parts of the world are quite low. I mean there are states in India for example that have a 50, 60% literacy rate. You put all of that together and it's easy to imagine that it's harder for some people to be able to deal with myths and disinformation when it's election related let alone stuff that is AI powered or deep fakes. So you know if you would advise companies or countries in this case to begin to think about tackling myths and disinformation when you have populations that aren't particularly tech savvy or media savvy or even literate how do you deal with that? Well to be honest I mean we have the same issue in advanced democracies with high literacy rates too. Exactly. But it's just exacerbated in countries with low literacy rates. Yeah I think that's right and that's why we really need a whole of society approach to this. You know the answer isn't going to be wave a magic wand and suddenly mandate that a company never allow a false information not feasible but number two the efforts that they would take to do that would end up over correcting in such a way that actually you undermine the extreme value of social media sites to share information. So you have to be really careful in the balancing act here of kind of how what those normative levers are that that you're able to pull. I mean I think the answer is some of the modifications and lessons that we've learned over the years around how do you signal boost what you know is authentic how do you signpost information that might be of questionable quality whether that's through labeling or fact checking or you know down rating something if it looks like it's of the spammy category as opposed to a quality site and then at the same time having media literacy be a really strong component of how governments across the world and societies across the world try to retool for this moment and again not one thing of those is going to be the silver bullet but hopefully through an integrated approach to have more resilient societies that can withstand this fragmented extremely democratized information environment in which we now live. Minister Lepofsky do you think tech companies are too powerful right now when it comes to their ability to just as Alexander was describing I mean they can signal boost false stuff or choose not to you are very good at reading my mind. I think it would be a huge mistake for governments to accept that something is not possible to regulate control or to somehow work with if it has possibility or capacity to endanger those governments and artificial intelligence is a great piece of technology which can deliver different kind of results so it needs to be developed such kind of regulation opposition to control of those technologies that the governments will be sure that it is not going against the interests of the governments and this is very important principle which basically every player will understand and this is the reason why we pose those questions how to work with them and I give you one very specific example my ministry of one of us is responsible for licensing process when Czechia sells weapons there is a threat of movement of weapons and ammunition and we need to give a stamp that we agree with this transaction that it is not going against interests of Czechia produce a gun so we will say ok there is not possible to sell them to Russia very easy example and same goes for the dual use products like CNC machinery which can be misused for production of let's say parts of ballistic missiles at the same is valid for software same is valid even for AI systems and we need to be sure that we are able to control and every state in the world solves the same problem because of course this is a matter of national security this is a matter of being able to control what the government is supposed to have in control and I think there will be solution this is the reason why I am calling for right not to be manipulated it's very easy principle it is not a given set of check marks you need to fulfill since and I absolutely agree that the system is non deterministic but it's a clear guidance for everyone what the results definitely should not be and if it's then you know that you need to go and let's say fix it and to have a capacity and understanding for that so we don't know it is a new new technology it's a new thing that you can chat with computer but if you chat with someone then you should know that you are chatting with computer or with a human person same goes for images same goes for video same might be valid for many other things let's say that the same technology which will give you the possibility to distinguish that your car is not a hitting person might be misuse for targeting purposes same piece of technology so how do we deal with that those are big questions they are indeed Alexandra quick and to when we're talking about AI as the arbiter of access to information I think there are real concerns with the world where the government gets to decide whether or not the AI is acting lawfully we do not live in a perfect world and there are plenty of governments around the world right now that are putting extreme pressure already on technology companies where the words of the opposition are misinformation that can no longer be shown so that's why this work is so hard the second you come in and you legislate that you really go into dangerous territory now do you want the CEO of a tech company deciding if information should be upranked or government minister deciding if the information should be upranked neither of those solutions is ideal right I think we can have or my community can but so instead I think we have to look at smart interventions that perhaps you know you can come down on different sides of this a place of ideas to solve out so that's where transparency mechanisms for reporting so you know how those decisions are being made is hugely important but a world where the government just gets to decide is this AI sanctioned or not yeah no your point is well taken I'll let you come back very quickly and then I'll bring in on so and therefore this is the reason why for example my country joined forces with Maldives, Mexico, Netherlands South Africa to promote in the General Assembly the protection of human rights in the context of digital technologies so the same human rights we have now are applied to the digital world and then you have countries like Russia, China which would like to create a new set of rules for the digital sphere which I don't think it's a good idea we should work with the good old rules we have and so we have a means how to sometimes decide maybe in a slower way but we should be able apply that to the digital world not in some kind of new set of rules André Jean-Pérez I would just like to come back to the question of innovation fundamentally what keep the overall ecosystem honest is through innovation you may have new initiative new elements that will give you different perspective fundamentally I don't think that the government can just decide what is right what is wrong if not you come to a system that is in reality not the power of the people but the power of the government having said that it's extremely important to have regulation that are not allowing to try new things but maybe to be able to regulate when you have abuses and it's extremely important to have this capability to come with new elements out of the box of thinking that can also challenge the way we see what is right what is wrong because if we just do things too much by regulation we can even not really be sure if something is right or wrong because the perspective may be biased and in a way through AI we may suddenly find different perspective and then one of the most important element is to educate people not in a classical sense but in a way that they are able to understand maybe not one single reality but different view and they make themselves their own opinion and decide what they think is right what is wrong thank you you know and as you're speaking we only have a few minutes left on this panel I'm going to welcome on stage one of our guests who's a little bit late Minister thank you for coming we're almost out of time so I think what I'm going to do is I'm going to ask you one question you haven't heard this discussion so far but I'll ask you a question about India which is going to the polls in a few months it's the biggest election in the history of the world for those of you who don't know when India votes it's spread over four or five weeks because it's that big you know we mentioned earlier in this panel that India is a country that has you know gotten online a big way in recent years partly through the smartphone and yet it's also confronted with low literacy rates especially in some states especially among women how are you thinking about combating as someone who's in government right now misinformation leading up to the elections so I'm late to the party because there was a man driving me through Davos and we are from the world's oldest democracy one of the aspects about the Indian political systems that is possibly unknown is not only the fight through government instruments with regards to disinformation not only partnering with media at large to ensure that citizens are better informed but if democracy is to truly fructify you need citizen engagement in governance and policymaking one of the most celebrated digital instruments for engagement is MyGov where citizens are requested to give their inputs as to how policy should be framed exactly two and a half weeks from now we will present the interim budget and a vote on account and that budget also has a reflection of inputs that are given by fellow Indians digitally through the platform when you talk about our elections it is notable that though we do it in five to seven phases from the last day of the vote being cast it takes us precisely three days to compute the results it is done electronically 945 million Indians today qualify as voters of which 94% already are bio-authorized 70% of these voters will cast their vote and the fact that they come to vote not only through systems that are electronically and digitally enabled but they are better informed because of social media engagements I'll give you a small example how is the digital democracy delivering when you talk about let's say deepfakes Prime Minister Modi speaks about watermarking each and every AI product so that we know of origins and can better inform our citizens with regards to the information source from which let's say they form an opinion we have the Ministry of Information and Broadcasting which calls out fake news if we find one and is brought to our attention through some media outlets so the government can give a clarification with regards to the news at hand I would also like to hear a state that when we talk about digitally in some way empowering the voter we also have engagements which are not only available at the status of the general election as a woman I proudly say this we have 1.5 million women who are voted into office at our grassroots so when we say our digital democracy and democracy per se has delivered it is not only delivered on the surface but it is percolated to the depths and the layers of administration and our population so India is not only digitally ready for the elections but also a democracy that celebrates our achievements digitally and gives possibly pathways for the future as AI grows as we augment our processes I can only say this since time is running out so let me just push you very quickly because time is running out and then we'll conclude but where are the checks and balances and what you're describing I mean doesn't this system that you're describing where you have my gov you have the media doesn't that make the government too powerful because the election commission is a process which is dealing from government the judicial system which is fair independent of government and we have a media that calls out if there is any such anomaly so it's not as though democracy is just served through the pillar just of government or politics democracy is equally served by other tenants which keep democracy alive which is an independent media a fair judiciary and a robust system of the democratic process and election commission that is de-linked from government or politicians Minister if we had more time I'd love to discuss how some of those pillars are being weakened but we'll leave that for next time I want to thank all of our panelists for joining us and also our audience for bearing with us thank you everyone and we'll continue the conversation have a great Davos week