Welcome to What's Next, a seminar series by IBM Research where we show you some of what we're working on. My name is David Cox, and I'm the IBM director of the MIT IBM Watson AI Lab here in Cambridge, Massachusetts. And I'm gonna tell you today about some of the work we're doing in efficient AI. Now, it goes without saying that there's a lot of excitement about AI today. And in particular, that's being driven by a technology called deep learning. And in 2015, Forbes declared that deep learning machine intelligence would quote, each the world. And I think to a first approximation, that's pretty much happened. Here's an example of the kinds of things we can do routinely now with AI. And this is an example actually from 2015. It's one of the first examples that really sealed for me as an AI researcher that deep learning was here to stay and that it was going to achieve amazing things. So this is a system that can not only identify objects and images, but it can actually create a beautiful natural language caption describing what's in the image. So you can give an AI this system a picture and it'll produce a caption like, a man in a black shirt is playing guitar or a construction worker in an orange safety vest is working on the road. It's easy to take these kinds of things for granted now because we've had these capabilities for a while, but it's really stunning and the progress in AI and deep learning in particular has been phenomenal. You know, there aren't that many games left that humans are better than machines at starting from Jeopardy, which IBM did in 2011 to the game of Go. Deep Mind created a system called AlphaGo that beat the world champion in Go. Even games like poker, a team at Carnegie Mellon produced a system that beat the world poker champion and even things like debate. We now have AI systems like Project Debater which IBM designed, which can carry on a lucid debate with an expert debater. So if you like AI systems that can argue with you, we've got your coverage even for that. And even things like art that we might have thought might never be the domain of a computer system or an AI system are increasingly, you know, amenable to AI as a tool. So everything from style transfer, where you can take a photograph and then re-render it in the style of any painter you like to images like these, which look like ordinary sort of high res pictures of a dog or a bubble, but are in fact not real at all. These are the figments of the imagination of an AI system completely confabulated out of thin air. So really amazing capabilities. And then of course, in the area of text and natural language processing, you have systems like GPT-3, which you might have heard of, which is able to create sort of spookily good sort of natural language, very, very realistic seeming text. And in fact, the Guardian created an article that was entirely written by the GPT-3 system. So starting with a prompt, I am not human, I am a robot. It then produced the entire rest of the article, which you'd be hard pressed to tell wasn't written by human. So the capabilities are getting really spectacular. But at the same time, the other trends have emerged. You know, we used to have computers on the desk and now increasingly computing is ubiquitous. It's everywhere. It's in our pockets. It's on our devices. The factories of the future increasingly have IoT devices Internet of Things devices, where you have computing all over, you know, many different facets of our life, running our world, communicating. And this trend towards mobile sort of presents a problem for AI. So for instance, this picture I showed you, this amazing model that can produce these pictures of this, you know, not real dog and this not real bubble is a system called BigGAN, which was created by DeepMind. And if we look though, how much compute and how much power it took to produce this model, it turns out it took the equivalent energy of the average US household for six months to train this just once. So an enormous amount of power to produce this AI system. And then meanwhile, this tech system, GPT-3, same story, you know, we're making big progress, but these are really, really big models. So for instance, we look at the number of parameters that you have to fit to train one of these models. GPT-3 has 175 billion parameters. So just think about how many numbers you need to even store to specify this model. And then it was trained with 300 billion tokens. So think about roughly 300 billion words to train this model. So these things are really, really big. And this article in the register did some back of the envelope math to learn that this required roughly 190,000 kilowatts of power, which produced 85,000 kilograms of CO2. And that's equivalent to a car driving from the Earth to the moon and back. So astronomical capabilities, but astronomical price in terms of power. And you know, there are limits, of course. And in fact, when I say there are limits, there are hard limits to how much further we can go down this road. So if we look at the power budget of the entire planet Earth, how much energy comes from the sun, how much thermal energy there is in the core of the Earth, how much stuff we can dig up out of the Earth and burn, we're actually on track to exceed that power budget in the next decade or two. So this was an estimate from the semiconductor industry association, where they looked at our year over year increase in computing power usage. So just each year, we use more and more computing. We're actually on track to hit that world energy production limit somewhere in the 2030s. So we literally cannot continue down this path of keeping to build bigger and bigger models. And if you look then at this sort of cost in terms of carbon, you can see today's even larger than GPT-3 models can consume half a million pounds of CO2 to train them. And you compare that to a car for a one lifetime or a flight between New York and San Francisco. And you can see that the power consumption of AI is actually a pretty big deal. As we do more of it, it's going to be an even bigger deal. And then of course, that's the power consumption cost. But the size of these models also means that we're paying a lot. So the estimates that it costs anywhere between $1 and $3 million to train one of these models once. So this is another sort of aspect of the problem that we have that our models are getting bigger and bigger and bigger. And of course, if you look at the hardware then that these things run on, this is what AI runs on in the data center. So these are a picture of an NVIDIA GPU. These cards are typically stacked then in boxes that are full of the GPUs. And these live in air-cooled data centers that are tremendously power intensive and also tremendously hot. And if you just take a thermal camera and point it inside of a modern computing system, you can just see how much heat they're producing. And someone at Intel produced this figure back in the 2000s that I like, where they were comparing the power density, the number of watts per unit area of the chip. And they actually showed that this was back in the era of the Core 2 processor. They're actually approaching and matching the power density of a nuclear reactor. So the actual number of watts per unit space in the nuclear reactor is actually comparable to that of a modern computing system. So this is a big deal. Computing is a very power-intensive thing. That's in the data center, but we also want to be able to run AI increasingly on the edge. So on our mobile devices, on robots, autonomous vehicles, we're trading off compute with battery life, in the case of all these devices. Think about applications of mobile devices and factories. There's just a lot of things we'd like to be able to do without having to always go up into the cloud. So I'm going to tell you a little bit about what we're working on at IBM Research to try and combat these issues and try and push us to a new regime where we can use AI without using so much power and really still gain the fruits of AI, but in a more sustainable way. So there's three trends that I'm going to tell you about today. First, I'm going to tell you about using AI to trim down AI models. Then I'm going to tell you about doing more with less. So this idea that sometimes less, and in particular, I mean less data, is actually desirable. And then the third thing I'll give you is just a little bit of a glimpse of are entirely new hardware architectures that we're working on that can achieve the benefits of AI but with a much smaller power footprint. So let's start by talking about using AI to trim down AI models. So a deep neural network, which is really the workhorse of this new revolution that we're seeing in AI, is what we call in technical jargon a nonlinear function approximator. And all that really means is it's something that takes complex input, like an image, in this case a picture of an apple, and it maps it to something we want, like an output like a label. So we might want to take pictures of apples and then label what is it. OK, we have this one hot coded vector that tells us it's an apple. Now, the trick with neural networks is we don't hand tune everything in between, but we train it with data. So we take lots of examples of what we have, the picture, and pair them with lots of examples of what we want, the labels that go with the pictures. And we train them, and then we train them, and train them. And then we fit all of these weights in these units that are inside the network. These are the artificial neurons in the neural network. And with lots and lots of data, we don't have to specify how to do it. The system learns how to do it on its own. Now, that's great. But if we wanted to make this smaller, we have a couple of things we can do, which are pretty straightforward. So the network consists of these units, which are the circles, and then they're connected to each other, and those connections have weights. So if we want to put the network on a diet, we can do two things. We can prune the synapses. We can take out synapses. And we can also prune neurons. We can take out the whole units. And by doing so, we just have to do less computation. So we can just get rid of some of the units, get rid of synapses, and put the network on a diet. Of course, this is not something that we know how to do on our own. It's not something even an expert would know how to do. But increasingly, there's a trend where we use AI to help us with the process of designing AI. And we sometimes call this auto-AI or network architecture search. This is an example. You may have seen visualizations like this before. This happens to be an example of an IBM product called NuNets, which does this process. And typically, what you do is you train an AI system to make decisions about how many layers should we have in our network, how big should the layers be, how small should they be, how many connections should we have. And it sort of automatically tunes those parameters, so you don't have to have an expert tinkering with every last detail. Now, typically, this is done to get the network that performs the best, that has the best accuracy at making whatever decisions you want to make. But we can also use these same techniques to help us build more efficient models. And this is one example of a piece of work that's come out of the MIT IBM Lab. It's a collaboration between Sung Han at MIT, a professor at MIT, and Chuang Gan, who's a researcher here in IBM Research Cambridge. And they created a method called once for all. What once for all does is it allows you to take a network and then progressively shrink it in such a way that you can now take that master network and then pick out subnetworks from inside that network for different purposes. So for instance, if you're running in the cloud on a server, you might use a large network, a large subnetwork that takes advantage of the resources available in a server in the cloud. But if you're running on a mobile phone, you might want to use a much smaller subnetwork than you would use on a big server with a big GPU. And furthermore, if the battery on the mobile device is full versus empty, you might want to use a different model again, because you might be less willing to trade your last little bit of battery for a little bit extra performance, whereas when the battery is full, you might be willing to make that trade off. So the magic of the once for all method is you can actually train the thing once and then dynamically pick out these subnetworks without having to retrain the model. And this actually is, so think of it kind of like these nested dolls, where inside the larger model exists many small models that can perform the task with different characteristics in terms of how much, how power-intensive they are for different hardware. Now, this actually makes a huge difference in terms of the power consumption of these models. So we look at MNASnet, which is a pretty standard neural network, you look at how much power, how much carbon it took to train, where on the order of half a million pounds of CO2 go into training, getting the parameters of the MNAS model. Now in contrast, once for all, you can do the same with just 340 pounds of carbon dioxide. And here's some other sort of typical activities like US car, including fuel for a lifetime and around trip flight from New York to San Francisco. You can see these are huge reductions in the amount of power, just by being a little bit smarter and allowing AI to help us do the design of efficient AI models. And we can act, so that was for training. We can also use similar ideas at inference time. So not when we're training the models, but when we're running them, there's a piece of work called MCU net V2, which is being presented at NeurIPS. And what the team has done with MCU net is to jointly optimize for the network, what should the network, how should the network be shaped to get optimal performance, but then also jointly optimizing that with the inference engine that's going to run that network. So you can control the memory for different, memory size, patch size, things like that for different devices. And that allows the team to even get these models relatively sophisticated models down onto a microcontroller like that one, which is a few dollars at scale, in contrast to a GPU, which costs thousands of dollars and lives in the cloud. So we can actually take relatively complicated AI workloads and shrink them down to the point where they could be running in devices ubiquitously. So very exciting things are possible when we allow AI to help us design smarter AI systems. The other theme that I want to tell you about is this idea that sometimes less is more. So you might have heard that data is the new oil and data is the fuel that drives AI and that's true. But sometimes you don't want more data, you actually can get by with less data. And when you can, there are a lot of opportunities to save energy. So for instance, if we have an AI system that's watching a soccer game, a video of a soccer game, maybe it's recognizing actions or recognizing players or whatever, the interesting thing intuitively about this game is, yeah, sure, we could watch every single pixel in the video, but it turns out that you don't need to process all of that data. And in fact, a few key frames here and there that show important parts of the action might be sufficient. We don't necessarily need to watch all of the video in full resolution. Sometimes maybe we can get by with half resolution or maybe even quarter resolution. And we don't have to watch it at full dynamic range. So we have these wonderful eight-bit cameras with lots of gradations of brightness, but sometimes we can get by with much shallower images. So what this team has done, this is a collaboration between Rameshwar Panda, who's a researcher here at IBM Research Cambridge, working together with Oda Liva, who's a researcher at MIT, and she's actually my counterpart at MIT. She's the MIT director of the MIT IBM Watson AI Lab. She and Rameshwar work together to create a system that dynamically decides what data the neural network should process. So again, we're using AI to help AI, but now we're doing it in real time. So we're in real time, we're deciding, do we need to look at this frame or not? Could we look at this frame at lower resolution and save on the amount of data we process? And in doing so, they can actually get up to a 40% reduction in the power and the energy consumption of these models, while not sacrificing the quality of the result at all. And again, just to plug some work that's happening this week at NURAPS, if you have a chance, this same team has a paper called AI Red Square, where they're applying the same idea to vision transformers. Now, you might remember, I told you earlier there was a model called GPT-3, which worked on text. That's an example of a transformer model for natural language processing. There are also transformer models for vision and they work in similar ways. But what the team here has done is to dynamically decide which parts of the image the transformer will process. And in doing so, it can greatly reduce the power consumption of these models. Interestingly, the results are also more explainable because by narrowing down what the model looks at, it gives the human observer a clearer picture of what was important and what was used to make the decision. Okay, finally, I wanna tell you a little bit about how the very hardware that we run these models on is poised to change. And in particular, I wanna tell you about some work that's happening, not here in Cambridge, but in IBM Research in Zurich. We have global labs all around the world and one of them is in Zurich looking at the problem of analog computing. So what is analog computing? Well, I think we're all familiar with what digital is and that's been the watchword of the last couple of decades. The idea that we can represent values as streams of zeros and ones. And that's digital technology has obviously been tremendously important and it's changing the world but that's not the only way to represent a value or a number. We can also represent a signal using analog. So rather than having zeros and ones, we can have continuously varying values that we can represent numbers with and that we can also process with. And think about like an analog watch in contrast to a digital one. The analog watch, it's the angle of the dial sort of varies continuously in proportion to the time. So it's like an analogy between where the dial is pointing and the value, which is this abstract thing, which is the time. That same way that we can represent time with a dial, we can represent values with varying voltages, for instance, inside of a computing device. And you can produce systems like this. So this is a system that you can actually try yourself and I would encourage you to do so. There's a link down here in the corner where you can run not, you can basically train a system to recognize handwritten digits. And rather than running with digital numbers and conventional digital system, you can run them in an analog computer that's actually doing that computation using the physics of electronic devices. And an awful lot of the power consumption, it turns out in traditional digital computers comes from keeping the ones ones and keeping the zeros zero. And when you perform these computations using analog voltages, instead you can achieve even up to 100 or a thousand fold reduction in power. So it's a fun demo and I encourage you to try it. And with that, I'll close. Hopefully I gave you a little bit of a picture, a glimpse of how we're tackling this problem of making AI sustainable. If you wanna learn more about what we're doing and particularly at the MIT IBM Lab, you can visit our website at mitibm.mit.edu. And thanks for taking the time to watch.


Lake Bam is approximately 100 or so kilometers from Ouagadougou to the north. It's in the Sahel Belt and the implication is that this is a very dry arid area and so water is of vital importance to whatever human activities that need to be done. Looking at these activities in closer detail, we find that they are characterized essentially by agriculture in terms of market gardening, livestock rearing and fishing. So agriculture is essentially market gardening and you have about 25,000 people who practice this activity around the lake. 25,000 people, that's a lot of people who depend on this lake for their subsistence. But you also find that there is a lot of livestock activity with animals in all this area coming down to the water to get a drink and you have a lot of fishermen who also practice fishing, although this is an activity that is increasingly on the decline. So we could say that this is a lake which is capital to most, if not all, of the livelihood activities in this area but it's about 12% of the entire population of the Bam province. So generally speaking, there are two main problems that we could talk about. One of the things that I observed is that the size of the lake is declining but also the fact that the quality of the water in the lake is less good. So this means that we have lower volumes of water and the water that is there is less appropriate for sustaining life within the water. What are the causes? Essentially you have the hills around the Lake Bam that have been deforested and so you have much more runoff and soil erosion. This is worsened by the fact that the banks, the immediate banks of the river are also where the market gardening is practiced. So you have nothing that holds the soil together and that prevents the deposits of silt within the lake rather. So over the years it has been noticed that between 1963 and about 2009 the lake has lost about one third of its depth due to the deposits of silt at the bottom. You also have people moving in during the drier seasons actually into the bed of the lake to practice farming, market gardening and so increasingly we have less and less of the water surface that is available and you also find that the fishermen are having a lot of trouble. There used to be a lot of fish. Today there's very little fish. A fisherman hardly gets up to five kilograms of fish per day and so they are trying to catch smaller and smaller fish. There's much less respect of norms, environmental norms by the market gardeners around the lake, fishing norms by these fishermen because these people are all struggling to earn a living under very difficult circumstances. So I think part of the solution is providing these communities that depend on the lake for their subsistence with better tools and land management systems that would reduce the erosion, the soil erosion that would permit them to produce in a more environmentally friendly manner and so this implies capacity building for them in agroforestry techniques. It implies sensitization and awareness raising about the environmental issues that the degradation of this environment faces and it means working with them to test different approaches to solving this problem. I think there are different roles for different people and C4 for instance and its partners of the CGIAR but also the national institutions for research and forestry have an important role to play in terms of setting up programs or projects that would help understand exactly what is happening what are the changes that are occurring in this landscape what are the causes or the factors that influence these changes what are the levers on which we can lean to be able to inverse the negative trends and how do we engage these communities and help them towards behavioral change and changes in practice to be able to adopt more sustainable land use and management practices. In this sense the CGIAR program on forestry and agroforestry has just begun a program globally called the Sentinel Landscape Program and in West Africa we are fortunate to have the West African Sentinel Landscape right here. Kungusi the Lak Bam is part of this landscape it's a transect that runs from southeast Mali passing through much of Bukina but also northern Ghana and northern Togo and so what we try to do as we begin this project we hope that we will be able to document the changes that are happening in the key or pilot sites within this landscape of which the Lak Bam is one to look at the human activities and how these are influencing the change in tree cover the change in availability of water, of land for farming and other uses but also how does this impact the livelihoods of the people so I think this is an important program in that it can inform government policy it can inform development organizations on the best strategies ones that can work, how to monitor these, how to determine their impacts and how to reverse some of these trends not only in the Lak Bam area but also in other areas further down south like Kasu which is another site where similar occurrences are being observed but that is still at an early stage so essentially I think our role is one of finding out, getting information generating this information and making it available to the various stakeholders from local community level through the NGO sector up to the decision and policy maker level and facilitating dialogue around these issues discussions to get to a common understanding of what the management objectives and perspectives should be and what would be the tools and strategies for achieving the desired results


I think it feels great. I mean, I think this is the first time a copywriter is at a loss for words. So yeah, I think when I heard the news, that's pretty speechless, but it's pretty exciting. It goes for pretty much every agency where numbers are important, otherwise servicing people would be out of jobs. But I guess what drives numbers is great businesses and great businesses definitely come from amazing ideas and great planned relationships. So I think we should never stop thinking and ideas definitely count at the end of the day.


Hi, so the Habitat Quality Model is useful in two key decision-making contexts. First, it can help assess the conservation status of habitat important for biodiversity by identifying areas vulnerable to anthropogenic impacts, and especially where habitat data, in terms of presence and absence, are scarce. It can also be applied in terms of assessing changes to species' habitat under different scenarios, such as future land cover or climate change threats. The Habitat Model does not treat biodiversity as an ecosystem service. Instead, it is used to assess the overlap and trade-offs among biodiversity conservation, ecosystem service provision, and other land use types. Habitat Quality is defined as the ability of environment to provide conditions for appropriate individual or population persistence. And Habitat Rarity is defined as the relative commonness of the habitat relative to the baseline land use scenario. Both indicators are used, I mean, both Habitat Quality and Rarity are used as indicators of status for biodiversity. So, generally, areas with large habitat extent and quality are likely to support higher biodiversity. The Habitat Quality Model depends on habitat suitability for the species of interest, which can range from a single species, such as a forest bird or even a larger category, like all mammals, or biodiversity in general, not being not species-specific. For instance, we can ask, does it prefer grassland open canopy forest or closed canopy forest? The model also depends on proximity and intensity of threats represented by two major categories, habitat loss and habitat fragmentation. And we can ask questions such as, how far away is the threat or how severe is the threat? So, habitat threats and degradation depends on the distance between the habitat and the threat. For instance, our cities as well as the relative weight of the threats. For instance, our cities more damaging than roads. Sensitivity of the habitat to the threats, such as is the forest more sensitive to road than grassland? Or how quickly the impact decays over a distance? Here's the model we can choose between linear or exponential relationships. And lastly, we can also account for accessibility or protection status, where the habitat types that are more protected are less likely to get degraded by illegal harvesting or hunting. But this does not help treat threats such like pollution, which can transcend boundaries. So this is a simple overview of how the model works. Let's say we have a bird species and live in a forested area that we designate in the model as habitat, using the language cover map. Then we have non-habitat in the landscape, such as agriculture, as well as threats such as roads and cities where people can come in and affect the habitat. And these threats can be linear or exponential by nature. So, and we can also have national protected areas that can help lessen those impacts. And using the habitat degradation map we can derive this habitat quality map. We can also apply the model to look at the rarer habitats, which are usually targeted for conservation, since they're more likely to disappear entirely along with the species and the processes in them. The rarity here is measured relatively between current or future language cover maps compared to a baseline land cover map, which represents an ideal historical state. And we identify those habits, those rare habitat based on the change in extent of each habitat in terms of number of grid cell between baseline and present or future conditions. Here, note that forest disappears between the baseline and current or projected land use cover. If we want to look for habitats with a high rarity score, here we see that most of the wetland disappears between the two land use cover maps. So, since it is more rare and seems to be disappearing, we probably want to protect that habitat. In general, habitats that are rare on both baseline and current or projected land use cover maps are likely more stable. Here is an overview of the input data needs to run the model. First, the model requires a land cover map with land cover classes. A sensitivity table will also be used for each land cover class. Threat raster maps, which are usually in geotiff formats and are used and must be represented on the same scale and metric. For instance, presence absence or kilometers or density. And the threat information is stored in a table, including their spatial impact and their weights. Optionally, the model can also incorporate a baseline land cover map, which would represent an ideal state or historical state of the landscape. It can also incorporate a future land cover map to look at projected changes across the landscape and how those would impact the habitat. And it can also incorporate an accessibility or protected area map, which basically tells us where legal protections is currently happening or physical barriers due to elevation, which can hinder threats impact. The sensitivity table, the threat table and the accessibility map are all scored between zero and one. And I'm going to explain how. So we assign values between zero and one because it is a relative ranking model. We do not quantify anything. So for suitability, zero means that the species does not like to live in this habitat, while one means that it is its preferred location. For sensitivity, zero means the habitat is not sensitive at all to the threat, while one means that it is very sensitive. Threat zero means inaccessibility, meaning that it's fully protected. People cannot enter the area. And one means that it is not protected and open for everyone. For threat zero means low impact on habitat, while one means very high impact on the habitat. So now looking at the structure of the workspace. So create a folder for the model and the input folder must be nested within that larger folder. And then within the input folder, we need to create another folder for the threat raster is where all the rasters need to be stored together in that folder. Note that at the end of each threat raster names, we include an underscore C to represent the current threat status and an underscore F for future threat status. Each threat names needs to also be under eight characters. So taking a deeper dive in the different input data for the model, the land use cover map is a geotip raster where each value corresponds to a land cover class and need to match the sensitivity table. The sensitivity table represent the land use land cover code which match the land cover maps so those two inputs are connected through that code. It helps us also define which land cover class is classified as habitat or non habitat, zero being used for not a habitat and one being used for habitats. And then we have the threads, which can be 123 depends on the study area or the question at hand. And each threat names must have L underscore at the beginning of the name that has nothing that has no implication for the model, but it is a relief from older model programming. All you need to know is that it needs to be included for the model to run. If we look at the weights, the sensitivity values under each thread. We noticed that those value basically represent the relative sensitivity of each habitat to each threat. So again, thinking of the relative ranking, one being highly sensitive and zero being not sensitive at all. It is also important to note that all the threads names must match the names of the threat maps in that nested folder. Then we have the threat table, which helps us list the threats, their weights and their spatial impact. The threat names must match. The threat names must match the maps of the threads I guess I just mentioned. And the table also include a max distance which is how far can that threat impact the habitat and these must be expressed in kilometers. If you use meters, the model will spend for a very long time and because the model will assume that those values are kilometers. The weights help measure the impact of each thread on habitat relative to other threats, one being the highest threat and zero being the lowest threat. The array column indicates the spatial impact of the threat. Is it either linear or exponential and can help indicates how quickly the threat decays over space until it reaches its max distance. The threats maps are rasters representing presence absence of the threat for instance here shown with presence of urban area or not same thing for roads and railways. They also represent density of threats such as nightlight or herders, which can be expressed in kilometers or density. And note in terms of GIS structure that all the no data in these rasters must be coded as zero. And all these rasters must be placed in the same folder nested in the input folder. You do not manually enter enter each raster in the model. We had the last optional input which is the accessibility layer, which represents different level of protection on the landscape with zero being completely protected and one being no protection at all. And also note that all the non defined areas are automatically treated as open, meaning assigning one value in the model. For the future land use cover maps. We can also include a future land use cover map which will present how the landscape is projected to change under current human or market drivers for instance. We can also include a baseline and use cover map which is used to calculate the rary index of habitat. And it's important to note that across all these land cover map if you put in the analysis, the values of the common land use cover that types must match across each raster. Once you have a new land use land cover type in the future maps, it needs to be assigned its own unique value. So, to set up the model, you need to download and install it using this link at the natural capital project dot edu slash software slash invest. Once you have downloaded, you can double click on the file and click next until it's completely and successfully installed. Once it is installed, you can open the habitat quality model. And the interface is going to look like this. So first, we need to set the, we need to point the workspace where we're operating. Then we need to include the current land use cover map in geotape format. Optionally, if it is part of the study or the research question, we can include a future and baseline land use cover map here for this example we didn't. Then we also need to point the folder, we need to point it towards the folder containing the threat rasters. And then the threat data table which is in CSV format. Here optionally we also included the accessibility layer for the level of protection across the landscape which is a vector so a shape file and the sensitivity table which is in CSV. And lastly, there's that half saturation constant and the default is 0.5. We also call a layman terms and what it does is basically adjust the spread and the distribution of the habitat quality values, but it doesn't change the ranking order of the habitat quality. So basically it helps us visualize the changes across the landscape more easily. First, the default is 0.5. And ultimately, you want to set this value to be equal to one half of the maximum degradation score. So the degradation score is obtained from the degradation map, which you obtain from running the model. So, therefore, to calibrate this value, you need to run the model twice. First, you run it with a default to identify what's the max score for the degradation score, and then you rerun the model using the half, the one half of that max value. So the model gives us two to three key outputs. The first one is the habitat degradation map. So here, which can help us, as I was just mentioning, set this house saturation factor. So here, looking at the habitat degradation map, we can see that the max value is 0.06. Meaning that for the second run of the model, we should set this factor 2.03 to help visualize the habitat map. The second key output is the habitat quality map, which range from zero to one, because it's a relative ranking system. And lastly, if we were to, if we had included a baseline layer, we would obtain a habitat rating output as well. It's important to note that all the values are relative to one another. We're not quantifying anything in absolute terms. So looking now at the habitat quality with the default saturation term, which is 0.05. So here, we can see some red spots in several areas, but it's really hard to tell if things vary. So looking at the scale, we see that there isn't much values between 0.1 and 0.99. So what does that tell us? We need to rerun the model and change that key factor. Once we do and set it to that half value of the degradation max score, now we start looking, now we start seeing some nuances across the landscapes. In every model, this model has limitations. The relative ranking is, the relative rankings are more important than absolute values here. And threats are treated in an additive fashion, when sometimes collective effects of threats are larger than the simple sum would suggest. The habitats are also using artificial boundaries because the model habitat is typically within a larger landscape and doesn't account for effects outside those boundaries. Sometimes one way to circumvent that is to improve the analysis is to model a larger area than the area of interest. We cannot consider patch size and connectivity when we know that small or isolated patches may not actually be suitable for species like large mammals. And feel free to contact us if you have questions or meet me during my office hours. Thank you.


Welcome to Toffy TV, these are my talking points from Everton One, Southampton One and the penalty shootout defeat in the Carabao Cup. First thing is, and I've just had that little bit of a rant about it on the matter of action, but why on earth we are changing our goalkeeper? Not even for a goalie that's going to challenge our number one. If it was Virginia, I kind of would have gone okay, having the luck to see what this kid's got. The hologram makes no sense for me, putting him in goal, it literally makes no sense. He's never going to challenge for the number one. He's not good enough. Pre-season showed you every shot he had out of my pre-season winning. He's not good enough, and it's not helped us at all tonight. The goal we conceded, why he drops to his knees, I don't know. I know it's deflected, but it's just bad goalkeeping. He made a good save in the second half from a header, from a corner, he should have just come and claimed he's six foot twelve or something, and he's rooted through his line. Not for a team that is allegedly trying to fight for the top six and get up there and try and win trophies and splash the cash around nonsense, because as much as you keep playing him, go giving the message out that you're not really serious about doing anything like that. If that's the case, tell the fans to say, unless we're not asked about this, we don't care. If we go out, we're going to play people who aren't good enough to see what happens. That's the first one. Second one is we need a centre forward. We need a quality, pacey centre forward. Love Gent Ocean, and I think within the squad there's definitely a place for him and I think he can do the job sometimes. But when games are like that, where he's not having any joy, we don't have anybody who can take the game to the opposition. We don't have anyone who can stretch them. Nias, come on, there's Nias. Now all right, he'll turn and say, well, I set the goal up by putting Walcott through. Yeah, you did mate, but you offered not much else really. But whatever it is supposed to be the next level, if you like, that's what it is. You need better centre forwards. You need better, you know, I think. But Jarlasdon, come on, I was incredibly poor tonight, but we know that he's really good and he's exciting and you'll have better games. But now I look fantastic for the large part. You know, Walcott didn't really get into the game, but took his goal brilliantly. I thought Luckman was good, I'll talk about him in a minute, but yeah, for me centre forward. Big Blair and Hull in our squad is the centre forward because you should have someone who's genuinely battling with Towson. Calvert Llewyn isn't the answer at the moment. He may well be in another year, I don't know. Nias certainly isn't, and that to me is a real issue. Third and final thing, Luckman. First 15 minutes he was horrendous. Getting pushed off the ball, wasn't running, but then he got into it and he done a couple of tricks and he started linking up. And all of a sudden he grew into the game and he became one of our better players and most, you know, a real threat.  And I really couldn't get my head round it. Towson would have been the player I'd have took off for the charlots. I'd have took Towson off and threw the charlots and just gone, either does it is, because Bernard's operating well on the left. Luckman's doing well on the right, I'll swap them over.  And if we're telling this kid that he's a big part of our future, I'm not saying his attitude's being good because he hasn't.  You see, I thought Markham didn't give himself any way to go with the nears up.  If he's coming on, take Towson off, just keep it as it is, but he didn't. He put him on and tried to go 4-4-2 in it. I mean, I'm never going to have a goreham for going with two strikers, but I just thought I just wouldn't have took Luckman off because he was a threat to me. I think he could have left it the way it was. He could have left two off front, but the charlots and could have replaced Towson. But Markham could have stayed where he was, Luckman on the other side, and then 15 minutes ago, if Luckman's fading a little bit or a bit tired, then taking him off and put a well-gotten. It was too early for me and I just don't think he'd offered. But the charlots didn't come on, it didn't really change anything. He went down. I don't know. The first one, when it might have been a penalty, I don't know, maybe he just went down too easily. But the one on the edge of the box, I thought he got caught. I'd have to see the game, but I thought he got taken out and then, yeah, they checked VAR and apparently didn't. So there you go. Yeah, that's it. We need a centre midfield player and we need a centre forward in January. I don't know who you could get.  The old someone and get a centre forward who's direct and who's pacey and who can get on the end of things and take us up a level because at the minute, we ain't got it. And it's what we need, probably more than anything. I'm going to need a ball playing the field player. You know, Davies and Snidel and we're bank average. I think tonight is the best way you're putting it. Yeah, there you go. That's my two-peneth worth, but what it's worth. We are out of the Carrival Cup unsurprising. You could always out of this round, or there, or thereabouts. So yeah, we're out again. So one down without the league really, so two down, so the FA Cup before it becomes another trophy of the season forever. If you haven't subscribed, please do so, take some minutes. Join us on Patreon if you want more videos. Check out Ted's Instagram match reaction. And yeah, see you later.


Hello my friends, and welcome to episode two of Running With Ryan. This is a series where I go running with runners and we chat about stuff. And today, I'm really excited to meet this guy. I've never met him in real life, but I've been following him for two years on his YouTube channel. He's a Nike Pro Trail athlete. He's been top 10 in Western states. He's won a ton of marathons. He got a degree from Stanford. He's a smarty pants. He ran for Stanford. He was a teammate of Ryan Hall's. And he's recently relocated to Boulder. Beautiful Boulder, my hometown. Ladies and gentlemen, Chris Mako. Look, there he is. It's Chris Mako. High five, bud. I found my diva sunglasses in the back of my car, so pretty excited. Chris just asked how many miles I want to go. What do you think? Your call. Down for anything. Two or three? Maybe 15 to 20, two or three hours? Yeah. I bet there's some Mako show fans out here. There may be, although I hope they're at work right now. This is time of day. But this is Boulder. This is a town where I don't know where people get their money. Nobody ever seems to work. They're always at coffee shops. Yeah, expensive coffee shops at that. I know. Well, we'll work with it. We'll work with it. We'll work with it. I'm gonna hold the camera today. You just get to run and talk. How do you think about that? Pretty much my dream. Yes, sir. So you just won the Chisholm Trail Marathon in Wichita and your calves, oh, not your calves, your quads were bleeding from chafing so bad. My number one goal has always been get back to Western States. I surprised myself and qualified in January. I've got a goal. So a lot of that pressure was behind me and I decided to go 100% in on the road scene. Nice. And I wanted to run really fast. That was the number one goal. Yep. Go Mako! And sure enough, as you alluded to, started having a little bit of pain in my upper thigh. And for reasons unbeknownst to me, I mean, I was properly lubricated beforehand. I'm actually sponsored by Squirrel's Nut Butter, which is the top. I love the number one rated lubrication brain in the sport. That's true. I love those guys. And as I crossed the finish line, I looked down and I'm just covered in blood. Fortunately, the scars have healed both mentally and physically. I think the viewers are still scarred. I'm a mama's boy. I love the video of you and your mom running into the Arizona rock and roll. My name is Maddie Mako. I'm 68 and I'm running my second half marathon tomorrow. Yeah. Talk to me about what it's like to cheer on your mama. Cause I do the same thing and there's no better feeling in the world than seeing your mom do something they didn't think was possible. Yeah. Well, I have a very vivid memory of about say sophomore year of high school. I'm at summer camp and the coolest kid in the camp, Remington was his name. Like so cool, you could pull off that name. Yeah, Remington gets settled into his room. It's an overnight camp.  Kisses her goodbye. And I'm like, what? You're allowed to do that. At that time it was like shunned to have like any affection towards your parents. And all of a sudden I realized like, hey, the coolest kid can do that. Maybe parents aren't so bad. Yeah. And ever since then, I've been a little bit more proud and affection towards my family. And it's crazy that we go through that time. I just remember looking back like, man, we can be pretty bad to our parents sometimes. Let's have a shout out a moment for all the parents in the world. Yes. Love you mom and data. So I saw some of your first videos or flashback videos. Yes, I love this. Embrace the mom. Embrace the mom. Especially in late 20s. That's even better. Exactly. Thank you Remington for inspiring that. If you're watching out there, Remington, this is for you.  If you know her story, I mean, five years ago she was not running at all. Not a step.  She's very fit, but like the thought of running 200 yards frightened her. Short of breath. And it was something that I could not relate to because I'm like, I've been doing this for so long that I just don't know what it's like to struggle like that. She decided she wanted to run an entire 5K. She walks them all the time, but wanted to actually run. I set out with a plan for her and my dad to both finish a 5K. They both did it.  And for that Christmas, I got them registered for a half marathon. And they did an amazing job of knocking out the training. Like, I think there's something not only inspiring for me to see them doing these workouts, like six by hell repeat, that sounds impossible. They just go out and do it like blindly. Might be naive, like not knowing how hard it's gonna be, but they did an amazing job. They both finished the race. Big finish, mama. Just so inspiring getting these emails every week. Like, oh, couldn't sleep last night. My legs were hurting so much. Is this what it's like to be a runner? Like doing long runs, the nervousness before workouts and just being excited and finding the joy, which like takes so much time. Like people are like running is the worst. It's so hard. Why would anyone do it? Like it's the punishment for most sports. If you do it consistently enough and you have something that you're shooting for, it just makes it so much better. Yeah. And she nailed it and it was incredible. How'd it go? I finished. How did you do? How did you make it?  A few moments later. All right, out of the bathroom. High five. Oh, hopefully we didn't wipe with that hand. All right, so we need to go. We're going to be running south today because the wind is just way too strong the other way. Your story to me and to others is, I think, very inspirational.  It's hard to do both. And you decided to give up the job, take a huge risk, make way less money, at least for now, and just go for it. Talk about what motivated you to do that. And maybe if you have any advice for somebody out here there who wants to pursue a dream, but they're a little scared. Yeah. Big question. I think it was a combination of a couple of things. One, I was just burnt out and that's something that I'm actually learning to identify more of. It's like, hey, if you're not doing well, you're not happy. If sleeping is a problem, something is going on and you gotta address it and tapping a lot with training recently and we might talk about that later, but with work, it was just this constant cycle of not being super happy with what I was doing and I did not initially see it as I'm the go pro in running. I saw it as I need a break. I'm qualified for Western States. It's in four months.    Yes, my thighs actually did get quite a bit smaller, but it was an amazing opportunity and fortunately, I've been working for 10 years. Anyone who knows me, I'm rather frugal. Shop at Costco, only 24 year old with an executive membership at Costco. By the way, he's still looking for that sponsorship. But yeah, I gave it a go and things could not have gone much better. I put $10,000 in my bank account and said when that hit zero, I gotta get a job. And won a couple of races, got the attention of some sponsors, found myself in a really good shape and before Western States signed a deal with Nike. I mean, the most important thing for me is working with companies that I actually believe in and their product and Nike is no exception and almost like a startup within their company. It's such a small niche in their entire company. Like Nike gets a bit of a bad raft sometimes because it's this massive corporation and the vibes that you see on the trail center is completely different. And yet Nike trail, like their kind of marquee brand is this Winnebago that's just decked out in trail gear and it's just so different than what you think of when you think of big corporation. If I look back, graduating from college, I was surrounded by some of the best runners in the country, the best athletes in the country and a lot of them went professional and that was certainly not an option for me. I knew where I was talent-wise and that meant barely making the top seven, fairly qualifying for NCAAs, but always improving and still loving it. And it took a decade after college to sign my first contract. And so whenever I see it hits much madness right now, whenever I see those 99% of college athletes will go pro in something else other than sports. I'm now like, you are one of those guys. I'm one of those guys. I come from a very conservative family when it comes to careers. My parents both worked for the government their entire lives. My brother looks like he's gonna be a lifer as well. And so the idea, not only switching jobs after just a couple of years, but it being my third job. Yeah. Not to mention that third job was gonna be, oh, just gonna run full-time. That was scary. Yeah, mom, I'm gonna make it work. Don't worry. And I think they are understanding it more. Like I bought them a copy of Once a Runner. They're both reading it now. Try to understand a little bit of the psyche that makes us tick, but they've also now experienced it through racing as well. So step one is, get your parents signed up for a rock and roll race. Yeah, so they can relate to your joy. But yeah, I think that it's just this awareness that if you're not happy, you need to change something. But I also think being a little too, it would be the right word there. Impulsive can also be risky. So I was definitely very conservative in my approach. And I basically decided around November, I'm not happy something needs to change. The thing I wanna try to do is this running thing. So while I still worked, I started investing more in my running because if I couldn't do it at like 80% with a job and find happiness, then how was I gonna do that at 100? Yeah. And sure enough, after three months, it was working out pretty well and I was ready to make the leap and definitely pretty major life decision and I'm very grateful for it. Pause. We got to cross a road. Gotta look both ways. Gotta be safe for Mama Mako here. Where were we? I think we were taking bets about who's gonna fall first on the street. Yeah, we're not really paying attention too much. We're like talking, blah, blah, blah. That's how beautiful it was. I knew that running was important to me but as many of you probably know, if you put 100% of your energy into training, there's two things that are not gonna happen. One, probably gonna get in pretty good shape. And two, you're very quickly gonna burn out because while it seems amazing to just exercise all day long, it's not sustainable. Your body needs to rest. You don't have something to do during that idle time and unfortunately I've watched about every Netflix show that exists so that wasn't gonna be an option. It would be particularly fulfilling. I had to find something to do with my days and so that's where YouTube is coming to play. That's where I do in a couple side projects, consulting has helped past the hours but I'm really focused on making sure I'm getting in my training but also finding something that gets me excited and really motivated to finish the run so I can get to this other thing and as funny as it sounds, Ryan probably knows, YouTube can be pretty fun. It's way fun. It's also pretty tiring, it takes a lot of time. Yes it does. And so that's been one great outlet but you have to find the outlet that works for you. Morning. Morning. I forgot that when you run with a pro, they go really fast. Woo, slow down, Chris. This year is a lot about redemption for me. I had a lot of missteps last year, last two years really and this year is about going back to those same races, finding redemption and so far it's worked out pretty well but Havelina is definitely one of them. Probably the low point in my season last year where I was starting to get really good fitness. Physically I was there but mentally I was completely checked out of the race. I heard this up and coming YouTube star from Boulder was gonna be in the race. Yeah, I've really intimidated the heck out of them. And yeah, I just wasn't there mentally and wanna go back in such a beautiful course and such great energy there. Woo, what's up? Yeah, look at you. It's hard entering a race where you don't feel like you can win for me. I like to be competitive whenever I can. I'm also very well aware of my limits and so for a lot of these ultra-distance races, unlike a marathon where from the gun I try to run for the win, stay with the leaders, make sure that I have a chance to win the race with ultra running. It's very much about, okay, we got some technical down coming up here. Technical downhill, pause. I said we were supposed to go conversation pace. This is not conversation pace. We were just talking about, there's only one goal and that's winning. Winning and also fighting a home on the street that you can actually afford. Yes, that's the true goal. I was just telling Chris, I would love to live on the street someday but these houses are 2 million and above so gotta start making a little bit more of that YouTube money. So I'm gonna amend that previous statement and have a lean of the goal as to try to win. But in general, it's about just running to my potential. And if you try to run someone else's race in an ultra, it almost inevitably is gonna go poorly where if you let somebody else dictate the pace and it's a pace you're not comfortable with, you've got 50, 60, 100 miles where things can go wrong. And instead I've adopted a very boring but very practical racing strategy where I run completely within myself, get to the halfway point, race is already halfway done and then start to race. I like that. And there's no better feeling in the world than picking people off, right? Absolutely, no better feeling than passing runners who are walking, throwing up. I just passed a buddy in a race who's nearly unconscious, which was not a good moment, but us tall boys have some issues with low clearance and he slammed his head on a trail, on a tree, on a narrow trail, so. But in general, it's nice to pass runners if they're not having a seizure. Absolutely, and it's so easy to get caught up in the excitement, Boston Marathon's next weekend. I've run that twice, failed twice and a lot of it's because, oh, there's so much energy. I'm running downhill. There's these women who are willing to kiss me in Wolsey College just for being a runner. This is amazing. Let's talk about YouTube.   I just completely underestimated the distance, the heat, the terrain, all of the above. I definitely saw it, not at first realize it, but now see it as a really great opportunity to connect with the trail running community. And it's not the largest community ever and I'm very well aware, like, okay, if I want this to be something a lot bigger, I need to start thinking about search engine optimization and having the right titles that are like five tips for faster marathon and eight ways to lose weight from your gut. That's how I'm going to title this video, by the way. I think more important than that is, yeah, building a community of super passionate, engaged fans and I don't see it every day. I show up to a race. It is so validating. I see, especially Avalina, where you're going back and forth on the same trails, the number of people cheering for you, calling you C-Money, a made up name that I gave myself on the YouTube channel. It is really, really awesome to see that and it's motivating for me and it also allows some level of accountability when I share my workouts and training plan. I now have a couple of thousand people who are checking in on me, looking at my Stravas. Hey, you're supposed to do five by three minute hill today. What happened? I realized that there's not a lot of top tier athletes. I say that very loosely with myself because there's quite a bit. Hey, you're on the same team as Colin Kaepernick now, dude. You're Nike, he's Nike. And we probably shouldn't comment on that subject because it's polarizing. As important as it is to have an opinion, probably not the best one, but I do appreciate that Nike's still willing to take a stand, even if it's not something you agree with because it's a lot easier to tiptoe that middle line. But yeah, I'm guessing you've gotten some really amazing inspirational messages from people saying, you've changed my life or I started running because of you. Am I right? I wish there were more, I wish there were more. But I'm sure you've gotten some. Yeah, well, I think there's one thing that I've done that was important. It was sharing a lot more about my personal struggles and I had a couple of videos, went on a couple of podcasts talking about eating disorders and how eating has been impacted a lot of my athletic and personal life and it's about something specifically called binge eating disorder. So when you think running, you often think about anorexia and bulimia, runners trying to get skinny as possible. Binge eating disorder is kind of the opposite side of that. We can't control your eating. I had no idea it was even a disorder because I've assumed that feeding your body too much, well, yeah, you're gonna put on weight, but at least it didn't get the nutritional value it needs and just didn't have any awareness for it. Decided to share that story. I think that alone has made this entire YouTube journey worthwhile because the number of messages I've gotten from people who, one, were embarrassed, didn't, two, didn't know who to talk to about and three, just weren't aware that it was a thing at all and I'll definitely put me in a very vulnerable state. I have no idea what inspired me to actually put that on my YouTube channel and, you know, as a runner, the fairly public social media presence when I am dating, type in Chris Mako into Google, even just goofy runner, Boulder, you're probably gonna see the two of us. Exactly. And having like that be one of the first links you see, it's like, wow, this first stage has got very real when they bring up. Oh yeah, I watch your YouTube channel at all. But yeah, I think sharing some of that is very important and a lot of people talk about how Instagram and social media is just showing the happy side of things and I honestly think it's kind of hard to share the negative sides on Instagram. Like, hey, here's a crummy photo of a cloudy day in Boulder. Nobody wants to see that, it's not pretty. But being able to share that, not everything is... Ah, windstorm. Not everything is a tailwind. Exactly, that came right at the right time. One of the resolutions for the year that I've shared with my family is, I don't need to be the fittest, I don't need to win the race, but I want to show up the best version of myself of that start line and I think that's something we all can do a little better job of.   And that's how you build your community, you know?  Never, but if you want to write to Chris Mako, he'll probably respond to your comment on YouTube and that's awesome. Yeah, I think it's incredibly important to respond and message back and I don't know how scalable that is and when that stops, but I'm still very aware that I'm grateful to be in the position I am and owe it a lot to the community around me and this is very meaningful loop that you picked out here because this is where I was introduced to Boulder. Really, right here? Wonderland Lake. Oh, nice. These are my home trails.  Right here. And we're both having a special moment and I think we've touched hands a few times already on this run, so we're bros. I thought that was an invitation for another one. I was like, I'm not quite ready for that. So one final thing, somebody's out there watching this. They want to just run a 5K like your mom did. Yeah. And they're intimidated by it. They think it's, you know, a 5K to them is an ultramarathon. How do they get there? Number one, it's consistency. Yeah. And we talked a little bit about before, but the first couple of times you go out running, even the first times I do after a long hiatus, break after a big race, it feels terrible. Yep. And your body doesn't feel like it's working. It's not natural.  But point two is you got to start small.  Yeah. If your goal's three miles, especially, but you got to start small and be realistic. And there are so many free resources available of like, hey, here's a 12 week training plan. Yeah. Get yourself a plan. Start as small as possible. Make your goals achievable. I think the hardest thing for me is when my workouts get so hard that I'm like, I can do a 10 mile tempo and you dread them and you fail to execute them. And instead of trying to overreach, do something that's achievable gets you excited and keeps you motivated and like that momentum going. And I think that's super important. If you set yourself up for failure, it's not going to end well. Yeah.  It's about getting outside, breathing fresh air, hanging out with friends, you know, join a running team. But if you really want to get serious, you should be looking into coaching, getting a coach. And I offer coaching services. Hey! Oh, shameless plug. But I think what I've seen in my athletes and even with my mom, when you start injecting real workouts and a structure and just the experience of an athlete who's gone through it before, it completely changes you as a runner. If you just go out every day and do the same one and a half mile loop, you're going to get to a certain level of fitness and then it's going to plateau. And if you have a coach who knows what needs to happen in order for you to really jumpstart the system and excite it in different ways, you can really see pretty remarkable improvements in your fitness. Awesome! Boom! My man! Ladies and gentlemen, Chris Mako, go to his channel, subscribe, his videos are very entertaining and inspirational. And good luck this year. We'll be cheering you on. Team Deuzer, cheer him on. All right, Chris, how far did we go? Well, I almost set a 10K record. We did 6.3 miles in 58 minutes, so. Hey! Count it! Yeah, that's actually pretty fast for this, for what we're doing. Usually I like to like barely jog, so we're not like out of breath when we're talking, but I think you have such good fitness, you can do it, no problem. And I'm like, wait, wait! We will see you for the next episode of Running with Ryan, not with him, unfortunately, but we'll find somebody else. Do you have any ideas? I don't think there's a lot of runners in Boulder, so you're probably gonna run on a hard time. Yeah, I guess I'm gonna have to go to Wichita to find somebody. Worst case, I will see you at Javalina. That's right, Javalina under it. We know we love that race. All right, we'll see you guys later.


That makes it very appropriate to come here to America because America will play a major role there in electric cars but also in the connectivity of the car in automotive clouds and that is why we are basically here. After the last couple of months we really reviewed our strategy and we are focusing now more on electric cars, more on the full connected cars, on autonomous driving cars and that made our appearance here very necessary.


Good afternoon, everyone. This is the 4 o'clock Tiger Fine National News Network market wrap-up for the week Friday the 15th of September. Basil Chapman here. We're looking at the Dow down 288 to 34,618. Giving back, well, it gave back more than the gains of yesterday in the sense that it gapped down from yesterday's open and it closed lower. So this is a very interesting, this little pattern that you can see on the data chart on the left. See what happened? It went above the inside track repellent zone. It got a propellant to push to the upside yesterday. Now it's back in that zone. And you can see that S says that the nine-period moving average went back to negative after being positive for a day. And that just says the cell mode so far is continuing in the Dow, looking at the S&P. Here we are. S&P was down 54 at 44,050, also gapped down. Went right to the 50-period moving average. If at any point this coming week, certainly the beginning of the week, it takes out 4430 support, that's going to be a big problem. It has to go above the high that was made yesterday, which was 43.11. I'd have to say 43.20 would be very good action if it can do that by Tuesday of next week. All the weekly charts are still holding really well. It's the daily charts that have been quite poor. QQQ talk about poor. Look at this. QQQ slumps today are getting repelled at that inside track repellent zone. And what happens? It closes down 647 at 370.80. If it starts to trade in the 360, A367 level early next week, that's going to be a problem. It makes this arch formation that we always look at the dreaded age. And we're looking at the upside has to be a 379 to 382 for any semblance of a recovery.  And I always think that where the semiconductors go, so the general market goes and so for a very ugly day today, look at this H pattern in the weekly chart. If the S&P semiconductors next week close any day below 141, that's going to be a real big problem for the weekly chart. It'll be the first time you get that kind of pattern. The IWM was very weak. It's stalled right at the 200 period moving average. Can't get above it. It was down a dollar and a half to 180 feet. Gold at a good session, but it couldn't close at the high of the day. It's trying to form establish some kind of support. But as long as the dollar, which is still looking very good, keeps rallying, that's going to be a big positive for the market and yields. Weekend and we will see what my day.