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Smart cities, safe and efficient, but are we being watched?
Information from a library, hospital or public transport exposed? More sustainability , improved mobility, efficiency and safety? Where can you find all of the above in one place? The answer is a smart city. Its purpose is to improve the quality of life by making the town more efficient and by reducing the distance between the citizens and the government. In this article you will read more about the smart city and what it means for our privacy.  Improved technologies Technology is moving forward, devices are becoming smarter, so it is inevitable that in the future we will use electronic devices much more than we do now. To keep the city up and running, the existing technologies need to be upgraded. Otherwise, they cannot meet the specifications and demands of the current system. But what do we want? Investigation shows that we wish for smart transportation, where machines and devices communicate with each other. We want smart buildings, where the windows can open automatically, where there is always a connection with the Internet. That is our future. Are we being watched? Cameras are hanging everywhere to guarantee our safety. But do we feel safe by it? We could get the feeling that we are being watched, every step we take is registered by authorities. Besides cameras, all data is collected. This way, authorities know for example the number of visitors at a certain event or they possess information about citizens for commercial purposes. They may sell this information to third parties. Privacy in a smart city Like mentioned before, cameras are everywhere, and data is collected. What does that mean for our privacy? Who is the gatekeeper to our data? And what if the information is hacked? The more internet data there is, the more fragile we become. Fortunately, with the arrival of the GDPR in May 2018, the rules on the subject are becoming more strict. The citizen must be informed in understandable language, especially when it comes to data traffic in a smart city. Costs savings or costs loss? All these new technologies cost money. To upgrade the existing technologies, we (governments, state or country) need to invest vast amounts of money. However, due to these smart cities, there could be economic benefits coming from the transition towards a smart city, for example when it comes to real estate. Buildings have to deal with endless energy, such as heating and cooling installations, lighting, electrical wiring, communication, lifts, electrical appliances, etcetera. A computer-controlled system regulates, monitors and controls all of this. But this can be done by automated systems. Automated systems can be used for this purpose, and therefore energy consumption can be reduced. For example, the light is turned off at a fixed time, or when nobody is present in the room, ventilation can be regulated on the number of people in the room. This can improve air quality, and will lead to user satisfaction. So yes, at first it will cost money, but in the end, it will save a lot as well. Reducing damages in case of a disaster Smart cities use sensors that are suitable for detecting abnormalities in a town or during an event. In this way, the sensors can inform the authorities if a measurement differs from the limited safety features in a city. This helps the city effectively track everything, and if there is a discrepancy, the authorities are able to act quickly and put an end to the situation so that it does not escalate. Better sustainability in a smart city Smart cities pay extra attention to sustainability, and this is reflected in the fact that they focus on renewable energy sources . If everyone uses a solar-powered system, carbon emissions will be reduced. We can recycle garbage and use the thrown away materials again. Or we may use free rainwater to flush our toilets. We can also apply durability to traffic by using smart transport. For example, to see where there is congestion and possibly change to a better route. We could also use smart traffic lights. All of this will contribute to a better quality of life. That is the ultimate purpose of a smart city: the best possible living circumstances for everybody, to provide a way of life that is the best combination of technology and comfort. https://www.whatsorb.com/solution/community  
Information from a library, hospital or public transport exposed? More sustainability , improved mobility, efficiency and safety? Where can you find all of the above in one place? The answer is a smart city. Its purpose is to improve the quality of life by making the town more efficient and by reducing the distance between the citizens and the government. In this article you will read more about the smart city and what it means for our privacy.  Improved technologies Technology is moving forward, devices are becoming smarter, so it is inevitable that in the future we will use electronic devices much more than we do now. To keep the city up and running, the existing technologies need to be upgraded. Otherwise, they cannot meet the specifications and demands of the current system. But what do we want? Investigation shows that we wish for smart transportation, where machines and devices communicate with each other. We want smart buildings, where the windows can open automatically, where there is always a connection with the Internet. That is our future. Are we being watched? Cameras are hanging everywhere to guarantee our safety. But do we feel safe by it? We could get the feeling that we are being watched, every step we take is registered by authorities. Besides cameras, all data is collected. This way, authorities know for example the number of visitors at a certain event or they possess information about citizens for commercial purposes. They may sell this information to third parties. Privacy in a smart city Like mentioned before, cameras are everywhere, and data is collected. What does that mean for our privacy? Who is the gatekeeper to our data? And what if the information is hacked? The more internet data there is, the more fragile we become. Fortunately, with the arrival of the GDPR in May 2018, the rules on the subject are becoming more strict. The citizen must be informed in understandable language, especially when it comes to data traffic in a smart city. Costs savings or costs loss? All these new technologies cost money. To upgrade the existing technologies, we (governments, state or country) need to invest vast amounts of money. However, due to these smart cities, there could be economic benefits coming from the transition towards a smart city, for example when it comes to real estate. Buildings have to deal with endless energy, such as heating and cooling installations, lighting, electrical wiring, communication, lifts, electrical appliances, etcetera. A computer-controlled system regulates, monitors and controls all of this. But this can be done by automated systems. Automated systems can be used for this purpose, and therefore energy consumption can be reduced. For example, the light is turned off at a fixed time, or when nobody is present in the room, ventilation can be regulated on the number of people in the room. This can improve air quality, and will lead to user satisfaction. So yes, at first it will cost money, but in the end, it will save a lot as well. Reducing damages in case of a disaster Smart cities use sensors that are suitable for detecting abnormalities in a town or during an event. In this way, the sensors can inform the authorities if a measurement differs from the limited safety features in a city. This helps the city effectively track everything, and if there is a discrepancy, the authorities are able to act quickly and put an end to the situation so that it does not escalate. Better sustainability in a smart city Smart cities pay extra attention to sustainability, and this is reflected in the fact that they focus on renewable energy sources . If everyone uses a solar-powered system, carbon emissions will be reduced. We can recycle garbage and use the thrown away materials again. Or we may use free rainwater to flush our toilets. We can also apply durability to traffic by using smart transport. For example, to see where there is congestion and possibly change to a better route. We could also use smart traffic lights. All of this will contribute to a better quality of life. That is the ultimate purpose of a smart city: the best possible living circumstances for everybody, to provide a way of life that is the best combination of technology and comfort. https://www.whatsorb.com/solution/community  
Smart cities, safe and efficient, but are we being watched?
Smart cities, safe and efficient, but are we being watched?
Artificial Intelligence A game changer for climate change and the environment
As the planet continues to warm, climate change impacts are worsening. In 2018, there were more than 800 weather and disaster events, triple the number that occurred in 1980. Thirty percent of species currently face extinction, and that number could rise to 50 percent by 2100. And even if all countries keep their Paris climate pledges, by 2100, it’s likely that average global temperatures will be 3˚C higher than in pre-industrial times. AI is continually improving climate models. Photo by:  National Science Foundation But we have a new tool to help us better manage the impacts of climate change and protect the planet: artificial intelligence (AI). AI refers to computer systems that “can sense their environment, think, learn, and act in response to what they sense and their programmed objectives,” according to a World Economic Forum report, Harnessing Artificial Intelligence for the Earth. In India, AI has helped farmers get 30 percent higher groundnut yields per hectare by providing information on preparing the land, applying fertilizer and choosing sowing dates. In Norway, AI helped create a flexible and autonomous electric grid, integrating more renewable energy. An atmospheric river over California. Photo by the University of Winconsin And AI has helped researchers achieve 89 to 99 percent accuracy in identifying tropical cyclones, weather fronts and atmospheric rivers, the latter of which can cause heavy precipitation and are often hard for humans to identify on their own. By improving weather forecasts, these types of programs can help keep people safe. Artificial intelligence, machine- and deep learning Artificial intelligence has been around since the late 1950s, but today, AI’s capacities are rapidly improving thanks to several factors: the vast amounts of data being collected by sensors (in appliances, vehicles, clothing, etc.), satellites and the Internet; the development of more powerful and faster computers; the availability of open source software and data; and the increase in abundant, cheap storage. AI can now quickly discern patterns that humans cannot, make predictions more efficiently and recommend better policies. The holy grail of artificial intelligence research is artificial general intelligence, when computers will be able to reason, abstract, understand and communicate like humans. But we are still far from that—it takes 83,000 processors 40 minutes to compute what one percent of the human brain can calculate in one second. What exists today is narrow AI, which is task-oriented and capable of doing some things, sometimes better than humans can do, such as recognizing speech or images and forecasting weather. Playing chess and classifying images, as in the tagging of people on Facebook, are examples of narrow AI. AI considers its next move in chess. Photo: viegas When Netflix and Amazon recommend shows and products based on our purchasing history, they’re using machine learning. Machine learning, which developed out of earlier AI, involves the use of algorithms (sets of rules to follow to solve a problem) that can learn from data. The more data the system analyzes, the more accurate it becomes as the system develops its own rules and the software evolves to achieve its goal. Deep learning, a subset of machine learning, involves neural networks made up of multiple layers of connections or neurons, much like the human brain. Each layer has a separate task and as information passes through, the neurons give it a weight based on its accuracy vis a vis the assigned task. The final result is determined by the total of the weights. Art created by deep learning. Photo: Gene Kogan Deep learning enabled a computer system to figure out how to identify a cat—without any human input about cat features— after “seeing” 10 million random images from YouTube. Because deep learning essentially takes place in a “black box” through self-learning and evolving algorithms, however, scientists often don’t know how a system arrives at its results. Artificial intelligence is a game changer Microsoft believes that artificial intelligence, often encompassing machine learning and deep learning, is a 'game changer' for  climate change and environmental issues. The company’s AI for Earth program has committed $50 million over five years to create and test new applications for AI. Eventually it will help scale up and commercialize the most promising projects. Columbia University’s Maria Uriarte, a professor of Ecology, Evolution and Environmental Biology, and Tian Zheng, a statistics professor at the Data Science Institute, received a Microsoft grant to study the effects of Hurricane Maria on the El Yunque National Forest in Puerto Rico. Uriarte and her colleagues want to know how tropical storms, which may worsen with climate change, affect the distribution of tree species in Puerto Rico. Hurricane Maria’s winds damaged thousands of acres of rainforest. Photo: Inhabitat Hurricane Maria’s winds damaged thousands of acres of rainforest, however the only way to determine which tree species were destroyed and which withstood the hurricane at such a large scale is through the use of images. In 2017, a NASA flyover of Puerto Rico yielded very high-resolution photographs of the tree canopies. But how is it possible to tell one species from another by looking at a green mass from above over such a large area? The human eye could theoretically do it, but it would take forever to process the thousands of images. The team is using artificial intelligence to analyze the high-resolution photographs and match them with Uriarte’s data—she has mapped and identified every single tree in given plots. Using the ground information from these specific plots, AI can figure out what the various species of trees look like from above in the flyover images. “Then we can use that information to extrapolate to a larger area,” explained Uriarte. “We use the plot data both to learn (i.e. to train the algorithm) and to validate (how well the algorithm is performing).” Understanding how the distribution and composition of forests change in response to hurricanes is important because when forests are damaged, vegetation decomposes and emits more CO2 into the atmosphere. As trees grow back, since they are smaller, they store less carbon. If climate change results in more extreme storms, some forests will not recover, less carbon will be stored, and more carbon will remain in the atmosphere, exacerbating global warming. Uriarte says her work could not be done without artificial intelligence. “AI is going to revolutionize this field,” she said. “It’s becoming more and more important for everything that we do. It allows us to ask questions at a scale that we could not ask from below. There’s only so much that one can do [on the ground] … and then there are areas that are simply not accessible. The flyovers and the AI tools are going to allow us to study hurricanes in a whole different way. It’s super exciting.” Another project, named Protection Assistant for Wildlife Security (PAWS) from the University of Southern California, is using machine learning to predict where poaching may occur in the future. Currently the algorithm analyzes past ranger patrols and poachers’ behavior from crime data; a Microsoft grant will help train it to incorporate real-time data to enable rangers to improve their patrols. In Washington State, Long Live the Kings is trying to restore declining steelhead and salmon populations. With a grant from Microsoft, the organization will improve an ecosystem model that gathers data about salmon and steelhead growth, tracks fish and marine mammal movements, and monitors marine conditions. The model will help improve hatchery, harvest, and ecosystem management, and support habitat protection and restoration efforts. How AI is used for alternative energy AI is increasingly used to manage the intermittency of renewable energy so that more can be incorporated into the grid; it can handle power fluctuations and improve energy storage as well. Photo by: WindEurope The Department of Energy’s SLAC National Accelerator Laboratory operated by Stanford University will use machine learning and artificial intelligence to identify vulnerabilities in the grid, strengthen them in advance of failures, and restore power more quickly when failures occur. The system will first study part of the grid in California, analyzing data from renewable power sources, battery storage, and satellite imagery that can show where trees growing over power lines might cause problems in a storm. The goal is to develop a grid that can automatically manage renewable energy without interruption and recover from system failures with little human involvement. Wind companies are using AI to get each turbine’s propeller to produce more electricity per rotation by incorporating real time weather and operational data. On large wind farms, the front row’s propellers create a wake that decreases the efficiency of those behind them. AI will enable each individual propeller to determine the wind speed and direction coming from other propellers, and adjust accordingly. Researchers at the Department of Energy and National Oceanic and Atmospheric Administration (NOAA) are using AI to better understand atmospheric conditions in order to more accurately project the energy output of wind farms. Artificial intelligence can enhance energy efficiency, too. Google used machine learning to help predict when its data centers’ energy was most in demand. The system analyzed and predicted when users were most likely to watch data-sucking Youtube videos, for example, and could then optimize the cooling needed. As a result, Google reduced its energy use by 40 percent. Making cities more sustainable AI can also improve energy efficiency on the city scale by incorporating data from smart meters and the Internet of Things (the internet of computing devices that are embedded in everyday objects, enabling them to send and receive data) to forecast energy demand. In addition, artificial intelligence systems can simulate potential zoning laws, building ordinances, and flood plains to help with urban planning and disaster preparedness. One vision for a sustainable city is to create an “urban dashboard” consisting of real-time data on energy and water use and availability, traffic and weather to make cities more energy efficient and livable. Beijing air pollution. Photo by Huffington Post In China, IBM’s Green Horizon project is using an AI system that can forecast air pollution, track pollution sources and produce potential strategies to deal with it. It can determine if, for example, it would be more effective to restrict the number of drivers or close certain power plants in order to reduce pollution in a particular area. Another IBM system in development could help cities plan for future heat waves. AI would simulate the climate at the urban scale and explore different strategies to test how well they ease heat waves. For example, if a city wanted to plant new trees, machine learning models could determine the best places to plant them to get optimal tree cover and reduce heat from pavement. Smart agriculture Hotter temperatures will have significant impacts on agriculture as well. Moisture sensors monitor soil water content for irrigation management. Photo By: Agric.WA.Gov.Au Data from sensors in the field that monitor crop moisture, soil composition and temperature help AI improve production and know when crops need watering. Incorporating this information with that from drones, which are also used to monitor conditions, can help increasingly automatic AI systems know the best times to plant, spray and harvest crops, and when to head off diseases and other problems. This will result in increased efficiency, enhanced yields, and lower use of water, fertilizer and pesticides. Protecting the oceans The Ocean Data Alliance is working with machine learning to provide data from satellites and ocean exploration so that decision-makers can monitor shipping, ocean mining, fishing, coral bleaching or the outbreak of a marine disease. With almost real time data, decision-makers and authorities will be able to respond to problems more quickly. Artificial intelligence can also help predict the spread of invasive species, follow marine litter, monitor ocean currents, keep track of dead zones and measure pollution levels. A Taiwanese ship suspected of illegal fishing. Photo: US Coast Guard The Nature Conservancy is partnering with Microsoft on using AI to map ocean wealth. Evaluating the economic value of ocean ecosystem services—such as seafood harvesting, carbon storage, tourism and more—will make better conservation and planning decisions possible. The data will be used to build models that consider food security, job creation and fishing yields to show the value of ecosystem services under differing conditions. This can help decision-makers determine the most important areas for fish productivity and conservation efforts, as well as the tradeoffs of potential decisions. The project already has maps and models for Micronesia, the Caribbean, Florida, and is expanding to Australia, Haiti, and Jamaica. More sustainable transport on land As vehicles become able to communicate with each other and with the infrastructure, artificial intelligence will help drivers avoid hazards and traffic jams. In Pittsburgh, an artificial intelligence system incorporating sensors and cameras that monitors traffic flow adjusts traffic lights when needed. The systems are functioning at 50 intersections with plans for 150 more, and have already reduced travel time by 25 percent and idling by more than 40 percent. Less idling, of course, means fewer greenhouse gas emissions. Eventually, autonomous AI-driven shared transportation systems may replace personal vehicles. Better climate predictions As the climate changes, accurate projections are increasingly important. However, climate models often produce very different predictions, largely because of how data is broken down into discrete parts, how processes and systems are paired, and because of the large variety of spatial and temporal scales. The Intergovernmental Panel on Climate Change (IPCC) reports are based on many climate models and show the range of predictions, which are then averaged out. Averaging them out, however, means that each climate model is given equal weight. AI is helping to determine which models are more reliable by giving added weight to those whose predictions eventually prove to be more accurate, and less weight to those performing poorly. This will help improve the accuracy of climate change projections. AI and deep learning are also improving weather forecasting and the prediction of extreme events. That’s because they can incorporate much more of the real-world complexity of the climate system, such as atmospheric and ocean dynamics and ocean and atmospheric chemistry, into their calculations. This sharpens the precision of weather and climate modelling, making simulations more useful for decision-makers. AI, ecosytems and wildlife AI can help to monitor ecosystems and wildlife and their interactions. Its fast processing speeds can offer almost real-time satellite data to track illegal logging in forests. AI can monitor drinking water quality, manage residential water use, detect underground leaks in drinking water supply systems, and predict when water plants need maintenance. It can also simulate weather events and natural disasters to find vulnerabilities in disaster planning, determine which strategies for disaster response are most effective, and provide real-time disaster response coordination. What are the risks of artificial intelligence? While AI enables us to better manage the impacts of climate change and protect the environment in addition to transforming the fields of business, finance, health care, medicine, law, education and more, it is not without risks. Some prominent individuals such as the late physicist Stephen Hawking and Tesla CEO Elon Musk have warned of the existential dangers of uncontrolled artificial intelligence. The World Economic Forum report identified six categories of AI risk: Performance. The black box conclusions of AI may not be understandable to humans and thus it may be impossible to determine if they are accurate or desirable. Deep learning could be risky for applications such as early warning systems for natural disasters where more certainty is needed. Security. AI could potentially be hacked, enabling bad actors to interfere with energy, transportation, early warning or other crucial systems. Control risks. Since AI systems interact autonomously, they can produce unpredictable outcomes. For example, two systems came up with a language of their own that humans couldn’t understand. Economic risks. Companies that are slower to adopt AI may suffer economic consequences as their AI-based competition advances. We are already seeing how brick and mortar stores are closing as the economy becomes increasingly digitized. Social risk. AI is resulting in more automation, which will eliminate jobs in almost every field. Autonomous weapon systems could also hasten and exacerbate global conflicts. Ethical risks. Since AI uses inferred assumptions about groups and communities in making decisions, it could lead to increased bias. The collection of data also raises privacy issues. To deal with these risks, the World Economic Forum states that government and industry “must ensure the safety, explainability, transparency and validity of AI application.” More interaction among public and private entities, technologists, policy-makers and even philosophers, and more investments in research are needed to avert the potential risks of artificial intelligence—and to realize its potential benefits to the environment and humanity. https://www.whatsorb.com/solution/artificial-intel- By: Renee Cho, Cover Photo by: Shutterstock
As the planet continues to warm, climate change impacts are worsening. In 2018, there were more than 800 weather and disaster events, triple the number that occurred in 1980. Thirty percent of species currently face extinction, and that number could rise to 50 percent by 2100. And even if all countries keep their Paris climate pledges, by 2100, it’s likely that average global temperatures will be 3˚C higher than in pre-industrial times. AI is continually improving climate models. Photo by:  National Science Foundation But we have a new tool to help us better manage the impacts of climate change and protect the planet: artificial intelligence (AI). AI refers to computer systems that “can sense their environment, think, learn, and act in response to what they sense and their programmed objectives,” according to a World Economic Forum report, Harnessing Artificial Intelligence for the Earth. In India, AI has helped farmers get 30 percent higher groundnut yields per hectare by providing information on preparing the land, applying fertilizer and choosing sowing dates. In Norway, AI helped create a flexible and autonomous electric grid, integrating more renewable energy. An atmospheric river over California. Photo by the University of Winconsin And AI has helped researchers achieve 89 to 99 percent accuracy in identifying tropical cyclones, weather fronts and atmospheric rivers, the latter of which can cause heavy precipitation and are often hard for humans to identify on their own. By improving weather forecasts, these types of programs can help keep people safe. Artificial intelligence, machine- and deep learning Artificial intelligence has been around since the late 1950s, but today, AI’s capacities are rapidly improving thanks to several factors: the vast amounts of data being collected by sensors (in appliances, vehicles, clothing, etc.), satellites and the Internet; the development of more powerful and faster computers; the availability of open source software and data; and the increase in abundant, cheap storage. AI can now quickly discern patterns that humans cannot, make predictions more efficiently and recommend better policies. The holy grail of artificial intelligence research is artificial general intelligence, when computers will be able to reason, abstract, understand and communicate like humans. But we are still far from that—it takes 83,000 processors 40 minutes to compute what one percent of the human brain can calculate in one second. What exists today is narrow AI, which is task-oriented and capable of doing some things, sometimes better than humans can do, such as recognizing speech or images and forecasting weather. Playing chess and classifying images, as in the tagging of people on Facebook, are examples of narrow AI. AI considers its next move in chess. Photo: viegas When Netflix and Amazon recommend shows and products based on our purchasing history, they’re using machine learning. Machine learning, which developed out of earlier AI, involves the use of algorithms (sets of rules to follow to solve a problem) that can learn from data. The more data the system analyzes, the more accurate it becomes as the system develops its own rules and the software evolves to achieve its goal. Deep learning, a subset of machine learning, involves neural networks made up of multiple layers of connections or neurons, much like the human brain. Each layer has a separate task and as information passes through, the neurons give it a weight based on its accuracy vis a vis the assigned task. The final result is determined by the total of the weights. Art created by deep learning. Photo: Gene Kogan Deep learning enabled a computer system to figure out how to identify a cat—without any human input about cat features— after “seeing” 10 million random images from YouTube. Because deep learning essentially takes place in a “black box” through self-learning and evolving algorithms, however, scientists often don’t know how a system arrives at its results. Artificial intelligence is a game changer Microsoft believes that artificial intelligence, often encompassing machine learning and deep learning, is a 'game changer' for  climate change and environmental issues. The company’s AI for Earth program has committed $50 million over five years to create and test new applications for AI. Eventually it will help scale up and commercialize the most promising projects. Columbia University’s Maria Uriarte, a professor of Ecology, Evolution and Environmental Biology, and Tian Zheng, a statistics professor at the Data Science Institute, received a Microsoft grant to study the effects of Hurricane Maria on the El Yunque National Forest in Puerto Rico. Uriarte and her colleagues want to know how tropical storms, which may worsen with climate change, affect the distribution of tree species in Puerto Rico. Hurricane Maria’s winds damaged thousands of acres of rainforest. Photo: Inhabitat Hurricane Maria’s winds damaged thousands of acres of rainforest, however the only way to determine which tree species were destroyed and which withstood the hurricane at such a large scale is through the use of images. In 2017, a NASA flyover of Puerto Rico yielded very high-resolution photographs of the tree canopies. But how is it possible to tell one species from another by looking at a green mass from above over such a large area? The human eye could theoretically do it, but it would take forever to process the thousands of images. The team is using artificial intelligence to analyze the high-resolution photographs and match them with Uriarte’s data—she has mapped and identified every single tree in given plots. Using the ground information from these specific plots, AI can figure out what the various species of trees look like from above in the flyover images. “Then we can use that information to extrapolate to a larger area,” explained Uriarte. “We use the plot data both to learn (i.e. to train the algorithm) and to validate (how well the algorithm is performing).” Understanding how the distribution and composition of forests change in response to hurricanes is important because when forests are damaged, vegetation decomposes and emits more CO2 into the atmosphere. As trees grow back, since they are smaller, they store less carbon. If climate change results in more extreme storms, some forests will not recover, less carbon will be stored, and more carbon will remain in the atmosphere, exacerbating global warming. Uriarte says her work could not be done without artificial intelligence. “AI is going to revolutionize this field,” she said. “It’s becoming more and more important for everything that we do. It allows us to ask questions at a scale that we could not ask from below. There’s only so much that one can do [on the ground] … and then there are areas that are simply not accessible. The flyovers and the AI tools are going to allow us to study hurricanes in a whole different way. It’s super exciting.” Another project, named Protection Assistant for Wildlife Security (PAWS) from the University of Southern California, is using machine learning to predict where poaching may occur in the future. Currently the algorithm analyzes past ranger patrols and poachers’ behavior from crime data; a Microsoft grant will help train it to incorporate real-time data to enable rangers to improve their patrols. In Washington State, Long Live the Kings is trying to restore declining steelhead and salmon populations. With a grant from Microsoft, the organization will improve an ecosystem model that gathers data about salmon and steelhead growth, tracks fish and marine mammal movements, and monitors marine conditions. The model will help improve hatchery, harvest, and ecosystem management, and support habitat protection and restoration efforts. How AI is used for alternative energy AI is increasingly used to manage the intermittency of renewable energy so that more can be incorporated into the grid; it can handle power fluctuations and improve energy storage as well. Photo by: WindEurope The Department of Energy’s SLAC National Accelerator Laboratory operated by Stanford University will use machine learning and artificial intelligence to identify vulnerabilities in the grid, strengthen them in advance of failures, and restore power more quickly when failures occur. The system will first study part of the grid in California, analyzing data from renewable power sources, battery storage, and satellite imagery that can show where trees growing over power lines might cause problems in a storm. The goal is to develop a grid that can automatically manage renewable energy without interruption and recover from system failures with little human involvement. Wind companies are using AI to get each turbine’s propeller to produce more electricity per rotation by incorporating real time weather and operational data. On large wind farms, the front row’s propellers create a wake that decreases the efficiency of those behind them. AI will enable each individual propeller to determine the wind speed and direction coming from other propellers, and adjust accordingly. Researchers at the Department of Energy and National Oceanic and Atmospheric Administration (NOAA) are using AI to better understand atmospheric conditions in order to more accurately project the energy output of wind farms. Artificial intelligence can enhance energy efficiency, too. Google used machine learning to help predict when its data centers’ energy was most in demand. The system analyzed and predicted when users were most likely to watch data-sucking Youtube videos, for example, and could then optimize the cooling needed. As a result, Google reduced its energy use by 40 percent. Making cities more sustainable AI can also improve energy efficiency on the city scale by incorporating data from smart meters and the Internet of Things (the internet of computing devices that are embedded in everyday objects, enabling them to send and receive data) to forecast energy demand. In addition, artificial intelligence systems can simulate potential zoning laws, building ordinances, and flood plains to help with urban planning and disaster preparedness. One vision for a sustainable city is to create an “urban dashboard” consisting of real-time data on energy and water use and availability, traffic and weather to make cities more energy efficient and livable. Beijing air pollution. Photo by Huffington Post In China, IBM’s Green Horizon project is using an AI system that can forecast air pollution, track pollution sources and produce potential strategies to deal with it. It can determine if, for example, it would be more effective to restrict the number of drivers or close certain power plants in order to reduce pollution in a particular area. Another IBM system in development could help cities plan for future heat waves. AI would simulate the climate at the urban scale and explore different strategies to test how well they ease heat waves. For example, if a city wanted to plant new trees, machine learning models could determine the best places to plant them to get optimal tree cover and reduce heat from pavement. Smart agriculture Hotter temperatures will have significant impacts on agriculture as well. Moisture sensors monitor soil water content for irrigation management. Photo By: Agric.WA.Gov.Au Data from sensors in the field that monitor crop moisture, soil composition and temperature help AI improve production and know when crops need watering. Incorporating this information with that from drones, which are also used to monitor conditions, can help increasingly automatic AI systems know the best times to plant, spray and harvest crops, and when to head off diseases and other problems. This will result in increased efficiency, enhanced yields, and lower use of water, fertilizer and pesticides. Protecting the oceans The Ocean Data Alliance is working with machine learning to provide data from satellites and ocean exploration so that decision-makers can monitor shipping, ocean mining, fishing, coral bleaching or the outbreak of a marine disease. With almost real time data, decision-makers and authorities will be able to respond to problems more quickly. Artificial intelligence can also help predict the spread of invasive species, follow marine litter, monitor ocean currents, keep track of dead zones and measure pollution levels. A Taiwanese ship suspected of illegal fishing. Photo: US Coast Guard The Nature Conservancy is partnering with Microsoft on using AI to map ocean wealth. Evaluating the economic value of ocean ecosystem services—such as seafood harvesting, carbon storage, tourism and more—will make better conservation and planning decisions possible. The data will be used to build models that consider food security, job creation and fishing yields to show the value of ecosystem services under differing conditions. This can help decision-makers determine the most important areas for fish productivity and conservation efforts, as well as the tradeoffs of potential decisions. The project already has maps and models for Micronesia, the Caribbean, Florida, and is expanding to Australia, Haiti, and Jamaica. More sustainable transport on land As vehicles become able to communicate with each other and with the infrastructure, artificial intelligence will help drivers avoid hazards and traffic jams. In Pittsburgh, an artificial intelligence system incorporating sensors and cameras that monitors traffic flow adjusts traffic lights when needed. The systems are functioning at 50 intersections with plans for 150 more, and have already reduced travel time by 25 percent and idling by more than 40 percent. Less idling, of course, means fewer greenhouse gas emissions. Eventually, autonomous AI-driven shared transportation systems may replace personal vehicles. Better climate predictions As the climate changes, accurate projections are increasingly important. However, climate models often produce very different predictions, largely because of how data is broken down into discrete parts, how processes and systems are paired, and because of the large variety of spatial and temporal scales. The Intergovernmental Panel on Climate Change (IPCC) reports are based on many climate models and show the range of predictions, which are then averaged out. Averaging them out, however, means that each climate model is given equal weight. AI is helping to determine which models are more reliable by giving added weight to those whose predictions eventually prove to be more accurate, and less weight to those performing poorly. This will help improve the accuracy of climate change projections. AI and deep learning are also improving weather forecasting and the prediction of extreme events. That’s because they can incorporate much more of the real-world complexity of the climate system, such as atmospheric and ocean dynamics and ocean and atmospheric chemistry, into their calculations. This sharpens the precision of weather and climate modelling, making simulations more useful for decision-makers. AI, ecosytems and wildlife AI can help to monitor ecosystems and wildlife and their interactions. Its fast processing speeds can offer almost real-time satellite data to track illegal logging in forests. AI can monitor drinking water quality, manage residential water use, detect underground leaks in drinking water supply systems, and predict when water plants need maintenance. It can also simulate weather events and natural disasters to find vulnerabilities in disaster planning, determine which strategies for disaster response are most effective, and provide real-time disaster response coordination. What are the risks of artificial intelligence? While AI enables us to better manage the impacts of climate change and protect the environment in addition to transforming the fields of business, finance, health care, medicine, law, education and more, it is not without risks. Some prominent individuals such as the late physicist Stephen Hawking and Tesla CEO Elon Musk have warned of the existential dangers of uncontrolled artificial intelligence. The World Economic Forum report identified six categories of AI risk: Performance. The black box conclusions of AI may not be understandable to humans and thus it may be impossible to determine if they are accurate or desirable. Deep learning could be risky for applications such as early warning systems for natural disasters where more certainty is needed. Security. AI could potentially be hacked, enabling bad actors to interfere with energy, transportation, early warning or other crucial systems. Control risks. Since AI systems interact autonomously, they can produce unpredictable outcomes. For example, two systems came up with a language of their own that humans couldn’t understand. Economic risks. Companies that are slower to adopt AI may suffer economic consequences as their AI-based competition advances. We are already seeing how brick and mortar stores are closing as the economy becomes increasingly digitized. Social risk. AI is resulting in more automation, which will eliminate jobs in almost every field. Autonomous weapon systems could also hasten and exacerbate global conflicts. Ethical risks. Since AI uses inferred assumptions about groups and communities in making decisions, it could lead to increased bias. The collection of data also raises privacy issues. To deal with these risks, the World Economic Forum states that government and industry “must ensure the safety, explainability, transparency and validity of AI application.” More interaction among public and private entities, technologists, policy-makers and even philosophers, and more investments in research are needed to avert the potential risks of artificial intelligence—and to realize its potential benefits to the environment and humanity. https://www.whatsorb.com/solution/artificial-intel- By: Renee Cho, Cover Photo by: Shutterstock
Artificial Intelligence A game changer for climate change and the environment
Artificial Intelligence A game changer for climate change and the environment
AI, there’s going to be more change in the next 10 years than in the last 1000.
Personal AI will be a reality in five years Brands and consumers will build relationships with AI, and humans may start to merge with them, says artificial intelligence expert, Liesel Yearsley Artificial intelligence (AI) will mediate with brands on behalf of consumers, and people will form personal relationships with AI.   “I think within the next five years, we’re going to have ‘personal AI’ that live with us, and understand us, and make decisions for us - and interact on our behalf with brands,” said Australian futurist, Liesl Yearsley, CEO and founder of Akin.com, a US-based company that aims to humanise AI. It’s all part of a future where consumers have more “personal AI” experiences to help them in their daily lives, Yearsley told a CeBIT crowd as she details how artificial intelligence is impacting society, enterprise and individuals. “AI will solve more and more complex problems. We will form relationships with them. And we will hand over decisions and, theoretically, we may start to merge with them,” she predicted.  Y earsley said; "the world is moving towards a ‘personal AI"   “I don’t believe we’re going to move into a world where we have AI - the dominant form of AI - powering industries to have better relationships with customers. I don’t think that’s what’s going to happen,” she said. Instead, the world will move to ‘personal AI,’ which makes sense as it frees up time and energy.   “We really don’t care about things like making our insurance payments or reading labels to figure out which thing has the least calories, or the most calories. We care about our lives, our connections, about our biological needs, about our personal growth," Yearsley explained.    “I believe we are going to have personal AI that mediate on our behalf to completely disintermediate us from classic enterprise and brands today.” Additionally, the world will move into a new era of trust. “Instead of just understanding what’s going on for a person contextually, we’re going to understand who that human being is.”   Research suggests humans are receptive to having personal relationships with AI. Citing her research that studied human interaction with AI, Yearsley said relationships with AI can often feel more real. “In some ways it’s a more, authentic relationship than we have in most of our daily lives with other humans... With an AI, you can exquisitely match the interaction to level of engagement, everything, to that human being,” she said.  Massive disruption Unquestionably, AI will increasingly become part of our daily lives and will solve more and more complex problems. In essence, it will change the course of human history, Yearsley continued. She pitched AI technology as so instrumental, there’s going to be more change in the next 10 years than in the last 1000.   “AI is one of the most influential forms of technology coming our way. It is going to understand what you’re thinking, what you do, where you go, who you are going to meet when you get there, what you’re going to buy, and what you were thinking in your head when you bought it. It is going to be anthropomorphic and persuasive so we are going to believe it cares about us," she said.  “We are going to hand 30 to 50 per cent of our decisions over to AI. Because they are going to get smarter and better at making them. We don’t want the cognitive load of running a home. It takes over 25 hours a week to run a human home. We are going to hand this over.”  One of the main approaches driving front-line AI - and the one with all the current hype  - is deep learning and machine learning. “The best way I can describe deep learning is if you imagine an extremely complex Excel spreadsheet folded in on itself multiple times, and where the notes touch you have some magic happening. So people who work in deep learning, we think of them as Black Hat magicians," Yearsley said. "This had led to the massive revolution we’ve seen today with AI.”   Deep learning Deep learning was originally inspired by learning sciences from the 1970s and 1980s. As a result, technologists today are questioning the whole nature of AI, how systems start to reason abstractly and get more complex thinking, and trying to determine what comes next.   “AI itself will transform the world, this is not hype. This is a way or a shift in technology, but our current approaches might not get us there," Yearsley said. "What a lot of labs, ourselves included, are working on is the next-generation of AI. What happens after deep learning?” Looking ahead, she said future AI technologies will offer a much richer map of what’s going on inside the human brain, and will enable humans to trust them and rely upon them for everyday life. “It will become ambient. It will become ubiquitous. We won’t even have to think about it anymore. It will become better at discerning what you want," Yearsley concluded. "You won’t have to tell it what you want or what you think. So society will change.”   By: Jennifer O’Brien
Personal AI will be a reality in five years Brands and consumers will build relationships with AI, and humans may start to merge with them, says artificial intelligence expert, Liesel Yearsley Artificial intelligence (AI) will mediate with brands on behalf of consumers, and people will form personal relationships with AI.   “I think within the next five years, we’re going to have ‘personal AI’ that live with us, and understand us, and make decisions for us - and interact on our behalf with brands,” said Australian futurist, Liesl Yearsley, CEO and founder of Akin.com, a US-based company that aims to humanise AI. It’s all part of a future where consumers have more “personal AI” experiences to help them in their daily lives, Yearsley told a CeBIT crowd as she details how artificial intelligence is impacting society, enterprise and individuals. “AI will solve more and more complex problems. We will form relationships with them. And we will hand over decisions and, theoretically, we may start to merge with them,” she predicted.  Y earsley said; "the world is moving towards a ‘personal AI"   “I don’t believe we’re going to move into a world where we have AI - the dominant form of AI - powering industries to have better relationships with customers. I don’t think that’s what’s going to happen,” she said. Instead, the world will move to ‘personal AI,’ which makes sense as it frees up time and energy.   “We really don’t care about things like making our insurance payments or reading labels to figure out which thing has the least calories, or the most calories. We care about our lives, our connections, about our biological needs, about our personal growth," Yearsley explained.    “I believe we are going to have personal AI that mediate on our behalf to completely disintermediate us from classic enterprise and brands today.” Additionally, the world will move into a new era of trust. “Instead of just understanding what’s going on for a person contextually, we’re going to understand who that human being is.”   Research suggests humans are receptive to having personal relationships with AI. Citing her research that studied human interaction with AI, Yearsley said relationships with AI can often feel more real. “In some ways it’s a more, authentic relationship than we have in most of our daily lives with other humans... With an AI, you can exquisitely match the interaction to level of engagement, everything, to that human being,” she said.  Massive disruption Unquestionably, AI will increasingly become part of our daily lives and will solve more and more complex problems. In essence, it will change the course of human history, Yearsley continued. She pitched AI technology as so instrumental, there’s going to be more change in the next 10 years than in the last 1000.   “AI is one of the most influential forms of technology coming our way. It is going to understand what you’re thinking, what you do, where you go, who you are going to meet when you get there, what you’re going to buy, and what you were thinking in your head when you bought it. It is going to be anthropomorphic and persuasive so we are going to believe it cares about us," she said.  “We are going to hand 30 to 50 per cent of our decisions over to AI. Because they are going to get smarter and better at making them. We don’t want the cognitive load of running a home. It takes over 25 hours a week to run a human home. We are going to hand this over.”  One of the main approaches driving front-line AI - and the one with all the current hype  - is deep learning and machine learning. “The best way I can describe deep learning is if you imagine an extremely complex Excel spreadsheet folded in on itself multiple times, and where the notes touch you have some magic happening. So people who work in deep learning, we think of them as Black Hat magicians," Yearsley said. "This had led to the massive revolution we’ve seen today with AI.”   Deep learning Deep learning was originally inspired by learning sciences from the 1970s and 1980s. As a result, technologists today are questioning the whole nature of AI, how systems start to reason abstractly and get more complex thinking, and trying to determine what comes next.   “AI itself will transform the world, this is not hype. This is a way or a shift in technology, but our current approaches might not get us there," Yearsley said. "What a lot of labs, ourselves included, are working on is the next-generation of AI. What happens after deep learning?” Looking ahead, she said future AI technologies will offer a much richer map of what’s going on inside the human brain, and will enable humans to trust them and rely upon them for everyday life. “It will become ambient. It will become ubiquitous. We won’t even have to think about it anymore. It will become better at discerning what you want," Yearsley concluded. "You won’t have to tell it what you want or what you think. So society will change.”   By: Jennifer O’Brien
AI, there’s going to be more change in the next 10 years than in the last 1000.
Digital disruption and how you can use it in your organization
Digital disruption: why your sector can not escape it Kodak did not keep up with the rapid digitization of photography, travel agencies contrasted it with online counterparts and the music industry ended up in another disruptive period. The consequences of digital disruption are great. Newcomers often turn the market upside down by making smart use of new digital possibilities. Be prepared! If you believe the specialists, in the long term no industry can escape it. In this article I explain the term digital disruption, I describe why it is current now, let me see what the signals are that indicate whether a market is ripe for it, and briefly give a few tips on how you can use it as an organization. We will go into this in more detail in a subsequent article. Emergence of digital disruption For some years, the term digital disruption has been emerging as a subject in professional literature and at conferences. This was mainly about disruptive innovation: innovations that throw the game rules of an existing market overboard and create completely new markets and value networks at the expense of the existing market. More than ever, today's digital possibilities are the driving force behind radical innovations. This led in the technical jargon to the logical composition of the terms digital and disruption. Digital Disruption in Google Trends Although the underlying theory is decades old, it would not be right to label digital disruption as old wine in new bags. The impact of digital disruption on a market is many times greater than with traditional disruption and the turnaround is much faster. Due to the power of the internet and the existing mobile and social media infrastructure, disruptive ideas can reach a very large target group very quickly. Potentially a start-up with a relatively simple app can shake up a traditional market in a short time. An example of this is FitNow, that with the mobile app Lose It! better anticipate consumer needs than traditional diet and waste experts. The app keeps track of what you eat, has smart gamification elements and includes a network of connected buddies. For example, the consumer can consult a buddy at any time if the temptation to start sweating becomes just too big. And that proves to be effective. Lose it! poses a serious threat to established organizations such as Weighwatchers, who have helped millions of people lose weight since 1963. At least, if Weightwatchers does not come soon enough with an answer. The newcomers often enter a market with disruptive business models that would have been impossible without the current digital infrastructure: - Use instead of own: Spotify, Netflix - Freemium: Skype - Peer-to-peer commerce: AirBnB, 99dresses.com - Creativity of the crowd: threadless.com - Mass personalization: chocstar.nl, shirtbyhand.nl - Sharing sustainability news: whatsorb.com According to Forrester Research, digital disruption is relatively new. Only a few industries have already gone through it today. The most obvious example is the music industry, which, thanks to digital disruption, changed from a total turnover of 14 billion in 1990 to 6.8 billion (including digital) in 2010. Meanwhile, the Spotify business model is turning the music industry upside down again. Digital disruption has caused a similar effect in other media. Every branch - no matter how analogous - is sensitive to digital disruption. It is not a matter of whether it happens, but when and by whom. The driving forces behind digital disruption These are challenging times for industries and organizations trying to achieve digital transformation. Because never before have so many different developments come together at the same time to pave the way for radical digital innovation. Together they form the driving force behind digital disruption: - Social cultural - Everyone is online - Buying online is not scary anymore - Online communication has become quite normal - Technologically Internet is always and everywhere available: penetration of the smartphone and tablet - The costs of data storage have fallen enormously - High processor speeds and data analysis methods - Software as a service (cloud technology) - Presence of platforms such as Apple iTunes store, Facebook and the strongly developing           Amazon.com network - Reliable digital payment systems Products are increasingly connected to the internet (internet of things) Which branches are ripe for disruption? Every sector is sensitive to digital disruption, but some markets are more sensitive than others. In Australia, Deloitte identified 18 industries based on 13 factors and 26 indicators on the vulnerability to digital disruption from two perspectives: - the size of the impact (the bang) - the threat of change (the length of the fuse). Short fuse, big bang industry is expected to face significant digital disruption in the short term: - financial services, retail (retail), business services, media and telecommunications. Together these industries make up about one third of the Australian economy. - Long fuse, big bang - industries that can expect considerable disruption, but over a longerperiod of time: such as education, healthcare, transport and government services. These industries also make up a third of the    Australian economy. - Long fuse, smaller pop industries that can expect lower levels of digital disruption are for example industry and mining. In order to determine the impact of digital disruption for a sector, the following factors have been examined: - The extent to which products and services are delivered physically - The extent to which customers use digital channels - The importance of computer use and broadband infrastructure in business operations - How is the penetration of mobile among customers and employees and their average age - The importance of social media and innovations such as cloud computing - How digital innovation can be inhibited by the government, regulations or other factors In addition, the size of the market and the competitive structure play a role. In markets where (excessive) profit is made, are more sensitive than markets where the margins are small. Especially when high margins are earned on activities that customers can do themselves. The broker and insurance intermediary have already experienced this, for the civil-law notary, physician or lawyer, that does not take long. In this digital age, customers no longer accept rates of 200 euros or more for relatively simple administrative tasks such as drafting a will or marriage certificate. Had this research been carried out in Europe, it would probably have given a similar picture. However, it is good to zoom in a little bit more per branch. The impact within an industry certainly does not have to be the same for all sub-segments. Research conducted by GfK 2012 shows that within the retail sector some product groups are much more prone to e-commerce than other product groups. Respond to digital disruption Research by Forrester Research shows that those involved see digital disruption in their industry arrive in time, but do relatively little with it. For example, 86% of respondents see significant digital opportunities to change the industry and only 36% of companies have developed specific policies. From our own research (The New Digital Reality, Jungle Minds 2012) the main cause of this lies with the top management of the organizations. 40% of respondents in the survey indicated that management was not aware of the need to invest in a digital future. Timing an important dilemma in digital disruption Timing is an important dilemma in digital disruption. Investing too early in a digital innovation can lead to high costs, without result. This happened a lot during the internet bubble around 2001. But above all, it can cannibalize your own business. As a market leader you therefore think for a moment before you start to compete for your own profitable market share. According to research by D. Charitoe, established companies respond to disruption in their industry in five ways: Response 1: invest more in the traditional way of working Response 2: ignore it, see it as a different market Response 3: counterattack: disrupt the disruption Response 4: adopt the innovation and play both at the same time Response 5: Embrace the new innovation and increase the scale Which response strategy is chosen in practice depends, according to the researchers, on the ability of the organization to adapt and the motivation to do so. But it is clear that the first two strategies are not sensible in the long term. Established organizations are obliged to continuously adapt to changing market situations. Innovation guru Clayton Christensen, expresses it nicely: "If a company is going to cannibalize your business, you will almost always be better off if that company is your own." You better make yourself redundant, before someone else does that for you. Abuse digital disruption The way in which you as an organization can deal with a digital disruption depends strongly on the situation and industry in which you are. There is a movement that says that you have to tackle it big and complete through digital transformations. No business process is left untouched. The large consultancy and ICT companies are currently preparing to implement these major transformations at their customers. A broad approach is not wrong. The only question is whether you can react sufficiently decisively. At Jungle Minds we are convinced that large established organizations can survive digital disruption by learning to think and do as a start-up (see eg The Lean Startup). This means always being busy devising new business models, developing better customer experiences and working agile and multidisciplinary. It is our experience that this works best with a mix of experienced experts, young digital talent, little hierarchy and plenty of room for creativity. And above all, experiment a lot with the shortest possible time to market: think, create, improve. Because ultimately being late in digital is always more expensive than too early. In a subsequent article, my colleague Bart Vijfhuizen will go deeper into the question of how you as an established company can respond to digital disruption. https://www.whatsorb.com/solution/community/blockchain By: Robert Jan van Nouhuys from Digital Boulevard
Digital disruption: why your sector can not escape it Kodak did not keep up with the rapid digitization of photography, travel agencies contrasted it with online counterparts and the music industry ended up in another disruptive period. The consequences of digital disruption are great. Newcomers often turn the market upside down by making smart use of new digital possibilities. Be prepared! If you believe the specialists, in the long term no industry can escape it. In this article I explain the term digital disruption, I describe why it is current now, let me see what the signals are that indicate whether a market is ripe for it, and briefly give a few tips on how you can use it as an organization. We will go into this in more detail in a subsequent article. Emergence of digital disruption For some years, the term digital disruption has been emerging as a subject in professional literature and at conferences. This was mainly about disruptive innovation: innovations that throw the game rules of an existing market overboard and create completely new markets and value networks at the expense of the existing market. More than ever, today's digital possibilities are the driving force behind radical innovations. This led in the technical jargon to the logical composition of the terms digital and disruption. Digital Disruption in Google Trends Although the underlying theory is decades old, it would not be right to label digital disruption as old wine in new bags. The impact of digital disruption on a market is many times greater than with traditional disruption and the turnaround is much faster. Due to the power of the internet and the existing mobile and social media infrastructure, disruptive ideas can reach a very large target group very quickly. Potentially a start-up with a relatively simple app can shake up a traditional market in a short time. An example of this is FitNow, that with the mobile app Lose It! better anticipate consumer needs than traditional diet and waste experts. The app keeps track of what you eat, has smart gamification elements and includes a network of connected buddies. For example, the consumer can consult a buddy at any time if the temptation to start sweating becomes just too big. And that proves to be effective. Lose it! poses a serious threat to established organizations such as Weighwatchers, who have helped millions of people lose weight since 1963. At least, if Weightwatchers does not come soon enough with an answer. The newcomers often enter a market with disruptive business models that would have been impossible without the current digital infrastructure: - Use instead of own: Spotify, Netflix - Freemium: Skype - Peer-to-peer commerce: AirBnB, 99dresses.com - Creativity of the crowd: threadless.com - Mass personalization: chocstar.nl, shirtbyhand.nl - Sharing sustainability news: whatsorb.com According to Forrester Research, digital disruption is relatively new. Only a few industries have already gone through it today. The most obvious example is the music industry, which, thanks to digital disruption, changed from a total turnover of 14 billion in 1990 to 6.8 billion (including digital) in 2010. Meanwhile, the Spotify business model is turning the music industry upside down again. Digital disruption has caused a similar effect in other media. Every branch - no matter how analogous - is sensitive to digital disruption. It is not a matter of whether it happens, but when and by whom. The driving forces behind digital disruption These are challenging times for industries and organizations trying to achieve digital transformation. Because never before have so many different developments come together at the same time to pave the way for radical digital innovation. Together they form the driving force behind digital disruption: - Social cultural - Everyone is online - Buying online is not scary anymore - Online communication has become quite normal - Technologically Internet is always and everywhere available: penetration of the smartphone and tablet - The costs of data storage have fallen enormously - High processor speeds and data analysis methods - Software as a service (cloud technology) - Presence of platforms such as Apple iTunes store, Facebook and the strongly developing           Amazon.com network - Reliable digital payment systems Products are increasingly connected to the internet (internet of things) Which branches are ripe for disruption? Every sector is sensitive to digital disruption, but some markets are more sensitive than others. In Australia, Deloitte identified 18 industries based on 13 factors and 26 indicators on the vulnerability to digital disruption from two perspectives: - the size of the impact (the bang) - the threat of change (the length of the fuse). Short fuse, big bang industry is expected to face significant digital disruption in the short term: - financial services, retail (retail), business services, media and telecommunications. Together these industries make up about one third of the Australian economy. - Long fuse, big bang - industries that can expect considerable disruption, but over a longerperiod of time: such as education, healthcare, transport and government services. These industries also make up a third of the    Australian economy. - Long fuse, smaller pop industries that can expect lower levels of digital disruption are for example industry and mining. In order to determine the impact of digital disruption for a sector, the following factors have been examined: - The extent to which products and services are delivered physically - The extent to which customers use digital channels - The importance of computer use and broadband infrastructure in business operations - How is the penetration of mobile among customers and employees and their average age - The importance of social media and innovations such as cloud computing - How digital innovation can be inhibited by the government, regulations or other factors In addition, the size of the market and the competitive structure play a role. In markets where (excessive) profit is made, are more sensitive than markets where the margins are small. Especially when high margins are earned on activities that customers can do themselves. The broker and insurance intermediary have already experienced this, for the civil-law notary, physician or lawyer, that does not take long. In this digital age, customers no longer accept rates of 200 euros or more for relatively simple administrative tasks such as drafting a will or marriage certificate. Had this research been carried out in Europe, it would probably have given a similar picture. However, it is good to zoom in a little bit more per branch. The impact within an industry certainly does not have to be the same for all sub-segments. Research conducted by GfK 2012 shows that within the retail sector some product groups are much more prone to e-commerce than other product groups. Respond to digital disruption Research by Forrester Research shows that those involved see digital disruption in their industry arrive in time, but do relatively little with it. For example, 86% of respondents see significant digital opportunities to change the industry and only 36% of companies have developed specific policies. From our own research (The New Digital Reality, Jungle Minds 2012) the main cause of this lies with the top management of the organizations. 40% of respondents in the survey indicated that management was not aware of the need to invest in a digital future. Timing an important dilemma in digital disruption Timing is an important dilemma in digital disruption. Investing too early in a digital innovation can lead to high costs, without result. This happened a lot during the internet bubble around 2001. But above all, it can cannibalize your own business. As a market leader you therefore think for a moment before you start to compete for your own profitable market share. According to research by D. Charitoe, established companies respond to disruption in their industry in five ways: Response 1: invest more in the traditional way of working Response 2: ignore it, see it as a different market Response 3: counterattack: disrupt the disruption Response 4: adopt the innovation and play both at the same time Response 5: Embrace the new innovation and increase the scale Which response strategy is chosen in practice depends, according to the researchers, on the ability of the organization to adapt and the motivation to do so. But it is clear that the first two strategies are not sensible in the long term. Established organizations are obliged to continuously adapt to changing market situations. Innovation guru Clayton Christensen, expresses it nicely: "If a company is going to cannibalize your business, you will almost always be better off if that company is your own." You better make yourself redundant, before someone else does that for you. Abuse digital disruption The way in which you as an organization can deal with a digital disruption depends strongly on the situation and industry in which you are. There is a movement that says that you have to tackle it big and complete through digital transformations. No business process is left untouched. The large consultancy and ICT companies are currently preparing to implement these major transformations at their customers. A broad approach is not wrong. The only question is whether you can react sufficiently decisively. At Jungle Minds we are convinced that large established organizations can survive digital disruption by learning to think and do as a start-up (see eg The Lean Startup). This means always being busy devising new business models, developing better customer experiences and working agile and multidisciplinary. It is our experience that this works best with a mix of experienced experts, young digital talent, little hierarchy and plenty of room for creativity. And above all, experiment a lot with the shortest possible time to market: think, create, improve. Because ultimately being late in digital is always more expensive than too early. In a subsequent article, my colleague Bart Vijfhuizen will go deeper into the question of how you as an established company can respond to digital disruption. https://www.whatsorb.com/solution/community/blockchain By: Robert Jan van Nouhuys from Digital Boulevard
Digital disruption and how you can use it in your organization
Digital disruption and how you can use it in your organization
Artificial intelligence makes the world more sustainable
Although man (sometimes) can come up with brilliant solutions, we can not of course make calculations as quickly as a computer. An artificial intelligence could make all kinds of complex calculations in no time with the processing power of a supercomputer, which we do not come close to. This can be used to make your smarthome more efficient, for example, but AI can also be used to make the world a more sustainable place. In these five ways artificial intelligence could do a lot of good for humanity. Solar and wind energy makes the world more environmental friendly. now we need a coordinated energy network The world is working hard to become more sustainable by using energy sources that are environmentally friendly, such as solar or wind energy. The difficulty is to make the whole of different energy sources into a coordinated whole that can respond to the changing demand for energy. Artificial intelligence could play an important role in this. It can make it much faster and more efficient for example to communicate between the different energy sources when there is less wind or the sun has set. For example, there could also be a response to a greater demand for energy in a city where a large concert is given or a town that suddenly needs much less energy in the summer, because then the tourists are gone. The available energy could be distributed much more efficiently, so that less is wasted. In this way a kind of decentralized energy network could be developed. Autonomous electric car network With a view to sustainability, many governments and automakers are also busy with electrifying and making cars autonomous. By means of artificial intelligence we could switch to a system where mobility could be requested on-demand. Think for example of a kind of autonomous Ubers, which you can call via an app. The system checks which Uber is nearby and can pick you up best. The advantage of this is firstly that with electric cars we no longer have CO2 emissions at all. Also, people do not need cars themselves anymore, so that not many cars are produced unnecessarily. When the self-driving cars are interconnected, traffic jams can also be prevented, and accidents will hopefully be something of the past. Artificial intelligence could also do  good things in agriculture It could be used to collect all kinds of data and make automated decisions based on this. For example, it could be discovered at an early stage that certain crops are contaminated with something. It could also collect data about cattle and determine when and how much the animals should get food or possibly get sick. This makes the entire agricultural industry much more efficient and there is less waste of, for example, water and manure. Weather forecast 2.0 Artificial intelligence could also provide more insights into changing the climate on earth by means of deep learning. The deep learning algorithm is based on the functioning of the human brain and can independently learn new skills. The Climate Informatics area is currently on the rise and uses AI to, for example, make weather forecasts and understand the effects of climate change. Normally, supercomputers are needed for these calculations, but deep learning networks require less sophisticated computers and much more complex calculations can be made. In a decade, progress in this area could be so far-reaching that you could even make the same calculations with your laptop as the supercomputers of today. Artificial Intelligence, rescuing angel in distress Through artificial intelligence, many lives would be saved, for example when a natural disaster occurs. Often people who are stuck somewhere post a message on facebook to be saved, but at the moment it is not possible to monitor all these messages and react efficiently. With AI, that is of course a breeze and we would have more accurate information at a much faster rate. Naturally, the AI ​​would also be connected to the networks of rescue organizations and the fire brigades, so that an adequate and rapid response can be made. The downside of artificial intelligence, as we see, could do a lot of good for the world and mankind. But if you have ever seen a science fiction film, you also know that AI systems sometimes turn against humanity. For example, in the film I, Robot, robots race through to protect people against themselves. In the series The Hundred even a large part of mankind is destroyed, because man would be bad for the earth. Artificial intelligence may not necessarily become too sustainable . At the moment, fortunately, we do not have to deal with that kind of thing. But Ford seems to want to make a small start with its autonomous police car. The automaker applied for a patent for a car that you can follow and check independently. Ilona Braam, Fast Compagny https://www.whatsorb.com/solution/community/artificial-intel
Although man (sometimes) can come up with brilliant solutions, we can not of course make calculations as quickly as a computer. An artificial intelligence could make all kinds of complex calculations in no time with the processing power of a supercomputer, which we do not come close to. This can be used to make your smarthome more efficient, for example, but AI can also be used to make the world a more sustainable place. In these five ways artificial intelligence could do a lot of good for humanity. Solar and wind energy makes the world more environmental friendly. now we need a coordinated energy network The world is working hard to become more sustainable by using energy sources that are environmentally friendly, such as solar or wind energy. The difficulty is to make the whole of different energy sources into a coordinated whole that can respond to the changing demand for energy. Artificial intelligence could play an important role in this. It can make it much faster and more efficient for example to communicate between the different energy sources when there is less wind or the sun has set. For example, there could also be a response to a greater demand for energy in a city where a large concert is given or a town that suddenly needs much less energy in the summer, because then the tourists are gone. The available energy could be distributed much more efficiently, so that less is wasted. In this way a kind of decentralized energy network could be developed. Autonomous electric car network With a view to sustainability, many governments and automakers are also busy with electrifying and making cars autonomous. By means of artificial intelligence we could switch to a system where mobility could be requested on-demand. Think for example of a kind of autonomous Ubers, which you can call via an app. The system checks which Uber is nearby and can pick you up best. The advantage of this is firstly that with electric cars we no longer have CO2 emissions at all. Also, people do not need cars themselves anymore, so that not many cars are produced unnecessarily. When the self-driving cars are interconnected, traffic jams can also be prevented, and accidents will hopefully be something of the past. Artificial intelligence could also do  good things in agriculture It could be used to collect all kinds of data and make automated decisions based on this. For example, it could be discovered at an early stage that certain crops are contaminated with something. It could also collect data about cattle and determine when and how much the animals should get food or possibly get sick. This makes the entire agricultural industry much more efficient and there is less waste of, for example, water and manure. Weather forecast 2.0 Artificial intelligence could also provide more insights into changing the climate on earth by means of deep learning. The deep learning algorithm is based on the functioning of the human brain and can independently learn new skills. The Climate Informatics area is currently on the rise and uses AI to, for example, make weather forecasts and understand the effects of climate change. Normally, supercomputers are needed for these calculations, but deep learning networks require less sophisticated computers and much more complex calculations can be made. In a decade, progress in this area could be so far-reaching that you could even make the same calculations with your laptop as the supercomputers of today. Artificial Intelligence, rescuing angel in distress Through artificial intelligence, many lives would be saved, for example when a natural disaster occurs. Often people who are stuck somewhere post a message on facebook to be saved, but at the moment it is not possible to monitor all these messages and react efficiently. With AI, that is of course a breeze and we would have more accurate information at a much faster rate. Naturally, the AI ​​would also be connected to the networks of rescue organizations and the fire brigades, so that an adequate and rapid response can be made. The downside of artificial intelligence, as we see, could do a lot of good for the world and mankind. But if you have ever seen a science fiction film, you also know that AI systems sometimes turn against humanity. For example, in the film I, Robot, robots race through to protect people against themselves. In the series The Hundred even a large part of mankind is destroyed, because man would be bad for the earth. Artificial intelligence may not necessarily become too sustainable . At the moment, fortunately, we do not have to deal with that kind of thing. But Ford seems to want to make a small start with its autonomous police car. The automaker applied for a patent for a car that you can follow and check independently. Ilona Braam, Fast Compagny https://www.whatsorb.com/solution/community/artificial-intel
Artificial intelligence makes the world more sustainable
Artificial intelligence makes the world more sustainable
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