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Big Data can help save the world

Statistics and analysis may sound dull but now big data is being roped into saving lives in the humanitarian hellholes of the world, writes ARTHUR GOLDSTUCK.

Once, it was a national park. Now, a vast area of Bangladesh has been turned into a sprawling refugee camp. Hundred of thousands of Rohingya people have poured across the border from Myanmar in recent months, forced out of their homes by a brutal army crackdown.

The vicious persecution aims at eradicating a Muslim presence from the primarily Buddhist country of Myanmar. Thousands have been killed for no other reason than being part of a community. Aside from the Rohingya themselves, the brunt of the anguish has been borne by Bangladesh, which has welcomed the refugees into a country that can barely cope with its own problems.

Aid workers have poured in from around the world to help. But that has sometimes only added to the confusion.

“How do you deal with an emergency in the chaos of a million people milling around?” asked Leonard Doyle, head of media and communications for the United Nations Migration Agency. “International and local agencies go piling in, installing tube wells next to water points that are contaminated. That’s not smart.

“Our role is to coordinate the response, which is a massive a challenge given that everyone is doing what they want. We have feedback channels and information points to help coordinate such disasters, but when information is collected out in the field where there is no Internet connectivity, and only submitted a few days later, it does not have the immediacy or urgency that is needed.”

The Agency finally turned to big data – the science of collecting and analysing large amounts of data, and using it for better decision-making. It developed an online platform to receive the information, as well as a software tool people could carry on their phones to collect and submit information.

“It’s a very simple app that allows people to log information and upload it to a response team, and view it on a mini-dashboard with quick statistics of all the feedback collected. It is easy to synchronise with a community response map, and data can be exported from platform and shared with other agencies via PDF and Excel, live data and infographics.

“It’s a very simple tool to collect information for every actor in the field. Now, information coming from these desperate people gets quickly fed into system. So, for example, if someone finds a boy who has lost his parents, and inputs that information, it creates a response procedure that ensures the boy us looked after immediately. We need better ways of getting aid to people, and this is one way.”

Doyle was speaking at the SAS Analytics Experience 2017 conference in Amsterdam, an event that draws both on 40 years of pioneering data science at the SAS Institute and on some of the most current case studies and strategies for turning data into decisions. Addressing humanitarian crises and human problems was a strong theme at the conference.

“The human being and mathematics are merging,” said Jon Briggs, the BBC broadcaster who also happens to be the British male voice of the iPhone voice assistant, Siri. Chairing the conference, he issued a powerful warning:  “The danger in relying purely on statistics is that it can have the effect of dehumanising what is often a very human tragedy.”

However, he pointed out that the work of the UN Migration Agency showed how data could save lives on global migratory routes. Also known as the International Organization for Migration (IOM), the body is currently dealing with 65-million displaced persons, 21-million refugees, and 41-million people displaced in their own countries. Almost a third of this latter group is in Africa. In one month in the DRC alone, 1.5-million people were displaced.

“These folks are the wretched of the earth,” said Doyle. “Already the human traffickers, sex exploiters, human slavers, are there. The vultures are circling. As these people become exploited and enslaved, there is an enormous danger of radicalisation. Yet, much of the suffering is unnecessary.

“These man made disasters may feel distant on our TV screens, but they have a habit of coming close to us. You have in your hands and brains and pockets many of the tools that could enormously help in dealing with the humanitarian issue.”

The message was reiterated by a member of the Dutch royal family, Pieter-Christiaan Michiel, Prince of Orange-Nassau, van Vollenhoven, who is also vice chairman of the board of the Dutch Red Cross.

“I believe big data can make the world better, more humanitarian and smarter,” he declared.

That was the thinking behind the creation by the Dutch Red Cross of a data unit called 510 Global, named for the 510-million square kilometres that make up the surface of the earth.

It is described as a “dedicated hybrid team of data scientists and information managers and researchers who apply their skills across humanitarian activities with Big Data”.

“From visualising and communicating information through interactive dashboards, maps and infographics, our team collects, collates and analyses big data, extracting insights and translating them into data-driven decisions, positively impacting humanitarian aid,” the organisaton says.

Prince Pieter-Christiaan presented a case study that is still raw in the memories of the Dutch: the devastation of the Netherlands territory of Sint Maarten in the Caribbean by last month’s Hurricane Irma. More than 7 out of 10 buildings were damaged or destroyed. The relief operations were a nightmare for aid organisations, the military and government.

The 510 Global team was tasked with both preparing data before the hurricane hit, and assessing the damage afterwards.

“We worked with Google, which was able to predict the path of hurricane, and first responders were able to share information via Google Maps. We knew the hurricane would hit the island, but we wanted to create an accurate picture of where people lived and map the houses on islands.

“We used satellite data to count houses and see where the roads are to reach them. A lot of illegal immigrants were living and working on the island, living in makeshift buildings. We used crowdsourcing to find how many unregistered buildings there were, and that map was used for the rescue operation.

“We used drones for damage assessment, and volunteers used satellite data to map and colour code the most devastated areas, to focus relief operations. We also used that for the recovery, to see how many roofs were needed for makeshift buildings.”

That still left aid workers scrambling for resources on the ground, but it helped divert these to where they were needed most.

The prince pointed out that the Dutch Red Cross was 150 years old, but was now spearheading the concept of smart aid. However, this was no luxury.

“We have a $25-billion budget, but a $35-billion need. There’s a big gap. We are always short of money. Smart aid pinpoints our smart responders, allowing them to be much more effective by seeing where the relief effort is needed most.”

  • Arthur Goldstuck is founder of World Wide Worx and editor-in-chief of Gadget.co.za. Follow him on Twitter on @art2gee and on YouTube.

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Now IBM’s Watson joins IoT revolution in agriculture

Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production

IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.

By 2050, the world will need to feed two billion more people without an increase in arable land [1]. IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.

Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”

New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.

Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.” 

The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 [2]. Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.

The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.

Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.

IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.

The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

[1] The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”

[2] Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10


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What if Amazon used AI to take on factories?

By ANTONY BOURNE, IFS Global Industry Director for Manufacturing

Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.

At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.

If Amazon did factories

If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.

Manufacturing with knowledge

Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.

In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.

As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.

Knowledge for manufacturing

For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.

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