IBM Research has announced a collaboration agreement with the City of Johannesburg and Council for Scientific and Industrial Research (CSIR) to apply technologies to help the City deliver on its air quality management plan.
Building on IBM’s global Green Horizons initiative, the partnership with the City of Johannesburg will leverage Internet of Things (IoT) combined with the analytical power of cognitive computing to provide insights and recommended actions to improve air quality and better protect the health of Johannesburg citizens. IBM is collaborating with South Africa’s CSIR on this project. This will build on IBM and CSIR’s Collaboration Framework Agreement that was signed formally in January 2015.
Researchers from IBM’s South Africa research lab will work closely with experts from government and CSIR to analyse historical and real-time data from environmental monitoring stations in the City of Johannesburg, with a long term plan to extend the project across the Gauteng province to include the City of Tshwane and the Vaal Industrial Triangle. The objective is to uncover greater insight about the nature and causes of air pollution as well as model the effectiveness of intervention strategies. In the second phase, the programme will also be extended to include high-accuracy air pollution forecasting for planning and decision support and enable a proactive approach to air quality management.
“For Johannesburg to be a world-class African city, we need world-class solutions to deliver on pressing problems like air pollution,” explained Nthatisi Modingoane from the City of Johannesburg. “This is where our partnership with IBM comes in. Using advanced decision analytics and pollution forecasting technologies, we will strengthen our air quality management strategies and gain greater situational awareness of the challenges at hand. Johannesburg is committed to ensuring a place where people and businesses are in harmony with the environment.”
Johannesburg is the economic hub of South Africa, generating 17% of the country’s GDP. Originally established as a mining town, the city’s mine dumps, residential and traffic emissions generated by the city’s population of approximately 4.5 million people are behind the city’s air pollution which includes ultrafine particulate matter particles – the most harmful to human health. With high rates of continued urbanization, the City of Johannesburg government has put in place a comprehensive Air Quality Management Plan (AQMP) to mitigate the risk of increased levels of air pollution and ensure that the City’s continued growth is not at the expense of environmental and public health. CSIR is currently working with the City of Johannesburg to update this plan.
IBM’s Green Horizons initiative draws on innovations from the company’s global network of research labs with contributions from leading environmental experts. At the heart of the initiative are air quality management systems which draw on vast amounts of environmental Big Data generated by thousands of sensors in environmental monitoring stations, traffic systems and meteorological satellites. Cognitive technologies understand this data, and use it to tune a predictive model that shows where the pollution is coming from, where it will likely go, and what will be its potential effect, allowing more informed decisions about how to improve air quality.
Machine learning technologies ensure that the Green Horizon system constantly self-configures, improving in accuracy and automatically adjusting the predictive models to different seasons and topographies. It blends various predictive models including traffic flow, weather forecasting, air pollution and economic data to help officials explore various ‘what if’ scenarios and better understand the consequences of certain actions, such as optimizing or changing traffic flows, relocating industry, switching to renewables and even introducing more green areas into the city. Feeding on the experience of other cities around the world, Green Horizons’ pollution forecasting and scenario modelling capabilities can also help city governments make informed decisions about the construction and location of future industry, power generation facilities and roads.
“Air pollution is now the world’s largest environmental health risk. While Johannesburg does not yet have the air pollution challenges to the scale of the world’s megacities, continued economic and demographic growth mean that the city government must take action now to safeguard the future health of the city and its people,” explained Solomon Assefa, Director of IBM’s South Africa Research Lab. “The combined power of Internet of Things and cognitive computing means that understanding, managing and forecasting air quality today is more technically and economically feasible than ever before.”
The new agreement builds on existing collaborations between IBM Research and the City of Johannesburg. In October, the two parties struck up a partnership to leverage IBM’s Watson social media analytics capabilities to better understand the pulse of citizens towards the city’s recent EcoMobility World Festival 2015 during which time people were encouraged to walk, cycle, car-share and use public transport as opposed to private vehicles.. During the Festival, IBM’s data scientists used the company’s world renowned Watson cognitive computing system to ingest and understand over 18,000 tweets from over 6,000 Twitter users. They provided the city with regular updates about public opinion towards the city’s ecomobile’ transformation.
“There is increasing focus on energy and environmental issues here in South Africa and around the world leading to new streams of investment and opportunities for innovation,” said Dr. Sibusiso Sibisi, CEO of CSIR. “Working together, CSIR and IBM will explore how advanced technologies can help to better understand and improve society’s relationship with the environment, bringing to bear local talent and best practices from some of the most environmentally challenged places on the planet.”
This also builds upon CSIR’s research into air quality and resultant health impacts in the city and province, including the recently completed Vaal Triangle Health Study and the ongoing project to assess and update the City of Johannesburg’s air quality management plan.
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 . 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 . 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.
 The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”
 Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10
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.