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IBM opens lab at Wits U

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IBM Research has opened its second research location on the African continent and announced new project collaborations in the areas of data driven healthcare, digital urban ecosystems and astronomy.

As part of a 10-year investment program through South Africa’s Department of Trade and Industry and working closely with the Department of Science and Technology, the new research lab is based at the University of the Witwatersrand (Wits). *The university was recently ranked amongst the top 10 in emerging economies by the Times Higher Education World University Rankings.

IBM researchers in South Africa with backgrounds in machine learning, mathematics, computer science, robotics, genomics and computational biology, are exploring the use of cognitive computing, the Internet of Things and Big Data to support South Africa’s national priorities, drive skills development and foster innovation-based economic growth.

“South Africa is a tremendous growth and transformation story, yet its increasing population and healthcare delivery shortfalls continue to pose challenges in the country,” said Solomon Assefa, director, IBM Research – Africa. “With the ability to detect patterns and discover new correlations, cognitive and cloud computing and the Internet of Things can provide potential solutions.”

The lab’s team of scientists is already collaborating extensively with local universities, research institutions, innovation centers, start-ups and government agencies. This will help foster South Africa’s emerging technology ecosystem and develop and scale new innovations.

“The launch of the IBM Research laboratory is an exciting milestone in the move towards a new era of globally competitive research, innovation and entrepreneurship that will be emerging out of the Tshimologong Precinct in Braamfontein,” said Professor Adam Habib, Vice-Chancellor and Principal of the University of the Witwatersrand. “Wits is delighted to be collaborating with IBM. We look forward to seeing top talent congregate to address the continent’s most intractable problems and work on the world’s next game changing technologies.”

IBM provided the following information:

Aligned with areas of strategic national importance, the lab’s focus areas include:

Data Driven Healthcare

·         In support of the World Health Organization’s End TB (Tuberculosis) Strategy, IBM scientists are designing wearable sensor technology connected to the Watson Internet of Things to trace the spread of highly infectious, communicable diseases. This innovation will help healthcare organizations and health officials develop prevention strategies and respond effectively.

·         IBM scientists are developing cognitive learning approaches to transform cancer reporting, prevention and precision medicine in Africa. In a proof of concept study, IBM scientists have discovered a basic molecular link between cancer causing genes and those associated with metastasis, the cause of 90% of cancer related deaths*. Preliminary results from this work have been presented recently. Using anonymous, unstructured data provided by the National Cancer Registry in South Africa and in collaboration with the University of Witwatersrand Medical School, the team is developing cognitive algorithms to automate the inference of national cancer statistics in South Africa. This technology is expected to reduce a five-year time lag in cancer statistics reporting to real-time.

·         With the support of the City of Johannesburg, IBM scientists have collected 65 samples of microbes and bacteria from 19 bus stations across the city as part of the global Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) international consortium. Once the samples are processed the results will be available to city planners, public health officials and scientists who will use the data to help officials predict and prepare for future disease outbreaks and discover new species and biological systems.

  • In early September, scientists from IBM, H3ABioNet and the University of Notre Dame will host a hackathon on anti-malarial drug resistance and drug combination prediction.

Digital Urban Ecosystems

·         Building on IBM’s global Green Horizons initiative, researchers at the new lab are working closely with experts from South Africa’s Council for Scientific and Industrial Research to analyze historical and real-time data from environmental monitoring stations. Using machine learning and cognitive models, the data collected in the City of Johannesburg, the City of Tshwane and the Vaal Industrial Triangle will help provide more insight about air pollution and model the effectiveness of intervention strategies. The project has recently been extended to predict ground level ozone and air quality forecasting.

·         Commuters in the City of Johannesburg currently spend 35 minutes extra travel time per day due to traffic congestion, according to the Tom Tom Traffic Index. Unreliable traffic light infrastructure provides challenges to traffic light management in the city. Using real time anonymized traffic data from TomTom combined with Twitter, IBM scientists have developed a traffic optimization recommendation tool which can help city officials dispatch traffic volunteers, known locally as pointsmen, to the intersections where they are most urgently needed.

  • The City of Cape Town often battles with devastating wild fires, due to its unique topography and vegetation. Using data from The Weather Company, an IBM business, and the City of Cape Town’s Open Data portal, IBM scientists have developed a cognitive dashboard. This can assess fire incidence risk and severity to help officials raise public awareness and prepare for emergency response.

·         The number of people living off-the-grid in Africa has grown by 114 million since 2000**. To help meet the energy needs of communities who are living remotely or would like to make use of renewable energy, IBM scientists have developed a mobile app which uses analytics to determine the solar requirements of users based on their energy needs and location.

Exploring the Universe 

·         In 2018 the, Square Kilometer Array (SKA), the world’s largest radio telescope, will be built in South Africa and Australia. IBM scientists are collaborating with SKA South Africa (SKA-SA) on the development of unsupervised algorithms which can make groundbreaking astronomical discoveries. Scientists expect to eventually apply the cognitive technology to other applications, including the development of new pharmaceuticals and genomics. IBM and SKA-SA have signed an agreement to explore the advancement of this technology and to lead some major developments in data science over the next decade.

·         IBM scientists in South Africa are joining NASA, the SETI Institute and Swinburne University to develop an Apache Spark application to analyze the 168 million radio events detected over the past 10 years by the Allen Telescope Array (ATA). The volume and complexity of the data requires advanced machine learning algorithms to separate noise from true signals of interest. These requirements are well suited to the scalable in-memory capabilities offered by Apache Spark when combined with the big data capabilities of the IBM Cloud and IBM Bluemix Spark.

Open Infrastructure, Sustainable Design

The new lab features an Infrastructure-as-a-Service (IaaS) platform based on OpenStack connected to IBM Storwize for efficiently provisioning 80TB of storage for research projects.

The lab is located in the Tshimologong Precinct in Braamfontein – an inner-city area which is today re-emerging as a vibrant Johannesburg district. The two-level, 900 square meter lab has a DIY maker space with electronic design equipment and a 3D printer.

Agile work spaces provide a collaborative environment for IBM scientists to train and mentor Wits students and local start-ups. Developer communities across Africa will also have access, at no charge, to a LinuxONE Community Cloud located in Johannesburg, which acts as a virtual R&D engine for creating, testing and piloting emerging applications via the cloud.

IBM Research Innovating for Africa

IBM has operated in Africa for almost 100 years. Today, its operations span 24 countries, including South Africa, Morocco, Egypt, Nigeria, Ghana, Angola, Kenya and Tanzania. IBM Research – Africa is the first commercial research organization on the continent, conducting applied and far-reaching exploratory research into Africa’s grand challenges and committed to delivering commercially-viable innovations that impact people’s lives.

IBM’s first African research lab was opened in Nairobi, Kenya in 2013. The South African research facility supports IBM’s Equity Equivalent Investment Programme (EEIP). In recent years, IBM has also invested in the development of an IBM Client Centre, an Innovation Centre, Service Delivery Centre and a number of offices and data centers across South Africa.

Africa News

Smart grids needed for Africa’s utilities

Power utilities across Africa should rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem, says COLIN BEANEY, Global Industry Director for Asset-intensive and Energy and Utilities at IFS.

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Africa’s abundant natural resources and urgent need for power mean that it is one of the most exciting and innovative energy markets in a world that is moving rapidly towards clean, renewable energy sources. The continent’s energy industry is taking new approaches to providing unserved and underserved communities with access to power, with an emphasis on smart technologies and greener energy sources.

Power systems are evolving from centralised, top-down systems as interest in off-grid technology grows among African businesses and consumers. And according to PwC, we will see installed power capacity rise from 2012’s 90GW to 380GW in 2040 in sub-Saharan Africa. Power utilities are needing to rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem.

Energy and utilities providers are transforming from centralised supply companies to more distributed, bi-directional service providers. They can only achieve this through the evolution of “smart grids” where sensors and smart meters will be able to provide the consumer with a more granular level of detail of power usage. This shift from an energy supplier to “lifestyle provider” will require a much more dynamic and optimised approach to maintenance and field service.

African companies must thus embrace digital transformation as an imperative. This transformation begins by embracing enterprise asset management to improve asset utilisation. The subsequent steps are enhancing upstream and downstream supply chain management; resource optimisation; introducing enterprise operational intelligence; embracing new technologies such as the Internet of Things, machine learning, and predictive maintenance; and becoming a smart utility.

Embracing mobility to drive ROI

Getting it right is about putting in place an enterprise backbone that accommodates asset and project management, multinational languages and currencies, new energies and markets, visualisation of the entire value chain, and mobility apps. Mobile technologies that support the field workforce have a vital role to play in driving better ROI from utilities’ investments in enterprise asset management and enterprise resource planning solutions.

Today’s leading enterprise asset management solutions feature powerful functionality for mobile management of the complete workflow of work orders – from logging status changes and updates, from receiving and creating new orders to concluding the job and reporting time, material and expenses. Such solutions are easy to deploy and intuitive for end users to learn and use.

Importantly for organisations operating in parts of the continent with poor telecoms infrastructure, connectivity is not an issue. The solutions work offline and synchronises when network connectivity is available. Users can work on any device—laptops, tablets, and smartphones—commercial or ruggedised.

By ensuring that field technicians have easy access to information and processes, the mobile solution enables technicians and maintenance engineers to easily do the following tasks:

·         Create a new work order on the fly and log new opportunities

·         Access both historical and planned work information when requested

·         Permit customers to sign when the job is completed

·         Capture measurements and inspection notes on route work orders

·         Create new fault reports on routing

·         Facilitate documentation through photo capturing

·         Provide easy access to technical data and preventive actions.

The power of mobility allows the engineer to be the origin of all data capture on a service event. They can easily inquire on asset history, record parts used or parts needed for repair, record labour hours, and expenses as they occur, and any notes of repairs performed. When coupled with workforce management tools, such solutions unlock significant productivity gains for utilities who are trying to get the most from their workforce and assets.

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How machine learning can save your life

Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

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Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

But what if we could have predicted the outbreak months before it happened, buying us time to take proactive measures to contain it and curb its spread?

With access to overwhelming volumes of data, the computational power needed to store and analyse this data in real time, and sophisticated algorithms that can find patterns in the data and alert authorities to health problems before they become, well, problems, pandemics don’t have to be as devastating as they have been in the past.

In fact, with advanced data analytics, we can better manage any disease – long-term, short-term or pandemic – resulting in better patient treatment, more efficient use of resources and cost savings.

It’s been done before.

By analysing data from social media, blogs, online forums and keyword searches, we were able to predict the 2012-2013 US flu season three months before the Center for Disease Control (CDC) issued its first official warning.

Imagine the impact if the same analytical power was applied across the entire healthcare spectrum – not only on a national and global level, but right down to the individual level.

Data evolution

In the past, health workers relied on manually intensive, paper-based systems to record infections and deaths during disease outbreaks. Not only was it easy for errors to slip through but because the data was anecdotal and historical, authorities did not get a complete understanding of the reach and impact of the outbreak.

During the Ebola outbreak, the CDC adopted a mobile data collection system that enabled health workers to instantly submit information to a database via text messages. This low-cost method of information gathering not only resulted in fewer errors but also allowed analysts to draw up detailed maps of population movements, which made it easier to understand how the disease was likely to spread, and where to set up treatment centres.

While this was certainly an improvement on the paper-based systems of old, the drawback was that mobile data was historic and did not provide researchers with the ability to track developments and population movements in real time.

Data-driven action

But mobile phones are just one source of data. Today, health authorities can overlay thousands of data sources – including social media, health and physician reports, keyword searches, media reports, transactional data from retailers and pharmacies, airline ticket sales, geospatial data and more – to not only better manage diseases and outbreaks when they do happen, but to see them coming months in advance – and what could happen if we don’t act on the information.

By mining structured and unstructured data, we can track the movements of infected populations and who they come into contact with; we can measure the success of containment policies, education campaigns and treatments – and what to do if they’re not working; we can determine the effect of weather and other environmental factors on the spread of diseases.

Never before have we been able to act on information to save lives, not just during pandemics but through better understanding and treatment of diseases.

Personalised treatment

Until now, standard treatments for diseases such as cancer and HIV have been applied to all patients, regardless of their unique profiles and with little understanding as to why some people respond well to certain treatments and others don’t.

But by analysing and creating ‘medical maps’ of individuals that take into account their anatomy, physiology, DNA, RNA and chemical composition, doctors can prescribe personalised treatments that have a greater chance of success.

There are many other benefits of data analysis in healthcare:

·        Personalised treatment can result in fewer hospital admissions and can produce faster results and better experiences for patients;

·        By better understanding the impact of lifestyle and diet on health, medical aid providers can educate their members with the aim of improving their health, which could result in cost savings for both the provider and the member;

·        Governments can use data to develop proactive approaches to protecting and promoting public health, to prioritise services and to find ways to cut costs so that they can provide healthcare to more citizens.

·        By sharing data and results from clinical trials and combining that data with academic, patient and industry data, medical researchers can better understand the genetics of viruses, why some strains are more deadly than others, and why some people are more resistant to viruses. This could spark innovation and generate new insights that ultimately improve treatment and outcomes.

AI and machine learning

As the use of intelligent algorithms, machine learning and natural language processing becomes more entrenched in advanced data analytics, technology will increasingly supplement the skills of humans to produce faster and more accurate medical diagnoses.

We’re already seeing successful applications of artificial intelligence (AI) in predicting relapse in leukaemia patients and in distinguishing between different types of cancer.

Machine learning can extract valuable insights from unstructured data like clinical notes and academic journals to provide even larger datasets that will transform the medical industry into a proactive front against diseases.

There are plenty of doomsday theories about how machines will supersede our intelligence and rise against us. But there aren’t enough stories about the potential of data analytics, AI and machine learning to supplement human skills and knowledge to drastically changes lives for the better – and even save them. Right now, it’s looking more likely that machines will actually help us to live longer – and I don’t know many people who would object to that.

 

  • ANESHAN RAMALOO, ‎Data Scientist and Senior Business Solutions Manager at SAS.
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