Artificial Intelligence in Finance, Education, Healthcare, Manufacturing, Agriculture, and Government

In follow-up to my previous post on Artificial Intelligence in the Smart Technology Era and this technology being at the center of the Machine Intelligence Institute of Africa(MIIA) partner activities with Silicon Cape, Barclays’ Rise Africa, and Cenfri’s Insights2Impact, we have some further exciting developments and upcoming activities in the areas of Finance, Education, Healthcare, Manufacturing, and Agriculture that I would like to share with this post. In addition to ongoing collaboration with the above-mentioned partners, MIIA has also started to collaborate with Stellenbosch University’s Innovus and LaunchLab who has introduced us to some promising spin-out and external companies that they host – more about this in the Eduction and Agriculture sections below. Some of MIIA’s upcoming activities this and next week include a presentation to Stellenbosch University’s Division for Institutional Research and Planning about AI / Machine Intelligence / Smart Technology and its applications in Education, a Rise Cape Town event “Rise to the Challenge: Big Data, AI, and Machine Learning” (on 20 September 2016), and a strategy and planning session in collaboration with the Cape Innovation and Technology Initiative (CITI) around economic development policy for the Western Cape Government with a focus on the digital economy. MIIA’s partner Insights2Impact also has a live stream event on 14 September 2016 which covers the increasing adoption by the private sector of alternative data sources and analytical methodologies for financial inclusion.

Intelligent Virtual Assistants and Robo-Advisors, one of the key cognitive applications of AI and technology revolutions in the coming years is also a major theme of my recent presentations as well as my own entrepreneurial activities (e.g., Bennit AI and Cortex Logic). Cognitive applications learn at scale, reason with purpose and interact with humans naturally. The concept of a virtual assistant and advisor looking after a range of our needs is fast becoming a reality. As Machine Intelligence progress at pace, virtual assistants and advisors are set to become our gateway to the internet and know more about us than we do ourselves. It is clear that Siri and Google Now are just the beginning. In the sections below I also make reference to some of these applications in Finance, Education, Healthcare, and Manufacturing.

Finance

Financial institutions are under immense pressure to utilize data more efficiently. As Machine Intelligence can help to make business technology solutions smarter via the transformation of structured and unstructured data collected from transactions, trade, payments, customer behavior, etc., it also comes with a lot of uncertainty of how to manage such solutions. Rise Cape Town supports the discussion around the current integration of machine intelligence into large institutions and the respective roles banks, startups and regulation is playing in bringing this focus area forward. The focus of a series of Rise community events in Cape Town was artificial intelligence, robo-advisors for wealth management, investment and insurance, big data and machine learning in finance as well as Blockchain technology. In the most recent event the emphasis was on how these technologies are impacting the Insurance industry (which has clearly not yet been impacting us much as wealth management and investment). Companies are introducing robo-advisors and virtual banking assistants to simplify communications and offer a more personalized experience for customers, that rely on natural language processing (NLP), data mining and human-like reasoning developed by deep learning algorithms. Blockchain, the software that underlies cryptocurrencies like Bitcoin, is another potentially transformative computational technology. Blockchains might be deployed as a secure cloud-based management system for all manner of robotic and otherwise automated operations, in cases as diverse as finance, manufacturing, hospitals, self-driving vehicles, and home environments. Experts are applying deep learning in blockchain to move and store data securely and privately, dropping the cost and complexity of financial transactions. See my presentation for more details on intelligent virtual assistants and machine learning in Finance (with some focus on Insurance and Wealth Management).

In the Appendix of the same presentation I also reference a wider class of applications that might be available for Blockchains and Machine Intelligence, where the latter can be more robustly implemented than before. Blockchains might be the always-on, auditable, safe, coordination mechanism, and reputation-connected administration for diverse Machine Intelligence applications. More sophisticated blockchain-based Machine Intelligence applications might increasingly include packages of smart contracts (automatically-executing code) in the form of Dapps (decentralized autonomous applications) and DAOs (decentralized autonomous organizations, engaging in a full suite of automated operations including their own self-instantiation, self-management, and self-retirement). Since blockchain features constitute a system of checks and balances wherein good-player behavior may be enforced, this might especially be useful in the future for Machine Intelligence operations.

MIIA will also present and participate in another Rise Cape Town event next week called “Rise to the Challenge: Big Data, AI, and Machine Learning“. Rise to the Challenge is an events series exploring the technology areas that are shaping the future of the financial industry (organized during the same week across all Rise sites globally). For this event, Rise is bringing together topic experts, startups, VCs, academia and Barclays senior leaders to host a thought-provoking discussion around Artificial Intelligence and Machine Learning. Rise to the Challenge aims to shake the current thinking and identify the opportunities and challenges that banks and startups face. Conversations will critically assess where to steer the future focus of investing in Fintech. Each event will identify a specific challenge area to help the innovators in the financial industry focus their curiosity and start experimenting with new ideas. Using Rise Crowd Rise to The Challenge Series will collate challenges the business face globally to identify new business opportunities, the most promising startups in respective areas and invite collaboration opportunities.

In my previous post, I’ve mentioned MIIA and Cenfri’s Insights2Impact (i2i) partnering to support i2i’s goal towards catalyzing the provision and use of data by private and public sector actors to improve financial inclusion through evidence-based, data-driven policies and client-centric product design. In one initiative, the i2i facility collaborates with partners such as MIIA and Barclays Rise, as well as open data initiatives in Africa and university data science departments aims to establish an Innovation Hub Competition for data scientists with a financial inclusion-related challenge and some datasets. The i2i facility also invites everyone that’s interested to join the live stream event at Barclays’ Rise in Cape Town which covers the increasing adoption by the private sector of alternative data sources and analytical methodologies for financial inclusion. See details below:

Education

With the massive impact of smart technology across all disciplines, economies and industries, the whole the Education sector, including Higher Education, is being disrupted and therefore at a crossroads and in need of fundamental transformation. This is especially so in this new era of reduced public funding, lagging personal incomes, increased accountability around outcomes, and educational ecosystems trends such as technology immersion, personalized learning paths, knowledge skills, economic alignment and globalization. The prerequisites for survival includes innovation, agility, accepting change, and being less risk-averse. The Education sector cannot afford to be complacent or reactive, but need clear direction, leadership, new financial model, and key player to help shape the Smart Technology era with other stakeholders.

In my presentation this week to Stellenbosch University’s Division for Institutional Research and Planning about Machine Intelligence, Smart Technology and its applications in Education, I’ll touch on these issues and also present some transformative solutions with respect to embracing smart technology, with special reference to Machine Intelligence, to improve outcomes, accessibility, and delivery of lower-cost but high-quality education. This would specifically involve the creation of smart data, learning analytics, personalized education using cognitive computing, and blending world-class certified MOOCs with best of traditional education and practical, relevant curricula. There are also the opportunity to provide new services that utilizes the Education sector’s experience in credentials, trust and identity through Blockchain certification, establishing a global identity, and aligning security processes. Higher Education can also implement a secure collaboration platform that caters for variety of users/students and includes ongoing life-long and life-wide learning services to their alumni network.

In my Silicon Cape Techtalk presentation on Artificial Intelligence I have introduced the concept of a Smart Technology Center of Excellence (CoE) at Universities that focus on inter- and trans-disciplinary on-line learning and research (a lot of breakthroughs and innovations happen where disciplines meet), an entrepreneurship program and startup incubation within predefined business areas that addresses Africa’s needs) as part of a education-entrepreneurship bridge building ecosystem. See the presentation and diagram below for more details (as well as a high-level talk that also references this).

MIIA has also started to collaborate with Stellenbosch University’s Innovus and LaunchLab who has introduced us to some promising spin-out and external companies that they host. Innovus is responsible for technology transfer, entrepreneurial support and development, and innovation at the university, whereas LaunchLab is the business incubator that offers various services to and opportunities for entrepreneurs. MIIA and LaunchLab has agreed to a presentation on Machine Intelligence and its applications at the LaunchLab facilities. MIIA is also already involved in some mentoring activities with some of their spin-out companies. One such exciting company with innovative solutions in Education is TheStudentHub.

The Student Hub Online aims to create a dynamic learner centered education system engineered to equip students to excel in their studies. They do it by reinventing and optimizing trading and learning methods in the current education system through disruptive innovation that benefits all stakeholders. One of their products is ERAOnline, a Student Portal that aims to increase productivity and performance through personalized digital learning. Unlike existing eLearning platforms, EraOnline provides a wide range of interactive Study Support Tools and university’s prescribed materials. This includes discounted prescribed eBooks, On-demand Content, 24/7 Assignment Support, Step-by-Step Solutions, Exam-Readiness Assessments, Revision tools, procrastination & information overload control, videos and more. Tools and materials are tailored to students’ course outlines and prescribed textbooks. Lecturers are able to view real time information from their students activities. Authors and publishers are able to view students reactions to their content and provide them with feedback.

Healthcare

Healthcare has long been viewed by many people as one of the most promising domains for Machine Intelligence technologies where applications could improve health outcomes and quality of life for millions of people in the years ahead. Many discussions around healthcare and smart technology have focused on personalized medicine, telemedicine, the internet of things, and robotics. Some of the key applications include patient monitoring and coaching, management of healthcare systems, automated devices to assist in surgery or patient care, and clinical decision support. We have recently also seen an example of where Machine Intelligence applications such as IBM Watson for Oncology has greatly speeded up the diagnosis of a rare form of leukemia in a patient (Watson has cross-referenced a patient’s genetic data to make a diagnosis that would have taken a human doctor weeks to do). Other examples include machine learning to predict patients at risk, mining social media to infer possible health risks, and robotics to support surgery.

A recent article by the World Economic Forum communicated three ways Machine Intelligence and Robotics will transform healthcare: human augmentation, social robots, and the open AI ecosystem. Whilst social or companion robotics use machine intelligence to understand people and respond appropriately, an open AI ecosystem refers to the idea that with an unprecedented amount of data available, combined with advances in natural language processing and social awareness algorithms, applications of Machine Intelligence will become increasingly more useful to consumers. Intelligent personal digital assistants, such as Alexa or Jibo, will be examples of this. Other examples of intelligent virtual assistant/advisor applications in Healthcare includes medical insurance and helping to optimize one’s physical and mental wellness every day.

In another example of Machine Intelligence being applied to healthcare, MIIA is currently involved with assisting the Vula team to apply Machine Learning to their ophthalmology data to create a more scalable solution that makes it easier to refer patients to specialists. Given the data available (mainly structured clinical data, images, and unstructured text) and the use cases specified (diagnosis and urgency classification), a combination of machine learning techniques/algorithms can be applied to learn the underlying input-output mappings for each of the use cases. Other data sets include cardiology, orthopaedics, burns, HIV, and dermatology. This type of application can also be further extended via an intelligent virtual assistant/advisor capability. Machine Learning experts and Data Scientist in the MIIA community that are interested to join this project can contact us through the Contacts page on MIIA’s website or via MIIA’s Slack channel.

Manufacturing

The Fourth Industrial Revolution or Smart Technology Era is driving collaboration and networking across the factory value chain. The manufacturing industry is in pursuit of a way to make machines, processes, and products more intelligent. Applying Machine Intelligence to manufacturing requires a number of key, foundational technologies and process innovations. From IBM‘s perspective, Cognitive Manufacturing enables manufacturing transformation by fully utilizing the data across systems, equipment and processes to derive actionable insight across the entire value chain from design through manufacture to support. Built on the foundations of Internet of Things and employing analytics combined with cognitive technology, cognitive manufacturing drives at key productivity improvements in quality, efficiency, and reliability of the manufacturing environment. GE has coined the term Brilliant Manufacturing, where a smart factory is a networked factory, in which data from supply chains, design teams, production lines and quality control are linked to form a highly integrated, intelligent creation engine. In another definition 21st Century Manufacturing has been described as where high technology tools and enterprise applications interconnect everything from the supplier to the distributor, leveraging Big Data, manufacturing intelligence and manufacturing operations management systems in a sophisticated interplay of information.

Intelligent virtual assistants and advisors is another key application of Machine Intelligence that can also be applied to the Manufacturing industry and layered across existing technologies to drastically simplify and improve access to information, helping industrial users make better decisions and improve business outcomes. One such pioneering example is Bennit.AI, which is an intelligent production assistant – like Siri or Alexa or Google Now – for manufacturers. Bennit eliminates waste for manufacturers, finding one extra minute each hour of every day for its users. Users talk to Bennit, and Bennit talks back. The natural language interface makes engaging with Bennit easy and comfortable. Just as today’s search engines seek and find information you want across the vast technology web of the Internet, Bennit securely connects to the myriad of internal and external systems and devices to return the answers users ask for – without them having to know where to look. Bennit leverages advanced Machine Intelligence to learn continuously, growing ever smarter and sharing what is learned with users. Bennit learns from the users, asking questions about their day and their work and their results. See something out of place?  Tell Bennit.  Hear a strange noise. Tell Bennit what it is. Bad day? Make sure Bennit knows.

Agriculture

We urgently need to rethink the future of agriculture. A recent World Economic Forum post highlighted some of the key challenges that our food systems face today:

• Approximately 795 million people in the world go hungry every day, while one third of food produced for human consumption is lost or wasted.
• The world now has even more obese people than hungry people, highlighting fundamental problems with both food quality and its allocation around the globe.
• More than 50% of the world’s hungry people are smallholder farmers, despite the fact that they produce up to 80% of the food supply in Asia and Sub-Saharan Africa.

Apart from these key challenges, we also need solutions to address the harmful affects of fertilizers to the environment and also human health. Machine Intelligence can be employed as part of innovative solutions to drive sustainable and climate-smart food systems by enhancing resilience and productivity through technology, enabling an environment for trade and value chain efficiency, and improving value-chain innovation in food production, processing and distribution.

LaunchLab has also introduced MIIA to another innovative spin-out company in the Agriculture sector that utilizes Machine Intelligence in their solution. MySmartFarmprovides a radically better way for farmers to work with their high-value and live data. It provides a one-stop-shop integrated solution for a farmer’s data and technology. Hosted in the cloud, driven by statistics and powered by Machine Intelligence, it is designed for easy real-time ‘anywhere-access’, to empower farmers with scientific advice, optimizing decision and to save time and money. MySmartFarm helps farmers who are frustrated with the collection and interpretation of information from multiple sources. The company automatically collect all kinds of data and aggregate it in order to present usable information for each field on an easy to use mobile dashboards.

Machine Intelligence Institute of Africa (MIIA)

Anyone interested to join MIIA and/or participate in using smart technologies to help address African problems such as those in education, finance, healthcare, energy, agriculture and unemployment is welcome to do this here. See How to participate for more details on various ways to help MIIA execute its mission.

View the current MIIA Community on the MIIA website as well as MIIA communications on Slack, the LinkedIn group, Meetup, Google+, FaceBook, and Twitter.