Africa is huge. Just how huge is rarely appreciated but this map helps. This massive landmass makes land transport difficult, physical internet cabling difficult, infrastructure difficult. But with two spots from one satellite, it is possible to cover the entire continent. Bad or non-exiting infrastructure is the condition for leapfrogging.
So here's a question.... What African leapfrogged the transport and energy sectors to such a degree that the oil economy look as though it’s on the way out? He did this by seeing the existing model as the problem – the oil economy - so created the self-driving, actually AI-driven, car and panels/batteries that change the way we power homes, even entire regions. He is, of course, Elon Musk, a leapfrogger. But Africa leaps over frogs in all sorts of ways, from mobile banking to drones for blood delivery.
The first technology, the stone axe, was invented by early hominoid species in the rift valley in Africa, that allowed us to leapfrog other species, who may have been stronger and faster, but lacked the technology to compete. The first writing in the Nile Valley, again in Africa, on the first flexible writing material, Papyrus, also invented in Africa, allowed the Egyptians to leapfrog other civilisations, a stable civilisation that lasted continuously for 4000 years, longer than any other Empire ever. The very tools and technology that the modern world is built on were first seen in Africa.
There’s a lesson here – the ‘Leapfrog Principle’. This is the idea that one can innovate in environments where precedents and incumbents are poor, primitive or absent, easier than in wealthier or technologically richer environments. Africa can, again, be the crucible for leapfrog ideas and development. In finance, healthcare, energy, agriculture and education, AI can augment and improve productivity.
Africa had little in the way of a retail banking infrastructure and most people did not have a bank account. Along comes the ubiquity of cheap mobile devices and Africa does what richer countries are only now waking up to – mobile banking. In its wake came advantages in communications, finding work, paying bills and agricultural information - markets, teachniques and so on. The runaway success of M-Pesa, the mobile money transfer service launched by Safaricom, Kenya’s largest mobile operator and Vodafone, in 2007, has allowed millins to pay bills, buy goods, receive remittances from abroad and even access learning. None of this would have been possible without AI-driven encryption and now AI as the new UI interfaces.
Take Zipline, in Rwanda, where drones deliver blood to rural locations. Doctors request blood for ‘at risk’ patients and drones deliver, dropping the protected packages by parachutes, from 30 feet, into the backyard of the clinic, aided by GPS and navigational software. This is fast, cheap, efficient and saves lives. Why Rwanda? Well the road infrastructure prohibits speed of delivery, there is less regulation to hold back these innovations and, as a small country, it is ambitious and willing to take more risks. Older countries tend to become more risk averse. Strangely enough it is sometimes the absence of physical infrastructure; roads, fixed line telephone networks, transport options, power stations, oil reserves, that make leapfrogging more likely. The investment in leapfrog technology has less competitive pressure from incumbent technology and infrastructure.
The International Energy Agency states that there are over 600 million people in sub-Saharan Africa that do not have access to electricity. An African startup, Off Grid Electric, backed by Solar City, wants to rack up the supply of solar panels across Africa, with at an affordable charge of $7 a month for the system. It already powers 125,000 households. Musk has taken technology built for the wealthy car industry and applies it in a modular, LED, robust, affordable way to an African problem – no infrastructure and low income. The project has the possibility of scale and sustainability to the 1.3 billion people globally who lack access to affordable electricity. In the continent of sunshine, solar leapfrogs other forms of energy supply.
The perfect storm of satellites or drones with analytics of water, wind damage, soil condition, temperature and so on, even predictive software may lead to step changes in productivity. Precision agriculture turns AI into real solutions, in everything from GPS guidance, control systems, sensors, robotics, drones, autonomous vehicles, GPS-based soil sampling and so on. Getting the most out of every centimetre of land, sensors for yield prediction, scanning for disease and damage, productivity gains are there for the taking. In agriculture, data can fed software to increase yields to feed people.
Leapfrog Investments is an investment company that specialises in African and Asian investments. This matters, as growth needs an ecosystem for sustainable success. It needs risk capital to make those leaps, not of faith, but of assessed risk. Others include Carlyle, TPG and Abraaj. Unfortunately, start-up finance in Africa is paltry at less than $150 million. Africa is going through a population explosion with young, tech savvy populations that are used to mobile solutions. We need to harness their energy and talents.
Musk is buiding a Starnet system which is a satellite system that delivers high bandwidth internet to the world, especially sparsely pupulated areas. This promises to accelerate progress in Africa. An African will have come full circle and delivered what the continent needs to do the things it wants.
Let’s apply this principle to learning. The current problem in Africa is poor schooling, and the need for vocational skills, along with sensitivity to local languages. This is where AI comes in. Think of these two letters as the hind legs that allow the frogs to leap. OK, I know this metaphor is being stretched a little but bear with me…. One of the barriers to leapfrogging is education. To escape from the trap of poverty, one needs vision, confidence and competence. Rather than rely on foreign workers to provide practical skills in building and tourism, we must focus on vocational skills. It is pointless investing in higher education when there is no middle ground. This must happen at school and college level. Africa is going through a population explosion with young, tech savvy populations that are used to mobile solutions.
It is in education that leapfrogging can have the greatest causal effect. AI can create online learning cheaply (WildFire), and through AI assisted translation, create such learning in multiple languages. AI can personalise learning through adaptive systems. (CogBooks). This helps build a platform of knowledge delivery, so hat teachers can focus on skills. In the same way that blood type has to be selected or every patient, learning needs to be delivered to each person in a way that suits their needs – and the diversity or variability of these needs is much greater in Africa, than in a developed country. This is not the primitive Hole-in-the Wall or tablets parachuted into villages approach but scalable, sustainable learning to help teachers teach and learners to learn.
Don’t dump devices in developing world. That’s not leapfrogging, it’s device dumping. Sugata Mitra and Negroponte have both made a career out of dumping devices into the developing world and teachers lap it up as if they’re some sort of saints. Listen carefully – they don’t like teachers and schools. Researchers, like Arora, from Erasmus University Rotterdam, “little real independent evidence, other than that provided by HiWEL“, accusing Mitra of “not comparing amount of time spent on hole-in-wall material with same time in school… making the comparison meaningless”. It was, she concluded,“self-defeating… ‘hole-in-the-wall’ has become the ‘computer-in-the-school”. This was confirmed by Mark Warschauer, Professor of Education at the University of California, who also visited sites, only to find that “parents thought the paucity of relevant content rendered it irrelevant“ and that “most of the time they were playing games…. with low level learning and not challenging”. The “internet rarely functioned” and “overall the project was not very effective”. I also visited a site, in Africa, and confirmed all of this and more. Read Mitra’s comment on my blog, “it took me 30 minutes to think about and write this response. I would have spent the time on planning a new project for very poor children. Would someone, perhaps Donald, like to take the responsibility for this wastage and the resultant loss to them.” Sugata Mitra. This is what happens when devices trump reason.
Google has opened up and AI research centre in Accra, Ghana. They see the potential and this is the first of the large tech companies to make such a move - it won't be the last. The African market is huge, its human potential maturing and the possibilities for the innovative use of AI certain.
But let’s not underestimate the problems. Corruption, unstable political environments, poverty and weak regulatory environments don’t encourage investment and sustainable growth. To leapfrog, one must have solid ground from which to leap. Without a stable platform, these will be leaps of faith or leaps into the darkness. Innovation is only innovation of it is sustainable, that means stable regulations, a war on corruption and an investment environment that supports staged growth. Let’s start with education.
The graduate demand for nursing is a filter excluding many who used to go into the profession. My mother was a nurse, my sister was a nurse for 30 plus years – neither would now have got into the profession because of the academic entrance demands. Working class youngsters, in particular, are being excluded and I do not believe for one minute that they are unsuitable. The nursing shortage is not only caused by this hurdle but it has been exacerbated by the need for a someone who wants to be a nurse to get University entrance qualifications, spend years at University, then exit into a relatively low paid profession, with a huge loan.
Alan Ryan, who was a nurse for 20 years, and knows a thing or two about training in the NHS, said something quite profound in Berlin recently. “All of our jobs (in NHS) are, in practice, apprenticehips, from consultants to cleaners”. His point was that healthcare is an eminently, practical affair. He supports alternative routes into healthcare professions.
In truth Higher Education in the UK has land-grabbed vocational education, mainly on the basis of increasing their revenues. What were adequate, shorter and more experiential, training courses are now degrees, making them longer and far more expensive, whether for the state or individual.
Universities may claim to be about critical thinking but a glance at some of the degrees on offer show that this is far from the truth – Dentists, Doctors, Nurses, Lawyers, Engineers and so on. In truth, as Roger Schank tells us, Universities are about “creating Scholars”, and, as he says “we have enough Scholars already”. I’d add that there may be a surplus, as shown by the ease at which adjuncts can be hired to do the ‘teaching’ even in top Universities. This is not the environment into which nursing easily fits.
We have many nurses from other countries, even the EU, such as Germany, who are hired without going through this University experience, so it is not as if it is a necessary condition for success. Those who deliver such courses will claim that a nurse’s job is more complex than it used to be and of that, I have no doubt. But complex does not necessarily mean more lecturing and theory. 'It is difficult to get a man to understand something, when his salary depends on his not understanding it’ said Upton Sinclair, and it is difficult to get something out of the world of lectures and essays once ‘Lecturers’ get a hold of it.
But part of the solution here is to reverse this policy of Nursing degrees, not by demolishing that option but opening up alternative routes, especially apprenticeships. A vocational route is badly needed, and should have been opened up years ago. In practice, we depended on migrant labour and extortionate agency fees. We didn’t have to pay for their education, which is neither good for us or the countries from which they came, but that is not a good excuse for the failure to train our own nurses. Brexit will at least slow that process bu we need an alternative route. The nursing assistant route is a start - we need much, much more.
AI just outperformed humans at reading, potentially putting millions of customer service jobs at risk of automation. Could it do the same in learning?
Something momentous just happened. An AI programme, from Alibaba, can now, for the first time, read a text and understand it better than humans. The purple line has just crossed the red line and the implications are huge.
Think through the consequences here, as this software, using NLP and machine learning, gets better ad better. The aim is to provide answers to questions. This is exactly what millions of people do in jobs around the world. Customer service in call centres, Doctors with patients, anywhere people reply to queries... and any interactions where language and its interpretation matter.
First we must be careful with these results, as it depends on two things 1) the nature of the text 2) what we mean by ‘reading’. Such approaches often work well with factual texts but not with more complex and subtle texts, such as fiction, where the language is difficult to parse and understand, and where there is a huge amount of ‘reading between the lines”. Think about how difficult it is to understand even that last sentence. Nevertheless, this is a breakthrough.
It is the first time a machine has out-done a real person in such a contest. They used the Stanford Question Answering Dataset, to assess reading comprehension. The test is to provide exact answers to more than 100,000 questions. As an open test environment, you can do it yourself, which makes the evidence and results transparent. Alibaba’s neural network model, based on a Hierarchical Attention Network, which reads down through paragraphs to sentences to words, identifies potential answers and their probabilities. Alibaba has already used this technology in their customer service chatbot, Dian Xiaomi, to an average of 3.5 million customers a day on the Taobao and Tmall platforms. (10 uses for chatbots in learning).
Indeed, the one area that is likely to benefit hugely from these advances is education and training. The Stanford dataset does have questions that are logically complex and, in terms of domain, quite obscure, but one should see this development as great at knowledge but not yet effective with questions beyond this. That’s fine as there is much that can be achieved in learning.We have been using this AI approach to create online learning content, in minutes not months, through WildFire. Using a similar approach, we identify the main learning points in any document, PPT or video, and build online learning courses quickly, with an approach based on recent cognitive psychology that focuses on retention. In addition, we add curated content.
The online learning is very different from the graphics plus multiple-choice paradigm. Rather than rely on the weak ‘select from a list’ MCQs (see critique here), we get learners to enter their answers in context. It focuses on open-input and retention techniques outlined by Roedinger and McDaniel in Make It Stick.
To give you some idea of the sheer speed of this process we recently completed 158 modules for a global company, literally in days, without a single face-to-face meeting with the project manager. The content was then loaded up to their LMS and is ready to roll. This was good content and they are very happy with the results.
An interesting outcome of this approach to creating content was the lack of heat generated during the production process. There was no SME/designer friction, as that was automated. That’s one of the reasons we didn’t need a single face-to-face meeting. It allowed us to focus on getting it done and quality control.
Organisations have been using this AI-created content as pre-training for face-to-face training for auditors in Finance, product knowledge and GMP in Manufacturing, health and safety, everything from nurse training to clinical guidelines in the NHS, apprenticeships in a global Hospitality company. All sorts of education and training in all sorts of contexts.
The breakthrough saw Microsoft and Baidu perform similarly, showing that the new AI-war is between China and the US. That’s a shame but we still have some edge here in Europe and the UK, if we could only overcome our tendency to see AI as a dystopian entity and start to use this stuff for social good, rather than being obsessed with ill-informed critiques. If we don’t, they will. These AI techniques have already hit the learning market. It is already automating the production of learning in that huge motherload of education and training: 101 courses and topics such as compliance, process, procedures, product knowledge and so on. Beyond this, AI-driven curation, which we use to supplement the core courses is also possible. If you want see how AI and WildFire can help you create content quickly, at much lower cost and increase retention, drop us a line and we’ll arrange a demo.
4. Practical environment where factory floor training more prevalent.
To this end we produced modules quickly and cheaply using WildFire, an AI service that takes any document, PowerPoint or video, and creates online learning in minutes not months. We have done this successfully in finance and healthcare but manufacturing posed different challenges.
1. Much of the training is text heavy from manuals without any sophisticated use of images. That we solved through quick and low cost photo-shoots. Literally shooting to a shot list as the online modules had already been created.
2. In not one case did we find a LMS (Learning management System), so we had to deliver from the WildFire server. This actually has one great advantage in that it freed us from the limitations of SCORM. We could gather oodles of data for monitoring and analysis.
3. Doing this learning at any time allows learners to train in down time or at anytime 24/7.
5. We could deliver to any service, especially mobile, which helped.
We are still delivering and analysing the results. Sure there have been issues, especially in the absence of L&D staff in the target organisations but when it works, it works beautifully. If we are to take productivity seriously in the UK we must realise that this means better training and therefore performance. Wouldn’t it be wonderful if AI helps increase productivity through online learning so that people can skill themselves into relevant employment? AI may automate parts of roles but it can also be used to skill for the newly created roles. If you want to find out more please inquire here.