Machine learning is going to alter our world, improving healthcare, the manufacturing industry and assisting in the prediction of supply and demand levels across various industries, writes RESHAAD SHA, Chief Strategy Officer and Executive Director at DFA.
For those who are into science fiction, the term ‘machine learning’ immediately conjures up images of computers taking over the world, either to send murderous terminators from the future to our present or to place us all inside the Matrix as living power batteries.
Fortunately, the truth about machine learning is not only far more prosaic, but also much more promising for the future of the human race. Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. The iterative aspect is especially important, as it means that as the computer is exposed to new data, it is able to independently adapt.
The process of machine learning is similar to that of data mining, in that both systems search through data to look for patterns. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it’s a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. He suggested that by 2000, computers would be able to ‘think’.
Turing was clearly not far off as the world is already seeing machine learning being put into practice across a range of sectors, and all of these vertical markets are benefiting enormously from the application of this technology. Some of the benefits are outlined below, but these are merely scratching the surface of where we might eventually go with this ground-breaking technology.
For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude. This means they are more likely to pick up individual health issues, and do so more rapidly and effectively than a human diagnosis could. Machine learning can also be used to understand risk factors for disease in large populations.
In customer-facing businesses, it is also enabling marketing personalisation. The more that a company understands about its customers, the better it can serve them, and machine learning algorithms are providing the kind of intimate picture of a customer that enables such personalisation to take place.
Perhaps the most obvious use of machine learning is its use in online search engines, where the engine uses this technology and watches how you respond to search results, learning from these and ensuring it delivers better results in the future.
Of all the uses for machine learning, one of the most exciting ones – particularly for those of us old enough to remember ‘Knight Rider’ and his self-driving Trans Am – is in the various types of smart cars now being developed. A recent IBM survey of top auto executives saw some 74% of these stating they expected there would be smart cars on the roads by 2025.
These vehicles will not only integrate into the Internet of Things (IoT), but also learn about their owners and their environment. A smart car might adjust the internal settings automatically, based on the driver, report and even fix problems itself, will certainly be able to drive itself, and will offer real time advice about traffic and road conditions.In extreme cases the vehicle may even take evasive action to avoid a potential collision.
A good example of such smart cars is the Tesla models fitted with the company’s version 7.0 Autopilot system. Tesla’s Autopilot system makes use of machine-learning techniques that are continuously learning from human actions. Over the past year or so, this system has quietly been monitoring drivers as they drive various routes. The more often the car drives on a particular route, the more the machine learns how the human approaches, for example, a particular corner.
The idea, according to Tesla, is for the vehicles featuring Autopilot to be self-driving capable from the moment legislation catches up to the technology. And because it requires only simple software updates to stay relevant, users who purchased a vehicle a year ago will still be capable of utilising this feature when it becomes legal.
Let the machine drive
Naturally, machine learning lies at the very core of this long-awaited self-driving car revolution, which is clearly one of its most advanced and complex applications. Self-driving vehicles, after all, need to not only be able to ‘understand’ the rules of driving and how to actually drive, but must also be able to monitor the movements and signals of other cars and infrastructure, as well as being capable of learning to negotiate exceptions and make split-second decisions.
It should be obvious then that driverless cars will require an immense amount of data gathering and analysis; they will also need to connect to cloud-based traffic and navigation services, and will draw on leading technologies in sensors, displays, on-board and off-board computing, in-vehicle operating systems, wireless and in-vehicle data communication, analytics, speech recognition and content management. All of this leads to considerable benefits and opportunities: reduced accident rates, increased productivity, improved traffic flow, lowered emissions and much more.
The question is, how are cars expected to access all this data? After all, we are talking about information transmitted not only from other vehicles, but potentially from traffic lights, nearby buildings and rail crossings, not to mention GPS signals and even pedestrians’ phones, just to name a few.
It is here that the IoT will become a crucial platform, as it will be IoT-enabled sensors that are used to transmit most of this data to and from the automated vehicle. This, in turn, means that the network that these objects and sensors connect to will have to be cost efficient, ubiquitous and reliable.
But is South Africa – a country renowned for high data costs and ongoing struggles with connectivity – going to be in a position any time soon to have the kind of network necessary to facilitate self-driving cars?
The good news is yes. In fact, in all likelihood, SA will have an effective IoT network long before the first local cars start driving themselves, thanks to SqwidNet, a wholly-owned subsidiary of Dark Fibre Africa (DFA), which is also the licensed Sigfox operator for SA. Sigfox has a global network that spans 29 countries and is specifically designed to deliver IoT connectivity.
The company also has access to a wide range of IoT-based solutions, many of which have already been deployed in cities around the world. This means that not only will we have the network to enable future smart everything, but also a range of other solutions that will already have been tried and tested in other environments. In other words, by the time of their deployment, the new technology kinks will already have been worked out.
There is no doubt that we are on the cusp of another technological revolution, one which is going to make everyone’s lives easier and more connected. The IoT and machine learning look set to fundamentally alter the way our world works – in a manner that is exactly the opposite of a killer robot from the future.
Seedstars seeks tech to reverse land degradation in Africa
A new partnership is offering prizes to young entrepreneurs for coming up with innovations that tackle the loss of arable land in Africa.
The DOEN Foundation has joined forces with Seedstars, an emerging market startup community, to launch the DOEN Land Restoration Prize, which showcases solutions to environmental, social and financial challenges that focus on land restoration activities in Africa. Stichting DOEN is a Dutch fund that supports green, socially-inclusive and creative initiatives that contribute to a better and cleaner world.
While land degradation and deforestation date back millennia, industrialization and a rising population have dramatically accelerated the process. Today we are seeing unprecedented land degradation, and the loss of arable land at 30 to 35 times the historical rate.
Currently, nearly two-thirds of Africa’s land is degraded, which hinders sustainable economic development and resilience to climate change. As a result, Africa has the largest restoration opportunity of any continent: more than 700 million hectares (1.7 billion acres) of degraded forest landscapes that can be restored. The potential benefits include improved food and water security, biodiversity protection, climate change resilience, and economic growth. Recognizing this opportunity, the African Union set an ambitious target to restore 100 million hectares of degraded land by 2030.
Land restoration is an urgent response to the poor management of land. Forest and landscape restoration is the process of reversing the degradation of soils, agricultural areas, forests, and watersheds thereby regaining their ecological functionality. According to the World Resources Institute, for every $1 invested in land restoration it can yield $7-$30 in benefits, and now is the time to prove it.
The winner of the challenge will be awarded 9 months access to the Seedstars Investment Readiness Program, the hybrid program challenging traditional acceleration models by creating a unique mix to improve startup performance and get them ready to secure investment. They will also access a 10K USD grant.
“Our current economic system does not meet the growing need to improve our society ecologically and socially,” says Saskia Werther, Program Manager at the DOEN Foundation. “The problems arising from this can be tackled only if a different economic system is considered. DOEN sees opportunities to contribute to this necessary change. After all, the world is changing rapidly and the outlines of a new economy are becoming increasingly clear. This new economy is circular and regenerative. Landscape restoration is a vital part of this regenerative economy and social entrepreneurs play an important role to establish innovative business models to counter land degradation and deforestation. Through this challenge, DOEN wants to highlight the work of early-stage restoration enterprises and inspire other frontrunners to follow suit.”
Applications are open now and will be accepted until October 15th. Startups can apply here: http://seedsta.rs/doen
To enter the competition, startups should meet the following criteria:
- Existing startups/young companies with less than 4 years of existence
- Startups that can adapt their current solution to the land restoration space
- The startup must have a demonstrable product or service (Minimum Viable Product, MVP)
- The startup needs to be scalable or have the potential to reach scalability in low resource areas.
- The startup can show clear environmental impact (either by reducing a negative impact or creating a positive one)
- The startup can show a clear social impact
- Technology startups, tech-enabled startups and/or businesses that can show a clear innovation component (e.g. in their business model)
Also, a specific emphasis is laid, but not limited to: Finance the restoration of degraded land for production and/or conservation purposes; big data and technology to reverse land degradation; resource efficiency optimization technologies, ecosystems impacts reduction and lower carbon emissions; water-saving soil technologies; technologies focused on improving livelihoods and communities ; planning, management and education tools for land restoration; agriculture (with a focus on precision conservation) and agroforestry; clean Energy solutions that aid in the combat of land degradation; and responsible ecotourism that aids in the support of land restoration.
The dark side of apps
Mobile device security threats are on the rise and it’s not hard to see why. In 2019 the number of worldwide mobile phone users is forecast to reach 4.68 billion of which 2.7 billion are smartphone users. So, if you are looking for a target, it certainly makes sense to go where the numbers are. Think about it, unsecured Wi-Fi connections, network spoofing, phishing attacks, ransomware, spyware and improper session handling – mobile devices make for the perfect easy target. In fact, according to Kaspersky, mobile apps are often the cause of unintentional data leakage.
“Apps pose a real problem for mobile users, who give them sweeping permissions, but don’t always check security,” says Riaan Badenhorst, General Manager for Kaspersky in Africa. “These are typically free apps found in official app stores that perform as advertised, but also send personal – and potentially corporate – data to a remote server, where it is mined by advertisers or even cybercriminals. Data leakage can also happen through hostile enterprise-signed mobile apps. Here, mobile malware uses distribution code native to popular mobile operating systems like iOS and Android to spread valuable data across corporate networks without raising red flags.”
In fact, according to recent reports, 6 Android apps that were downloaded a staggering 90 million times from the Google Play Store were found to have been loaded with the PreAMo malware, while another recent threat saw 50 malware-filled apps on the Google Play Store infect over 30 million Android devices. Surveillance malware was also loaded onto fake versions of Android apps such as Evernote, Google Play and Skype.
Considering that as of 2019, Android users were able to choose between 2.46 million apps, while Apple users have almost 1.96 million app options to select from, and that the average person has 60-90 apps installed on their phone, using around 30 of them each month and launching 9 per day – it’s easy to see how viral apps take several social media channels by storm.
“In this age where users jump onto a bandwagon because it’s fun or trendy, the Fear of Missing Out (FOMO) can overshadow basic security habits – like being vigilant on granting app permissions,” says Bethwel Opil, Enterprise Sales Manager at Kaspersky in Africa. “In fact, accordingly to a previous Kaspersky study, the majority (63%) of consumers do not read license agreements and 43% just tick all privacy permissions when they are installing new apps on their phone. And this is exactly where the danger lies – as there is certainly ‘no harm’ in joining online challenges or installing new apps.”
However, it is dangerous when users just grant these apps limitless permissions into their contacts, photos, private messages, and more. “Doing so allows the app makers possible, and even legal, access to what should remain confidential data. When this sensitive data is hacked or misused, a viral app can turn a source into a loophole which hackers can exploit to spread malicious viruses or ransomware,” adds Badenhorst.
As such, online users should always have their thinking caps on and be more careful when it comes to the internet and their app habits including:
- Only download apps from trusted sources. Read the reviews and ratings of the apps as well
- Select apps you wish to install on your devices wisely
- Read the license agreement carefully
- Pay attention to the list of permissions your apps are requesting. Only give apps permissions they absolutely insist on, and forgo any programme that asks for more than necessary
- Avoid simply clicking “next” during an app installation
- For an additional security layer, be sure to have a security solution installed on your device
“While the app market shows no signs of slowing down, it is changing,” says Opil. “Consumers download the apps they love on their devices which in turn gives them access to content that is relevant and useful. The future of apps will be in real-world attribution, influenced by local content and this type of tailored in-app experience will lead consumers to share their data more willing in a trusted, premium app environment in exchange for more personalised experiences. But until then, proceed with caution.”