In 2018, innovations will emerge out of the foundational technologies conceived over the past few years. Most of these innovations are still in their early stages, and may only start gaining ground several years from now, but they hold the potential to change the way we live and the way we work and we may see precursors of this in 2018. Here are our top 5 predictions for this year:
The Convergence of Blockchain and IoT
Blockchain technologies are gaining even greater momentum, if you still haven’t heard of it yet; it is a distributed ledger that provides a trusted and cryptographically secure method for storing records online. Its main feature is that it’s decentralized – unlike centralized systems where a single party assures a systems authenticity (like banks or the government) the authenticity of the block chain is assured by every member having access to a transparent ledger that holds records of every transaction made.
Combine this with the Internet of Things – a network of physical devices connected through the internet – and you have a formidable means of disrupting existing business models in areas such as supply chain management. One key issue with supply chains is the inability to capture and share information about the items moving through the chain in a transparent and trusted fashion. Too much trust needs to be placed on third party stakeholders, especially in extended supply chains, creating the potential for fraud and malpractice. Introducing blockchain and IoT technology to the supply chain will provide a means to address this need – everything that happens to an item can be captured through IoT solutions and recorded using a blockchain ledger making it visible to all parties involved, and all records are immutable, creating trust amongst all stakeholders. This technology is also cost effective as verification is carried out using computing power rather than man power.
Smart devices (e.g. self-driving cars, wearable devices), because of their digitally connected nature are a gold mine for user data. The connected devices create a digital mesh, but all the data collected through each individual device is processed through a single or small number of servers that may be located miles away from the location of the smart device. This means that a large volume of raw data must be transported through the network and most of the data cannot be processed in real time; placing strains on bandwidth and deeming a large quantity of the data obsolete. Edge computing will allow data to be processed at the edge of the mesh i.e.; within the smart device itself, allowing larger quantities of data to be processed with no latency, and in turn, providing businesses with more actionable insights.
Due to rapid developments in Artificial Intelligence, 2018 will see a rise in devices with the capability of learning from their environment and modifying their behavior based on external stimuli. A real-world application of this technology is Honda’s Safe Swarm concept which instills heightened AI capabilities to their autonomous smart cars. The cars mimic swarm intelligence – a form of intelligence transfer among constituents within a group – most notably present in the natural world (ants or bees for example use swarm intelligence for inter-group communications enabling them to seamlessly function almost like a single entity). Safe Swarm was designed to enable cars to tune into cues from other vehicles or apply intelligence derived from past data to modify their behavior independent of the driver; minimizing the risk of accidents.
As an increasing number of physical artefacts from everyday life join the internet of things and employ this more sophisticated form of intelligent response, more valuable data will be transmitted to both businesses and researchers alike.
The Digital Employee
We will see more organizations relegating menial and repetitive tasks that were formally performed by humans, to programs. This could be in the form of a computer software that runs in the background, or a physical robot. Chatbots for example are programs that run on a chat interface and help customer support teams by fielding entry level questions, allowing them to focus on more complex issues that require a higher level of creativity and intelligence to solve.
The digital workforce will make it a lot easier for organizations to streamline their processes, and help empower their employees, and most significant of all give human employees more time for creativity and innovation.
For the longest time, what set higher forms of artificial intelligence apart was their ability to articulate a response using existing internal models, even when confronted with a new external problem. Recent developments in AI however take it a step further, allowing systems to consider how they learn; either through existing data or through experience, helping it learn more effectively as a result. Compared to the rest of the technologies listed in this article, meta-learning is relatively new – the term meta-learning had only started to solidify towards late 2017. Currently most applications for meta-learning revolve around the processing of data. Most data scientists spend a large quantity of their time preparing the data for analysis but by employing meta-learning technology, data scientists and businesses can delegate data cleaning and classification to the computer, allowing them to perform higher level processes, such as analysis and decision making.
All of the technologies listed above are in different stages of their development, and have not fully expressed their potential but will permanently impact and change the way businesses and organizations function.