The digital future is in essence about how new digital technology or updated existing technology can have an impact on the way we work, the way we interact, the way we make decisions and the way we image new futures again. Not all promising innovations will become mainstream and not all potential improvements will succeed. It is smart to be open minded on new ideas and to analyze what could happen when they become reality. It is also smart to be skeptical about the world=changing aspects of new technology and take the time to investigate its true potential first.
At the core a digital system takes input, makes some computations and displays some kind of output. At the start of the computer era this was all done on one device: the mainframe computer. Nowadays there are many devices, and they can be linked together through a network. They can handle a multitude of data that can be taken in on a local level or from some kind of centralized location. There are also many shapes output can take, although most of these will be either visual or audible. In a laboratories they have managed to take brainwaves from other person (input) thinking on certain words and transfer these thoughts through a system (computer) to another person on a different continent directly into his brain (output)
Internet of Things
In the digital future there might be more means to gather data, to get input for the system. The Internet of Things is collection of sensors that can be put in all kinds of objects to gather all kinds of data to analyze and respond to. This can for instance help to get a more accurate image on events like the weather. One of the many ideas using the Internet of Things concept is connecting the rain sensors on cars that control the automatic windshield wipers with the meteorological service and get a much more accurate insight on where it is raining and what the potential road conditions are.
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Another form of innovation at the input side of digital systems are voice-based systems. The way we interact with computers at the moment is mainly through a keyboard and through a click on a screen (directly or by using a mouse or trackpad). Talking to a digital systems still has ways to grow and mature. It is expected that in the near-future we will talk more with computers using digital assistants like Alexa or Google Assistant. It might also be the case that we will text or app with these assistants to access all kinds of services who are now living on our smartphone as an enormous collection of apps.
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Artificial Intelligence – Automation
In order for a digital assistant to recognize our voice and our commands the computer behind the screens will use a form of Artificial Narrow Intelligence (ANI). Artificial Intelligence is at this moment an hot subject. There is a lot of anticipation of what artificial intelligence can become in both positive and negative ways. There are even warnings that AI might one day turn on humanity and wipe us out. That seems Science Fiction at the moment. At the core Artificial Intelligence are based on algorithms that can calculate probable outcomes based on a large amount of input. There are many different ways to look at Artificial Intelligence and to define different types. For me there is a distinction between AI systems that are basic and rigid on one side and where the output is defined by a set of rules (automation) and AI systems that are a bit more smart and where the output is computed as probable outcomes of the systems own analysis (machine learning). The chess computer that has beaten world champion Kasparov is an example of a rule-based system: it calculated the best moves based on the situation on the board without taking past moves in consideration. Any time the same situation on the board would be presented to the system it will make the same move, over and over again.
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Artificial Intelligence – Machine Learning
Self-driving cars are an example of machine learning. A programmer cannot anticipate the situations the car will find itself in. The car has to learn how to deal by itself, although it will still have set boundaries limiting its choices upfront. A self-driving car is impressive. Not only because it can drive better (safer) than most humans, it will not fall asleep or get distracted. It is also a showcase on how the tasks we humans find easy are actually very complicated for a computer to learn.
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A self-driving car is a kind of robot: it takes in all kinds of information, analyzes the data and processes the probable outcomes and as a consequence performs an action. Robotics is all about translating the outcome of an algorithm into some kind of specific output. When we think of robots we most likely think of the robots in a factory doing the repetitive and heavy work on an assembly belt. Or we see the cute? humanoid robots that can interact with us and perform some much more limited tasks. We might think of our robot lawn mower or robot vacuum cleaner. Robots perform a specific tasks or set of tasks based on automated processes.
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Another form of output of a digital system are all kinds of virtual and augmented realities. Using these techniques it becomes possible to run simulations of a new factory or a complex surgery and experience the consequences of certain decisions before the factory is actually build or the surgery takers place. These are both examples of the concept of a Digital Twin: a digital representation that can be manipulated and tested before putting it in practice in reality. It is possible to design a robot in a digital twin environment, test it in all kind of circumstances before it is put into production. The concept of digital twins can take all kinds of forms, from running simple models to a simulation of a complete mirror image of a complex system like a factory or a human patient.
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Cloud Computing and Enterprise Services
A digital system can become quite complex. It will require a good infrastructure, well-managed maintenance and regular updates. Over the past decades the available computing power has expanded exponentially, creating many new possibilities. With the growth in computing power came also a growth of infrastructure bandwidth, allowing more connections and the transfer of larger datasets. Where IT systems and infrastructure used to be mostly localized with companies running their own data center and networks, now all these functions have become globally available through cloud computing. For most IT departments cloud computing is about bringing the data center into a shared environment and being able to used shared resources to improve performance and to build in extra redundancy. From a user perspective cloud computing makes new functionality almost instantly available, without the need to have to buy software and install it on local systems to be managed by IT: Enterprise (micro) Services.
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Quantum Computing has moved on from a theoretical discussion into the experimental phase. The promise of Quantum Computing is even more computing power. This might open new opportunities for digital systems and the digital future.
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