While we are currently in the digital age, most of the current progress is merely incremental. However, looking towards the long term future, there are several technological problems that we will bump into. The currently used digital technology was originally conceived with the purpose of doing arithmetic and logic functions. We have however managed to get this technology to do more, but that does not mean that it does these other tasks efficiently. Considering that our needs are moving beyond simple automation of on/off and towards more complicated tasks, more similar to what we humans are doing, there are various problems with using just digital principles. After all biological systems are not digital, they are none deterministic and this should be reflected in the next set of systems that we develop. However, this requires us to deal with uncertainty, and designing that into our systems rather than the current approach of designing it out by added circuitry. This would help us a great deal also when it comes to overcoming some of our technological challenges. For example, silicon manufacturing starts to suffer more from what we call reliability issues, but these are only really reliability issues when one performs deterministic computing. Biological systems do not suffer from such problems. Unfortunately, the digital principle is however the only option considered in a lot of contexts, which limits technological developments as such. 

Many decades ago digital systems started off rather slowly and have meanwhile taken the market by storm, but the initial energy is out of its developments and we are reaching more of a steady state, therefore new developments that can take us from where we are towards new heights need to be investigated. It is also in this context that quantum computing has a huge potential, but then not quite as a computer that purely deals with 0’s and1’s, as its potentials as a none deterministic computing platform are huge. 

 In order to develop these ideas, a large set of different disciplines would need to come together and look at doing developments of none deterministic systems, what their requirements are, how one can use them for computational tasks, what their required accuracy is, whether there are any thresholds below which they stop working and so on. 

 Biology is the best example of none deterministic systems and the fact that this principle simply works. If not, I would not be able to write this text and you would not be able to read and understand it. So, in modelling some of this, one needs to think about how biological systems operate, but that would then also allow us to better model them and let them perform functions similar to what we are able to do, and that with the same efficiency. 

From an applications perspective the usefulness of designing systems that operate more similarly to ourselves are huge, especially if they are artificial, as they would not be restricted by emotions, need to sleep and so on. Therefore they would be able to help with providing useful information towards the context in which people work, and that beyond our current searching abilities. This could bring significant benefits in e.g. the medical world, where research results from across the world would be provided when relevant towards the patient that people are dealing with. This could save lives and move medical care forward massively. One could even move this technology in the development of new technology by better understanding what has been tried already and what is currently being developed elsewhere, and therefore developments would be more unique and less overlapping, allowing for an overall faster progress of technology as such. 

A few further notes that are important towards this development: 

1) Our current systems have mainly been designed at component/part-level rather than at system-level. While these parts form building blocks to larger systems, efficiencies are often only considered at the level of these parts, and not at the system level as such. Larger, system level design is therefore essential in all future developments, as efficiency needs to be considered at this larger level. This will require the industry to either design at system level and/or there will be a need for more standardisation for parts of different vendors to work together efficiently, because they will need to communicate more to achieve such system level efficiency. Such larger integration can only be achieved by people working more closely together and being aware of what others our doing through the aid of intelligent assistants. 

2) Unfortunately some current developments are hindered by “conventional” thinking, which is due to the fact that we only teach people certain solutions and not to let them think more widely about problems. After all, it is well known that education teaches creativity out of children, as they are more creative before they start school than once they have finished. This is due to the fact that our minds tend to search for solutions within the “database" of what we learned, and so teaching existing technology does not necessarily help them, unless it is put into the larger context of solutions, and not presented as “the solution”. Changing education within this context will be essential and start from the lower levels and then move upwards. It will also keep students more engaged with their materials, rather than asking them to reiterate material that is currently available through the web anyway. Consequently, people will be more engaged, enthusiastic and happier.