Slow technological evolution

From time to time some technology cats up hype state, like smart phones a decade ago or AI today. For some time there is an omnipotent euphoria and if feels like the world history is changing in an eye-blick.  

Normally after this come a hangover and at least the enlightened individuals feels empasserst. In most cases after this drop the real development and changing the world begins. There is still another aspect to this. The organisation that produces the fundamental change seldom is capable to create any business out of it.


Here is an revealing example. Eastman Kodak was one of the largest players in the film and photography industry. It was able to develop a digital camera, where the old celluloid film was replaced by image sensor. But the company was not ready to start to develop new business model base on new technology. This was done by Japanese companies like Sony and Canon. Eastman Kodak went bankrupt in 2012 after strong decline in businesses from year 2000. This shows clearly that companies – big and small – are shaped by their technologies. Organisational structures, people’s’ skills work processes all are tuned to the technologie. So the whole business logic is heavily leaning on the chosen technologies. This is quite universal phenomenon. Almost the same happened to the Japanese camera companies as the pocket cameras moved into smartphones.  


At the change of millenium the ideas Object-Oriented design and programming hyped in the IT community. The explosive usage growth in the OO-language usage especially Sun Microsystems Java. Sun promoted successfully it 3-tier server-client architecture with clear logical division of duties. The word look very bright from our – OO-enthusiastics point of view.

Grady Booch said in some OO-conference in the beginning the 2000s that “ in 5 years time there will not be any OO-conferences, because OO has become mainstream ”. Well the prediction was correct, but the reasoning behind was not. The OO-languages became mainstream, but OO-analysis or design did not. If this had happened, it would had made life much more easy for many, but the step or the change was too big and too difficult for most in the industry and as so many times in the history, the big short term interests defeated the long term benefits. Here one again the big companies with huge economical power were facing the threat of being destroyed, because the size of the change was far too big for them to make it. This is why big database companies like Oracle and IBM introduced SOA  (Service Oriented Architecture) , which was actually very rude Trojan horse  to turn the history backward at least two spets. The whole concept is so complicated and fuzzy, that it was clear from the beginning the it  was there just to muddy water and at the same time it appealed to masses of older developers that had big difficulties to understand the OO-concept. This is a big lost of opportunities that are gone at least for long time.  Perhaps we get our revance when the neural networks take over the application design and implementation in the future.


This fact is good to keep in mind, when following new hypes. The current hype is AI. It can be seen everywhere. When everyone is trying to get their share of this, the boundaries and interpretation of what is actually AI are heavily stretched. The current deep learning implementation with current computes are poor, because the computer architecture doesn’t  match at all with neural networks. The real breakthru is yet to been seen, because it requires neurons on chip design. IBM’s TrueNorth neurochips has demonstrated that the trick can be done. They have made the first workable version, but as it was a army project, it is likely that we don’t hear about that any more. Other organisation thriving to that goal are so much behind, that is we are not able to predict the timetable. Current achievement with computers are good and we can benefit from them in many ways in everyday life. At the same time we should understand the even if not today but just round the corner the neuromachines are waiting to be used. They will be from 100 to 1000 times faster than the current solution and the size of their neuronetwork will be magnitudes bigger that the current solutions. All this makes it totally impossible to predict their capabilities and understanding. Those remains to be seen when the time goes by.