FIVE hundred years from now: can you predict the Software Landscape in anno 2525?

No

ah go on.

Here was Software in 1956 -- on Altair IV:

Back to the Future of AI.
More Kool Aid

MORE HUMAN THAN EVER, COMPUTER IS LEARNING TO LEARN
https://www.nytimes.com/1987/09/15/scie ... learn.html

About the Archive
This is a digitized version of an article from The Times’s print archive, before the start of online publication in 1996. To preserve these articles as they originally appeared, The Times does not alter, edit or update them.
Occasionally the digitization process introduces transcription errors or other problems; we are continuing to work to improve these archived versions.
COMPUTER scientists, taking clues from how the brain works, are developing new kinds of computers that seem to have the uncanny ability to learn by themselves.
 
The new systems offer hope of being able to perform tasks such as recognizing objects and understanding speech that have so far stymied conventional computers. Moreover, with the ability to learn by themselves, such machines would not require the laborious programming of rules and procedures that is now required to allow computers to work.
 
The new computers are called neural networks because they contain units that function roughly like the intricate network of neurons in the brain. Early experimental systems, some of them eerily human-like, are inspiring predictions of amazing advances.
 
''I'm convinced that this will be the next large-scale computer revolution,'' said Pentti Kanerva of the Research Institute for Advanced Computer Science in Mountain View, Calif., run by a consortium of universities. But he and other experts note that the technology is still in its infancy and there are many obstacles to surmount.
 
Among the recent developments are these:
 
* At Johns Hopkins University Terrence Sejnowski developed a program that teaches itself to read out loud. The system is given no rules about how letters are to be pronounced; its errors are merely corrected. At first, the talk is mere gibberish. After a while it begins to utter some baby-like sounds as it learns to distinguish between consonants and vowels. After a night of computing, it reads with few mistakes.
 
* At Avco Financial Services in Irvine, Calif. a neural network learned how to evaluate loan applications after being fed data on 10,000 past loans. One test showed that had the neural system been used in place of the company's existing computerized evaluation system, it would have increased profits 27 percent.
 
* At Los Alamos National Laboratory, researchers used a neural network to predict whether particular DNA sequences represented genetic codes for the manufacture of proteins. The network seemed to work with greater than 80 percent accuracy, better than conventional statistical techniques.
 
In each of these cases, a computer was ''trained'' with a set of tasks or problems and a set of correct answers. As it completed each task, it compared its results with the correct answers. When it was wrong, it altered its own program and tried again. Gradually, it ''learned'' the right approach.
 
Up to a point, the more the computer is ''trained'' the more accurate it gets. But no one can tell at the outset how long the training may take.
 
Lured by the promise of this technology, universities and electronics companies like TRW, I.B.M. and A.T.&T. are pursuing work on such computer systems. And more than two dozen small neural network companies have been formed, mainly in the last two years, according to Intelligence, a New York newsletter that follows the field.

Electronic 'Brain' Teaches Itself

July 13, 1958

https://www.nytimes.com/1958/07/13/arch ... tself.html

Productivity experts have long emphasized system thinking and processes, strategies that have supported improvements for at least 100 years giving us Henry Ford’s assembly line, JIT manufacturing, and process re-engineering. But one expert says there is too much emphasis today on tasks, such as mail and writing reports: “Today’s companies buy apps to complete tasks or make groups of tasks easier. But the tasks are not isolated; assembled together they make up processes, which make up businesses. We’re all buying apps for tasks, but is anyone thinking about how they affect the whole? To understand how the tasks fit into the big picture, we need to apply systems thinking.“
 
These recent trends suggest that AI will be much harder to implement than previous information technologies. As the New York Times says: “The AI revolution is coming, but not as fast as some people think.”