The Complexity Barrier (2003)

Short of super-intelligent general AI writing programs for us yadda yadda, I believe the answer will be to adopt formal methods more widely: Bake the intended semantics of your software with regards to some formal system into the program itself and have it only be valid if you or the computer can prove that it meets that specification (and have the specification be complete and correct of course (which can be difficult when you consider what e.g. cryptographic strength or "real-time properties" actually mean); if proof is not feasible, add in checks that make it fail gracefully at least or generate tests).

Where today the "engineering mindset" is more prevalent (Look, I can do things with it! And it does them correctly I hope.), there needs to be a shift to a more mathematical view on computation (Your program is an instance of a certain calculus and claiming to fulfill a formal specification, which needs proof), if the software complexity mentioned is to be managed better (there's a whole zoo of specification techniques and low-level formal systems available, which doesn't make this any easier).

Unfortunately support for any of this is severely lacking in popular tools (yes, there's a lot going on in academia, but I don't feel this has spilled over yet): Languages lacking a formal spec and changing frequently, compilers not proven correct, programs running on buggy processors, operating systems with unclear semantics, written in (from a type-system PoV) weakly-expressive languages using meaningless types (void, ugh), black-box peripherals. In that light I'm amazed my computer does anything at all, but clearly most of the time these things don't matter.

Then again, I'm not aware of a fully-featured and usable (comparatively to today's personal computers) formally-specified (whatever this would mean here, surely there's more than one way to define an OS for instance) and proven computing system that goes from high-level language to operating system and application ecosystem to whatever machinery is used at the bottom.

Coq and co. to the rescue (see e.g. http://compcert.inria.fr/)?

"increasingly concerned by new rather subtle diseases (SARS, HIV, BSE etc,) which are very difficult to understand, often difficult to diagnose or only become apparent long after the infection, and which often seem to originate through some change introduced by modern practices, such as use of antibiotics on farm animals or a change in the practices used in preparing meat."

Except new diseases have been constantly arising through human history and those examples arise from the usual ancient zoonotic transmission routes: eating wild meat and living in close proximity with animals. (With the exception of BSE, which inherently will never be anything but an extremely trivial disease.) The only thing 'modern' or 'complex' about them is the names, and that medicine has advanced past witchcraft and is able to notice them.

Highly optimized systems are great, until they aren't. F1 race cars are notoriously touchy and require a team of specialists to keep running, while a 1950's pickup truck I happen to know of may be slow, uncomfortable and horribly polluting, but has been in operation for pushing 70 years and is currently maintained by a single person who doesn't have to do much to it.

Just in time inventory control is great for margins, until, for instance, the bulk of the hard drive production capacity in the world is flooded.

Optimization and resilience frequently sit in tension, if not outright opposition.

Collapse can be, and occasionally has been, prevented through making simplifications, but there's usually resistance to that. There's only one civilization left. If it collapses, collapse will be global.

These systems will _gain_ from chaotic input rather than fail.

For More: https://www.quantamagazine.org/20140122-a-new-physics-theory...

The party may not last forever, but VLSI is basically a profession of scale. It stands for Very Large Scale Integration. Tools continue to improve, allowing ever more complex designs to be managed by the same team of people. And we aren't talking factors of 2x.

"... And hatred for the Knowledge Crash. If you could hate something that might not even have happened. That was perhaps the surpassing irony: no one was ever quite sure if the Knowledge Crash had even taken place. Some argued that the very state of being uncertain whether or not the Crash had occurred proved that it had.

Briefly put, the K-Crash theory was that Earth had reached the point where additional education, improved (but more expensive) technology, more and better information, and faster communications had negative value.

If, the theory went on, there had not been a Knowledge Crash, the state of the world information economy would be orderly enough to confirm the fact that it hadn’t happened. That chaos and uncertainty held such sway therefore demonstrated that the appropriate information wasn’t being handled properly. QED, the Crash was real.

An economic collapse had come, that much was certain. Now that the economy was a mess, learned economists were pointing quite precisely at this point in the graph, or that part of the table, or that stage in the actuarial tables to explain why. Everyone could predict it, now that it had happened, and there were as many theories as predictions. The Knowledge Crash was merely the most popular idea.

But correct or not, the K-Crash theory was as good an explanation as any for what had happened to the Earth’s economy. Certainly there had to be some reason for the global downturn. Just as certainly, there had been a great deal of knowledge, coming in from many sources, headed toward a lot of people, for a long time.

The cultural radicals—the Naked Purples, the Final Clan, all of them—were supposed to be a direct offshoot of the same info-neurosis that had ultimately caused the Crash. There were whole communities who rejected the overinformed lifestyle of Earth and reached for something else—anything else—so long as it was different. Raphael did not approve of the rads. But he could easily believe they were pushed over the edge by societal neuroses.

The mental institutions of Earth were full of info-neurotics, people who had simply become overwhelmed by all they needed to know. Information psychosis was an officially recognized—and highly prevalent—mental disorder. Living in the modern world simply took more knowledge than some people were capable of absorbing. The age-old coping mechanisms of denial, withdrawal, phobic reaction and regression expressed themselves in response to brand-new mental crises.

Granted, therefore, that too much data could give a person a nervous breakdown. Could the same thing have happened to the whole planet?

The time needed for the training required to do the average technical job was sucking up the time that should have gone to doing the job. There were cases, far too many of them, of workers going straight from training program to retirement, with never a day of productive labor in between. Such cases were extreme, but for many professions, the initial training period was substantially longer than the period of productive labor—and the need for periodic retraining only made the situation worse.

Not merely the time, but the expense required for all that training was incredible. No matter how it was subsidized or reapportioned or provided via scholarship or grant program, the education was expensive, a substantial drain on the Gross Planetary Product.

Bloated with information, choked with the of a world-girdling bureaucracy required to track information and put it to use, strangled by the data security nets that kept knowledge out of the wrong hands, lost in the endless maze of storing and accessing all the data required merely to keep things on an even keel, Earth’s economy had simply ground to a halt. The world was so busy learning how to work that it never got the chance to do the work. The planet was losing so much time gathering vital data that it didn’t have a chance to put the data to use. Earth’s economy was writhing in agony. ..."

The problem is, how do we apply that to real things? I guess I'll hear "robots" somewhere on the answer, but don't really know where.

Edit: wow you guys. I'm impressed. 2003 it is.