Comment by sytelus | Hacker News Reader

Comment by sytelus | Hacker News Reader

"Twenty years ago, in November 1985, Windows 1.0 debuted with approximately 400 documented function calls.5 Ten years later, Windows 95 had well over a thousand.6

Today we are ready for the official release of the .NET Framework 2.0. Tabulating only MSCORLIB.DLL and those assemblies that begin with word System, we have over 5,000 public classes that include over 45,000 public methods and 15,000 public properties..."

This is our constant struggle in the modern world where the complexity is simply outstripping the capacity of the human brain. It's happening in physics, biology, genetics... Some authors question why we need all this complexity? Isn't complexity bad? I'm forming a view that the complexity is in fact necessary and we try out best to minimize it but beyond a certain level, it's simply isn't possible to get rid of it. This is only accelerating. Classic physics was easy to understand but all the advances have made physics (and other fields) slowly inaccessible to a larger and larger population without going through intense add-on multi-year narrowly scoped learning. It seems we are at the point that to comprehend the latest in quantum mechanics you need yet another 16 years of learning after you already had 16 years of high school+college. In other words, time to climb the knowledge pyramid doubles approximately every 100 years. By that speculation, in year 2100 the knowledge pyramid might have grown so big that to reach top might need 48 years! At this juncture, it might become apparent that the human life span is simply not enough to climb the knowledge pyramid starting from the basic arithmetic to the latest and greatest in physics anymore. We reach tipping point where biological limitation of life spans, memory capacity, recalls and search become truly limiting. I think this is where AI would be super important. From that point onward, we will need to rely on machines to tell us the simplified, intuitive but inaccurate version of the truth that we can sort of comprehend with our limited hardware, like automated machine-generated 1000+ steps proofs. That would be an interesting world.

No. Reaching the top of the knowledge pyramid in your subfield over time has remained constant (typically in your early to mid 20s, as you complete your PhD).

We have more sub fields, but that’s not particularly a problem - once you’re an effective researcher, jumping branches - and even trunks - is far from impossible. A few months surrounded with talented people and dedication is typically enough - eg my grad school advisor, a CS HCI professor, got into genomics when meeting his wife and went on to publish, as first author, genomics papers in nature.

See also the recent rise of machine learning - many of the top contributors to that field were not doing machine learning research 10 years ago.

Similarly, motivated high schoolers now learn things that you needed a PhD to tackle 100 years ago.

Knowledge is weird and non linear, and as humans we have many super powers: we stand on the shoulders of our predecessors, we build better ways to preserve knowledge, to share it, better ways to teach, better ways to test our theories, etc.

It took thousands and thousands of years of man hours of work to get us to basic calculus, something that a motivated high schooler can get very comfortable with in a few weeks at most. The most educated Roman citizen would take a bit of time to compute something as straightforward as (8238*2949), which is now something you expect elementary school students to compute in minutes at most.

If we could jump in time 100 years in the future and see what 18 year olds know and can do then, we’d probably be blown away.

>See also the recent rise of machine learning - many of the top contributors to that field were not doing machine learning research 10 years ago.

Machine learning is older than you think.

The previous commenter was not saying that machine learning didn't exist 10 years ago. They were saying that "many of the TOP CONTRIBUTORS TO THAT FIELD were not doing machine learning research 10 years ago."

There are also people who were doing machine learning 30 years ago and are still doing so. But there are people who were doing, say, fluid dynamics simulations 10 years ago who self-taught machine learning, and are now making significant contributions to machine learning, by transferring their knowledge of programming, optimization, calculus, etc.

Arguably, we're already past the point where it's impossible for a single human brain to retain all knowledge needed to build today's society, or even to fully dominate a field right physics, biology or genetics?

Complexity isn't "bad" - complexity is a property of all evolving things, from code to life. In sciences, it just means we're collectively learning more, faster and better. Complexity is beautiful!

It's not even arguable. I mean, think about, say, databases. I've worked with people who have far deeper knowledge about them than I ever will. A few old timers working at Oracle know more about database keys than anyone else ever will, and that expertise is necessary to build a modern system.

And that's just one thing, databases. There are way more technologies out there. Even building my metal coffee cup requires deep expertise in metallurgy, manufacturing, industrial design, product design, supply chains, etc. etc. etc.

I think it's past the point for a single human brain to retain all knowledge about databases, unless they're a profound genius (don't underestimate how much there is to know), and that is one tiny thing that humans do.

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