4 ms·

Oh wow these are way off. I wonder what went wrong?

by mrfusion 8y ago

ardy42 8y ago
> Oh wow these are way off. I wonder what went wrong? Ray Kurzweil went wrong, they're his predictions.
VMG 8y ago
His predictions went wrong
- mrfusion 8y ago
  Maybe he didn’t take into account how much things depend on software. He just assumed faster hardware will make things happen. And even on that front, Moore’s law died somewhere in the 2010s. That might have thrown these off.
  - jaked89 8y ago
    Moore's law didn't die, it's been para(lle)lized.
- neuronic 8y ago
  At least he sold books? Good for him.
base698 8y ago
It's impossible to predict the location of a bouncy ball when thrown in a small home with a cat present. Now extrapolate that to all of human activity twenty years out. Because we are confronted by speculation at every turn, in print, on video, on the net, in conversation, we may eventually conclude that it must have value. But it doesn’t. Because no matter how many people are speculating, no matter how familiar their faces, how good their makeup and how well they are lit, no matter how many weeks they appear before us in person or in columns, it remains true that none of them knows what the future holds. http://larvatus.com/michael-crichton-why-speculate/ http://larvatus.com/michael-crichton-why-speculate/
caymanjim 8y ago
This implies that there was ever a conceivable way any of the predictions would be right.
jcranmer 8y ago
A few things went wrong. The first is the evolution of microprocessors under Moore's Law. Since about 2005, we've come to realize that we can't afford to cool chips anymore if we crank up the frequency. What that means is that the advancements in building silicon is much less efficient at being turned into sustained computation rates. So building the exponential growth curves predicted required (and still requires) revolutionary thinking about how to build computers, rather than just easy gains each generation. The second thing that went wrong was what Derek Lowe refers to as the "Andy Grove Fallacy." This fallacy is that every hard problem (particularly in biological systems) can be solved by just shoveling computational power at it. In the modern day, we still can't model the neurological system of a nematode that has just 302 neural cells, let alone understanding how the entire organism works. Multiply that to the scale [1] of a human being, and we are truly ignorant of how things work except in the most general sweeping statements possible. The final thing is, well, people tend to underestimate the difficulty of problems. Things such as bipedal motion or turning a doorknob are problems that seem utterly trivial to us human beings (because we learn them before we can remember the process of learning!) but are absolutely maddening to robots. [1] Recall that complexity tends to grow exponentially with scale, not linearly--each pairwise, three-way, four-way, etc. interaction contributes to complexity.
gooseus 8y ago
I didn't read his book, but I would guess it was his failure to properly account for the diminishing returns of increased complexity in all the systems that these technological advances (increasingly) depend on. This includes the economic, political, ecological, and social systems, as well as the actual technological systems.

Oh wow these are way off. I wonder what went wrong?

ardy42 8y ago

> Oh wow these are way off. I wonder what went wrong? Ray Kurzweil went wrong, they're his predictions.

VMG 8y ago

His predictions went wrong

mrfusion 8y ago
Maybe he didn’t take into account how much things depend on software. He just assumed faster hardware will make things happen. And even on that front, Moore’s law died somewhere in the 2010s. That might have thrown these off.
- jaked89 8y ago
  Moore's law didn't die, it's been para(lle)lized.
neuronic 8y ago
At least he sold books? Good for him.

base698 8y ago

It's impossible to predict the location of a bouncy ball when thrown in a small home with a cat present. Now extrapolate that to all of human activity twenty years out. Because we are confronted by speculation at every turn, in print, on video, on the net, in conversation, we may eventually conclude that it must have value. But it doesn’t. Because no matter how many people are speculating, no matter how familiar their faces, how good their makeup and how well they are lit, no matter how many weeks they appear before us in person or in columns, it remains true that none of them knows what the future holds. http://larvatus.com/michael-crichton-why-speculate/ http://larvatus.com/michael-crichton-why-speculate/

caymanjim 8y ago

This implies that there was ever a conceivable way any of the predictions would be right.

jcranmer 8y ago

A few things went wrong. The first is the evolution of microprocessors under Moore's Law. Since about 2005, we've come to realize that we can't afford to cool chips anymore if we crank up the frequency. What that means is that the advancements in building silicon is much less efficient at being turned into sustained computation rates. So building the exponential growth curves predicted required (and still requires) revolutionary thinking about how to build computers, rather than just easy gains each generation. The second thing that went wrong was what Derek Lowe refers to as the "Andy Grove Fallacy." This fallacy is that every hard problem (particularly in biological systems) can be solved by just shoveling computational power at it. In the modern day, we still can't model the neurological system of a nematode that has just 302 neural cells, let alone understanding how the entire organism works. Multiply that to the scale [1] of a human being, and we are truly ignorant of how things work except in the most general sweeping statements possible. The final thing is, well, people tend to underestimate the difficulty of problems. Things such as bipedal motion or turning a doorknob are problems that seem utterly trivial to us human beings (because we learn them before we can remember the process of learning!) but are absolutely maddening to robots. [1] Recall that complexity tends to grow exponentially with scale, not linearly--each pairwise, three-way, four-way, etc. interaction contributes to complexity.

gooseus 8y ago

I didn't read his book, but I would guess it was his failure to properly account for the diminishing returns of increased complexity in all the systems that these technological advances (increasingly) depend on. This includes the economic, political, ecological, and social systems, as well as the actual technological systems.