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> Each time there is an algorithmic advancement, people quickly rush to apply it to both existing and new problems, demonstrating quick advancements. Then we te
by Matthyze 2y ago
> Each time there is an algorithmic advancement, people quickly rush to apply it to both existing and new problems, demonstrating quick advancements. Then we tend to hit a kind of plateau for a number of years until the next algorithmic solution is found.
That seems to be how science works as a whole. Long periods of little progress between productive paradigm shifts.
- semi-extrinsic 2y agoPunctuated equilibrium theory.
- tyronehed 2y agoThis is actually a lazy approach as you describe it. Instead, what is needed is an elegant and simple approach that is 99% of the way there out of the gate. Soon as you start doing statistical tweaking and overfitting models, you are not approaching a solution.
- klabb3 2y agoIn a way yes. For models in physics that should make you suspicious, since most of our famous and useful models found are simple and accurate. However, in general intelligence or even multimodal pattern matching there’s no guarantee there’s an elegant architecture at the core. Elegant models in social sciences like economics, sociology and even fields like biology tend to be hilariously off.
- the13 2y ago[dead]
- ahazred8ta 2y agoIt's been described as fumbling around in a dark room until you find the light switch. At which point you can see the doorway leading to the next dark room.
- calmbell 2y agoThat is how science seems to work as a whole. What worries me is that the market views the emergence of additional productive paradigm shifts in AI as only a matter of money. A normal scientific advancement plateau for another five years in AI would be a short-term disaster for the stock market and economy.