2 ms·

I used to think that the rarity of human mastery of games, and games' abstraction from the physical world, were what prevented most humans from perceiving machi

I used to think that the rarity of human mastery of games, and games' abstraction from the physical world, were what prevented most humans from perceiving machine intelligence as intelligence. Then the 2005 DARPA Grand Challenge for self-driving vehicles showed that machines could perform a task that most adult Americans can perform, that no non-human animals have ever been taught to perform, and that requires significant awareness of the physical world. But AFAICT it didn't cause a sea change in how most people think about intelligence, human and otherwise. There has been an uptick in people pondering the economic implications of driverless vehicles and a more robotic future. That discussion seems kind of oddly isolated from re-considering the nature of intelligence, human and otherwise. It's as if after the Industrial Revolution people kept narrowly scoping the meaning of "power" to "muscle power" rather than acknowledging mechanical forms. Oh, yes, that coal fired pump can remove water from the mine faster than I can... but it just uses clever tricks for faking power.

laughinghan 10y ago
> a task that most adult Americans can perform, that no non-human animals have ever been taught to perform Woah wait what? Non-human animals successfully navigate >7 miles of mountain terrain all the time. Machine intelligence doesn't seem like "real" intelligence because it just doesn't seem as generalizable. Taking the engines and hydraulics used to great effect in water pumps and applying them to construction cranes required engineering work, sure, but no new physics. But you can't just take the convolutional neural nets that are breaking new ground in computer vision and apply them to natural language processing, you need new computer science research to develop long short-term memory networks. The cool thing about AlphaGo, from my understanding, was that it was able to train the deep learning-based heuristics for board evaluation by playing a ton of games against itself. This is especially awesome because those heuristics are (were?) our main edge over machines [1]. But in CV and NLP, playing against yourself isn't really a thing, so again, this work doesn't automatically generalize the way engines and hydraulics did. [1]: https://en.wikipedia.org/wiki/Anti-computer_tactics https://en.wikipedia.org/wiki/Anti-computer_tactics
deleted 10y ago
[deleted]

laughinghan 10y ago

> a task that most adult Americans can perform, that no non-human animals have ever been taught to perform Woah wait what? Non-human animals successfully navigate >7 miles of mountain terrain all the time. Machine intelligence doesn't seem like "real" intelligence because it just doesn't seem as generalizable. Taking the engines and hydraulics used to great effect in water pumps and applying them to construction cranes required engineering work, sure, but no new physics. But you can't just take the convolutional neural nets that are breaking new ground in computer vision and apply them to natural language processing, you need new computer science research to develop long short-term memory networks. The cool thing about AlphaGo, from my understanding, was that it was able to train the deep learning-based heuristics for board evaluation by playing a ton of games against itself. This is especially awesome because those heuristics are (were?) our main edge over machines [1]. But in CV and NLP, playing against yourself isn't really a thing, so again, this work doesn't automatically generalize the way engines and hydraulics did. [1]: https://en.wikipedia.org/wiki/Anti-computer_tactics https://en.wikipedia.org/wiki/Anti-computer_tactics

deleted 10y ago

[deleted]