3 ms·

>The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. There was a point when it was the other way around, this is not static but

>The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. There was a point when it was the other way around, this is not static but the result of resources being poured. The data heavy, computational heavy, black box style of ML gives power to large business over small business. So it's seen as a safer bet than symbolic A.I. This in turn makes it work better, which makes it an even safer bet. Notice that startups dream of being big business so they still pick ML. Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles.

lostdog 5y ago
> Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles. Classical AI has failed in robotics. There's practically no field where it does worse than in robotics. It's getting cut out of every part of every system one piece at a time, and being replaced by ML methods that really work. Even Kalman Filters aren't safe.
- iamcurious 5y ago
  It's been a few years since I investigated, but Boston dynamics was using mostly old non ML methods. See [1] IIRC and Waymo vehicles used ML for some of its perception but also depended heavily on the lidar and a rules based approach. Sadly I can't find a link at the moment. What examples are you thinking of? [1] https://www.quora.com/How-does-Boston-Dynamics-use-AI-machine-learning https://www.quora.com/How-does-Boston-Dynamics-use-AI-machin...
  - lostdog 5y ago
    Here's Tesla's AI day presentation on how they are going to use AI in their planning system because traditional methods aren't scalable enough: https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s (1:20:15). You can see hints of this in presentations from the other self-driving companies. Boston Dynamics was definitely an exception for a long time. Certainly the perception systems that they are adding use ML, but you are right that they use expressly use classical methods for their control system.

lostdog 5y ago

> Also notice that in some domains ML is still behind symbolic A.I., for instance a lot of robotics and autonomous vehicles. Classical AI has failed in robotics. There's practically no field where it does worse than in robotics. It's getting cut out of every part of every system one piece at a time, and being replaced by ML methods that really work. Even Kalman Filters aren't safe.

iamcurious 5y ago
It's been a few years since I investigated, but Boston dynamics was using mostly old non ML methods. See [1] IIRC and Waymo vehicles used ML for some of its perception but also depended heavily on the lidar and a rules based approach. Sadly I can't find a link at the moment. What examples are you thinking of? [1] https://www.quora.com/How-does-Boston-Dynamics-use-AI-machine-learning https://www.quora.com/How-does-Boston-Dynamics-use-AI-machin...
- lostdog 5y ago
  Here's Tesla's AI day presentation on how they are going to use AI in their planning system because traditional methods aren't scalable enough: https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s https://www.youtube.com/watch?v=j0z4FweCy4M&t=4392s (1:20:15). You can see hints of this in presentations from the other self-driving companies. Boston Dynamics was definitely an exception for a long time. Certainly the perception systems that they are adding use ML, but you are right that they use expressly use classical methods for their control system.