3 ms·

Symbolic AI is a well known topic. https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence?wprov=sfla1 https://en.wikipedia.org/wiki/Symbolic_artificial

Symbolic AI is a well known topic. https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence?wprov=sfla1 https://en.wikipedia.org/wiki/Symbolic_artificial_intelligen... As the article notes > Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the late 1980s. And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale. It simply brute force calculated the value of as many position trees as it could up to ply 8, and the "value calculator" was a set of rules based on human expert inputs. Creating Deep Blue's hardware in 1997 was the most impressive achievement. Anyway, symbolic AI still potentially may be the path to AGI, but obviously ML/DL techniques utilizing very large datasets snd very large architectures bore a lot of useful fruit.

forgotpwd16 6y ago
Is symbolic AI dead nowadays? All AI papers I'm seeing is on machine learning.
- YeGoblynQueenne 6y ago
  Have you looked at recent publications in AAAI and IJCAI? Those are the big conferences of AI without specific focus on machine learning. You may also want to have a look at KRR (Knowledge Representation and Reasoning) and one of the robotics conferences, whose names unfortunately I don't remmeber by heart. Again, those don't tend to focus on machine learning. Finally, there is such a thing as symbolic machine learning. For example, see the IJCLR conference (unifying a bunch of conferences from the symbolic machine learning field, like ILP, STAR, NeSy etc).
YeGoblynQueenne 6y ago
>> And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale. Modern chess engines use the same approach as Deep Blue, on much smaller hardware. IBM certainly tried to position Deep Blue as a triumph of large hardware, but its success is widely recognised as being owed to its minimax with alpha-beta cutoff algorithm. I can't imagine anyone who would call minimax "brute force"- it's a search algorithm and alpha-beta is a heuristic pruning technique. It's very hard to see pruning of any kind as "brute force"; rather that's the whole point, you prune a search tree to avoid an exhaustive search. The evaluation function was hard-coded, yes, according to human expertise and chess theory. Note that Deep Blue also had an extensive "opening book" which if I remember correctly made it possible to play a very strong early game. In any case, it took a few decades to create a system like Deep Blue and it was far from "a bit of symbolic manipulation in Lisp" but scaled up. I recommend the Adversarial Search section in Russel and Norvig if you want to develop a more thorough understanding of the relevant approaches (probably fourth edition will do). >> Creating Deep Blue's hardware in 1997 was the most impressive achievement. Can you point to other achievements in symbolic AI that were not as impressive as Deep Blue? Do you know of any others that you could compare to Deep Blue?

forgotpwd16 6y ago

Is symbolic AI dead nowadays? All AI papers I'm seeing is on machine learning.

YeGoblynQueenne 6y ago
Have you looked at recent publications in AAAI and IJCAI? Those are the big conferences of AI without specific focus on machine learning. You may also want to have a look at KRR (Knowledge Representation and Reasoning) and one of the robotics conferences, whose names unfortunately I don't remmeber by heart. Again, those don't tend to focus on machine learning. Finally, there is such a thing as symbolic machine learning. For example, see the IJCLR conference (unifying a bunch of conferences from the symbolic machine learning field, like ILP, STAR, NeSy etc).

YeGoblynQueenne 6y ago

>> And, yes, Deep Blue was just "a bit of symbolic manipulation you can do in Lisp" at a very large hardware scale. Modern chess engines use the same approach as Deep Blue, on much smaller hardware. IBM certainly tried to position Deep Blue as a triumph of large hardware, but its success is widely recognised as being owed to its minimax with alpha-beta cutoff algorithm. I can't imagine anyone who would call minimax "brute force"- it's a search algorithm and alpha-beta is a heuristic pruning technique. It's very hard to see pruning of any kind as "brute force"; rather that's the whole point, you prune a search tree to avoid an exhaustive search. The evaluation function was hard-coded, yes, according to human expertise and chess theory. Note that Deep Blue also had an extensive "opening book" which if I remember correctly made it possible to play a very strong early game. In any case, it took a few decades to create a system like Deep Blue and it was far from "a bit of symbolic manipulation in Lisp" but scaled up. I recommend the Adversarial Search section in Russel and Norvig if you want to develop a more thorough understanding of the relevant approaches (probably fourth edition will do). >> Creating Deep Blue's hardware in 1997 was the most impressive achievement. Can you point to other achievements in symbolic AI that were not as impressive as Deep Blue? Do you know of any others that you could compare to Deep Blue?