3 ms·

I really don't get Gary Marcus' fundamental problem. He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed a

I really don't get Gary Marcus' fundamental problem. He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? Surely no one is being held academically ransom to Deep Learning. If Ps (and Qs) is a better approach then do the research and publish the results. Instead he just seems to whine about it on Twitter all day and attack people who are actually "doing the research and publishing the results."

deleted 8y ago
[deleted]
Eridrus 8y ago
Gary isn't a technologist, but he wants to set the stage with critique so that when/if a fusion of the ideas does better he can claim to have been important to that, regardless of the fact that the idea is obvious, and the implementation is what is hard
- deleted 8y ago
  [deleted]
YeGoblynQueenne 8y ago
>> He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? It's a slightly garbled (and so not immediately recognisable, perhaps) pun on the verse "All that we're saying/ is give peace a chance" from John Lennon's "Give Peace a Chance". So it's not addressed to anyone- it's just his attempt at injecting a bit of levity in the debate. EDIT: >> If Ps (and Qs) is a better approach then do the research and publish the results. Well, people have done that, yes. For example, the Evans and Grefenstette paper Marcus' article cites towards the end has shown in a very clear manner the power of combining symbolic with sub-symbolic approaches, as has the work of, off the top of my head, Artur D'avila Garcez (neuro-symbolic computation), Luciano Serafini (a differentiable logic), and many others. And yet, my intuition at least is that most people who have heard about Deep Learning, haven't heard about that work.
- laichzeit0 8y ago
  This comes across a bit like the Tanenbaum–Torvalds monolithic/microkernel debate in the 90s. Tanenbaum in this case is Marcus and Torvalds is LeCunn. There are so many well established benchmarks out there. Just build whatever this symbolic system is that you're advocating and beat the benchmarks. Or maybe create your own benchmarks and then we can also apply Deep Learning to those problems too and see who wins? Until then, no one really has to pay any serious attention to these clamourings.
  - YeGoblynQueenne 8y ago
    That's what the work I mentioned above does- it beats benchmarks and shows impressive results. The work by Evans and Grefenstette in particular is all out of chewing gum [1]. Somewhat depressingly, your comment makes me guess that you really think that none of that work has actually been done, that it's somehow all theoretical or speculative, or just a lot of twitter talk by Gary Marcus. That couldn't be farther from the truth. It's really the case that many people have no idea about AI beyond a quick article in the popular press here and there, and despite this, they have already formed very strong opinions about it. _________________ [1] Here: https://deepmind.com/blog/learning-explanatory-rules-noisy-data/ https://deepmind.com/blog/learning-explanatory-rules-noisy-d... Note that the above overview of their paper is published on DeepMind's website (the two are DeepMind employees). You can rest assured that DeepMind in particular, would never support the publication of a paper that doesn't demonstrate impressive results.
empath75 8y ago
Isn’t the kind of programming that most of us do every single day just symbol manipulation of the kind he describes? On this site, I click reply, the web server interprets my intention and presents me with a text box, etc. There’s a level of intelligence in pretty much any computer program. I think we take it for granted now because it’s so engrained in our data to day lives, but it’s remarkable isn’t it? Just look at how many decisions take place entirely within the ‘minds’ of computers when you try and buy a product on amazon and have it shipped to you. Everything from the pricing of the goods, to approving your credit worthiness to the logistics of getting it to your house is decided on by computers. All of that would have to have been done by human beings not very long ago. If anything, symbol manipulation has become such a dominant paradigm for artificial intelligence that we don’t actually recognize any of those activities as requiring intelligence any more — which is always the case every time we mechanize thought. The question is really whether either of those paradigms gets us to a “general” ai (whatever that is), or both, or neither.
drb91 8y ago
This is an article in a popular magazine, and there is no “problem” nor any “whining”. Except here in the comments, oddly!
mindgam3 8y ago
As stated in the article, Marcus' fundamental problem is: 1) "the notion that deep learning is without demonstrable limits and might, all by itself, get us to general intelligence, if we just give it a little more time and a little more data, as captured in a 2016 suggestion by Andrew Ng" and 2) "Leaders in AI like LeCun acknowledge that there must be some limits, in some vague way, but rarely (and this is why Bengio’s new report was so noteworthy) do they pinpoint what those limits are, beyond to acknowledge its data-hungry nature."

deleted 8y ago

[deleted]

Eridrus 8y ago

Gary isn't a technologist, but he wants to set the stage with critique so that when/if a fusion of the ideas does better he can claim to have been important to that, regardless of the fact that the idea is obvious, and the implementation is what is hard

deleted 8y ago
[deleted]

YeGoblynQueenne 8y ago

>> He ends the essay with "All I am saying is to give Ps (and Qs) a chance.". But who exactly is this aimed at? It's a slightly garbled (and so not immediately recognisable, perhaps) pun on the verse "All that we're saying/ is give peace a chance" from John Lennon's "Give Peace a Chance". So it's not addressed to anyone- it's just his attempt at injecting a bit of levity in the debate. EDIT: >> If Ps (and Qs) is a better approach then do the research and publish the results. Well, people have done that, yes. For example, the Evans and Grefenstette paper Marcus' article cites towards the end has shown in a very clear manner the power of combining symbolic with sub-symbolic approaches, as has the work of, off the top of my head, Artur D'avila Garcez (neuro-symbolic computation), Luciano Serafini (a differentiable logic), and many others. And yet, my intuition at least is that most people who have heard about Deep Learning, haven't heard about that work.

laichzeit0 8y ago
This comes across a bit like the Tanenbaum–Torvalds monolithic/microkernel debate in the 90s. Tanenbaum in this case is Marcus and Torvalds is LeCunn. There are so many well established benchmarks out there. Just build whatever this symbolic system is that you're advocating and beat the benchmarks. Or maybe create your own benchmarks and then we can also apply Deep Learning to those problems too and see who wins? Until then, no one really has to pay any serious attention to these clamourings.
- YeGoblynQueenne 8y ago
  That's what the work I mentioned above does- it beats benchmarks and shows impressive results. The work by Evans and Grefenstette in particular is all out of chewing gum [1]. Somewhat depressingly, your comment makes me guess that you really think that none of that work has actually been done, that it's somehow all theoretical or speculative, or just a lot of twitter talk by Gary Marcus. That couldn't be farther from the truth. It's really the case that many people have no idea about AI beyond a quick article in the popular press here and there, and despite this, they have already formed very strong opinions about it. _________________ [1] Here: https://deepmind.com/blog/learning-explanatory-rules-noisy-data/ https://deepmind.com/blog/learning-explanatory-rules-noisy-d... Note that the above overview of their paper is published on DeepMind's website (the two are DeepMind employees). You can rest assured that DeepMind in particular, would never support the publication of a paper that doesn't demonstrate impressive results.

empath75 8y ago

Isn’t the kind of programming that most of us do every single day just symbol manipulation of the kind he describes? On this site, I click reply, the web server interprets my intention and presents me with a text box, etc. There’s a level of intelligence in pretty much any computer program. I think we take it for granted now because it’s so engrained in our data to day lives, but it’s remarkable isn’t it? Just look at how many decisions take place entirely within the ‘minds’ of computers when you try and buy a product on amazon and have it shipped to you. Everything from the pricing of the goods, to approving your credit worthiness to the logistics of getting it to your house is decided on by computers. All of that would have to have been done by human beings not very long ago. If anything, symbol manipulation has become such a dominant paradigm for artificial intelligence that we don’t actually recognize any of those activities as requiring intelligence any more — which is always the case every time we mechanize thought. The question is really whether either of those paradigms gets us to a “general” ai (whatever that is), or both, or neither.

drb91 8y ago

This is an article in a popular magazine, and there is no “problem” nor any “whining”. Except here in the comments, oddly!

mindgam3 8y ago

As stated in the article, Marcus' fundamental problem is: 1) "the notion that deep learning is without demonstrable limits and might, all by itself, get us to general intelligence, if we just give it a little more time and a little more data, as captured in a 2016 suggestion by Andrew Ng" and 2) "Leaders in AI like LeCun acknowledge that there must be some limits, in some vague way, but rarely (and this is why Bengio’s new report was so noteworthy) do they pinpoint what those limits are, beyond to acknowledge its data-hungry nature."