Deep Learning – Is It Really Over?

this is one of those cases where paying attention brought two very different articles to contrast.  and am not sure which way to go with this – stay tuned.

first, I read this article from Futurism on the end of the era of Deep Learning.  Quoting from it:

Artificial intelligence developers may soon find themselves on the brink of a paradigm shift. Deep learning has dominated the field for several years — but may be on its way out.

The field of AI has shifted focus roughly every decade since it began in the 1950s. Now, a new analysis suggests that the 2020s will be no different.

it is a short article, but makes a valid point.  we do change our focus fairly often on what we look at: during the 1990s classification became “the thing”, 2000s? understanding language, followed by deep learning.  what will come next?

well, if MIT knows anything — deep learning is just getting started.

as this article on MIT tech review shows, the advances from deep learning are not just staying in place, but are fueling many more innovations in other areas.

Last June, a Google deep-learning system that had been shown 10 million images from YouTube videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats. Google also used the technology to cut the error rate on speech recognition in its latest Android mobile software.

In October, Microsoft chief research officer Rick Rashid wowed attendees at a lecture in China with a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his own voice uttering them in Mandarin.

That same month, a team of three graduate students and two professors won a contest held by Merck to identify molecules that could lead to new drugs. The group used deep learning to zero in on the molecules most likely to bind to their targets.

which brings the question — which one is right?  is deep learning over? or is it just beginning?

(i must confess, never liked the term deep learning – sounds like marketing-made, and most likely it is – but i understand that we are trying to differentiate between general machine learning and stronger methods that go beyond simple cognition… not sure why there is a need for such a differentiation tbh – but i just work here)

what do you think? what are you seeing? is DL over?

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