Deep Learning’s Diminishing Returns

www.spectrum.ieee.org ^ | 24 SEP 2021 | NEIL C. THOMPSON KRISTJAN GREENEWALD KEEHEON LEE GABRIEL F. MANSO

[Jonty30]

DEEP LEARNING IS NOW being used to translate between languages, predict how proteins fold, analyze medical scans, and play games as complex as Go, to name just a few applications of a technique that is now becoming pervasive. Success in those and other realms has brought this machine-learning technique from obscurity in the early 2000s to dominance today.

Although deep learning's rise to fame is relatively recent, its origins are not. In 1958, back when mainframe computers filled rooms and ran on vacuum tubes, knowledge of the interconnections between neurons in the brain inspired Frank Rosenblatt at Cornell to design the first artificial neural network, which he presciently described as a "pattern-recognizing device." But Rosenblatt's ambitions outpaced the capabilities of his era—and he knew it. Even his inaugural paper was forced to acknowledge the voracious appetite of neural networks for computational power, bemoaning that "as the number of connections in the network increases...the burden on a conventional digital computer soon becomes excessive."

[Jonty30]

The good news is that there may not be as many jobs lost to AI, because the trade-off on costs to make computers smarter may not pay off in the long run.

[Gene Eric]

As the Woke have sabotaged a mature society, so will it do to AI.

[Veggie Todd]

Because I’m good enough, I’m smart enough, and, doggonit, people like me!