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> Failing on the last 2% (or whatever small number we want to set) isn't a shortcoming of deep learning Citation needed. Deep learning is used to power some of
by justapassenger 6y ago
> Failing on the last 2% (or whatever small number we want to set) isn't a shortcoming of deep learning
Citation needed. Deep learning is used to power some of the biggest money making machines in the world (like google or facebook ads) and they're still bad. It's also used to power products like voice recognition and it'll still add dog to my shopping list instead of soap.
- ethbr0 6y agoI would counter that those systems are as good as they need to be. Google and Facebook have realized advertisers are dumb. So why waste a ton of people time optimizing past what moves the revenue needle? Where I'd expect to see interesting developments (hopefully published openly) is in sentiment analysis / disinformation identification, as companies begin to see it as an existential threat to the unregulated nature of their businesses and fund accordingly.