[ExI] Neural networks score higher than humans in reading and comprehension test

[Dylan Distasio]

John-

I'm not trying to take anything away from the accomplishments, but current
deep learning architectures are nowhere close to strong AI, and in my
humble opinion are likely a dead end in that regard.

I will quote the inventor of Keras which is a heavily used deep learning
library layered on top of Tensorflow (or other) architecture to sum up my
views:

"Neural networks" are a sad misnomer. They're neither neural nor even
networks. They're chains of differentiable, parameterized geometric
functions,  trained with gradient descent (with gradients obtained via the
chain rule). A small set of highschool-level ideas put together

and

"I'd say ML is both overhyped and underrated. People overestimate the
intelligence & generalization power of ML systems (ML as a magic wand), but
underestimate how much can be achieved with relatively crude systems, when
applied systematically (ML as the steam power of our era)"



On Tue, Jan 16, 2018 at 5:35 PM, John Clark <johnkclark at gmail.com> wrote:

> On Tue, Jan 16, 2018 at 1:31 PM, Dylan Distasio <interzone at gmail.com>
> wrote:
>
>
>> ​> ​
>> There is zero comprehension.
>>
>
> ​
> If that is true and if neural networks can can now answer questions about
> what they just read better than a human can (and they can) then I don't
> know what "comprehension" means,
> ​but ​
> whatever it means it can't be anything very important.
>
>
>> ​>​
>>  The nets are dead behind the eyes.
>
>
> ​If that is true then being "​
> dead behind the eyes
> ​" is an advantage because it makes you more intelligent. Regardless of
> how you want to spin it the fact remains that as of today a computer can
> answer questions about what it just read better than a human can, and
> the implications of that fact are momentous.
>
> John K Clark
>
>
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