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

[Dylan Distasio]

For those interested, this is a very good write up on how the Microsoft
architecture works:

https://codeburst.io/understanding-r-net-microsofts-superhuman-reading-ai-23ff7ededd96

The actual paper is here:
https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf

On Jan 18, 2018 8:17 PM, "Rafal Smigrodzki" <rafal.smigrodzki at gmail.com>
wrote:

>
>
> On Tue, Jan 16, 2018 at 1:31 PM, Dylan Distasio <interzone at gmail.com>
> wrote:
>
>> This is the Achilles heel of all the deep learning that I've been exposed
>> to.  Deep nets are also great at image recognition until an adversarial
>> attack is injected that will fool a net, but not a human child.  There is
>> zero comprehension.  The nets are dead behind the eyes.  I believe the
>> folks attempting to replicate actual biological neuron function (the bulk
>> of deep learning is not much more than high school math and a gradient
>> descent function to find a global minima) have the best chance of building
>> something that actually understands what it is doing.
>>
>>
> ### I agree with the general tenor of your remarks but I would not go as
> far as saying "zero comprehension". Current deep learning systems are very
> brittle, much more so than humans but they are on to something - small
> parts of the world models that humans build over decades of learning are
> already present in the networks. They are not fleshed out with enough
> cross-references to different but complementary representations of aspects
> of the world, which is why they are brittle. I cannot guess whether the
> Microsoft or Baidu systems have 1% or 10% of adult human understanding of
> the world but I would argue it is not zero.
>
> I also doubt that the secret of human intelligence is in human neurons.
> Neurons have been around a long time and human neurons are about 99%
> identical biologically to monkey neurons - but we have more of them and
> especially more of them wired to perform high-level integrative data
> analysis. In effect, our brain's networks are just deeper than other deep
> learning networks in existence, whether animal or artificial, but still
> using only high school math to do their job. I guess.
>
> Rafal
>
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