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Can we unlock the Deep Learning black box?

Troy Sadkowsky - Wednesday, June 28, 2017

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Can we unlock the Deep Learning black box?

Artificial intelligence has experienced a boom in recent years due to the increasing automation and generation of big data. Due to deep learning and artificial neural networks, machines are acquiring new perceptual abilities, such as recognizing images, speech, or reading handwriting, paving the way for an infinite number of new applications in human lives. However, this leap forward in AI processing comes at a cost – the reasoning behind the choices that deep learning networks make has become inscrutable to the engineers that built them.

Progress in automated vision greatly benefits from deep learning because general visual processing is far too complex to code by hand. A program that can learn by observing human example or through complicated training sets, and can generate its own algorithms through an interconnected network of a dozen to several hundred layers of simulated neurons, positions us that much closer to a self-driving car, or other AI programs capable of sophisticated automated decision-making. But if we cannot understand a program’s individual decisions – why it chose to drive into a tree, why a patient’s medications should be changed, why one individual was hired for the job and another flagged as a terrorist – what ethical and moral boundaries are we crossing? And if an artificial intelligence program is not 100% accurate, as they rarely are, what are the dangers of putting ourselves at the mercy of a machine’s mistakes?

According to an article in MIT Technology Review (https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/), trust will be a key factor in future applications of deep learning. Once machines can explain their reasoning to humans, we will better be able to learn from and act on their insights. Research is underway to develop tools for machine learning programs to be able to explain themselves. Regina Barzilay, at MIT, is developing a system that can collaborate with doctors by extracting snippets of text that represent patterns it has discover, and Carlos Guestrin’s system, from the University of Washington, highlights significant keywords or parts of an image to support a particular decision.

However, interpretable deep learning is far off, and even then, explanations offered by machines will always be simplified to a degree, which will always make trusting machine learning programs controversial. As a consequence, the European Union might soon pass legislation to make explanations for automated decisions a fundamental legal right, required of the companies that implement AI machine programs.

Jeff Clune, an investigator at the University of Wyoming testing deep neural networks, offers that, just as with human intelligence and decision-making, it is perhaps “the nature of intelligence that only part of it is exposed to rational explanation. Some of it is just instinctual, or subconscious, or inscrutable”. As deep learning offers more appealing possibilities in medicine, technology and other industries, when and should, society take the deep learning leap of faith?

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Can we unlock the Deep Learning black box?

Troy Sadkowsky - Wednesday, June 28, 2017

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Times; color: #000000; -webkit-text-stroke: #000000} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Times; color: #000000; -webkit-text-stroke: #000000; min-height: 14.0px} span.s1 {font-kerning: none} span.s2 {text-decoration: underline ; font-kerning: none; color: #0000ff; -webkit-text-stroke: 0px #0000ff}

Can we unlock the Deep Learning black box?

Artificial intelligence has experienced a boom in recent years due to the increasing automation and generation of big data. Due to deep learning and artificial neural networks, machines are acquiring new perceptual abilities, such as recognizing images, speech, or reading handwriting, paving the way for an infinite number of new applications in human lives. However, this leap forward in AI processing comes at a cost – the reasoning behind the choices that deep learning networks make has become inscrutable to the engineers that built them.

Progress in automated vision greatly benefits from deep learning because general visual processing is far too complex to code by hand. A program that can learn by observing human example or through complicated training sets, and can generate its own algorithms through an interconnected network of a dozen to several hundred layers of simulated neurons, positions us that much closer to a self-driving car, or other AI programs capable of sophisticated automated decision-making. But if we cannot understand a program’s individual decisions – why it chose to drive into a tree, why a patient’s medications should be changed, why one individual was hired for the job and another flagged as a terrorist – what ethical and moral boundaries are we crossing? And if an artificial intelligence program is not 100% accurate, as they rarely are, what are the dangers of putting ourselves at the mercy of a machine’s mistakes?

According to an article in MIT Technology Review (https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/), trust will be a key factor in future applications of deep learning. Once machines can explain their reasoning to humans, we will better be able to learn from and act on their insights. Research is underway to develop tools for machine learning programs to be able to explain themselves. Regina Barzilay, at MIT, is developing a system that can collaborate with doctors by extracting snippets of text that represent patterns it has discover, and Carlos Guestrin’s system, from the University of Washington, highlights significant keywords or parts of an image to support a particular decision.

However, interpretable deep learning is far off, and even then, explanations offered by machines will always be simplified to a degree, which will always make trusting machine learning programs controversial. As a consequence, the European Union might soon pass legislation to make explanations for automated decisions a fundamental legal right, required of the companies that implement AI machine programs.

Jeff Clune, an investigator at the University of Wyoming testing deep neural networks, offers that, just as with human intelligence and decision-making, it is perhaps “the nature of intelligence that only part of it is exposed to rational explanation. Some of it is just instinctual, or subconscious, or inscrutable”. As deep learning offers more appealing possibilities in medicine, technology and other industries, when and should, society take the deep learning leap of faith?

Comments
anchortext commented on 13-Jun-2018 10:24 AM
Yes, even though all that listing down, in the end you need to sit and
compose an entire response, exactly the same you'll write any essay.
Each format pressupposes some formation plus design for citing rephrased and echoed resources in favor of all various printed, internet, as well
as other types of resources. If you say because again and again, one and only thing
your reader is going to be conscious of happens because -
it's going to stifle your argument and it is near the top of their list of things you should avoid with your academic work.
asia 88 commented on 13-Jun-2018 11:03 AM
Terrific article! This is the kind of info that are meant to be shared around the internet.
Shame on Google for not positioning this publish upper!
Come on over and visit my site . Thank you =)
лазерная гравировка commented on 13-Jun-2018 02:34 PM
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website before but after browsing through some of the post I realized it's new to me.
Nonetheless, I'm definitely happy I found it and I'll be book-marking and checking back often!
Shari commented on 13-Jun-2018 05:05 PM
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Yasmin commented on 13-Jun-2018 05:17 PM
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anchortext commented on 14-Jun-2018 02:01 AM
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across, write employing their language and write while considering their degree of comprehension.
centiplex the worst website commented on 14-Jun-2018 04:16 AM
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I really found you by mistake, while I was looking on Askjeeve for something else, Anyways I
am here now and would just like to say thank you for a
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so when I have time I will be back to read a lot more, Please do keep up the excellent job.
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However imagine if you added some great graphics or videos to give your posts more, "pop"!
Your content is excellent but with images and videos, this
website could definitely be one of the very best in its field.
Terrific blog!
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put this short article together. I once again find myself spending way too much time both reading and leaving comments.
But so what, it was still worthwhile!
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all while focusing on as small an area of investigation as
possible. To ensure that these folks will understand the message that you're trying to find across,
write using their language and write while considering their a higher level comprehension.
https://gust.com commented on 14-Jun-2018 04:33 PM
Howdy! Do you know if they make any plugins to safeguard against hackers?

I'm kinda paranoid about losing everything I've worked
hard on. Any tips?
Self Help Groups commented on 14-Jun-2018 10:54 PM
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Post a Comment




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