[OPLIN 4cast] OPLIN 4cast #528: Deep learning

[OPLIN Support]

Email not displaying correctly? View it in your browser.
<http://www.oplin.org/4cast/>
[image: OPLIN 4Cast]

OPLIN 4cast #528: Deep learning
February 8th, 2017

[image: Deep learning] Last week, Facebook announced that its users will be
able to search for photos
<http://techcrunch.com/2017/02/02/facebooks-ai-unlocks-the-ability-to-search-photos-by-whats-in-them/>
based on a description of the photo, something Google Photos has also
worked on. Note that this is not just searching the metadata (tags,
alternative text, etc.) that someone has added to the photo; this is
actually looking for patterns in the image itself. The technology is an
extension of work that had been going on to create automatic alternative
text <https://github.com/ageitgey/show-facebook-computer-vision-tags> for
images, to assist blind users of the internet. And all of this is based on
so-called deep learning neural networks, the “brains” behind artificial
intelligence.
- Deep learning will radically change the ways we interact with technology
<https://hbr.org/2017/01/deep-learning-will-radically-change-the-ways-we-interact-with-technology>
(Harvard Business Review | Aditya Singh)  “Think of the difference between
modern voice-assistants like Siri or Alexa, which allow you to ask for
things in various ways using natural language, vs. automated phone menu
systems, which only perform if you use the specific set of non-negotiable
words that they were programmed to understand. By contrast, deep
learning-based systems make sense of data for themselves, without the need
of an explicit algorithm. Loosely inspired by the human brain, these
machines learn, in a very real sense, from their experience.”
- Building a deep learning neural network startup
<https://medium.com/@startuphackers/building-a-deep-learning-neural-network-startup-7032932e09c#.mcbu4vy07>
(Medium | Varun)  “First came the ‘cat experiment’ demo, where researchers
fed still-image pictures of cats from cat videos on Youtube to a neural
network, and it was able to identify cats in pictures where they were not
labelled so. Pass the champagne moment for researchers. Then, the real
turning point came Nov 2016 when Google switched to using a deep learning
neural network for its Google Translate service, making a drastic switch
from prior 10 years of work building algorithms programmatically as the
results from the deep learning neural network were *orders of magnitude
superior*.”
- What deep learning really means
<http://www.networkworld.com/article/3166029/application-development/what-deep-learning-really-means.html>
(Network World | Martin Heller)  “Understanding *why* deep learning
algorithms work is nontrivial. I won’t say that *nobody* knows why they
work, since there have been papers on the subject, but I will say there
doesn’t seem to be widespread consensus about why they work or how best to
construct them. The Google Brain people creating the deep neural network
for the new Google Translate didn’t know ahead of time what algorithms
would work. They had to iterate and run many weeklong experiments to make
their network better, but sometimes hit dead ends and had to backtrack.”
- AI software learns to make AI software
<https://www.technologyreview.com/s/603381/ai-software-learns-to-make-ai-software/>
(MIT Technology Review | Tom Simonite)  “If self-starting AI techniques
become practical, they could increase the pace at which machine-learning
software is implemented across the economy. Companies must currently pay a
premium for machine-learning experts, who are in short supply. Jeff Dean,
who leads the Google Brain research group, mused last week that some of the
work of such workers could be supplanted by software. He described what he
termed ‘automated machine learning’ as one of the most promising research
avenues his team was exploring.”

*Articles from Ohio Web Library <http://ohioweblibrary.org>:*

   - Combining Newton interpolation and deep learning for image
      classification.
      <http://search.ebscohost.com.proxy.oplin.org/login.aspx?direct=true&db=cph&AN=100117428>
      (*Electronics Letters*, 1/8/2015, p.40-41 | Yongfeng Zhang and
      Changjing Shang)
      - Deep learning.
      <http://search.ebscohost.com.proxy.oplin.org/login.aspx?direct=true&db=aph&AN=103043672>
      (*Nature*, 5/28/2015, p.436-444 | Yann LeCun, Yoshua Bengio and
      Geoffrey Hinton)
      - The deep-learning revolution.
      <http://search.ebscohost.com.proxy.oplin.org/login.aspx?direct=true&db=buh&AN=118302290>
      (*Fortune*, 10/1/2016, p.96-106 | Roger Parloff)

------------------------------
The *OPLIN 4cast* is a weekly compilation of recent headlines, topics, and
trends that could impact public libraries. You can subscribe to it in a
variety of ways, such as:

   - *RSS feed.* You can receive the OPLIN 4cast via RSS feed by
   subscribing to the following URL: http://www.oplin.org/4cast/
   index.php/?feed=rss2.
   - *Live Bookmark.* If you're using the Firefox web browser, you can go
   to the 4cast website (http://www.oplin.org/4cast/) and click on the
   orange "radio wave" icon on the right side of the address bar. In Internet
   Explorer 7, click on the same icon to view or subscribe to the 4cast RSS
   feed.
   - *E-mail.* You can have the OPLIN 4cast delivered via e-mail (a'la
   OPLINlist and OPLINtech) by subscribing to the 4cast mailing list at
   http://lists.oplin.org/mailman/listinfo/OPLIN4cast
   <http://lists.oplin.org/mailman/listinfo/OPLIN4cast>.

© 2016 Ohio Public Library Information Network
[image: Find us on Slideshare] <http://www.slideshare.net/oplin>  [image:
Find us on Facebook] <http://www.facebook.com/oplin.org>  [image: Find us
on Google+] <https://plus.google.com/107751358238995507967>  [image: Find
us on Twitter] <http://www.twitter.com/oplin>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.oplin.org/pipermail/oplin4cast/attachments/20170208/67d7bff6/attachment.html>