NIPS is NeurIPS

Its a big day for regeneration, for non neural cognition and bioelectric mechanisms. The lowly flat worm has had its day. At NeurIPS 2018 up Montreal way.

Michael Levin from Tufts just blew the minds of the NeurIPS attendees with two-headed planaria and other amazing anatomical manipulations #NeurIPS https://t.co/l1IrZNkRC5

— Thomas G. Dietterich (@tdietterich) December 4, 2018

Interested in how the primate visual system might use recurrence, or have a favorite hypothesis? Come to our poster on building deep ConvRNNs #NeurIPS2018 at 5 pm today, poster 20 with @aran_nayebi @qbilius @NeuroAILab @SussilloDavid @SuryaGanguli @dyamins https://t.co/eWSBTP9BxV

— Daniel Bear (@recursus) December 4, 2018

Streamline the mixed precision user experience and enable researchers to leverage mixed precision training in their models more conveniently with NVIDIA Apex, a lightweight @PyTorch extension. #NeurIPS2018 https://t.co/h54Z2xFzl6 pic.twitter.com/3Awu8vhVma

— NVIDIA AI Developer (@NVIDIAAIDev) December 4, 2018

#NeurIPS2018 Best Paper Awards 1): "Non-Delusional Q-Learning and Value- Iteration" by @CraigBoutilier, @tylerlu and Dale Schuurmans from @GoogleAI
Read the full paper at https://t.co/XutJidKNCt pic.twitter.com/vxcComdHLN

— Synced (@Synced_Global) December 4, 2018

Apple talk at #NeurIPS2018 showing their on device models pic.twitter.com/7GWeqRoeCs

— Arch (@archer_mcgee) December 4, 2018

Detecting glaucoma from raw OCT with deep learning framework

''A team of scientists from IBM and New York University is looking at new ways AI could be used to help ophthalmologists and optometrists further utilize eye images, and potentially help to speed the process for detecting glaucoma in images. In a recent paper, they detail a new deep learning framework that detects glaucoma directly from raw optical coherence tomographic (OCT) imaging.''

''Logistic regression was found to be the best performing classical machine learning technique with an AUC* of 0.89. In direct comparison, the deep learning approach achieved AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection.''

Read more at: https://phys.org/news/2018-10-deep-glaucoma.html#jCp
Also https://arxiv.org/abs/1807.04855v1
* "Area under the ROC Curve."

GPUs speed computation

The Science for Life Lab uses GROMACS on NVIDIA GPUs to accelerate drug design. The research group is studying the mechanisms behind various molecular phenomena that occur at human cellular membranes. GROMACS is a molecular dynamics application designed to simulate Newtonian equations of motion for systems with hundreds to millions of particles. The researchers write: The highly iterative nature of fitting the parameters of the kinetic models used to simulate the electrical current curves and running compute heavy simulations for each consumes both time and resources. Slower simulations mean fewer iterations.
Adding GPU acceleration provides a significant performance boost.
Read more. Shown at left: Voltage sensing protein doman.

Platform for Terror

Facebook Admits It Was Used to Incite Violence in Myanmar https://t.co/ZKrA1TaDCu

— Jack Vaughan at TT (@JackVaughanatTT) November 8, 2018

A plaint too plain

They dont call it the Web for nothing

Was remembering when the Web first caught on: There have been a lot of changes in system and data architecture since the. One thing I remember back then is people saying “yeah, it is pretty cool, but, you know, it is stateless.” As most of what I heard on this issue was from enterprise software vendors, with all the bias that could entail, I should have taken what I was told with a grain of salt. The first big problem these folks saw with the Web was its statelessness, which made it far different from the synchronously connect clients and servers (at that time, Java servers) they were used to. Wrote this up for a podcast page related to a podcast ...

 Podcast Page
https://itknowledgeexchange.techtarget.com/talking-data/web-what-have-you-wrought-on-strata-microservices-and-more/
Podcast https://cdn.ttgtmedia.com/Editorial/2016/PodcastTechTarget/Talking_Data_Podcast_092418_withmusic.mp3

Name that tune, Now Playing!

A recent note on the Google AI blog discusses the company’s use of a deep neural network for music recognition on mobile devices. As it brings extreme-scale noodling (convolution) to bandwidth limited devices (smart phones) it could be a breakthrough on par with MPEG and JPEG, which dramatically transformed music distribution beginning in the 1990s. It’s known as Now Playing, and it can use a sequence of embeddings that run your music against its network and recognize the song, while conserving energy on the device. Each embedding has 96 to 128 dimensions. An embedding threshold is raised for obscure songs – which is the town where I live. I guess when you look at what Google has done with Search, it shouldn’t be that surprising – but the idea that so much of the work occurs on the Thing (device), is pretty astounding. I  asked it ‘what’s that song’ and it got it right. Slam dunk. “Ride Your Pony” by Lee Dorsey. Now, Shoot! Shoot! Shoot! Shoot!  Jack Vaughan

RELATED 

https://ai.googleblog.com/2018/09/googles-next-generation-music.html  - Google AI Blog, Sept 14, 2018

Speaking of Name That Tune – why not a little vignette from the time when Humans Walked the Earth?

Opaque algorithms with singular purpose

Today's article  by DiResta:

"Opaque algorithms with their singular purpose—“keep watching”—coupled with billions of users is a dangerous recipe

...

Most RT viewers don’t set out in search of Russian propaganda. The videos that rack up the views are RT’s clickbait-y, gateway content: videos of towering tsunamis, meteors striking buildings, shark attacks, amusement park accidents, some that are years old but have comments from within an hour ago. This disaster porn is highly engaging; the videos been viewed tens of millions of times and are likely watched until the end. As a result, YouTube’s algorithm likely believes other RT content is worth suggesting to the viewers of that content—and so, quickly, an American YouTube user looking for news finds themselves watching Russia’s take on Hillary Clinton, immigration, and current events. These videos are served up in autoplay playlists alongside content from legitimate news organizations, giving RT itself increased legitimacy by association.

...

The social internet is mediated by algorithms: recommendation engines, search, trending, autocomplete, and other mechanisms that predict what we want to see next."

https://www.wired.com/story/free-speech-is-not-the-same-as-free-reach/

The Cavalcade of Falsehood

Notes - The Fog falls on Facebook-When Deception becomes the norm - Manipulation of public opinion is nothing new. But assorted characteristics of the social media platform Facebook make of public opinion a very new mixer. But for context let’s not go back as far as the Lusitania – lets go to the Ukraine.

The downing of a Malaysian jet liner in July 2014 may have pretty immediately been laid at Russia’s door, but it did not catch the Bear without the wherewithal to respond in ingrained fashion. A disinformation campaign was soon launched, with cascading and contradictory alternative stories of lies, half-truths and some truths.

The formula is becoming familiar now in America, but in 2014 it was already very familiar to countries bordering Putin’s dark lair. Reading the report “Fog of Falsehood: Russian Strategy of Deception and the Conflict in Ukraine” bears this out. It is in fact an analysis of the Kremlin kiddos strategic deception, which has had incredible influence in America, and which is at the point of  shaking the foundations of friends and families.

Like others, the Fog of Falsehood editors, Katie Pynnoniemi and Adras Racz, point to Putin’s pre-history, the Soviet Era, to gain historical view on deep trickeration as a tact.  -Jack Vaughan