Gaming platforms

With Facebook we see algorithms have replaced editorial boards... a lot of people welcome that... but they may have not have entirely thought through the implications. The Facebook and Twitter platforms have been gamed/amplified by clever/nefarious state-backed programmers. A lot of positive work done to engineer the Internet, has, like the snake eating its tail, begun to devour itself. Her work is not "light reading" but Renee DiResta is someone who I find has really thought through this stuff, and is thinking several steps ahead of the bad guys. - Vaughan

Related

https://www.c-span.org/video/?449088-1/senate-intelligence-committee-holds-hearing-social-media-foreign-influence

https://www.youtube.com/watch?v=QOdqXZWs3yg

https://www.nytimes.com/2017/11/12/technology/social-media-disinformation.html

Facebook fights broadcasts of confusion

Facebook continues to be used as a vehicle for disinformation. This is done by publishing provocative news (not always fake, but certainly presented with nefarious gusto)  under false pretenses to influence the division in large population. Facebook said on Tuesday that it had identified several new Iranian and Russian influence campaigns on its platform designed to mislead people in different countries and regions. Able Renee DiResta of the New Knowledge Research Group said "malicious narratives are spreading to mislead people around the world". The news comes on the same day as reports that Microsoft has found Russian government affiliated websites that masquerade as websites of prominent American conservative think tank websites. The saw of confusion cuts in all directions. Know your links, or you may be sharing falsehoods that have suspicious origin and negatively disruptive intention. - Vaughan

How well can neurals generalize across hospitals?

Which features in any quantity influence a convolutional neural network’s (CNN’s) decision? To find the answer in radiology, work is needed, writes researcher John Zech on Medium. The matter gains increased importance as researchers look to ‘go big’ with their data, and to create models based on X-rays obtained from different hospitals.

Before tools are used to crunch big data for actual diagnosis "we must verify their ability to generalize across a variety of hospital systems" writes Zech.

Among findings:

that pneumonia screening CNNs trained with data from a single hospital system did generalize to other hospitals, though in 2 / 4 cases their performance was significantly worse than their performance on new data from the hospital where they were trained.

he goes further:

CNNs appear to exploit information beyond specific disease-related imaging findings on x-rays to calibrate their disease predictions. They look at parts of the image that shouldn’t matter (outside the heart for cardiomegaly, outside the lungs for pneumonia). Initial data exploration suggests they appear rely on these more for certain diagnoses (pneumonia) than others (cardiomegaly), likely because the disease-specific imaging findings are harder for them to identify.

These findings come against a backdrop: An early target for IBM’s Watson cognitive software has been radiology diagnostics. Recent reports question the efficacy thereof. Zech and collaborators’ work shows another wrinkle on the issue, and the complexity that may test estimates of early success for deep learning in this domain. - Vaughan

Related
https://arxiv.org/abs/1807.00431
https://medium.com/@jrzech/what-are-radiological-deep-learning-models-actually-learning-f97a546c5b98
https://en.wikipedia.org/wiki/Convolutional_neural_network
https://www.clinical-innovation.com/topics/artificial-intelligence/new-report-questions-watsons-cancer-treatment-recommendations

DeepMind AI eyes ophthalmological test breakthrough

Eye ball to eye ball with DeepMind.

DeepMind, the brainy bunch of British boffins whom Google pickedup to carry forward the AI torch, has reported in a scientific journal that it succeeded in employing a common ophthalmological tests to screen for many health disorders.

So reports Bloomberg.

DeepMind’s software used two separate neural networks, a kind of machine learning loosely based on how the human brain works. One neural network labels features in OCT images associated with eye diseases, while the other diagnoses eye conditions based on these features.

Splitting the task means that -- unlike an individual network that makes diagnoses directly from medical imagery – DeepMind’s AI isn’t a black box whose decision-making rationale is completely opaque to human doctors, [a principal said].

The group, which encountered controversy over its use of patient data in the past, said it has cleared important hurdles and  hopes to move to clinical tests in 2019.

Related
https://www.bloomberg.com/news/articles/2018-08-13/google-s-deepmind-to-create-product-to-spot-sight-threatening-disease

these deep neural nets just sort of keep getting deeper and bigger

Making intelligence intelligible with Dr. Rich Caruana https://t.co/s3jGUR4QRV

— Jack Vaughan at TT (@JackVaughanatTT) June 5, 2018

hard to open up those many layered neurals.

to wrap your head around a hundred million weights.

that's harder to udnerstand compared to linear regression.

these deep neural nets just sort of keep getting deeper and bigger.

cc: ummings

ODSC placekeeper

Sorry I missed the Open Data Science Conference & Expo in Boston earlier this month. I could even have taken that there bus on the left. It was one of those things. This year has a scattered plot. I would have liked to accelerate my data science knowledge, training, and do some networking.  ODSC East 2018 is one of the largest applied data science conferences in the world. But let's think of this as a book mark for a placekeeper for a mnemonic jigger to pick up where we left off.  Find out more: https://odsc.com/boston

Recalling good old Obama days

The NYTimes had an editorial about Facebook data privacy yesterday.  In it they recall Obama’s efforts in this regard. Which we saw firsthand at an MIT event back in 2014. I got to cover it as part of my job.

I remember thinking at the time that Obama’s Data Privacy Fact Finding committee was likely to be sidetracked (and co-opted by advertising giants Facebook and Google and telecoms like Verizon and Comcast and their soldiers among the MIT high tech intelligentsia).

That feeling emerged as the conference events ensued, which revolved around encryption and differential privacy and other of the hemming and hawing that characterize the corridors of technology power.

A colleague and I agreed the theme that emerged most prominently was that data was the "new gold" or the "new oil"  -- it seems overblown (why not the "new tulips"?), until you see a room full of policy and commerce people discussing how much data is going to change the world as we know it. Ad nauseum.

Whether they were right or wrong, we more or less settled, was less important than the palpable sense that something akin to gold or oil ''fever'' was in the air. Which brings us back to Facebook, seen in a new light, given the way its data (your data) ended up in the hands of Cambridge Analytica.

The Times's recent editorial avows there is no reason to start from scratch when it comes to data privacy today, that Obama's privacy proposals of 2012 and thereafter, for a basis for data rights. I am not so sure there was much inthe way of real changeat work there. I don't want to sound relativistic like the Trump cracker contingent, but there wasnt much different between the left and right when push came to shove on privacy back in 2004. - Jack IgnatiusVaughan

Related
https://www.nytimes.com/2018/04/01/opinion/facebook-lax-privacy-rules.html
https://itsthedatatalking.blogspot.com/2014/03/encryption-and-differential-privacy.html 

Orwell's Bad Dream Lives

Provided uninterrupted

False news travels faster

Steve Lorhr's  “Why we are easily seduced by false news” recalls an old adage: It takes two to tango.

Yes the IRA attacked America in the soft underbelly known as the Facebook newsfeed, but what made that tummy so flaccid? It was not just the broadcaster - the broadcaster found receivers - many of them. Oafs, retired and semiretired; students, part time and less; nightwatchmen and nightwatchwomen, clicking on their smart phones.

They danced with the Ruskie night riders. And they danced on the winds of false news, which, Lohr reports, follows a unique trajectory. He focuses on an the MIT study that found false news travels faster than true news - that false claims were 70% more likely than the truth to be shared on Twitter

It took true stories about six times longer than false ones to reach 1500 people the MIT study disclosed.

The research was published in Science magazine. It examined stories posted to Twitter from 2006 until 2017, tracking 126,000 stories tweeted by roughly 3.0 million people more than 4.5 million times. News was defined broadly.

What is it about people that makes them more likely to share the false news? It's said here that true news inspired more anticipation, sadness and joy - while false claims elicited greater surprise and disgust. I guess you can say what is false is more visceral.

Should journalism classes be required of citizens in the 21st-century democracy? As I recall, the 20th century journalism teachers told us -- first day of class -- that you did not have to go to journalism school to be a journalist. We're people different then? Was the environment different than today's? - Jack Vaughan


===
I remember in the run up to the election losing my temper with all the false things I was seeing - cant say really understood what was going on but I really wailed away on Facebook . Visceral, one night. Yes, yes. Take this y'all who is reposteth Breitbart, I railed too.

https://www.nytimes.com/2018/03/08/technology/twitter-fake-news-research.html