“Fake news,” broadly defined as false or misleading information masquerading as legitimate news, is frequently asserted to be pervasive online with serious consequences for democracy. Using a unique multimode dataset that comprises a nationally representative sample of mobile, desktop, and television consumption, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most 14.2% of Americans’ daily media diets. Second, to the extent that Americans do consume news, it is overwhelmingly from television, which accounts for roughly five times as much as news consumption as online. Third, fake news comprises only 0.15% of Americans’ daily media diet. Our results suggest that the origins of public misinformedness and polarization are more likely to lie in the content of ordinary news or the avoidance of news altogether as they are in overt fakery.
Working with Zach and Aaron on the app. I think we’ll have something by this weekend
Added a starting zero on the regression
Added the regression to the json file, and posted to see if Zach can reach
Set up the hooks for export to excel workbook, with one tab per active country. I’ll work on that later today – done! countries
Got clarification from Wayne on some edits. Going to turn those around this morning and try to submit before COB today. Maryland is at 580 confirmed cases as of yesterday. I’d expect to see nearly 800 when they update the site this morning. Sent over all the edits. It’s in!
Waking up to the news these days makes me want to stay in bed with the radio off
Working on automating the process of downloading the spreadsheet, parsing out the countries, and calculating daily rates. The goal is to have a website up this weekend so you can see how your country is doing.
Set up converter class – done
download spreadsheet – done
parse out countries – working on it
Made mockups of the mobile and webpage displays, and refined a few times based on comments
Got notes for Chapter 11 from Wayne. Switching gears and rolling that in. Put in changes for all the items I could read. There are a few still outstanding. I’ll submit tonight if Wayne doesn’t come back for a discussion.
Back to Docker. Need to connect to the WLS. Done!
AIMS – status for all, plus technichal glitches. We’ll try Teams next time. Vadim has made GREAT progress. We might be able to get a real Yaw Flip soon as well
A2P – Infor demo. Meh.
Stampede theory proposal deadline was delayed a couple of days
I found the data sources for the dashboard in the previous few posts. Yes, everything still looks grim:
So rather than working on my dissertation, I thought I’d take a look at the data for the last 9(!) days in Excel:
This is for the USA. The data is sorted based on the cumulative total of new cases confirmed. If you look at the chart on the right, everything is in line with a pandemic in exponential growth. However, that’s not the whole story.
I like to color code the cells in my spreadsheets because colors help me visualize patterns in the data that I wouldn’t otherwise see. And one of the things that really stands out here is the red rows with one yellow cell on the left. These are all cases where the rate of confirmed new cases dropped to zero overnight. And they’re not near each other. They are in WA, NY, and CA. Is this a measuring problem or is something going right in these places?
Maybe we’ll find out more in the next few days. Now that I know how to get the data, I can do some of my own visualizations that look for outliers. I can also train up some sequence-to-sequence ML models to extrapolate trends.
One more thing. I had heard earlier (Twitter, I think?) that Vietnam was handling the crisis well. And it looks like it was, but things are back to being bad:
Ok, back to work
8:00 – 4:30 ASRC PhD, GOES
Working on the process section – done!
Working on the TACJ bookend – done! Made a new figure:
Submitted to Wayne. Here’s hoping it doesn’t fall through the cracks
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us remain focused on important parts of our world without distraction from irrelevant details. Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck. By constraining access to only a small fraction of the visual input, we show that their policies are directly interpretable in pixel space. We find neuroevolution ideal for training self-attention architectures for vision-based reinforcement learning tasks, allowing us to incorporate modules that can include discrete, non-differentiable operations which are useful for our agent. We argue that self-attention has similar properties as indirect encoding, in the sense that large implicit weight matrices are generated from a small number of key-query parameters, thus enabling our agent to solve challenging vision based tasks with at least 1000x fewer parameters than existing methods. Since our agent attends to only task-critical visual hints, they are able to generalize to environments where task irrelevant elements are modified while conventional methods fail.
Today’s dashboard snapshot (more data here). My thoughts today are about supression and containment, which are laid out in the UK’s Imperial College COVID-19 report. The TL;DR is that suppression is the only strategy that doesn’t overwhelm healthcare. Suppression is fever clinics, contact tracing, and enforced isolation, away from all others (in China, this was special isolation clinics/dorms). This has clearly worked in China (and a town in Italy), though Hong Kong and Singapore seem to be succeeding in different (more cultural?) ways. The thing that strikes me is that suppression is just putting a lid on things. The moment the lid comes off, then infections start up again? I guess we’ll see over the next few months in China.
There appear to be vaccines in (human already!) testing. Normally, there is an extensive evaluation process to see if the treatment is dangerous, but that was sidestepped during the AIDS crisis (the parallel track policy). I wonder if at risk populations (People older than 70?), will allowed to use less-tested drugs. My guess is yes, probably within a month.
Finished all the dissertation revisions and made a document that contains only those revisions. Need to make a change tableand then send (full and revisions only) to Wayne today.
Whoops! No I didn’t. After putting together the change table, I realize there are still a few things to do. Dammit!
Update SDaaS paper as per John’s edits
Phone call with Darren at 2:00
Start a google doc that has all the parts of a proposal, plus a good introduction.
Also the idea of sims came up again as ways to define, explain, train ML, and test a problem/solutions
Today’s view of the dashboard. Looking at the numbers, it’s pretty clear that China has things under control, which means that we can get an idea of what it will look like in the US on the other side. The symptomatic population was (3,111 deaths + 55,987 recovered) = 59,098. That means that the mortality rate for that (infected? symptomatic?) population (59,098/3,111) is 5.26%. The median age in China is 38.4 years. Interestingly, that’s about the same as the USA.
Working from home for the duration of the COVID-19 pandemic. It’s estimated that we are approximately 10 days behind Italy, So I’m hoping that when things start to get better there, it will be a head’s up that things might start to get better here.
Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take many hours on even the fastest supercomputers. Emulators, algorithms that quickly approximate these detailed simulations, offer a shortcut. Now, work posted online shows how artificial intelligence (AI) can easily produce accurate emulators that can accelerate simulations across all of science by billions of times.
Deceptive claims surround us, embedded in fake news, advertisements, political propaganda, and rumors. How do people know what to believe? Truth judgments reflect inferences drawn from three types of information: base rates, feelings, and consistency with information retrieved from memory. First, people exhibit a bias to accept incoming information, because most claims in our environments are true. Second, people interpret feelings, like ease of processing, as evidence of truth. And third, people can (but do not always) consider whether assertions match facts and source information stored in memory. This three-part framework predicts specific illusions (e.g., truthiness, illusory truth), offers ways to correct stubborn misconceptions, and suggests the importance of converging cues in a post-truth world, where falsehoods travel further and faster than the truth.
It turns out that the alienware OEM power supply uses standard connectors in a non-standard way. When I had to use the OEM SATA low-profile connector, I tripped the power supply and also blew out the HD. Ordered a replacement SATA SSD
Rebuild travel folders
Copy laptop’s d: dev and program files folders onto new SSD