Happy post-Thanksgiving everyone! Hope yours was lovely. I went mostly computer free so if you’ve emailed me or sent me something recently, I promise it’s not off my radar. I didn’t get much reading in, but I did get sent two interesting pop culture graphics that are worth a gander.

First up, a visual representation of how accurate “based on a true story” movies are. Shows not only how often it’s inaccurate, but where those inaccuracies take place and how inaccurate they are. For example, here’s Selma (the highest rated) vs Imitation Game (one of the lowest rated). Bright red means false, light red means false-ish, grey is unknown, light blue is true-ish, dark blue is true. :

Check out the actual site, as you can click on each bar to see exactly what the scene was that got the rating. I was interested to see what they called “unknown”, and it appears that those are mostly things like conversations between two characters who definitely spoke, and almost certainly about that topic, but no specific record of or reference to the conversation exists.

Next up, from John: Are pop lyrics getting more repetitive? Using the same algorithm used to compress digital photos in to smaller file sizes, this guy tries to measure how repetitive the lyrics in the Billboard Top 100 songs for the last few decades. Not only is this an interesting project, but he spells out his methodology, assumptions, the outliers and his step by step process REALLY nicely. He shows examples of songs ranked highly repetitive, why he chose to use a log scale for his axis, and how his algorithm would evaluate a regular paragraph of text. Seriously, if scientific papers in general had methodology sections this robust we wouldn’t have a replication crisis.

So what was the most repetitive song in the 15,000 he looked at? Around the World by Daft Punk. Considering that song is just the phrase “Around the World” repeated 100+ times, this makes sense. He breaks down the most repetitive songs by decade, which I thought might be of interest to folks here. Remember, these are only songs that made it to the Billboard Hot 100:

1960s top 3:

• Chain of Fools (Part 1) – Jimmy Smith, 1968 (92% size reduction)
• Jingo – Sanata, 1969  (85% size reduction)
• Any Way You Want It – The Dave Clark Five, 1964 (83% reduction)

(Note to my Dad – You Really Got Me by the Kinks was #5 for the decade at 81%)

1970s top 3:

• Let’s All Chant – The Michael Zager Band, 1978 (88% size reduction)
• Keep it Comin’ Love – KC and the Sunshine Band, 1977 (87%)
• Who’d She Coo? – Ohio Players, 1976 (86%

1980s top 3:

• Pump Up the Jam – Technotronic, 1989 (85%)
• Funkytown – Lipps Inc. 1980 (85%)
• Got My Mind Set On You – George Harrison, 1987 (80%)

1990s top 3:

• Around the World – Daft Punk, 1997 (98%)
• The Rockafeller Skank – Fatboy Slim, 1998 (95%)
• Send Me On My Way – Rusted Root, 1995 (85%)

2000s top 3:

• Better Off Alone – Alice Deejay, 2000 (84%)
• Thong Song – Sisqo, 2000 (81%)
• Dance With Me – 112, 2001 (81%)

2010s top 3:

• Get Low – Dillon Francis & DJ Snake, 2015 (90%)
• Barbra Streisand – Duck Sauce, 2011 (89%)
• Feliz Navidad – Jose Feliciano, 2017 (89%)

Overall, songs did get more repetitive, both overall and the top 10 from each year. In 1960 the average song on the Top 100 was 46% compressible, while in 2015 it was 56% compressible. Interestingly, the top 10 songs are always more repetitive than the rest of them by about 2-6% or so.

There’s also a lot of interesting breakdowns by artist. I learned that the Guess Who was particularly repetitive for the 70s, and that country is much less repetitive than pop music. Apparently this even applies within artists, as Taylor Swift showed a sharp rise in repetitive lyrics after she switched from country to pop.

Anyway, go check it out, the graphics are great!

 

Well, my audit went well last week. The inspector called us “the most boring audit he’d ever had”, which quite frankly is what you want to hear from a regulator. Interest = violations = citations = sad BS King.

As someone who has now dealt with quite a few inspectors over the years, I am always interested to see how exactly they choose to go about surveying everything given the time constraints. This particular inspector had an interesting tactic: he ran down the list of regulations we should be following, and asked us verbally if we followed it or not. Everything tenth one or so, he would suddenly pivot and ask us to provide proof. He mentioned afterwards that he put a lot of weight on how quickly we were able to produce what he asked for. From what I can tell, his theory was that if you produce proof for random questions easily and without hesitation, you probably prepared for everything fairly well. Not a bad theory. Luckily for me, our preparation strategy had been to read through every standard, then prepare a response for it. Thus, we were boring, and my sanity is restored.

I was thinking about all this as I sat down to relax this weekend and picked up the book “Bad Blood: Secrets and Lies in a Silicon Valley Startup” by John Carreyrou. This book covers the rise and fall of Theranos and its founder Elizabeth Holmes, a topic I’ve mentioned on this blog before. To say I couldn’t put it down is a near literal statement: I started it at 5pm last night and finished it by noon today. The book converges on many of my interests: health, medicine, technology, data, and how very smart people can be deceived in to believing something that isn’t true. It also doesn’t hurt that the companies founder is a woman about my age who was once touted as being the first self-made female billionaire in a field I have actually worked in.

For those unfamiliar with Theranos, I’ll give the short version. Theranos was a company started in 2003 by then 19 year old Stanford drop out Elizabeth Holmes. Her vision was to create a blood analyzer that could run regular lab tests on just a few drops of blood, so patients could use a finger stick (like with home glucose monitoring) rather than get their blood drawn the conventional way. Ten years in, the company was worth almost $10 billion, but there was an issue: their product didn’t really work the way they claimed, and the company was using extreme tactics to cover this up. Eventually, in a bid to get somebody to pay attention to this, the story was brought to the attention of a Wall Street Journal reporter (John Carreyrou, who wrote the book) and he managed to untangle the web. Despite the highlights all being pretty well publicized at the time, I found the details and timeline reconstruction to be a fascinating read.

What interested me most about the book was that my characterization in my blog post 2 years ago was a little bit wrong. I had snarked that Carreyrou was one of the first to question them, but as I read the book I discovered that actually a lot of people had questioned Theranos, even during its prime. It actually restored my faith in humanity to see how many people had attempted to raise concerns about what they saw. Many of these people were young, with student debt, or marketing people unfamiliar with science, or simply people with ethics who just got uncomfortable, and many of them only stopped pushing when they were on the receiving end of some downright frightening legal (and sometimes not so legal) intimidation tactics. Additionally, many people who were deceived really couldn’t be blamed. In one particularly bizarre anecdote, Carreyrou mentions that a fellow Wall Street Journal reporter had gone to a meeting with Theranos and they had promised to show him how the machine worked. It turns out the machine didn’t work, but they’d written a program to hide any error messages with a progress screen, and then when he left the room they swapped out his sample and ran it on a regular analyzer they had in another room. Not really his fault for not picking up on that. She got her deal with Walgreens by performing a similar slight of hand. Since the initial WSJ articles, Theranos has paid out millions in lawsuits claiming that they intentionally deceived investors, and Holmes and Ramesh Balwani (her #2 guy and former boyfriend) are under indictment.

Throughout the book, Carreyrou returns to two related but slightly different central points:

1. Holmes and her investors wanted to believe she was the next Steve Jobs or Bill Gates.
2. Healthcare doesn’t work like other tech sector products. Claiming your technology works before it’s ready could kill someone.

It was interesting for me to reflect that if Holmes hadn’t entered the healthcare realm, she might have actually succeeded. While the biographies of people like Steve Jobs are actually littered with the stories of broken promises, many of the people who flipped on Holmes stated that they were compelled to resign their jobs or talk to reporters because they feared the shoddy work was going to kill someone.

So if this was so obvious, how did Theranos get to $10 billion? And how did they end up with people like Henry Kissinger, George Schulz and James Mattis on the board? A few lessons I gleaned:

1. Watch out for the narrative, ask for data. One of the few things everyone agrees upon in this story was that Holmes was a compelling CEO. She could spin a strong narrative to anyone who asked, and was kind and easy to work with as long as you let her stick to the story. Throughout the story though, anyone who asked for proof of anything she said was met with responses ranging from frosty to belligerent. This is what initially reminded me of my inspection. We were able to provide proof just as readily as we were able to provide verbal confirmation, which is why our inspector ended up believing us.
2. Look for real experts. After Carreyrou published his first article about the concerns with the company, he notes that Theranos issued quite a few heavily worded denials and legal threats to the Wall Street Journal. Luckily for him, he noted that post-publication several other media outlets jumped in and started asking questions. He noted that one of the reasons they were so quick to pounce is that a quick look at Theranos’s board and investors revealed that no one involved really knew anything about biotech. While names like Henry Kissinger are impressive, people quickly started noting that the board was mostly military men and diplomats. The lack of any medical leadership seemed out of place. Additionally, some investing groups (like Google Health) that specialize in biotech had passed on Theranos. This was enough to cause other news outlets to turn up the heat on Holmes, as the lack of real experts struck everyone as suspicious.
3. Look at the history. In an interview he gave, Carreyrou pointed out that it wasn’t the initial investors in Theranos who screwed up, as early investors are often gambling on half-baked ideas. The people who failed their due diligence were those who invested a decade in. He notes that those people should have been pushing harder for financial statements and peer reviewed studies, and that didn’t happen. For Theranos not to have peer reviewed studies in their first year was understandable. To still be lacking them in their tenth year was a very bad sign.
4. Apply the right standards to the right industry. Healthcare isn’t the same as a cell phone. There are laws, and regulating bodies that can and will shut you down. A 1% product failure rate can kill people. Don’t get so excited by the idea of “disruption” that you ignore reality.

Come to think of it, with a few tweaks these are all pretty good life lessons about how to avoid bad actors in your personal life as well. I really do recommend this book, if only as a counter-narrative to the whole “everyone said we couldn’t do it, but we proved the naysayers wrong!” thing. Sometimes naysayers are right.

Although maybe not forever. As an interesting end note: according to this article, Holmes is currently fundraising in Silicone Valley for another start up.