You cannot trace everybody, so be smart about who you trace. This article points out the impracticality of massive contact tracing, and how to build a learning system to make it useful anyway. Contact tracing is hard, and when there are too many cases it starts to break down. But we need to figure it out, especially in high-priority settings and in places with limited outbreaks. There are also many idiosyncrasies in Covid infection patterns. A well-executed learning system can gradually make smarter judgments about where to look for cases, who to test, who to quarantine, and when to lift the quarantine.
As we build our nation’s tracing operations, we need to ensure that they are effective at identifying contacts while attempting to quarantine as few people as possible, for as short a duration as possible. To ensure contact tracing remains viable at scale, we must develop data-driven metrics to evaluate and adapt our contact tracing efforts. Historically, successful contact tracing has been measured by its sensitivity [based on more is better]. However, at scale “more is better” breaks down. We must have corresponding metrics for specificity, to … exclude from quarantine those people who have not themselves become carriers of the virus.
But will America’s current political decision-making paralysis, chaos, and suspicion allow the systematic tracing program that would be required? At the national level it seems unlikely. But this approach can be done by states or smaller units. There are probably some states with enough leadership and public willingness to be serious about suppressing Covid before it wipes out another 6 months of jobs and education!
Whiskey is aged in oak barrels, and oak wood is highly variable. But barrel-making can still become much more scientific.
“Twenty-five years ago, it was more art than science. Now we have a healthy dose of science in with the art.” Larry Combs, the general manager for Jack Daniel’s
Recently, the two companies completed the decade-long Single Oak Project, in which they made 192 barrels, each using the wood from a single log, to find what constituted the “perfect” bourbon. (Among other things, they found that wood from the bottom of a tree made for the best aging.). Computers track each stave as it moves through assembly, while sensors analyze staves for density and moisture content. Instead of guessing how much to toast a barrel, operators use lasers and infrared cameras to monitor the temperature of the wood and the precise chemical signature that the heat coaxes to the surface — all subject to the customer’s desired flavor profile.“They’ve developed technologies so that if we say we want coconut flavors, they can apply this or that process” — like applying precise amounts of heat to different parts of the wood to tease out certain flavors — “and we’ll have it,” said Charles de Pottere, the director of production and planning at Jackson Family Wines…
… Black Swan makes barrels with a honeycomb design etched on the inside, which increases surface area and reduces a whiskey’s aging time.
Their approach: learn by experimentation, and use the new knowledge for tight process control. Same approach as machining, aviation, …. And this is a 400+ year old industry. Now I just need a word that’s better than “science” to describe this approach. (See my previous post.)
Last comment: according to the article, one of the main forces driving willingness to learn was competition from superior French barrels.
This article describes the efforts of Facebook, Youtube, and similar hosts of user-generated content, to screen unacceptable material. (Both speech and images.) It’s apparently a grim task, because of the depravity of some material. For the first decade, moderation methods were heavily ad hoc, but gradually grew more complex and formalized in response to questions such as when to allow violent images as news. In aviation terms, it was at Stage 2: Rules + Instruments. Now, some companies are developing Stage 3 (standard procedures) and Stage 4 (automated) methods.
This company also took an experimental approach to banning players. And hired 3 PhDs in Cognitive Science to develop it. (Just to be clear, their experiments did not appear to be automated A/B style experiments.) After the jump is a screen shot from that system.
But, I’m not tempted to play League of Legends to study player behavior and experiment with getting banned! (I don’t think I’ve ever tried an MMO beyond some prototypes 15 years ago.) If any players want to post your observations here, great.
I recently audited some lectures by friend and China expert Prof. Susan Shirk. She bans computers in her lectures. But one student sitting near me had his machine out and was “busy” with the usual distractions. (Didn’t he know the Associate Dean was a few seats away?) I asked Susan about him after class. “He told me he can’t take notes without a computer.” Obviously the computer is not the big issue on his note taking. Actually, it probably IS the issue – but in a negative way.
Not one computer mirrors the overheads.
James Kwak has beaten the distraction of cell phones – by removing most apps, including browsers.
I know that its enormous powers of distraction also make me lose focus on work, tune out in meetings, stay up too late at night, and, worst of all, ignore people in the same room with me. We all know this. We’re addicted to the dopamine hit we get when we look at our email and there’s actually something good in there, so we keep checking our email hoping to feel it again.
Clay Shirky, an Internet sociologist, has a good discussion of why he recently banned computers in his classrooms. Excerpt:
I came late and reluctantly to this decision — I have been teaching classes about the internet since 1998, and I’ve generally had a laissez-faire attitude towards technology use in the classroom. This was partly because the subject of my classes made technology use feel organic, …. And finally, there’s not wanting to infantilize my students, who are adults, even if young ones — time management is their job, not mine.
Despite these rationales, the practical effects of my decision to allow technology use in class grew worse over time. The level of distraction in my classes seemed to grow, even though it was the same professor and largely the same set of topics, …
Over the years, I’ve noticed that when I do have a specific reason to ask everyone to set aside their devices (‘Lids down’, in the parlance of my department), it’s as if someone has let fresh air into the room. The conversation brightens, and more recently, there is a sense of relief from many of the students. Multi-tasking is cognitively exhausting — when we do it by choice, being asked to stop can come as a welcome change.
So this year, I moved from recommending setting aside laptops and phones to requiring it, adding this to the class rules: “Stay focused. (No devices in class, unless the assignment requires it.)” …
Very surprising. When cardiologists are away from the hospital, deaths after heart failure or cardiac arrest declined. I’ll probably use this in my course this Spring. (Or perhaps in both courses: Big Data, and Operations Quality in Healthcare.)