Oddly, there’s been a few recent outbreaks of measles. It struck me how when I was a kid, a few hundred kids getting measles wouldn’t have been news at all. However, even a handful makes the news now, since in 2000 the Center for Disease Control declared measles eradicated in the United States.
So how can an eradicated disease come back? How did we eradicate it to start with? The answers tell a pretty interesting tale of science applying to everyday life.
In 2016, a Tesla Model S T-boned a tractor trailer at full speed, killing its lone passenger instantly. It was running in Autosteer mode at the time, and neither the driver nor the car’s automatic braking system reacted before the crash. The US National Highway Traffic Safety Administration (NHTSA) investigated the incident, requested data from Tesla related to Autosteer safety, and eventually concluded that there wasn’t a safety-related defect in the vehicle’s design (PDF report).
But the NHTSA report went a step further. Based on the data that Tesla provided them, they noted that since the addition of Autosteer to Tesla’s confusingly named “Autopilot” suite of functions, the rate of crashes severe enough to deploy airbags declined by 40%. That’s a fantastic result.
Because it was so spectacular, a private company with a history of investigating automotive safety wanted to have a look at the data. The NHTSA refused because Tesla claimed that the data was a trade secret, so Quality Control Systems (QCS) filed a Freedom of Information Act lawsuit to get the data on which the report was based. Nearly two years later, QCS eventually won.
Looking into the data, QCS concluded that crashes may have actually increased by as much as 60% on the addition of Autosteer, or maybe not at all. Anyway, the data provided the NHTSA was not sufficient, and had bizarre omissions, and the NHTSA has since retracted their safety claim. How did this NHTSA one-eighty happen? Can we learn anything from the report? And how does this all align with Tesla’s claim of better-than-average safety line up? We’ll dig into the numbers below.
But if nothing else, Tesla’s dramatic reversal of fortune should highlight the need for transparency in the safety numbers of self-driving and other advanced car technologies, something we’ve been calling for for years now.
In the middle of the East Coast’s slow broil in the summer of 2018, a curious phenomenon surfaced. As a tropical air mass settled in and smothered the metropolitan New York area, a certain breed of stock speculator began feeling the financial heat as the microwave signals linking together various data centers and exchanges began to slow down. These high-frequency traders rely on getting information a fraction of a second before other traders see the same thing and take advantage of minuscule price differences to make money hand over fist.
While you won’t catch us shedding many tears over the billions these speculators lost during the hot spell, we did find the fact that humidity can slow microwave propagation enough to make this a problem for them a fascinating subject, enough so that we covered it in some detail at the time. While financial markets come and go and the technology to capitalize them changes at a breakneck pace, physics stays the same, and it can make or break deals with no regard to the so-called fundamentals.
So it was with great interest that we happened upon Tom Scott’s recent video outlining how one new stock exchange is using physics to actually slow down stock trades, in an attempt to gain a competitive advantage over the other exchanges. In light of the billions lost over the summer to propagation delays amounting to a mere 10 microseconds, we couldn’t help but wonder how injecting a delay 35 times longer using a “magic shoebox” was actually good for business. It turns out to be an interesting story.
Representatives from SpaceX, Blue Origin, and United Launch Alliance participated in a forum last week held by NASA to determine the future of humans on the moon. This isn’t just how they will live, how long they will stay, or what they will do; no, this is far more interesting: this was how humans will travel from lunar orbit from the surface of the moon. The future of the next generation of lunar lander is being determined right now.
The plan right now is entirely unlike Apollo, which sent a pair of spaceships in orbit around the moon, sent one to the surface, then returned to the mother ship for the trip back to Earth. Instead of something somewhat simple, the next era of lunar exploration will happen from a gateway orbiting in cis-lunar space. What makes this so amazing is how weird the orbit is, and the reasons behind it.
Our digital world is so much more interactive than the paper one it has been replacing. That becomes very obvious in the features of Jupyter Notebooks. The point is to make your data beautiful, organized, interactive, and shareable. And you can do all of this with just a bit of simple coding.
We already leveraged computer power by moving from paper spreadsheets to digital spreadsheets, but they are limited. One thing I’ve seen over and over again — and occasionally been guilty of myself — is spreadsheet abuse. That is, using a spreadsheet program to do something I probably ought to write a program to do. For those times that you want something quick but want something more than a spreadsheet, you should check out Jupyter Notebooks. The system is most commonly associated with Python, but it isn’t Python-specific. There are over 100 languages supported — many community-developed. You can even install a C++ interpreter backend for it. Because of the client/server architecture, it is very simple to share notebooks with other users.
You can — in theory — use Jupyter for anything you could use Python for. In practice, it seems to get a lot of workout with people analyzing large data sets, doing machine learning, and similar tasks.
The Good: Simple, Powerful, Extensible
The idea is simple. Think of a Markdown-enabled web page that can connect to a backend (a kernel, in Jupyter-speak). The backend can run on your machine or remotely and will support some kind of language — often Python. The document has cells that line up vertically (like a single wide spreadsheet column). For example, here’s a simple notebook I created to explain how a bunch of sine waves add up to a square wave:
It’s no secret that I rather enjoy connecting things to the Internet for fun and profit. One of the tricks I’ve learned along the way is to spin up simple APIs that can be used when prototyping a project. It’s easy to do, and simple to understand so I’m happy to share what has worked for me, using Web2Py as the example (with guest appearances from ESP8266 and NodeMCU).
Barring the times I’m just being silly, there are two reasons I might do this. Most commonly I’ll need to collect data from a device, typically to be stored for later analysis but occasionally to trigger some action on a server in the cloud. Less commonly, I’ll need a device to change its behavior based on instructions received via the Internet.
Etherscan is an example of an API that saves me a lot of work, letting me pull data from Ethereum using a variety of devices.
In the former case, my first option has always been to use IoT frameworks like Thingsboard or Ubidots to receive and display data. They have the advantage of being easy to use, and have rich features. They can even react to data and send instruction back to devices. In the latter case, I usually find myself using an application programming interface (API) – some service open on the Internet that my device can easily request data from, for example the weather, blockchain transactions, or new email notifications.
Occasionally, I end up with a type of data that requires processing or is not well structured for storage on these services, or else I need a device to request data that is private or that no one is presently offering. Most commonly, I need to change some parameter in a few connected devices without the trouble of finding them, opening all the cases, and reprogramming them all.
At these times it’s useful to be able to build simple, short-lived services that fill in these gaps during prototyping. Far from being a secure or consumer-ready product, we just need something we can try out to see if an idea is worth developing further. There are many valid ways to do this, but my first choice is Web2Py, a relatively easy to use open-source framework for developing web applications in Python. It supports both Python 2.7 and 3.0, although we’ll be using Python 3 today.
How large is the cache of discarded electronics in your home? They were once expensive and cherished items, but now they’re a question-mark for responsible disposal. I’m going to dig into this problem — which goes far beyond your collection of dead smartphones — as well as the issues of where this stuff ends up versus where it should end up. I’m even going to demystify the WEEE mark (that crossed out trashcan icon you’ve been noticing on your gadgets), talk about how much jumbo jets weigh, and touch on circular economies, in the pursuit of better understanding of the waste streams modern gadgets generate.
Our lives are encountering an increasing number of “how do I dispose of this [X]” moments, where X is piles of old batteries, LCDs, desktop towers, etc. This leads to relationship-testing piles of garbage potential in a garage or the bottom of a closet. Sometimes that old gear gets sold or donated. Sometimes there’s a handy e-waste campaign that swings through the neighborhood to scoop that pile up, and sometimes it eventually ends up in the trash wrapped in that dirty feeling that we did something wrong. We’ve all been there; it’s easy to discover that responsible disposal of our old electronics can be hard.
Fun fact: the average person who lives in the US generates 20 kg of e-waste annually (or about 44 freedom pounds). That’s not unique, in the UK it’s about 23 kg (that’s 23 in common kilograms), 24 kg for Denmark, and on and on. That’s quite a lot for an individual human, right? What makes up that much waste for one person? For that matter, what sorts of waste is tracked in the bogus sounding e-waste statistics you see bleated out in pleading Facebook posts? Unsurprisingly there are some common definitions. And the Very Serious People people at the World Economic Forum who bring you the definitions have some solutions to consider too.
We spend a lot of time figuring out how to build this stuff. Are we spending enough time planning for what to do with the gear once it falls out of favor? Let’s get to the bottom of this rubbish. Continue reading “The Woeful World Of Worldwide E-Waste”→
