Interworks Blog: 10 Questions

interworks-blog

Interworks has a series of interviews with people working in data visualization. Check out this blog for their valuable experiences.

Reference: Interworks Blog: 10 Questions

Cheaper Robots, Fewer Workers

nyt-robotic-manufacturing

This NYT article continues to echo the movement of robotic manufacturing. It is an interesting special report on the manufacturing trend in China, where the government would like maintain its leadership in low cost manufacturing in the world.

Listed below are my past blog about robots and automation:

Scientists are skeptical about the secret blood test that has made Elizabeth Holmes a billionaire

elizabeth-holmes

The skepticism scientists have about the revolutionary blood testing of Wlizabeth Holmes remains a concern to our scientific society, if she chooses not to share her work with the community. However, on a side not, the idea of performing testing using the least amount of sample and taking the least amount of time will benefit my recent blog about our food inspection process.

Reference: Scientists are skeptical about the secret blood test that has made Elizabeth Holmes a billionaire

Neither agencies nor Blue Bell tested products for Listeria

blue-bell ice cream

Doesn’t this sound like a stone age in our 21st century?

… Listeria is never tested for frozen ice scream, because of its low risk factor. … The 67 licensed manufacturers of frozen desserts in Texas, including 17 in the Dallas-Fort Worth area, are inspected every four to six weeks … Low-risk foods, such as ready-to-eat bakery items, candy and ice, are inspected by the state in factories every 18 to 24 months. Medium-risk foods — jams and jellies and canned fruit — are inspected every 12 to 18 months. Higher-risk foods, including canned vegetables, require specialized processing and are inspected every three to 12 months … We have 62 food inspectors and 14,000 food manufacturers, so we do have to prioritize the resources based on risk …

I think this is a perfect new business opportunity for automation. When we have a process with few skilled people to work on it, with our advancement in computing technology, why don’t we let robots and automatic process to help? Besides, food inspection is a global problem, not just in the US. It has a global market for growth!

This is like inventing a giant baking machine, where companies can send their food samples by UPS/FedEx/USPS, drop off the samples, and receive their test results in emails or over the internet. Inside the giant baking machine, robots route and grow samples. They can then take the time needed for different types of samples before they make the final inspection to create reports. Do you know anyone who has thought about this challenge?

Reference: Neither agencies nor Blue Bell tested products for Listeria

Nate Silver’s Interview in Freakonomics Radio

Freakonomics

Nate Silver is America’s favorite statistical guru of the past – well, maybe ever. He has been devilishly accurate in predicting electoral outcomes. Before that, he joined the small but influential fraternity of statheads who work with data in sports, particularly baseball. He’s written an excellent book called The Signal and the Noise, which is essentially about the folly of prediction.

In his interview in the Freaknomics Radio, he offered very good insight about data analysis and a lot of misunderstanding people have today.

… Big Data … Oftentimes it’s not about the amount of data you have but how much you vetted that data. If a data set is virginal, as they call it, no one’s looked at it before really. You’re gonna have a lot of problems and one problem with a really large data set is that if you’re running some algorithms, some quick and dirty way to find the most influential data points, a lot of times those are bugs and outliers, right? And the reason why you have that anomaly is because someone coded it in wrong. Or you made some mistake in the analysis.

… I think people love new technology but they overestimate how much of the kind of human factor gets in the way. I’m not trying to be cute about that, I just mean that people need to learn how to use these tools, what they can do, what they can’t do, you know, no amount of data is a substitute for scientific inference and hypothesis testing, and kind of structured analysis of a system. I think one of the false promises that was made early on is that, well if you have a billion data points or a trillion data points, you’re going to find lots and lots of correlations through brute force. And you will, but the problem is that a high percentage of those, maybe the vast majority, are false correlations, are false positives. Where there could be significance, but you have so many lottery tickets when you can run an analysis on a trillion data points, that you’re going to have some one in million coincidences just by chance alone. If you bet all your money on them, you might wind up looking very foolish in the end.

Reference:

Diamonds Are a Marriage Counselor’s Best Friend

Freakonomics

This podcast is quite interesting. Here is a list of interesting items I found. If you are interested, you should check it out, too.

  • Do you know that diamond is actually a commodity which is over-valued? Just like regular commodity, its value drops, when supply is high. This is why diamond resale value is low. You are not getting the money you spent, if you try to sell yours later.
  • “Chekhov’s Gun” principal by Anton Chekhov: “You mustn’t put a loaded rifle on stage if no one intends to fire it. You shouldn’t make promises.” The use of Chekhov’s Gun implies a certain number of things — for example that there’s something inherently dangerous. Someone will get hurt.

In particular, …

A special example of the Chekhov’s Gun is called The “Lottery Ticket.” Basically, a married couple believe they may have won the lottery. They go off in their fantasies about how they might spend the money and the husband’s imaginings are quite different from the wife’s. And by the end they’re miserable.

And he looked at his wife, not with a smile now, no, but with hatred. She glanced at him too, also with hatred and anger. She had her own daydreams, her own plans, her own reflections; she understood perfectly well what her husband’s dreams were. And she knew who would be the first to try to grab her winnings.

Chekhov always has his microscope on intimate relationships. And the sort of more superficial moral might be that you bring some kind of windfall into an intimate relationship like a marriage and you’re worse off in the end from it.

… We now have something to do with this … and we don’t know what to do with it that will make us both feel okay with it because now we both have such negative feelings about it that whoever wins is not really winning. You know, we’re both kind of losing, if that makes sense. …

… Keeping the … is just being a prisoner of the illusion. … to sell the …, and you might find someone else who’s a prisoner of the illusion …

Reference: Diamonds Are a Marriage Counselor’s Best Friend

Great Github list of public data sets

github

Another great listing of open data resource.

Reference: Great Github list of public data sets