BioCoder 6: iGEM's first Giant Jamboree, an update from the #ScienceHack Hack-a-thon, the Open qPCR project, and more.
Once again, we’re interested in your ideas and in new content, so if you have an article or a proposal for an article, send it in to BioCoder@oreilly.com. We’re very interested in what you’re doing. There are many, many fascinating projects that aren’t getting media attention. We’d like to shine some light on those. If you’re running one of them — or if you know of one, and would like to hear more about it — let us know. We’d also like to hear more about exciting start-ups. Who do you know that’s doing something amazing? And if it’s you, don’t be shy: tell us.
Above all, don’t hesitate to spread the word. BioCoder was meant to be shared. Our goal with BioCoder is to be the nervous system for a large and diverse but poorly connected community. We’re making progress, but we need you to help make the connections.
Marco Arment did an excellent job of offering constructive criticism to a company that he genuinely loves.
The point he made is one I’ve tweeted about (though not written about) a number of times over the years. The big problem facing Apple isn’t a deficit of innovation, but bitrot creeping into their codebase. You don’t have to look far to see it: for example, there’s a race condition in text handling that has been there since at least OS X 10.3.
The point needed to be made by someone who knows and loves Apple, someone who is a leader in their developer community, and someone who has a reputation for being fair and even-handed. That’s Marco. He did an excellent job of offering constructive criticism to a company that he genuinely loves.
Yes, there is a risk that anything you write will turn into a media storm. That’s a risk you have to live with; it’s unfortunate, but it’s not going away. It’s also important to say what needs to be said. Self-censorship is not the way forward. Read more…
A look at what lies ahead in the disenchanted age of postmodern computing.
Sometime last summer, I ran into the phrase “postmodern computing.” I don’t remember where, but it struck me as a powerful way to understand an important shift in the industry. What is different in the industry? How are 2014 and 2015 different from 2004 and 2005?
If we’re going to understand what “postmodern computing” means, we first have to understand “modern” computing. And to do that, we also have to understand modernism and postmodernism. After all, “modern” and “postmodern” only have meaning relative to each other; they’re both about a particular historical arc, not a single moment in time.
Some years back, I was given a history of St. Barbara’s Greek Orthodox Church in New Haven, carefully annotated wherever a member of my family had played a part. One story that stood out from early in the 20th century was AHEPA: the American-Hellenic Progressive Association. The mere existence of that organization in the 1920s says more about modernism than any number of literary analyses. In AHEPA, and in many other similar societies crossing many churches and many ethnic groups, people were betting on the future. The future is going to be better than the present. We were poor dirt farmers in the Old Country; now we’re here, and we’re going to build a better future for ourselves and our children. Read more…
Drones might never find meaningful retail delivery work, but they might find practical employment in warehouses.
After writing my short post about the use of drones to deliver packages, it occurred to me that there’s one more realistic use case. Unfortunately (or not), this is a use case that you’ll never see if you’re not an Amazon employee. But I think it’s very realistic. And obviously, I just can’t get drones out of my head.
As I argued, I don’t think you’ll see drones for retail delivery, except perhaps as a high-cost, very conspicuous consumption frill. What could get more conspicuous? Drone pilots are expensive, and I don’t think we’ll see regulations that allow autonomous drones flying in public airspace any time soon. Drones also aren’t terribly fast, and even if you assume that the warehouses are relatively close to the customers, the number of trips a drone can make per hour are limited. There’s also liability, weather conditions, neighbors shooting the drones down, and plenty of other drawbacks.
These problems all disappear if you limit your use of drones to the warehouse itself. Don’t send the drone to the customer: that’s a significant risk for an expensive piece of equipment. Instead, use the drones within the warehouse to deliver items to the packers. Weather isn’t an issue. Regulation isn’t an issue; the FAA doesn’t care what you do inside your building. Autonomous flight isn’t just a realistic option, it’s preferable: one massive computing system can coordinate and optimize the flight paths of all the drones. Amazon probably has some of that system built already for its Kiva robots, and Amazon is rather good at building large computing architectures. Distance isn’t an issue. Warehouses are big, but they’re not that big, and something (or someone) has to bring the product to the packing station, whether it’s a human runner or a Kiva robot. Read more…
Biological products have always seemed far off. BioFabricate showed that they're not.
The products discussed at BioFabricate aren’t what I thought they’d be. I’ve been asked plenty of times (and I’ve asked plenty of times), “what’s the killer product for synthetic biology?” BioFabricate convinced me that that’s the wrong question. We may never have some kind of biological iPod. That isn’t the right way to think.
What I saw, instead, was real products that you might never notice. Bricks made from sand that are held together by microbes designed to excrete the binder. Bricks and packing material made from fungus (mycelium). Plastic excreted by bacteria that consume waste methane from sewage plants. You wouldn’t know, or care, whether your plastic Lego blocks are made from petroleum or from bacteria, but there’s a huge ecological difference. You wouldn’t know, or care, what goes into the bricks used in the new school, but the construction boom in Dubai has made a desert city one of the world’s largest importers of sand. Wind-blown desert sand isn’t useful for industrial brickmaking, but the microbes have no problem making bricks from it. And you may not care whether packing materials are made of styrofoam or fungus, but I despise the bag of packing peanuts sitting in my basement waiting to be recycled. You can throw the fungal packing material into the garden, and it will decompose into fertilizer in a couple of days. Read more…
For the time being, we won't see drone delivery outside of a few very specialized use cases.
I read with some interest an article on the Robotenomics blog about the feasibility of drone delivery. It’s an interesting idea, and the article makes a better case than anything I’ve seen before. But I’m still skeptical.
The article quotes direct operating costs (essentially fuel) that are roughly $0.10 for a 2-kilogram payload, delivered 10 kilometers. (For US-residents, that’s 4.4 pounds and about six miles). That’s reasonable enough.
The problem comes when he compares it to Amazon’s current shipping costs, of $2 to $8. But it sounds roughly like what Amazon pays to UPS or FedEx. And that’s not for delivering four pounds within a six-mile range. And it’s not just the fuel cost: it’s the entire cost, including maintenance, administrative overhead, executive bonuses, and (oh, yes) the driver’s salary. Read more…
Antha is a high-level, open source language for specifying biological workflows.
Editor’s note: This is part of our investigation into synthetic biology and bioengineering. For more, download the new BioCoder Fall 2014 issue here.In a couple of recent posts, I’ve written about the need for a high-level programming language for biology. Now we have one. Antha is a high-level, open source language for specifying biological workflows (i.e., describing experiments). It’s available on Github.
A programming language for scientific experiments is important for many reasons. Most simply, a scientist in training spends many, many hours of time learning how to do lab work. That sounds impressive, but it really means moving very small amounts of liquid from one place to another. Thousands of times a day, thousands of days in preparation for a career. It’s boring, dull, and necessary work, and something that can be automated. Biologists should spend most of their time thinking about biology, designing experiments, and analyzing results — not handling liquids. Read more…
Uber has built a great service. Why do they feel the need to use dirty tricks to succeed?
Tim O’Reilly has said that Uber is an example of designing for how the world ought to be. Their app works well, their cars are clean, their drivers are pleasant, and they usually arrive quickly. But more goes into the experience of a company than just an app. Corporate behavior is also part of the company’s design; perhaps not as noticeable as their Android or iPhone app, but a very real part. That’s where Uber falls down. They have increasingly been a bad actor, on many counts:
- Coercing their black car (Uber) drivers into driving for the low cost UberX service, which is much less profitable.
- Being disingenuous about the economics of driving for them. Justin Singer does an excellent job of deconstructing their claims. $90,000/year for a 40-hour work week? Think $40K. For a 70-hour work week.
- Badmouthing a competitor (Lyft) that is raising capital. As Fred Wilson says, this practice may be common, but it’s unethical and unproductive.
- Predatory (“surge”) pricing during peak hours, as much as seven times normal prices.
- Playing fast and loose with drivers’ background checks.
- And now one of their senior VPs has suggested researching and exposing the private lives of reporters who criticize them. He’s apologized, and said he never meant anything of the sort. Right. It’s not what you apologize for that counts; it’s not doing stuff you need to apologize for in the first place.
If you really want to understand the effect data is having, you need the models.
Writing my post about AI and summoning the demon led me to re-read a number of articles on Cathy O’Neil’s excellent mathbabe blog. I highlighted a point Cathy has made consistently: if you’re not careful, modelling has a nasty way of enshrining prejudice with a veneer of “science” and “math.”
Cathy has consistently made another point that’s a corollary of her argument about enshrining prejudice. At O’Reilly, we talk a lot about open data. But it’s not just the data that has to be open: it’s also the models. (There are too many must-read articles on Cathy’s blog to link to; you’ll have to find the rest on your own.)
You can have all the crime data you want, all the real estate data you want, all the student performance data you want, all the medical data you want, but if you don’t know what models are being used to generate results, you don’t have much. Read more…