Nate Oostendorp on manufacturing and the industrial Internet, and Tim O'Reilly and Rod Smith discuss emerging tech.
The Industrial Revolution had a profound effect on manufacturing — will the industrial Internet’s effect be as significant? In this podcast episode, Nate Oostendorp, co-founder and CTO of Sight Machine, says yes — where mechanization ruled the Industrial Revolution, data-driven automation will rule this next revolution:
“I think that when you think about manufacturing 20 years from now, the computer and the network is going to be much more fundamental. Your factories are going to look a lot more like data centers do, where there’s a much greater degree of automation that’s driven by the fact that you have good data feeds off of it. You have a lot of your administration of the factory that will be done remotely or in a back office. You don’t necessarily need to have engineers on a floor watching a machine in order to know what’s going on. I think fundamentally, the number of players in a factory will be much smaller. You’ll have much more technical expertise but a fewer number of people overall in a factory setting.”
According to Oostendorp, we’re already seeing the early effects today in an increased focus on quality and efficiency. Read more…
Glowing plants disrupt the GMO narrative.
Unlike many of his generational peers, Glowing Plant chief scientific officer Kyle Taylor was never put off by genetically modified organism (GMO) crops. On the contrary: Kansas-born and bred, cutting-edge agriculture was as natural to him as the torrid summers and frigid winters of the southern plains.
“GMO corn first hit the market while I was still in high school,” says Taylor, “and I have to admit I was fascinated by it. It was Roundup resistant, meaning that it you could spray it with the most commonly used herbicide in commercial agriculture and it would remain unaffected. I found that really profound, a breakthrough.” Read more…
We're at the start of a revolution in biology, and it's time for a biological commons.
A few months ago, I singled out an article in BioCoder about the appearance of open source biology. In his white paper for the Bio-Commons, Rüdiger Trojok writes about a significantly more ambitious vision for open biology: a bio-commons that holds biological intellectual property in trust for the good of all. He also articulates the tragedy of the anticommons, the nightmarish opposite of a bio-commons in which progress is difficult or impossible because “ambiguous and competing intellectual property claims…deter sharing and weaken investment incentives.” Each individual piece of intellectual property is carefully groomed and preserved, but it’s impossible to combine the elements; it’s like a jigsaw puzzle, in which every piece is locked in a separate safe.
We’ve certainly seen the anticommons in computing. Patent trolls are a significant disincentive to innovation; regardless of how weak the patent claim may be, most start-ups just don’t have the money to defend. Could biotechnology head in this direction, too? In the U.S., the Supreme Court has ruled that human genes cannot be patented. But that ruling doesn’t apply to genes from other organisms, and arguably doesn’t apply to modifications of human genes. (I don’t know the status of genetic patents in other countries.) The patentability of biological “inventions” has the potential to make it more difficult to do cutting-edge research in areas like synthetic biology and pharmaceuticals (Trojok points specifically to antibiotics, where research is particularly stagnant). Read more…
New issue: bioreactors and food production, modeling a worm's brain on a computer and letting it drive a robot, and more.
The fifth issue of BioCoder is here! We’ve made it into our second year: this revolution is in full swing.
Rather than talk about how great this issue is (though it is great), I’d like to ask a couple of questions. Post your answers in the comments; we won’t necessarily reply, but we will will read them and take them into account.
- We are always interested in new content, and we’ll take a look at almost anything you send to BioCoder@oreilly.com. In particular, we’d like to get more content from the many biohacker labs, incubators, etc. We know there’s a lot of amazing experimentation out there. But we don’t know what it is; we only see the proverbial tip of the iceberg. What’s the best way to find out what’s going on?
- While we’ve started BioCoder as a quarterly newsletter, that’s a format that already feels a bit stodgy. Would you be better served if BioCoder went web-native? Rather than publishing eight or 10 articles every three months, we’d publish three or four articles a month online. Would that be more useful? Or do you like things the way they are?
And yes, we do have a great issue, with articles about a low-cost MiniPCR, bioreactors and food production, and what happens when you model a worm’s brain on a computer and let it drive a robot. Plus, an interview with Kyle Taylor of the glowing plant project, the next installment in a series on lab safety, and much more. Read more…
Predixion service could signal a trend for smaller health facilities.
Analytics are expensive and labor intensive; we need them to be routine and ubiquitous. I complained earlier this year that analytics are hard for health care providers to muster because there’s a shortage of analysts and because every data-driven decision takes huge expertise.
Currently, only major health care institutions such as Geisinger, the Mayo Clinic, and Kaiser Permanente incorporate analytics into day-to-day decisions. Research facilities employ analytics teams for clinical research, but perhaps not so much for day-to-day operations. Large health care providers can afford departments of analysts, but most facilities — including those forming accountable care organizations — cannot.
Imagine that you are running a large hospital and are awake nights worrying about the Medicare penalty for readmitting patients within 30 days of their discharge. Now imagine you have access to analytics that can identify about 40 measures that combine to predict a readmission, and a convenient interface is available to tell clinicians in a simple way which patients are most at risk of readmission. Better still, the interface suggests specific interventions to reduce readmissions risk: giving the patient a 30-day supply of medication, arranging transportation to rehab appointments, etc. Read more…
The revolutionary thing about desktop machines is that they'll make experimentation easier.
“Mr. Frankel, who started this program, began to suffer from the computer disease that anybody who works with computers now knows about,” [Richard] Feynman later explained. “The trouble with computers is you play with them.”
— George Dyson, describing the beginning of the Manhattan Project’s computing effort in Turing’s Cathedral.
I’ve been reading George Dyson’s terrific history of the early development of the digital computer, and the quote above struck me. Even when they were little more than room-sized adding machines that had to be painstakingly programmed with punchcards, computers offered an intoxicating way to experiment. Most programmers can probably remember their first few scripts and the thrilling feeling of performing millions of operations in seconds. Computers let us take some abstracted human process and repeat it quickly, at almost no cost, with easy modification along the way. Read more…