The O'Reilly Radar Podcast: Tim Gardner on the synthetic biology landscape, lab automation, and the problem of reproducibility.
Editor’s note: this podcast is part of our investigation into synthetic biology and bioengineering. For more on these topics, download a free copy of the new edition of BioCoder, our quarterly publication covering the biological revolution. Free downloads for all past editions are also available.
Tim Gardner, founder of Riffyn, has recently been working with the Synthetic Biology Working Group of the European Commission Scientific Committees to define synthetic biology, assess the risk assessment methodologies, and then describe research areas. I caught up with Gardner for this Radar Podcast episode to talk about the synthetic biology landscape and issues in research and experimentation that he’s addressing at Riffyn.
Defining synthetic biology
Among the areas of investigation discussed at the EU’s Synthetic Biology Working Group was defining synthetic biology. The official definition reads: “SynBio is the application of science, technology and engineering to facilitate and accelerate the design, manufacture and/or modification of genetic materials in living organisms.” Gardner talked about the significance of the definition:
“The operative part there is the ‘design, manufacture, modification of genetic materials in living organisms.’ Biotechnologies that don’t involve genetic manipulation would not be considered synthetic biology, and more or less anything else that is manipulating genetic materials in living organisms is included. That’s important because it gets rid of this semantic debate of, ‘this is synthetic biology, that’s synthetic biology, this isn’t, that’s not,’ that often crops up when you have, say, a protein engineer talking to someone else who is working on gene circuits, and someone will claim the protein engineer is not a synthetic biologist because they’re not working with parts libraries or modularity or whatnot, and the boundaries between the two are almost indistinguishable from a practical standpoint. We’ve wrapped it all together and said, ‘It basically advances in the capabilities of genetic engineering. That’s what synthetic biology is.'”
The O'Reilly Radar Podcast: Mike Belshe on making bitcoin secure and easy enough for the mainstream.
In this week’s O’Reilly Radar Podcast episode, I caught up with Mike Belshe, CTO and co-founder of BitGo, a company that has developed a multi-signature wallet that works with bitcoin. Belshe talks about about the security issues addressed by multi-signature wallets, how the technology works, and the challenges in bringing cryptocurrencies mainstream. We also talk about his journey into the bitcoin world, and he chimes in on what money will look like in the future. Belshe will address the topics of security and multi-signature technology at our upcoming Bitcoin & the Blockchain Radar Summit on January 27, 2015, in San Francisco — for more on the program and registration information, visit our Bitcoin & the Blockchain website.
Multi-signature technology is exactly what it sounds like: instead of authorizing bitcoin transactions with a single signature and a single key (the traditional method), it requires multiple signatures and/or multiple machines — and any combination thereof. The concept initially was developed as a solution for malware. Belshe explains:
“I’m fully convinced that the folks who have been writing various types of malware that steal fairly trivial identity information — logins and passwords that they sell super cheap — they are retooling their viruses, their scanners, their key loggers for bitcoin. We’ve seen evidence of that over the last 12 months, for sure. Without multi-signature, if you do a bitcoin transaction on a machine that’s got any of this bad stuff on it, you’re pretty much toast. Multi-signature was my hope to fix that. What we do is make one signature happen on the server machine, one signature happen on the client machine, your home machine. That way the attacker has to actually compromise two totally different systems in order to steal your bitcoin. That’s what multi-signature is about.”
With remote connectivity and remote updates, companies are able to iterate and add value to products customers already own.
Editor’s note: this is an excerpt from our recent report, When Hardware Meets Software, by Mike Barlow. The report looks into the new hardware movement, telling its story through the people who are building it. For more stories on the evolving relationship between software and hardware, download the free report.The Internet of Things doesn’t presage a return to the world of smoke-belching factories and floors covered with sawdust. But it does signify that change is afoot for any business or activity related to the information technology or communications industries.
“Not everyone will become a hardware designer,” says Joi Ito, director of the MIT Media Lab. But many students, software engineers, and entrepreneurs will see the advantages of learning how to work with hardware. “It’s never too late to learn this stuff,” says Ito, “if you decide that you want to do it.”
At minimum, software engineers should learn as much about design and manufacturing as possible. “Buy an Arduino and start building. Everything you need to learn is on the web,” urges Jordan Husney, an avid hardware hacker who serves as strategy director at Undercurrent, an organizational transformation firm and digital think tank in lower Manhattan.
In the same way that software people will have to reconfigure their modes of thinking, hardware people will need to learn new technical skills and new ways of looking at problems, says Husney. “They will have to become more comfortable with uncertainty occurring later and later in the process,” he says. “Hardware engineers will keep things in the realm of bits (as opposed to committing them to atoms) by sharing designs using digital collaboration and simulation tools virtually, while testing multiple physical prototypes. I think we’re going to see the supply chain start to shift around these concepts.” Read more…
In this O'Reilly Radar Podcast: Edd Dumbill on the data lake, and Rajiv Maheswaran on the science of moving dots.
In a recent blog post, Edd Dumbill, VP of strategy at Silicon Valley Data Science, wrote about the phrase “data lake.” Likening it to a dream, he described a data lake as “a place with data-centered architecture, where silos are minimized, and processing happens with little friction in a scalable, distributed environment…Data itself is no longer restrained by initial schema decisions, and can be exploited more freely by the enterprise.” He explained that he called it a “dream” because “we’ve a way to go to make the vision come true” — but noted he’s optimistic the dream can be realized.
The core principle in bitcoin is decentralization, and it has important implications for security.
Editor’s note: this is an excerpt from Chapter 10 of our recently released book Mastering Bitcoin, by Andreas Antonopoulos. You can read the full chapter here. Antonopoulos will be speaking at our upcoming event Bitcoin & the Blockchain, January 27, 2015, in San Francisco. Find out more about the event and reserve your spot here.Securing bitcoin is challenging because bitcoin is not an abstract reference to value, like a balance in a bank account. Bitcoin is very much like digital cash or gold. You’ve probably heard the expression “Possession is nine tenths of the law.” Well, in bitcoin, possession is ten tenths of the law. Possession of the keys to unlock the bitcoin, is equivalent to possession of cash or a chunk of precious metal. You can lose it, misplace it, have it stolen, or accidentally give the wrong amount to someone. In every one of those cases, end users would have no recourse, just as if they dropped cash on a public sidewalk.
However, bitcoin has capabilities that cash, gold, and bank accounts do not. A bitcoin wallet, containing your keys, can be backed up like any file. It can be stored in multiple copies, even printed on paper for hardcopy backup. You can’t “backup” cash, gold, or bank accounts. Bitcoin is different enough from anything that has come before that we need to think about bitcoin security in a novel way too.
The core principle in bitcoin is decentralization and it has important implications for security. A centralized model, such as a traditional bank or payment network, depends on access control and vetting to keep bad actors out of the system. By comparison, a decentralized system like bitcoin pushes the responsibility and control to the end users. Because security of the network is based on Proof-Of-Work, not access control, the network can be open and no encryption is required for bitcoin traffic. Read more…
A look at the social and moral implications of living in a deeply connected, analyzed, and informed world.
We’ll now look at both the light and the shadows of this new dawn, the social and moral implications of living in a deeply connected, analyzed, and informed world. This is both the promise and the peril of big data in an age of widespread sensors, fast networks, and distributed computing.
Solving the big problemsThe planet’s systems are under strain from a burgeoning population. Scientists warn of rising tides, droughts, ocean acidity, and accelerating extinction. Medication-resistant diseases, outbreaks fueled by globalization, and myriad other semi-apocalyptic Horsemen ride across the horizon.
Can data fix these problems? Can we extend agriculture with data? Find new cures? Track the spread of disease? Understand weather and marine patterns? General Electric’s Bill Ruh says that while the company will continue to innovate in materials sciences, the place where it will see real gains is in analytics.
It’s often been said that there’s nothing new about big data. The “iron triangle” of Volume, Velocity, and Variety that Doug Laney coined in 2001 has been a constraint on all data since the first database. Basically, you could have any two you want fairly affordably. Consider:
- A coin-sorting machine sorts a large volume of coins rapidly, but assumes a small variety of coins. It wouldn’t work well if there were hundreds of coin types.
- A public library, organized by the Dewey Decimal System, has a wide variety of books and topics, and a large volume of those books — but stacking and retrieving the books happens at a slow velocity.
What’s new about big data is that the cost of getting all three Vs has become so cheap it’s almost not worth billing for. A Google search happens with great alacrity, combs the sum of online knowledge, and retrieves a huge variety of content types. Read more…
The blockchain is like layers in a geological formation — the deeper you go, the more stability you gain.
Editor’s note: this is an excerpt from Chapter 7 of our recently released book Mastering Bitcoin, by Andreas Antonopoulos. You can read the full chapter here. Antonopoulos will be speaking at our upcoming event Bitcoin & the Blockchain, January 27, 2015, in San Francisco. Find out more about the event and reserve your spot here.The blockchain data structure is an ordered back-linked list of blocks of transactions. The blockchain can be stored as a flat file, or in a simple database. The bitcoin core client stores the blockchain metadata using Google’s LevelDB database. Blocks are linked “back,” each referring to the previous block in the chain. The blockchain is often visualized as a vertical stack, with blocks layered on top of each other and the first block serving as the foundation of the stack. The visualization of blocks stacked on top of each other results in the use of terms like “height” to refer to the distance from the first block, and “top” or “tip” to refer to the most recently added block.
Each block within the blockchain is identified by a hash, generated using the SHA256 cryptographic hash algorithm on the header of the block. Each block also references a previous block, known as the parent block, through the “previous block hash” field in the block header. In other words, each block contains the hash of its parent inside its own header. The sequence of hashes linking each block to its parent creates a chain going back all the way to the first block ever created, known as the genesis block. Read more…
As we increasingly depend on connected devices, primary concerns will narrow to safety, reliability, and survivability.
Editor’s note: this interview with GE’s Bill Ruh is an excerpt from our recent report, When Hardware Meets Software, by Mike Barlow. The report looks into the new hardware movement, telling its story through the people who are building it. For more stories on the evolving relationship between software and hardware, download the free report.More than one observer has noted that while it’s relatively easy for consumers to communicate directly with their smart devices, it’s still quite difficult for smart devices to communicate directly, or even indirectly, with each other. Bill Ruh, a vice president and corporate officer at GE, drives the company’s efforts to construct an industrial Internet that will enable devices large and small to chat freely amongst themselves, automatically and autonomously. From his perspective, the industrial Internet is a benign platform for helping the world become a quieter, calmer, and less dangerous place.
“In the past, hardware existed without software. You think about the founding of GE and the invention of the light bulb — you turned it on and you turned it off. Zero lines of code. Today, we have street lighting systems with mesh networks and 20 million lines of code,” says Ruh. “Machines used to be completely mechanical. Today, they are part digital. Software is part of the hardware. That opens up huge possibilities.”
A hundred years ago, street lighting was an on-or-off affair. In the future, when a crime is committed at night, a police officer might be able to raise the intensity of the nearby street lights by tapping a smart phone app. This would create near-daylight conditions around a crime scene, and hopefully make it harder for the perpetrators to escape unseen. “Our machines are becoming much more intelligent. With software embedded in them, they’re becoming brilliant,” says Ruh. Read more…
The evolving marketplace is making new data applications and interactions possible.
Here’s a look at some options in the evolving, maturing marketplace of big data components that are making the new applications and interactions we’ve been looking at possible.
First used in social network analysis, graph theory is finding more and more homes in research and business. Machine learning systems can scale up fast with tools like Parameter Server, and the TitanDB project means developers have a robust set of tools to use.
Are graphs poised to take their place alongside relational database management systems (RDBMS), object storage, and other fundamental data building blocks? What are the new applications for such tools?
Inside the black box of algorithms: whither regulation?It’s possible for a machine to create an algorithm no human can understand. Evolutionary approaches to algorithmic optimization can result in inscrutable, yet demonstrably better, computational solutions.
If you’re a regulated bank, you need to share your algorithms with regulators. But if you’re a private trader, you’re under no such constraints. And having to explain your algorithms limits how you can generate them.
As more and more of our lives are governed by code that decides what’s best for us, replacing laws, actuarial tables, personal trainers, and personal shoppers, oversight means opening up the black box of algorithms so they can be regulated.
Years ago, Orbitz was shown to be charging web visitors who owned Apple devices more money than those visiting via other platforms, such as the PC. Only that’s not the whole story: Orbitz’s machine learning algorithms, which optimized revenue per customer, learned that the visitor’s browser was a predictor of their willingness to pay more. Read more…