Elon Musk tweeted this morning that an update that enables Tesla’s most advanced autopilot functions will roll out to the entire fleet on Monday. The HW2 software will be fully functional in 1,000 cars and run in “shadow mode” for the rest while data continues to be collected.
Music: audiovisual artist Kamiel Rongen used his own frozen urine to create visuals in the music video for Dutch musician Marle Thomson's debut track Levitation (+ movie). (more…)
A team of scientists at Oxford Eye Hospital aim to create a bionic eye, that will make people who lost their sight to see again. A special chip is placed behind the retina and a small processor sends signal to the optic nerve and brain process it thus enabling the person in treatment to see.
In Lewis’ case, after just two days of tests, she was able to make out basic shapes and see outside for the first time in over a decade.
It helped that in her case, Lewis’ optic nerve was still intact. But it still took some time for her brain to adjust to actually seeing for the first time in years. The first test was to see blinking lights on a computer screen, which she passed with ease. But when it came to discerning a white plate on a black table cloth, not so much. Trying to not feel overly defeated, Lewis came back the next day and was able to make out the shape, and more.
A new form of retina implant returns sight to the blind – [Link]
The post A new form of retina implant returns sight to the blind appeared first on Electronics-Lab.
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M1. Photo: Naval History
This story originally appeared on War Is Boring.
A century ago submarines presented as much of a challenge for naval war planners as drones do today. Like airborne and surface stealth decades later, underwater stealth was a game-changer—but how to use it?
Until the missile age, a wide variety of naval missions and vessels tried applying the submarine’s advantages to best use. From early on, planners envisioned submarines carrying out commerce raiding, minelaying, shore bombardment and intelligence collection, besides the sub’s obvious role in fleet attack.
The limited undersea endurance of pre-atomic subs shaped design and tactics. At the time, submarines largely cruised on the surface and only submerged for brief periods. Early concepts of operations foresaw subs scouting ahead of the battle fleet, then submerging below an enemy fleet to surface and attack it from behind.
During World War I, sub-surface torpedo attacks on warships and merchant vessels became the submarine’s preferred attack technique. However, many engagements involved substantial surface combat.
Deck armament often equaled that of coastal and riverine gunboats—one or two artillery pieces plus several machine-gun mounts for anti-aircraft and surface attack. During these engagements, a five- or six-inch deck gun could wreak havoc on upperworks, steering gear or shoreline structures. Machine guns could rake decks and lifeboats.
But convoys of armed merchantmen and their destroyer escorts were another matter. So was stealthy shore bombardment of coastal bases and defenses. These missions inspired truly remarkable undersea “cruisers.” These were truly big-gun submarines.
A model showing the M1‘s gun. Photo: Andy Dingley/Wikimedia Commons
Undersea cruisers
The British Admiralty appears to have kicked off the idea with its extraordinary response to reports of the Kaiser’s new U139-class subs, which packed 5.9-inch guns. In 1916, the Committee on Submarine Development chose to mount 12-inch guns—battleship guns—on submarines.
The exact military requirements for such a weapon shifted as the concept developed. Originally envisioned as stealthy coastal attack platforms, the “submarine monitors” became high-power anti-ship weapons. Or would have, except they never saw wartime use. Perhaps the Admiralty realized the threat such subs with guns posed to their own Royal Navy if their use proliferated.
Four of the disastrous K-class steam submarines (K18 through K21) were reconfigured into the diesel-powered M-class. The giant subs were more than 295 feet long and were 24 feet in diameter, and each carried a single surplus 12-inch naval rifle from the Formidable-class battleships in a watertight turret.
While the M-class boats mounted four 18-inch torpedo tubes with a reload apiece, doubts about the torpedoes’ efficacy buttressed arguments for the guns. From a submerged posture, the subs could lob 850-pound shells over the better part of a mile.
Targeting and firing the gun proved both remarkably crude and ruggedly simple. Crews called it the “dip-chick” method.
An M-class monitor lined up on her target at periscope depth then surfaced to expose some six feet of her weapon’s barrel. Using the periscope as an optical sight, the commander aligned a simple bead sight on the barrel’s tip—like aiming a submerged rifle using its bead sight and a pair of binoculars—and ordered the gun fired.
The sub then immediately submerged, the whole operation taking only 30 seconds. An all-up ammunition magazine and lift supplied 50 rounds, but the gun could not be reloaded while submerged.
That such odd limitations severely constricted the M-class subs’ wartime effectiveness was moot. None of the subs saw action in World War I, but inter-war experimentation within treaty restrictions continued throughout the 1920s and 1930s. Surprisingly, the “submarine cruiser” concept survived the war and reached its baroque apex in some very large (and strange!) subs.
X1. Photo: Wikimedia Commons
X-ed out
By the time the Royal Navy’s X-1 launched on November 16, 1923, the 1922 Washington Naval Treaty had dramatically reshaped the post-war balance of sea power. The treaty restricted the size of guns permitted aboard submarines, and banned the targeting of merchant vessels.
Into these waters sailed the X-1 with its twin dual 5.2-inch turrets, high speed and long range. The largest, most heavily armed sub in the world when launched, her main batteries complemented six torpedo tubes. Rate of fire and ammunition supply were problematic. Targeting used a nine-foot-wide retractable rangefinder behind the conning tower. The sub required 58 men to crew the turrets alone.
Despite crippling mechanical shortcomings, especially her terrible engines, the X-1 made a great submarine cruiser, a type of warship the Washington Treaty specifically forbade. Consequently, the British Government remained very cagey about the X-1. Her poor maintenance record didn’t help. After the vessel literally fell over in a drydock in 1936, the Admiralty scrapped her.
The M-1 suffered equally bad luck. A Swedish freighter accidentally knocked her turret off and she sank with all hands in 1925. After the loss of the M-1, the Royal Navy removed the turrets from M-2 and M-3 and scrapped M-4 altogether. M-3 became a large stealth minelayer with capacity for 100 sea mines mounted in a deployment rack running along the back of the vessel.
The M-2 received a watertight hangar, a crane and a collapsible floatplane and explored aircraft-launching operations between 1927 and 1932. In 1932, M-2 sank with all hands during training maneuvers.
Surcouf. Photo: Wikimedia Commons
Les sous-marins corsaires
Even as the British tried to operationalize the big-gun sub, the French navy doubled down on the concept with its planned “corsair submarines,” a thoroughly dashing Gallic term. Only the first of the three, the Surcouf (named for a famous French privateer) was ever built.
But she was a monster.
Like the X-1, the French giant sub was the largest in the world when launched in October 1929. It was over 363 feet long, almost 30 feet in diameter, with a range of 7,800 miles at 13 knots. Surcouf could bring to bear her twin eight-inch guns on targets and track them with a 16-foot rangefinder from nearly seven miles away.
To supplement her reconnaissance and gunnery range, Surcouf, like M-2, carried a collapsible floatplane in a watertight hangar. Though never used in combat, the small Besson MB.411 seaplane could call in long-range ship sightings and gunfire corrections to the submarine.
Surcouf‘s leisurely submergence rate and poor submerged handling contrasted with her “corsairing” qualities, including two auxiliary boats and a brig equipped for 40 prisoners. A cruiser this big and complex required a 118-man crew, enough for three regular subs.
Appropriately perhaps for such a quasi-piratic vessel, Surcouf led a troubled life. She participated in the seizure of some small Vichy-controlled islands off Newfoundland before disappearing with all hands under mysterious circumstances early in World War II.
The later, even larger Japanese I-400-class submarine aircraft carriers extended the aircraft-as-long-range-artillery concept to its mid-century conclusion. The huge subs mounted five-inch deck guns and anti-aircraft machine guns, but relied on their embarked aircraft and their weapons for main firepower.
With the advent of nuclear power and the guided missile, the seemingly goofy idea of a heavy surface-attack submarine became an impressive and chilling reality. A sub-launched ballistic missile armed with multiple hydrogen bombs represents the acme of long-range naval fire. Most recently, sub-launched cruise missiles have taken out Libyan air defenses and Syrian rebel strongholds.
Navies around the world are arming their undersea forces with missiles filling the roles guns filled long ago—shore bombardment, anti-ship and anti-air. Will energy weapons perhaps eventually fill this longstanding role in unexpected ways?
Forget all those fancy trend reports and hyperbolic tech outlooks by consulting firms. Rather spend an hour with Kevin Kelly - founding executive editor of Wired magazine - (plus Paul Saffo) and learn all about the most important foresight tweaks and tricks:
Worth a watch.
The ever growing demand for housing in the world is undoubtedly one of the greatest challenges of sustainable architecture of the new century. To design and build houses and buildings that are not only aesthetically pleasing, but that also promote comfort and sustainability is what Colombian architects Miguel Niño and Johanna Navarro from Sumart Diseño y Arquitectura SAS aimed to do when creating the BT – Bloque Termodisipador (HB – Heatsink Brick) – a clay brick designed with an irregular cross section and a large angled face that helps protect the brick from solar radiation. This allows ventilation to pass through the bricks, quickly dissipating the heat and reducing the temperature inside the building.
Read More at Interesting Engineering
Swedish scientists have developed a sheet of paper that can store a significant amount of energy. The sheet is very small, you’d probably never imagine what it “has in store” just by looking at it. In terms of dimensions, the sheet of paper is 15 cm in diameter and slightly less than 0.5 mm thick, and it can store up to 1 farad of electric capacitance, about as much as supercapacitors we use today do.
The scientists from Linköping University’s Laboratory of Organic Electronics who created the “Power Paper”, used two main materials: nano-cellulose and a conductive polymer; this allows a single sheet the capacity to recharge not only once but hundreds of times and what’s best: in only a few seconds!
Read More at Interesting Engineering
Beautiful new design for The Biesbosch Museum in the Netherlands, set for completion this coming summer. From MyModernMet:
The Biesbosch Museum, located near the city of Dordrecht in the Netherlands, recently went through a massive redesign and has now been opened to the public. The 8-month-long transformation includes the addition of an organic restaurant, an outdoor seating area, a new wing extension, and a rooftop that covers the entire building with grass, herbs and other flora. A rooftop walkway winds through the new grassy knolls and ends in a lookout that allows visitors to admire the surrounding parkland. In the cooler months, a biomass stove maintains the building’s ambient temperature, while an indoor water feature works to cool the museum in the warmer months.
A 1975 Highway Map of Northern California by the American Automobile Association. Image: University of Toronto's Map and Data Library
The author John Steinbeck once remarked that “maps are not reality at all—they can be tyrants.”
Take road maps, for example. These maps aren’t just designed to provide geographic and navigational information. Since the early 1900s, road maps have been designed to advance capitalism, promoting the ventures of businesses and governments, and in the process, influencing where and how people travel. These ulterior mapping objectives aren’t only found in older paper highway maps, but modern digital mapping applications as well.
The production of paper highway maps in the early twentieth century was a time-consuming and expensive process. So, to offset production costs, most early highway maps were supplemented with advertisements. Because these maps were widely used by motorists, they were great opportunities for advertising for local businesses, such as gas stations, restaurants, and hotels.
A 1968 map of Ontario sponsored by Shell. Image: University of Toronto's Map and Data Library
By the mid-twentieth century, oil companies such as Shell and Esso were mass producing highway maps in North America. Their maps were often distributed for free at their gas stations, superimposed with advertisements for their products and brands. Some companies went so far as to only show the locations of their gas stations amongst the network of highways, thus persuading motorists to stop at their gas stations rather than their competitors.
Beyond advertising specific brands and products, highway maps were also designed to promote auto travel in general. The more people drove, the more certain industries—namely, oil companies and car manufacturers—stood to benefit. So, to further encourage people to drive, highway maps often included idealistic drawings of places and landscapes shown on the map that people could drive to. Such imagery invoked ideas of freedom of mobility and the great outdoors, with the hopes of persuading people to become motoring tourists.
A 1941 map of Colorado by the State Highway Department. Image: University of Toronto's Map and Data Library
Designing maps also involves choices of how real world features are represented—which are omitted, and which are emphasized. People are more likely to stop at tourist destinations and businesses that are included on their map, rather than those that are not.
In France, some of the first road maps were published by tire-manufacturer Michelin, and were often paired with guide books, which provided travel advice and recommended hotels and places to eat. If the location of a business was mentioned it could mean more customers, particularly if it had favourable reviews (the star rankings for restaurants originating in Michelin’s guide books are now one of the most sought after culinary awards worldwide).
A 1922 Map of Toulouse from a Michelin Guide of France. Image: University of Toronto's Map and Data Library
Even the style in which roads were drawn could influence where people travelled.
Roads drawn with thicker lines and brighter colours indicated faster routes, and as such, people were more likely to travel on these roads rather than others. In some cases, roads were emphasized specifically to encourage people to drive along them. In Ontario, for example, some highway maps produced by the Provincial government highlighted the province’s highway and infrastructure projects with more prominent lines compared to those roads built by local governments—even when roads had similar surfaces and speed limits.
Similarly, some maps funded by local businesses would highlight roads that passed through their towns, because that’s where their businesses were located.
A 1955 map of Ontario published by the Provincial Government. Prominent red roads are provincial highways while solid blue are similarly paved roads, but funded by local government. Image: University of Toronto's Map and Data Library
Since the turn of the century, however, the use of paper highway maps has dwindled with the development of GPS devices, Google Maps, and other mapping applications. But like their predecessors, these digital maps also influence users often under a guise of navigational accuracy.
People are more likely to travel along the routes recommended to them by mapping applications than those that are not. If a map shows multiple routes, those shown first or those with faster travel times are more likely to be selected. As a result, because car travel is often the fastest mode of transportation, mapping applications can actually deter people from using public transit or other forms of travel. In other cases, this deterrence is less subtle; a partnership between Google and the ridesharing service Uber means that Google Maps will compare walking or public transit to Uber when suggesting directions—Uber often being faster, albeit more expensive—if users have Uber installed on their smartphone.
Google Maps uses a variety of factors to determine which shops and restaurants to show by default, and which ones to hide. Image: Screengrab
And like their predecessors, mapping applications such as Google Maps have become crucial for advertising retail businesses. The more a store appears on a map, the more likely people will shop there rather than at a competing store. Having a business show up at higher map zoom levels or being at the top of a list when keywords are searched is an important marketing strategy. Generally, these rankings are determined via algorithms based on a combination of business location, number of searches, having complete information, and favourable reviews—but the things that a user searches for, or the places a user visits, can affect what is displayed on a map, too.
What’s important to remember is that maps are not merely representations of the physical world. They are also cultural texts, lenses into the societies that create and use them, and they are often used as instruments of persuasion. This is not to say that most maps, old or new, are inaccurate, but they do not display a whole truth—only a selected vision of the world.
Deepfield Robotics has developed a machine that can distinguish weeds from crops using pattern recognition software, then removes or stamps out the former without harming the latter. This design will help farmers reduce reliance on dangerous pesticides and expensive fertilizers.
The Bonirob robot is about the size of a car and uses advanced shape recognition software to recognize different plant shapes and leaf types, able to deploy a rod that stamps weeds back down into the ground (both eliminating them as a threat and conveniently recycling them into the soil).
Additionally, its onboard systems can monitor and track crop growth, identify pest problems and water needs. It also gets smarter as it goes, adding new information in the field, so to speak.
“Over time, based on parameters such as leaf colour, shape, and size, Bonirob learns how to differentiate more and more accurately between the plants we want and the plants we don’t want” said Professor Amos Albert of Deepfield Robotics.
Rising Temperatures Kick-Start Subarctic Farming In Alaska
We’ve heard a lot about the negative effects of climate change in the arctic and subarctic. But some Alaskans, like farmer Tim Meyers, are seeing warming temperatures as an opportunity.
Full Story: NPR
We are so used to some things that we stopped wondering about them. Like light. What is light? Some kind of wavy thing, right? Kind of. Listen to Youtuber “In a Nutshell” as he explains the science behind light.
The post What Is Light? [Science Video] appeared first on Geeks are Sexy Technology News.
From talking toasters to self-aware fridges, more and more devices are getting connected as part of the much-hyped Internet of Things. But one UK research project is taking the idea of digitizing everything well beyond personal devices: It’s connecting a whole city.
Bristol Is Open is managed in collaboration between Bristol City Council and the University of Bristol. It’ll see all manner of sensors—think traffic lights, pollution sensors, and CCTV cameras—hooked up to a citywide network that will carry massive amounts of data for the university’s supercomputer to make sense of. The team prefers the term “programmable city” to “smart city”: The aim is to use this urban data to reconfigure all aspects of city life to “program” Bristol into a more efficient, democratic, and generally nicer place to live for its almost half-million residents.
That’s the dream, but there are obvious concerns too. Who has access to this data? What happens when something malfunctions? What if it’s hacked?
As a pioneer of smart cities in the UK, Bristol Is Open is turning over these questions as it goes. Though the infrastructure is being rolled out in a real city, it’s experimental in nature. “A city as a laboratory where the citizens are not treated as guinea pigs is how we describe it,” said Stephen Hilton, Director of Futures at Bristol City Council.
Three fast networks make up the backbone of the project: a “wireless mile;” a mesh network bouncing across 1500 lampposts; and a superfast fibre network running through abandoned cable TV ducts that the Council had the foresight to buy up 10 years ago.
The brains of the project can be found at the University of Bristol, where Professor of High Performance Networks Dimitra Simeonidou oversees an “operating system” for cities. She compares the OS to Android: anyone can build applications to sit on top of it, and it can be programmed for use with different cities. Crucially, it’s open source.
For the first episode of the UK Motherboard Show, made possible by CISCO, we visited Bristol and got a glimpse of the kind of information that might end up running through the system. We saw a control room where CCTV images of the city’s streets are beamed from a wall of screens as monitors communicate with police to track suspect activity. It’s easy to dismiss as Big Brother-style surveillance, with all the concerns that evokes, but in the same room others are keeping watch in a different way: Over headsets, operators check in with elderly people who have activated a personal or home alarm (often accidentally). This kind of monitoring allows them to remain independent for longer.
With the networks in place, Bristol Is Open and its partners plan to explore many different applications for connected tech. Ideas involving air quality monitors, traffic data from driverless cars, and sensors inside residential homes are all in the works.
To discuss the implications of the “internet of everything” we invited some experts in different aspects of digitization to share their thoughts. Former LulzSec hacker Jake Davis emphasised quite how dangerous it can be if a bad actor hacks your internet-enabled waste bin, while Stephen Hilton offered the local government perspective on some of the issues raised. Anab Jain, who’s worked on connected prototypes as cofounder of design studio Superflux and the Internet of Things Academy, reminded us of the potential present in this technology, but only if we approach it an informed, measured, and democratic way.
The Bristol project shows that this kind of hyperconnected future is already on its way—and now’s the time to decide how we want to use it.
Register for our free webcast "Building IoT Systems with Web Standards," which will be hosted by Vlad Trifa and Dominique Guinard on December 8, 2015, at 10 a.m. PT.
When the term "IoT" was first coined, the idea was to move from a model where data is generated by humans bridging media gaps between the physical and the virtual worlds to a model where data is gathered by the Things themselves.
Fifteen years later, we're moving in the right direction to make this a reality, but we still have several challenges ahead. One major challenge is interoperability: many Things do talk using the Internet, but they don't talk the same language. Having been involved in the IoT for about as long as it's been around, I'm pretty sure of one thing: a universal networking protocol for the IoT will never exist — and for a good reason! The IoT is a vast world where the needs of one field (e.g. Industry 4.0) to another (e.g. the smart home) are fundamentally different. As a consequence, the list of automation protocols is actually growing, not shrinking.
A consequence of these different needs is the focus on the connectivity aspect of the IoT. This is not unusual, but as we ascend the pyramid of IoT needs, we must think about the data interoperability of Things. We need to build APIs for Things that are interoperable; in short, we need an application layer for the IoT.
Why? Let's look at a simple example: a smart hotel. The hotel owner would like to digitally connect the appliances in all the rooms of the hotel. This gives guests access to a variety of services, from controlling their room (lights, air conditioning, entertainment, etc.), to booking hotel facilities, to ordering food and drinks — all from a mobile phone. This would also enable the owner to coordinate and optimize all aspects of the hotel (e.g. energy consumption) in a centralized and efficient manner, without having to use a variety of siloed applications and tools.
Building a smart hotel system will likely require electronic door locks made by one company, security cameras from another company, and a control application to manage all of this made by yet another company. Making these devices and systems talk and work with each other will require a lot of custom system integration work. The hotel owner likely will have to contract a specialist company and spend well-earned savings on a substantial project that will take months to deliver. Why? Because of the lack of a common application layer.
The best example of interoperability at the application layer is the Web. The Web made the Internet successful by creating an open, simple, and highly interoperable layer where data can be exchanged between servers and consumed by applications. I believe that technical details about how physical things talk to each other over the Web will make a massive difference to whether or not the IoT realizes its true vision, or becomes a complex mesh of proprietary or semi-proprietary standards.
Together with my colleague Vlad Trifa, we have been promoting this approach for more than a decade now and call it the Web of Things. This approach also underpins the IoT platform of the company Vlad and I co-founded: EVRYTHNG; these principles have been applied, and the platform deployed in the wild to connect millions of products, from bottles of whisky or soda, to smart plugs and lighting systems. The core of the Web of Things approach is quite simple: re-use Web standards to build the APIs of Things.
[caption id="attachment_80888" align="aligncenter" width="570"]
Image courtesy of Dominique Guinard and EVRYTHNG.[/caption]
There are many areas where the Web can help:
There are a lot more Web tools to leverage to build the application layer of the IoT, such as those for security (e.g. TLS, oAuth), semantics (e.g. JSON-LD, RDFa, Schema.org), or to create composite applications (e.g. Mashups).
As the Web embraces the IoT (and vice-versa), the IoT will also influence the new developments of the Web. For instance, the recently approved HTTP/2 standard implements a number of improvements directly impacting the IoT. CoAP is another example: a protocol for low-power Things inspired by Web patterns that can be translated to HTTP and WebSockets. These are steps in the right direction.
However, just as Web APIs created thousand of isolated Web silos, a Web of Things where we only agree on using the same protocols creates accessible silos, but still silos of Things.
The next step toward true interoperability is to go beyond the protocols and look at common syntax and semantics for Things. This is the gap that platforms like Google/Nest Weave, Apple HomeKit, and the EVRYTHNG API are filling: they integrate via Internet protocols at the network layer, Web protocols at the Web layer, and define the models and semantics of Things.
We are at a critical turning point in the development of the IoT, and relying on commercial de-facto standards can be detrimental to consumers. Independent institutions like W3C are essential to balance individual commercial agendas, ensuring that the IoT moves from a complicated maze of disjointed standards to an open, universal Web-based system. I love what the power of the World Wide Web has helped our societies and economies achieve so far, and believe it makes all the sense in the world to take the same approach with the IoT.
To address this need for a Web-based approach, EVRYTHNG, alongside partners from the COMPOSE Project (with members from IBM, W3C, Barcelona Supercomputing Centre, Fraunhofer Institute, and other universities), has authored an official W3C Member Submission called the Web Thing Model, graphically represented below. This submission contributes to the pre-standard work around the Web of Things at W3C within the WoT Interest Group.
[caption id="attachment_80889" align="aligncenter" width="570"]
Image courtesy of Dominique Guinard and EVRYTHNG.[/caption]
The submission starts by providing guidelines on how to implement RESTful APIs for Things. It also discusses different integration patterns (Gateways, Cloud, Direct). On the semantic level, we focus on five simple constructs as illustrated below:
[caption id="attachment_80890" align="aligncenter" width="570"]
Image courtesy of Dominique Guinard and EVRYTHNG.[/caption]
Things are the physical objects. They are semantically described by simple Models based on the Web Linking standard, and semantic extensions using JSON-LD are supported. This allows a Thing to extend its description using a well-known semantic format such as the Schema.org Product schema. Using this approach, existing services like search engines can automatically get metadata about Things.
Properties are the variables of a device, such as location, temperature sensor ready, or state. These are meant to be changed by the Things themselves (or by a gateway / cloud). Properties can be observed by clients using push mechanisms such as WebSockets or WebHooks via Subscriptions.
If an external application wants to change the state of a device, it must do so by sending Actions to the device — for example, lightState, garageDoorState.
The Web Thing Model also provides guidelines on how to implement these constructs using Web standards, focusing on HTTP and WebSockets. However, it could also be applied and used with other application protocols such as CoAP or MQTT.
While this submission is not yet a standard, we hope it will bootstrap the discussions in several areas:
No matter the outcome, these discussions are what we are looking for to finally implement the powerful vision of the Internet of Things.
Image by Jud McCranie on Wikimedia Commons.
When building a biotech start-up, there is a certain inevitability to every conversation you will have. For investors, accelerators, academics, friends, baristas, the first two questions will be: "what do you want to do?" and "have you got a patent yet?"
Almost everything revolves around getting IP protection in place, and patent lawyer meetings are usually the first sign that your spin-off is on the way. But what if there was a way to avoid the patent dance, relying instead on implementation? It seems somewhat utopian, but there is a precedent in the technology world: open source.
What is open source? Essentially, any software in which the source code (the underlying program) is available to anyone else to modify, distribute, etc. This means that, unlike typical proprietary development processes, it lends itself to collaborative development between larger groups, often spread out across large distances. From humble beginnings, the open source movement has developed to the point of providing operating systems (e.g. Linux), Internet browsers (Firefox), 3D modelling software (Blender), monetary alternatives (Bitcoin), and even integrating automation systems for your home (OpenHab).
The obvious question is then, "OK, but how do they make money?" The answer to this lies not in attempting to profit from the software code itself, but rather from its implementation as well as the applications which are built on top of it. For the implementation side, take Red Hat Inc., a multinational software company in the S&P 500 with a market cap of $14.2 billion, who produce the extremely popular Red Hat Enterprise Linux distribution. Although open source and freely available, Red Hat makes its money by selling a thoroughly bug-tested operating system and then contracting to provide support for 10 years. Thus, businesses are not buying the code; they are buying a rapid response to any problems.
How about profiting on the applications? Google's Android, the operating system that runs a large percentage of the world's phones, is open source. This allows others to make their own variants, but the majority of phone and tablet owners will remain with the pre-installed Android version — and purchase their apps from the Google Play store with its associated distributor fees. In this manner, Google profits not from the source code, but from the actions of people using this code.
A more "physical" version would be Tesla, which opened up its patent portfolio for anyone to use. This doesn't seem to make sense at first glance, but Tesla is also heavily investing in battery production capacity — thus, encouraging the use of its electric technology will also increase the income from its Gigafactory and SuperCharger network. Again, open source acts as a basis to build further income on top.
So, that's software. How does open source work in biology? Examples lie on a spectrum ranging from "garage" to "academic lab."
Biohackers, for one, in many ways resemble the original "two nerds in a garage" origins of the computer movement. Biohackers use open source protocols and designs for equipment, such as PCR to set up personal laboratories that would normally be beyond the scope of casual tinkerers. This is assisted by recent attempts to standardize genetic elements, as seen, for example, in the BioBrick movement (which curates various DNA sequences designed to easily clone together into a biological circuit) or the OpenPlant collaborative initiative (which promotes an open source approach to plant synthetic biology). Supported by a surprising number of open, collaborative labs around the world, these groups aim to bring about the same sort of changes as were seen with the start of the PC era.
At the other end, we have institutions such as CambiaLabs and the BiOS Initiative, which aim to support open source IP initiatives for biological systems via collaborative licensing agreements. A good example of their work would be the Transbacter project, an attempt to perform an end-run around the multitude of Agrobacteria-mediated plant engineering techniques patents by identifying other vectors — which were then released to the community.
Both of these are attempts to democratize biological research and development, and tie into a general increase in popular interest over biotechnology — as can be seen by the success of the crowdfunded "Glowing Plants" synthetic biology project.
However, there are significant differences between the acceptance of open source software and open source biology, primarily boiling down to regulation and safety issues (after all, a badly written program can crash your computer, but a badly formed bacteria can kill you). The number of regulations that need to be followed when legally producing a transgenic organism are immense, particularly in ensuring that they are both non-harmful and unlikely to spread throughout the wild. These regulatory — and thus financial — burdens severely limit the degree to which any individual biohacker can take their ideas and develop them. Note, however, that this is individual biohackers — larger firms can naturally afford to bring developments through this stage to market. Can a larger firm thus make money from open source biology? We believe so, provided the company uses a method similar to Red Hat, Google, or Tesla, in using the open source component to drive customers toward their own market strength — for example, by releasing blueprints and software for lab automation, then selling that equipment and support.
Open source biology is taking off, but will it ever provide enough income to build a business around? When we look at the history of open source software, it seems inevitable.
To learn more about synthetic biology, download O'Reilly BioCoder for free. You can also join Tim O'Reilly from O'Reilly Media and Roger Chen from O'Reilly AlphaTech Ventures discussing open source biology at the SynBioBeta San Francisco Conference on November 4-6, 2015.
Image via Wellcome Images on Wikimedia Commons.
Hexagonal timber frame sauna with reciprocal roof in Swindon, England.
Contributed by Sierra Carpenter.
Preventing inflammation in obese fat tissue may hold the key to preventing or even reversing type 2 diabetes, new research has found.
Researchers from Melbourne's Walter and Eliza Hall Institute and the RIKEN Institute, Japan found they could 'reverse' type 2 diabetes in laboratory models by dampening the inflammatory response in fat tissue.
Dr Ajith Vasanthakumar, Dr Axel Kallies and colleagues from the institute discovered that specialised immune cells, called regulatory T cells (Tregs), played a key role in controlling inflammation in fat tissue and maintaining insulin sensitivity.
Most people (including myself) are drawn to Julia by its lofty goals. Speed of C, statistical packages of R, and ease of Python?—it sounds two good to be true. However, I haven't seen anyone who has looked into it say the developers behind the language aren't on track to accomplish these goals.
Having only been around since 2012, Julia's greatest disadvantage is a lack of community support. If you have an obscure Julia question and you google it, you probably won't find the answer, whereas with Python or R or Java you would. This also means less package support. The packages for linear algebra, plotting, and other stuff are there, but if you want to do computer vision or nlp, you'd be among the few.
However, it is definitely worth looking into. I'm not quite a pro, but I'm getting to the point where if I code something in Python, I can easily transfer it to Julia. Then sometimes I test the speed for fun. Julia always wins.
Recently, I wrote an article about linear algebra, with accompanying code in Python. Below is basically the same article, with the code in Julia. If you need help with the basic syntax, I also wrote a basic syntax guide, kind of a compressed version of the documentation. I included the concept descriptions, but they're no different from my original article. The point of this is just to show how easy it is to do linear algebra in Julia.
Here is the iPython notebook on my github. (You can write Julia code in iPython...it's awesome).
matrix – a rectangular array of values
vector – one dimensional matrix
identity matrix I – a diagonal matrix is an n x n matrix with one’s on the diagonal from the top left to the bottom right.
i.e.
[[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]]
When a matrix A is multiplied by it’s inverse A^-1, the result is the identity matrix I. Only square matrices have inverses. Example below.
Note – the inverse of a matrix is not the transpose.
Matrices are notated m x n, or rows x columns. A 2×3 matrix has 2 rows and 3 columns. Read this multiple times.
You can only add matrices of the same dimensions. You can only multiply two matrices if the first is m x n, and the second is n x p. The n-dimension has to match.
Now the basics in Julia.
When appending an array, make sure whatever is being appended is an an array itself.
# 1d arrays
arr = [1,2,3,4,5] append!(arr, [6]) # append function.
# number of dimensions
println("Array arr has ", ndims(arr), " dimension.")
println("Array arr is size ", size(arr)) # size of the arrayprintln(arr)
Array arr has 1 dimension.
Array arr is size (6,)
[1,2,3,4,5,6]
You can also define matrices using reshape(). Notice how [1:12] returns an array of numbers ranging from 1 to 12.
A = [1 2 3; 4 5 6; 7 8 9]
println(size(A))
# I can't seem to find an argument allowing you to reshape by row
B = reshape([1:12], 3, 4)
(3,3)
3x4 Array{Int64,2}:
1 4 7 10
2 5 8 11
3 6 9 12
Multiplying a matrix by the identity matrix will return the original matrix. The identity matrix is represented by eye() in most languages, Julia included.
A = reshape([1.0,2.0,3.0,4.0], 1,4)println(A)
println(A * eye(4)) # yields the same result
[1.0 2.0 3.0 4.0]
[1.0 2.0 3.0 4.0]
inv() returns the inverse of a matrix. Remember only square matrices have inverses!
B = [1 2; 3 4]
B * inv(B) # B * inv(B) returns the identity matrix
2x2 Array{Float64,2}:
1.0 0.0
8.88178e-16 1.0
In Julia, use a ' symbol, or transpose(A) to return the transpose of a matrix.
A = [1 2 3; 4 5 6]
println(A)
println(A') # A transpose
[1 2 3
4 5 6]
[1 4
2 5
3 6]
An eigenvalue of a matrix A is something you can multiply some vector X by, and get the same answer you would if you multiplied A and X. In this situation, the vector X is an eigenvector. More formally –
Def: Let A be an n x n matrix. A scalar λ is called an eigenvalue of A if there is a nonzero vector X such that AX = λX.
Such a vector X is called an eigenvector of A corresponding to λ.
There is a way to compute the eigenvalues of a matrix by hand, and then a corresponding eigenvector, but it’s a bit beyond the scope of this tutorial.
# ************* Eigenvalues and Eigenvectors *********** #
A = [2 -4; -1 -1] x = [4; -1]
eigVal = 3
println(A * x)
println(eigVal * x)
[12,-3]
[12,-3]
Now that we know A has a real eigenvalue, let's compute it with Julia's built in function.
w, v = eig(A)
print(w) # these are the eigenvalues
[3.0,-2.0]
# ok, so the square matrix A has two eigenvalues, 3 and -2. # but what about the corresponding eigenvector?v
# this is the normalized eigenvector corresponding to w[0], or 3.
# let's unnormalize it to see if we were right.
# the length of our original eigenvector xlength = sqrt(x[1]^2 + x[2]^2)
println(v[:, 1] * length)
println("Our original eigenvector was [4, -1]")
[4.0,-1.0]
Our original eigenvector was [4, -1]
Note - all multiples of this eigenvector will be an eigenvector of A corresponding to lambda.
Determinants are calculated value for a given square matrix. They are used in most of linear algebra beyond matrix multiplication.
We can see where this comes from if we look at the determinant for a 2 x 2 matrix.
Imagine we have a square matrix A.
We can define it’s inverse using the formula below.
That bit in the denominator, that’s the determinant. If it is 0, the matrix is singular (no inverse!).
It has a ton of properties, for example, the determinant of a matrix equals that of it’s transpose.
They are used in calculating a matrix derivative, which is used in a ton of machine learning algorithms (i.e. normal equation in linear regression!).
# ************************ Determinants ****************** #
A = [1 2; 3 4]
print("det(A) = ", det(A))
det(A) = -2.0
SVD is a technique to factorize a matrix, or a way of breaking the matrix up into three matrices.
SVD is used specifically in something like Principal Component Analysis. Eigenvalues in the SVD can help you determine which features are redundant, and therefore reduce dimensionality!
It’s actually considered it’s own data mining algorithm.
It uses the formula M = UΣV, then uses the properties of these matrices (i.e. U and V are orthogonal, Σ is a diagonal matrix with non-negative entries) to furthur break them up.
Here is a bit more math-intensive example.
# ****** Singular Value Decomposition ****** #
A = [1 2 3 4 5 6 7 8; 9 10 11 12 4 23 45 2; 5 3 5 2 56 3 6 4]
U, s, V = svd(A)
(
3x3 Array{Float64,2}:
-0.181497 0.0759015 0.980458
-0.652719 0.736431 -0.177838
-0.735538 -0.672241 -0.0841178,
[63.2736,49.1928,7.77941],
8x3 Array{Float64,2}:
-0.153834 0.0679486 -0.133773
-0.143769 0.111793 -0.00897498
-0.180203 0.100975 0.0725716
-0.158513 0.158485 0.208183
-0.70659 -0.697668 -0.0667992
-0.289349 0.312578 0.197973
-0.554039 0.602472 -0.211356
-0.0900781 -0.0123776 0.919288 )
There are a few other things you should know for convenience!
randn(x) returns x normally distributed numbers. rand() is your typical random function, between 0-1.
# 10 random numbers from a standard normal distributionA = randn(10)
B = rand(10) # 10 random numbers in [0,1]
A = [3 3 3]
B = [2 3 4]
A .< B
1x3 BitArray{2}:
false false true
# Indexing matrices
A = [1 2 3; 4 5 6; 7 8 9]
A[:] # flattens array by column
A[1, :] # first row
A[:, 1] # first column
A[2, 3] # second row, third column
# There are also basic statistic operations like mean(), std(), var() # as well as math functions like exp(), sin(), etc.
You can get a much more thorough guide through the Julia documentation.
Via Game n Gadgets.
Little Shop of Physics has built a miniature plasma cutter using a pencil lead together with 4 x 9 volt batteries and a couple of clips allowing you to cut through aluminium foil.
Learn more about how to create your very own plasma cutter and how it can be used to cut thin metal foil using a mechanical lead pencil, Watch the video below.
Parts required to make your own include :
– 4 x 9 V battery
– 2 x Clip leads
– 1 x 5 mm pencil lead
– Aluminium foil
– Box or tub
– Rubber bandThis demonstration is only for the experienced! Little Shop of Physics took several precautions to do this demonstration safely. Don’t try this yourself unless you understand and appreciate the difficulties and the dangers.
“We have a few cyborgs on staff. Ben Popper is arguably the reporter best known for peeling back his skin to insert a piece of technology, which he chronicled in his feature, Cyborg America. But others have gone under the knife. I wanted to know why. You know, because I have crippling FOMO.”
Dezeen and MINI Frontiers: Royal College of Art graduate Frank Kolkman has built an open-source machine that could enable people to perform keyhole surgery on themselves using a Playstation controller (+ movie). (more…)
From Scott Sumner:
Why then are they planning on raising interest rates? They seem to be relying on flawed NK models that suggest tight labor markets cause higher inflation. And they notice that unemployment has recently fallen to 5.3%, and may decline further.
But these NK models are simply wrong; low unemployment does not cause inflation. Rather unexpectedly high inflation (when caused by demand shocks) causes low unemployment. And monetary policy drives inflation. The NK models have causation reversed. The Fed is acting like a bystander waiting for the economy to bring inflation on line, whereas actually the Fed determines inflation. But to do so they need to ease monetary policy when they are likely to fall short of their target.
Going visual:
You can get many combinations of inflation and unemployment, depending on the shocks that hit the system. For example, in the mid and late 90s, a combination of a positive productivity shock, for a while accompanied by a negative oil price shock, is behind falling inflation and unemployment. In the early 00s, a negative demand shock (Fed mistake) which brought NGDP growth down was accompanied by rising unemployment, while inflation didn´t move much at all.
In 2008-9, a very strong negative demand shock (the Fed again) brings inflation forcefully down and unemployment jumps.
Although they appear not to like being bystanders, monetary policy makers act as if they were. But they are “restless”, especially since many of them have never had the chance to increase interest rates. With that, they begin to “see things” (like an economy that´s “picking up” bringing inflation in its wake!)
With a low and stable NGDP growth that would be quite a feat!
Almost five years later, they are very likely to pull a “Trichet trick”. And we well know the consequences.
