Category: Electronics


An international team of scientists has come up with a blueprint for a large-scale quantum computer

By Hugo Angel,

‘It is the Holy Grail of science … we will be able to do certain things we could never even dream of before’
Courtesy Professor Winfried Hensinger
Quantum computing breakthrough could help ‘change life completely‘, say scientists
Scientists claim to have produced the first-ever blueprint for a large-scale quantum computer in a development that could bring about a technological revolution on a par with the invention of computing itself.
Until now quantum computers have had just a fraction of the processing power they are theoretically capable of producing.
But an international team of researchers believe they have finally overcome the main technical problems that have prevented the construction of more powerful machines.
They are currently building a prototype and a full-scale quantum computer – many millions of times faster than the best currently available – could be built in about a decade.
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Scientists invent invisible underwater robots based on glass eels
Such devices work by utilising the almost magical properties found in the world of the very small, where an atom can apparently exist in two different places at the same time.
Professor Winfried Hensinger, head of the Ion Quantum Technology Group at Sussex University, who has been leading this research, told The Independent: “It is the Holy Grail of science, really, to build a quantum computer.
And we are now publishing the actual nuts-and-bolts construction plan for a large-scale quantum computer.
It is thought the astonishing processing power unleashed by quantum mechanics will lead to new, life-saving medicines, help solve the most intractable scientific problems, and probe the mysteries of the universe.
Life will change completely. We will be able to do certain things we could never even dream of before,” Professor Hensinger said.
You can imagine that suddenly the sky is the limit.
This is really, really exciting … it’s probably one of the most exciting times to be in this field.
He said small quantum computers had been built in the past but to test the theories.
This is not an academic study any more, it really is all the engineering required to build such a device,” he said.
Nobody has really gone ahead and drafted a full engineering plan of how you build one.
Many people questioned, because this is so hard to make this happen, that it can even be built.
We show that not only can it be built, but we provide a whole detailed plan on how to make it happen.
The problem is that existing quantum computers require lasers focused precisely on individual atoms. The larger the computer, the more lasers are required and the greater the chance of something going wrong.
But Professor Hensinger and colleagues used a different technique to monitor the atoms involving a microwave field and electricity in an ‘ion-trap’ device.

What we have is a solution that we can scale to arbitrary [computing] power,” he said.

Fig. 2. Gradient wires placed underneath each gate zone and embedded silicon photodetector.
(A) Illustration showing an isometric view of the two main gradient wires placed underneath each gate zone. Short wires are placed locally underneath each gate zone to form coils, which compensate for slowly varying magnetic fields and allow for individual addressing. The wire configuration in each zone can be seen in more detail in the inset.
(B) Silicon photodetector (marked green) embedded in the silicon substrate, transparent center segmented electrodes, and the possible detection angle are shown. VIA structures are used to prevent optical cross-talk from neighboring readout zones.
Source: Science Journals — AAAS. Blueprint for a microwave trapped ion quantum computer. Lekitsch et al. Sci. Adv. 2017;3: e1601540 1 February 2017
Fig. 4. Scalable module illustration. One module consisting of 36 × 36 junctions placed on the supporting steel frame structure: Nine wafers containing the required DACs and control electronics are placed between the wafer holding 36 × 36 junctions and the microchannel cooler (red layer) providing the cooling. X-Y-Z piezo actuators are placed in the four corners on top of the steel frame, allowing for accurate alignment of the module. Flexible electric wires supply voltages, currents, and control signals to the DACs and control electronics, such as field-programmable gate arrays (FPGAs). Coolant is supplied to the microchannel cooler layer via two flexible steel tubes placed in the center of the modules.
Source: Science Journals — AAAS. Blueprint for a microwave trapped ion quantum computer. Lekitsch et al. Sci. Adv. 2017;3: e1601540 1 February 2017
Fig. 5. Illustration of vacuum chambers. Schematic of octagonal UHV chambers connected together; each chamber is 4.5 × 4.5 m2 large and can hold >2.2 million individual X-junctions placed on steel frames.
Source: Science Journals — AAAS. Blueprint for a microwave trapped ion quantum computer. Lekitsch et al. Sci. Adv. 2017;3: e1601540 1 February 2017

We are already building it now. Within two years we think we will have completed a prototype which incorporates all the technology we state in this blueprint.

At the same time we are now looking for industry partner so we can really build a large-scale device that fills a building basically.
It’s extraordinarily expensive so we need industry partners … this will be in the 10s of millions, up to £100m.
Commenting on the research, described in a paper in the journal Science Advances, other academics praised the quality of the work but expressed caution about how quickly it could be developed.
Dr Toby Cubitt, a Royal Society research fellow in quantum information theory at University College London, said: “Many different technologies are competing to build the first large-scale quantum computer. Ion traps were one of the earliest realistic proposals. 
This work is an important step towards scaling up ion-trap quantum computing.
Though there’s still a long way to go before you’ll be making spreadsheets on your quantum computer.
And Professor Alan Woodward, of Surrey University, hailed the “tremendous step in the right direction”.
It is great work,” he said. “They have made some significant strides forward.

But he added it was “too soon to say” whether it would lead to the hoped-for technological revolution.

ORIGINAL: The Independent
Ian Johnston Science Correspondent
Thursday 2 February 2017

IBM, Local Motors debut Olli, the first Watson-powered self-driving vehicle

By Hugo Angel,

Olli hits the road in the Washington, D.C. area and later this year in Miami-Dade County and Las Vegas.
Local Motors CEO and co-founder John B. Rogers, Jr. with “Olli” & IBM, June 15, 2016.Rich Riggins/Feature Photo Service for IBM

IBM, along with the Arizona-based manufacturer Local Motors, debuted the first-ever driverless vehicle to use the Watson cognitive computing platform. Dubbed “Olli,” the electric vehicle was unveiled at Local Motors’ new facility in National Harbor, Maryland, just outside of Washington, D.C.

Olli, which can carry up to 12 passengers, taps into four Watson APIs (

  • Speech to Text, 
  • Natural Language Classifier, 
  • Entity Extraction and 
  • Text to Speech

) to interact with its riders. It can answer questions like “Can I bring my children on board?” and respond to basic operational commands like, “Take me to the closest Mexican restaurant.” Olli can also give vehicle diagnostics, answering questions like, “Why are you stopping?

Olli learns from data produced by more than 30 sensors embedded throughout the vehicle, which will added and adjusted to meet passenger needs and local preferences.
While Olli is the first self-driving vehicle to use IBM Watson Internet of Things (IoT), this isn’t Watson’s first foray into the automotive industry. IBM launched its IoT for Automotive unit in September of last year, and in March, IBM and Honda announced a deal for Watson technology and analytics to be used in the automaker’s Formula One (F1) cars and pits.
IBM demonstrated its commitment to IoT in March of last year, when it announced it was spending $3B over four years to establish a separate IoT business unit, whch later became the Watson IoT business unit.
IBM says that starting Thursday, Olli will be used on public roads locally in Washington, D.C. and will be used in Miami-Dade County and Las Vegas later this year. Miami-Dade County is exploring a pilot program that would deploy several autonomous vehicles to shuttle people around Miami.
ORIGINAL: ZDnet
By Stephanie Condon for Between the Lines
June 16, 2016

Former NASA chief unveils $100 million neural chip maker KnuEdge

By Hugo Angel,

Daniel Goldin
It’s not all that easy to call KnuEdge a startup. Created a decade ago by Daniel Goldin, the former head of the National Aeronautics and Space Administration, KnuEdge is only now coming out of stealth mode. It has already raised $100 million in funding to build a “neural chip” that Goldin says will make data centers more efficient in a hyperscale age.
Goldin, who founded the San Diego, California-based company with the former chief technology officer of NASA, said he believes the company’s brain-like chip will be far more cost and power efficient than current chips based on the computer design popularized by computer architect John von Neumann. In von Neumann machines, memory and processor are separated and linked via a data pathway known as a bus. Over the years, von Neumann machines have gotten faster by sending more and more data at higher speeds across the bus as processor and memory interact. But the speed of a computer is often limited by the capacity of that bus, leading to what some computer scientists to call the “von Neumann bottleneck.” IBM has seen the same problem, and it has a research team working on brain-like data center chips. Both efforts are part of an attempt to deal with the explosion of data driven by artificial intelligence and machine learning.
Goldin’s company is doing something similar to IBM, but only on the surface. Its approach is much different, and it has been secretly funded by unknown angel investors. And Goldin said in an interview with VentureBeat that the company has already generated $20 million in revenue and is actively engaged in hyperscale computing companies and Fortune 500 companies in the aerospace, banking, health care, hospitality, and insurance industries. The mission is a fundamental transformation of the computing world, Goldin said.
It all started over a mission to Mars,” Goldin said.

Above: KnuEdge’s first chip has 256 cores.Image Credit: KnuEdge
Back in the year 2000, Goldin saw that the time delay for controlling a space vehicle would be too long, so the vehicle would have to operate itself. He calculated that a mission to Mars would take software that would push technology to the limit, with more than tens of millions of lines of code.
Above: Daniel Goldin, CEO of KnuEdge.
Image Credit: KnuEdge
I thought, Former NASA chief unveils $100 million neural chip maker KnuEdge

It’s not all that easy to call KnuEdge a startup. Created a decade ago by Daniel Goldin, the former head of the National Aeronautics and Space Administration, KnuEdge is only now coming out of stealth mode. It has already raised $100 million in funding to build a “neural chip” that Goldin says will make data centers more efficient in a hyperscale age.
Goldin, who founded the San Diego, California-based company with the former chief technology officer of NASA, said he believes the company’s brain-like chip will be far more cost and power efficient than current chips based on the computer design popularized by computer architect John von Neumann. In von Neumann machines, memory and processor are separated and linked via a data pathway known as a bus. Over the years, von Neumann machines have gotten faster by sending more and more data at higher speeds across the bus as processor and memory interact. But the speed of a computer is often limited by the capacity of that bus, leading to what some computer scientists to call the “von Neumann bottleneck.” IBM has seen the same problem, and it has a research team working on brain-like data center chips. Both efforts are part of an attempt to deal with the explosion of data driven by artificial intelligence and machine learning.
Goldin’s company is doing something similar to IBM, but only on the surface. Its approach is much different, and it has been secretly funded by unknown angel investors. And Goldin said in an interview with VentureBeat that the company has already generated $20 million in revenue and is actively engaged in hyperscale computing companies and Fortune 500 companies in the aerospace, banking, health care, hospitality, and insurance industries. The mission is a fundamental transformation of the computing world, Goldin said.
It all started over a mission to Mars,” Goldin said.

Above: KnuEdge’s first chip has 256 cores.Image Credit: KnuEdge
Back in the year 2000, Goldin saw that the time delay for controlling a space vehicle would be too long, so the vehicle would have to operate itself. He calculated that a mission to Mars would take software that would push technology to the limit, with more than tens of millions of lines of code.
Above: Daniel Goldin, CEO of KnuEdge.
Image Credit: KnuEdge
I thought, holy smokes,” he said. “It’s going to be too expensive. It’s not propulsion. It’s not environmental control. It’s not power. This software business is a very big problem, and that nation couldn’t afford it.
So Goldin looked further into the brains of the robotics, and that’s when he started thinking about the computing it would take.
Asked if it was easier to run NASA or a startup, Goldin let out a guffaw.
I love them both, but they’re both very different,” Goldin said. “At NASA, I spent a lot of time on non-technical issues. I had a project every quarter, and I didn’t want to become dull technically. I tried to always take on a technical job doing architecture, working with a design team, and always doing something leading edge. I grew up at a time when you graduated from a university and went to work for someone else. If I ever come back to this earth, I would graduate and become an entrepreneur. This is so wonderful.
Back in 1992, Goldin was planning on starting a wireless company as an entrepreneur. But then he got the call to “go serve the country,” and he did that work for a decade. He started KnuEdge (previously called Intellisis) in 2005, and he got very patient capital.
When I went out to find investors, I knew I couldn’t use the conventional Silicon Valley approach (impatient capital),” he said. “It is a fabulous approach that has generated incredible wealth. But I wanted to undertake revolutionary technology development. To build the future tools for next-generation machine learning, improving the natural interface between humans and machines. So I got patient capital that wanted to see lightning strike. Between all of us, we have a board of directors that can contact almost anyone in the world. They’re fabulous business people and technologists. We knew we had a ten-year run-up.
But he’s not saying who those people are yet.
KnuEdge’s chips are part of a larger platform. KnuEdge is also unveiling KnuVerse, a military-grade voice recognition and authentication technology that unlocks the potential of voice interfaces to power next-generation computing, Goldin said.
While the voice technology market has exploded over the past five years due to the introductions of Siri, Cortana, Google Home, Echo, and ViV, the aspirations of most commercial voice technology teams are still on hold because of security and noise issues. KnuVerse solutions are based on patented authentication techniques using the human voice — even in extremely noisy environments — as one of the most secure forms of biometrics. Secure voice recognition has applications in industries such as banking, entertainment, and hospitality.
KnuEdge says it is now possible to authenticate to computers, web and mobile apps, and Internet of Things devices (or everyday objects that are smart and connected) with only a few words spoken into a microphone — in any language, no matter how loud the background environment or how many other people are talking nearby. In addition to KnuVerse, KnuEdge offers Knurld.io for application developers, a software development kit, and a cloud-based voice recognition and authentication service that can be integrated into an app typically within two hours.
And KnuEdge is announcing KnuPath with LambdaFabric computing. KnuEdge’s first chip, built with an older manufacturing technology, has 256 cores, or neuron-like brain cells, on a single chip. Each core is a tiny digital signal processor. The LambdaFabric makes it possible to instantly connect those cores to each other — a trick that helps overcome one of the major problems of multicore chips, Goldin said. The LambdaFabric is designed to connect up to 512,000 devices, enabling the system to be used in the most demanding computing environments. From rack to rack, the fabric has a latency (or interaction delay) of only 400 nanoseconds. And the whole system is designed to use a low amount of power.
All of the company’s designs are built on biological principles about how the brain gets a lot of computing work done with a small amount of power. The chip is based on what Goldin calls “sparse matrix heterogeneous machine learning algorithms.” And it will run C++ software, something that is already very popular. Programmers can program each one of the cores with a different algorithm to run simultaneously, for the “ultimate in heterogeneity.” It’s multiple input, multiple data, and “that gives us some of our power,” Goldin said.

Above: KnuEdge’s KnuPath chip.
Image Credit: KnuEdge
KnuEdge is emerging out of stealth mode to aim its new Voice and Machine Learning technologies at key challenges in IoT, cloud based machine learning and pattern recognition,” said Paul Teich, principal analyst at Tirias Research, in a statement. “Dan Goldin used his experience in transforming technology to charter KnuEdge with a bold idea, with the patience of longer development timelines and away from typical startup hype and practices. The result is a new and cutting-edge path for neural computing acceleration. There is also a refreshing surprise element to KnuEdge announcing a relevant new architecture that is ready to ship… not just a concept or early prototype.”
Today, Goldin said the company is ready to show off its designs. The first chip was ready last December, and KnuEdge is sharing it with potential customers. That chip was built with a 32-nanometer manufacturing process, and even though that’s an older technology, it is a powerful chip, Goldin said. Even at 32 nanometers, the chip has something like a two-times to six-times performance advantage over similar chips, KnuEdge said.
The human brain has a couple of hundred billion neurons, and each neuron is connected to at least 10,000 to 100,000 neurons,” Goldin said. “And the brain is the most energy efficient and powerful computer in the world. That is the metaphor we are using.”
KnuEdge has a new version of its chip under design. And the company has already generated revenue from sales of the prototype systems. Each board has about four chips.
As for the competition from IBM, Goldin said, “I believe we made the right decision and are going in the right direction. IBM’s approach is very different from what we have. We are not aiming at anyone. We are aiming at the future.
In his NASA days, Goldin had a lot of successes. There, he redesigned and delivered the International Space Station, tripled the number of space flights, and put a record number of people into space, all while reducing the agency’s planned budget by 25 percent. He also spent 25 years at TRW, where he led the development of satellite television services.
KnuEdge has 100 employees, but Goldin said the company outsources almost everything. Goldin said he is planning to raised a round of funding late this year or early next year. The company collaborated with the University of California at San Diego and UCSD’s California Institute for Telecommunications and Information Technology.
With computers that can handle natural language systems, many people in the world who can’t read or write will be able to fend for themselves more easily, Goldin said.
I want to be able to take machine learning and help people communicate and make a living,” he said. “This is just the beginning. This is the Wild West. We are talking to very large companies about this, and they are getting very excited.
A sample application is a home that has much greater self-awareness. If there’s something wrong in the house, the KnuEdge system could analyze it and figure out if it needs to alert the homeowner.
Goldin said it was hard to keep the company secret.
I’ve been biting my lip for ten years,” he said.
As for whether KnuEdge’s technology could be used to send people to Mars, Goldin said. “This is available to whoever is going to Mars. I tried twice. I would love it if they use it to get there.
ORIGINAL: Venture Beat

holy smokes

,” he said. “It’s going to be too expensive. It’s not propulsion. It’s not environmental control. It’s not power. This software business is a very big problem, and that nation couldn’t afford it.

So Goldin looked further into the brains of the robotics, and that’s when he started thinking about the computing it would take.
Asked if it was easier to run NASA or a startup, Goldin let out a guffaw.
I love them both, but they’re both very different,” Goldin said. “At NASA, I spent a lot of time on non-technical issues. I had a project every quarter, and I didn’t want to become dull technically. I tried to always take on a technical job doing architecture, working with a design team, and always doing something leading edge. I grew up at a time when you graduated from a university and went to work for someone else. If I ever come back to this earth, I would graduate and become an entrepreneur. This is so wonderful.
Back in 1992, Goldin was planning on starting a wireless company as an entrepreneur. But then he got the call to “go serve the country,” and he did that work for a decade. He started KnuEdge (previously called Intellisis) in 2005, and he got very patient capital.
When I went out to find investors, I knew I couldn’t use the conventional Silicon Valley approach (impatient capital),” he said. “It is a fabulous approach that has generated incredible wealth. But I wanted to undertake revolutionary technology development. To build the future tools for next-generation machine learning, improving the natural interface between humans and machines. So I got patient capital that wanted to see lightning strike. Between all of us, we have a board of directors that can contact almost anyone in the world. They’re fabulous business people and technologists. We knew we had a ten-year run-up.
But he’s not saying who those people are yet.
KnuEdge’s chips are part of a larger platform. KnuEdge is also unveiling KnuVerse, a military-grade voice recognition and authentication technology that unlocks the potential of voice interfaces to power next-generation computing, Goldin said.
While the voice technology market has exploded over the past five years due to the introductions of Siri, Cortana, Google Home, Echo, and ViV, the aspirations of most commercial voice technology teams are still on hold because of security and noise issues. KnuVerse solutions are based on patented authentication techniques using the human voice — even in extremely noisy environments — as one of the most secure forms of biometrics. Secure voice recognition has applications in industries such as banking, entertainment, and hospitality.
KnuEdge says it is now possible to authenticate to computers, web and mobile apps, and Internet of Things devices (or everyday objects that are smart and connected) with only a few words spoken into a microphone — in any language, no matter how loud the background environment or how many other people are talking nearby. In addition to KnuVerse, KnuEdge offers Knurld.io for application developers, a software development kit, and a cloud-based voice recognition and authentication service that can be integrated into an app typically within two hours.
And KnuEdge is announcing KnuPath with LambdaFabric computing. KnuEdge’s first chip, built with an older manufacturing technology, has 256 cores, or neuron-like brain cells, on a single chip. Each core is a tiny digital signal processor. The LambdaFabric makes it possible to instantly connect those cores to each other — a trick that helps overcome one of the major problems of multicore chips, Goldin said. The LambdaFabric is designed to connect up to 512,000 devices, enabling the system to be used in the most demanding computing environments. From rack to rack, the fabric has a latency (or interaction delay) of only 400 nanoseconds. And the whole system is designed to use a low amount of power.
All of the company’s designs are built on biological principles about how the brain gets a lot of computing work done with a small amount of power. The chip is based on what Goldin calls “sparse matrix heterogeneous machine learning algorithms.” And it will run C++ software, something that is already very popular. Programmers can program each one of the cores with a different algorithm to run simultaneously, for the “ultimate in heterogeneity.” It’s multiple input, multiple data, and “that gives us some of our power,” Goldin said.

Above: KnuEdge’s KnuPath chip.
Image Credit: KnuEdge
KnuEdge is emerging out of stealth mode to aim its new Voice and Machine Learning technologies at key challenges in IoT, cloud based machine learning and pattern recognition,” said Paul Teich, principal analyst at Tirias Research, in a statement. “Dan Goldin used his experience in transforming technology to charter KnuEdge with a bold idea, with the patience of longer development timelines and away from typical startup hype and practices. The result is a new and cutting-edge path for neural computing acceleration. There is also a refreshing surprise element to KnuEdge announcing a relevant new architecture that is ready to ship… not just a concept or early prototype.”
Today, Goldin said the company is ready to show off its designs. The first chip was ready last December, and KnuEdge is sharing it with potential customers. That chip was built with a 32-nanometer manufacturing process, and even though that’s an older technology, it is a powerful chip, Goldin said. Even at 32 nanometers, the chip has something like a two-times to six-times performance advantage over similar chips, KnuEdge said.
The human brain has a couple of hundred billion neurons, and each neuron is connected to at least 10,000 to 100,000 neurons,” Goldin said. “And the brain is the most energy efficient and powerful computer in the world. That is the metaphor we are using.”
KnuEdge has a new version of its chip under design. And the company has already generated revenue from sales of the prototype systems. Each board has about four chips.
As for the competition from IBM, Goldin said, “I believe we made the right decision and are going in the right direction. IBM’s approach is very different from what we have. We are not aiming at anyone. We are aiming at the future.
In his NASA days, Goldin had a lot of successes. There, he redesigned and delivered the International Space Station, tripled the number of space flights, and put a record number of people into space, all while reducing the agency’s planned budget by 25 percent. He also spent 25 years at TRW, where he led the development of satellite television services.
KnuEdge has 100 employees, but Goldin said the company outsources almost everything. Goldin said he is planning to raised a round of funding late this year or early next year. The company collaborated with the University of California at San Diego and UCSD’s California Institute for Telecommunications and Information Technology.
With computers that can handle natural language systems, many people in the world who can’t read or write will be able to fend for themselves more easily, Goldin said.
I want to be able to take machine learning and help people communicate and make a living,” he said. “This is just the beginning. This is the Wild West. We are talking to very large companies about this, and they are getting very excited.
A sample application is a home that has much greater self-awareness. If there’s something wrong in the house, the KnuEdge system could analyze it and figure out if it needs to alert the homeowner.
Goldin said it was hard to keep the company secret.
I’ve been biting my lip for ten years,” he said.
As for whether KnuEdge’s technology could be used to send people to Mars, Goldin said. “This is available to whoever is going to Mars. I tried twice. I would love it if they use it to get there.
ORIGINAL: Venture Beat

Google Built Its Very Own Chips to Power Its AI Bots

By Hugo Angel,

GOOGLE
GOOGLE HAS DESIGNED its own computer chip for driving deep neural networks, an AI technology that is reinventing the way Internet services operate.
This morning, at Google I/O, the centerpiece of the company’s year, CEO Sundar Pichai said that Google has designed an ASIC, or application-specific integrated circuit, that’s specific to deep neural nets. These are networks of hardware and software that can learn specific tasks by analyzing vast amounts of data. Google uses neural nets to identify objects and faces in photos, recognize the commands you speak into Android phones, or translate text from one language to another. This technology has even begin to transform the Google search engine.
Big Brains
Google’s called its chip the Tensor Processing Unit, or TPU, because it underpins TensorFlow, the software engine that drives its deep learning services.
 
This past fall, Google released TensorFlow under an open-source license, which means anyone outside the company can use and even modify this software engine. It does not appear that Google will share the designs for the TPU, but outsider can make use of Google’s own machine learning hardware and software via various Google cloud services.
Google says it has been running TPUs for about a year, and that they were developed not long before that.Google is just one of so many companies adding deep learning to a wide range of Internet services, including everyone from Facebook and Microsoft to Twitter. Typically, these Internet giants drive their neural nets with graphics processing units, or GPUs, from chip makers like Nvidia. But some, including Microsoft, are also exploring the use of field programmable gate arrays, or FPGAs, chips that can be programmed to specific tasks.
GOOGLE
According to Google, on the massive hardware racks inside the data centers that power its online services, a TPU board fits into the same slot as a hard drive, and it provides an order of magnitude better-optimized performance per watt for machine learning than other hardware solutions.
TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation,” the company says in a blog post. “Because of this, we can squeeze more operations per second into the silicon, use more sophisticated and powerful machine learning models and apply these models more quickly, so users get more intelligent results more rapidly.
This means, among other things, that Google is not using chips from companies like Nvidia—or using fewer chips from these companies. It also indicates that Google is more than willing to build its own chips, which bad news from any chipmaker, most notably the world’s largest: Intel. Intel processor power a vast major of the computer servers inside Google, but the worry, for Intel, is that the Internet giant will one day design its own central processing units as well.
Google says it has been running TPUs for about a year, and that they were developed not long before that. After testing its first silicon, the company says, it had it running live applications inside its data centers within 22 days.
 
ORIGINAL: Wired
By Cade Metz
05.18.2016 

First Human Tests of Memory Boosting Brain Implant—a Big Leap Forward

By Hugo Angel,

You have to begin to lose your memory, if only bits and pieces, to realize that memory is what makes our lives. Life without memory is no life at all.” — Luis Buñuel Portolés, Filmmaker
Image Credit: Shutterstock.com
Every year, hundreds of millions of people experience the pain of a failing memory.
The reasons are many:

  • traumatic brain injury, which haunts a disturbingly high number of veterans and football players; 
  • stroke or Alzheimer’s disease, which often plagues the elderly; or 
  • even normal brain aging, which inevitably touches us all.
Memory loss seems to be inescapable. But one maverick neuroscientist is working hard on an electronic cure. Funded by DARPA, Dr. Theodore Berger, a biomedical engineer at the University of Southern California, is testing a memory-boosting implant that mimics the kind of signal processing that occurs when neurons are laying down new long-term memories.
The revolutionary implant, already shown to help memory encoding in rats and monkeys, is now being tested in human patients with epilepsy — an exciting first that may blow the field of memory prosthetics wide open.
To get here, however, the team first had to crack the memory code.

Deciphering Memory
From the very onset, Berger knew he was facing a behemoth of a problem.
We weren’t looking to match everything the brain does when it processes memory, but to at least come up with a decent mimic, said Berger.
Of course people asked: can you model it and put it into a device? Can you get that device to work in any brain? It’s those things that lead people to think I’m crazy. They think it’s too hard,” he said.
But the team had a solid place to start.
The hippocampus, a region buried deep within the folds and grooves of the brain, is the critical gatekeeper that transforms memories from short-lived to long-term. In dogged pursuit, Berger spent most of the last 35 years trying to understand how neurons in the hippocampus accomplish this complicated feat.
At its heart, a memory is a series of electrical pulses that occur over time that are generated by a given number of neurons, said Berger. This is important — it suggests that we can reduce it to mathematical equations and put it into a computational framework, he said.
Berger hasn’t been alone in his quest.
By listening to the chatter of neurons as an animal learns, teams of neuroscientists have begun to decipher the flow of information within the hippocampus that supports memory encoding. Key to this process is a strong electrical signal that travels from CA3, the “input” part of the hippocampus, to CA1, the “output” node.
This signal is impaired in people with memory disabilities, said Berger, so of course we thought if we could recreate it using silicon, we might be able to restore — or even boost — memory.

Bridging the Gap
Yet this brain’s memory code proved to be extremely tough to crack.
The problem lies in the non-linear nature of neural networks: signals are often noisy and constantly overlap in time, which leads to some inputs being suppressed or accentuated. In a network of hundreds and thousands of neurons, any small change could be greatly amplified and lead to vastly different outputs.
It’s a chaotic black box, laughed Berger.
With the help of modern computing techniques, however, Berger believes he may have a crude solution in hand. His proof?
Use his mathematical theorems to program a chip, and then see if the brain accepts the chip as a replacement — or additional — memory module.
Berger and his team began with a simple task using rats. They trained the animals to push one of two levers to get a tasty treat, and recorded the series of CA3 to CA1 electronic pulses in the hippocampus as the animals learned to pick the correct lever. The team carefully captured the way the signals were transformed as the session was laid down into long-term memory, and used that information — the electrical “essence” of the memory — to program an external memory chip.
They then injected the animals with a drug that temporarily disrupted their ability to form and access long-term memories, causing the animals to forget the reward-associated lever. Next, implanting microelectrodes into the hippocampus, the team pulsed CA1, the output region, with their memory code.
The results were striking — powered by an external memory module, the animals regained their ability to pick the right lever.
Encouraged by the results, Berger next tried his memory implant in monkeys, this time focusing on a brain region called the prefrontal cortex, which receives and modulates memories encoded by the hippocampus.
Placing electrodes into the monkey’s brains, the team showed the animals a series of semi-repeated images, and captured the prefrontal cortex’s activity when the animals recognized an image they had seen earlier. Then with a hefty dose of cocaine, the team inhibited that particular brain region, which disrupted the animal’s recall.
Next, using electrodes programmed with the “memory code,” the researchers guided the brain’s signal processing back on track — and the animal’s performance improved significantly.
A year later, the team further validated their memory implant by showing it could also rescue memory deficits due to hippocampal malfunction in the monkey brain.

A Human Memory Implant
Last year, the team cautiously began testing their memory implant prototype in human volunteers.
Because of the risks associated with brain surgery, the team recruited 12 patients with epilepsy, who already have electrodes implanted into their brain to track down the source of their seizures.
Repeated seizures steadily destroy critical parts of the hippocampus needed for long-term memory formation, explained Berger. So if the implant works, it could benefit these patients as well.
The team asked the volunteers to look through a series of pictures, and then recall which ones they had seen 90 seconds later. As the participants learned, the team recorded the firing patterns in both CA1 and CA3 — that is, the input and output nodes.
Using these data, the team extracted an algorithm — a specific human “memory code” — that could predict the pattern of activity in CA1 cells based on CA3 input. Compared to the brain’s actual firing patterns, the algorithm generated correct predictions roughly 80% of the time.
It’s not perfect, said Berger, but it’s a good start.
Using this algorithm, the researchers have begun to stimulate the output cells with an approximation of the transformed input signal.
We have already used the pattern to zap the brain of one woman with epilepsy, said Dr. Dong Song, an associate professor working with Berger. But he remained coy about the result, only saying that although promising, it’s still too early to tell.
Song’s caution is warranted. Unlike the motor cortex, with its clear structured representation of different body parts, the hippocampus is not organized in any obvious way.
It’s hard to understand why stimulating input locations can lead to predictable results, said Dr. Thoman McHugh, a neuroscientist at the RIKEN Brain Science Institute. It’s also difficult to tell whether such an implant could save the memory of those who suffer from damage to the output node of the hippocampus.
That said, the data is convincing,” McHugh acknowledged.
Berger, on the other hand, is ecstatic. “I never thought I’d see this go into humans,” he said.
But the work is far from done. Within the next few years, Berger wants to see whether the chip can help build long-term memories in a variety of different situations. After all, the algorithm was based on the team’s recordings of one specific task — what if the so-called memory code is not generalizable, instead varying based on the type of input that it receives?
Berger acknowledges that it’s a possibility, but he remains hopeful.
I do think that we will find a model that’s a pretty good fit for most conditions, he said. After all, the brain is restricted by its own biophysics — there’s only so many ways that electrical signals in the hippocampus can be processed, he said.
The goal is to improve the quality of life for somebody who has a severe memory deficit,” said Berger. “If I can give them the ability to form new long-term memories for half the conditions that most people live in, I’ll be happy as hell, and so will be most patients.
ORIGINAL: Singularity Hub

Bridging the Bio-Electronic Divide

By Hugo Angel,

New effort aims for fully implantable devices able to connect with up to one million neurons
A new DARPA program aims to develop an implantable neural interface able to provide unprecedented signal resolution and data-transfer bandwidth between the human brain and the digital world. The interface would serve as a translator, converting between the electrochemical language used by neurons in the brain and the ones and zeros that constitute the language of information technology. The goal is to achieve this communications link in a biocompatible device no larger than one cubic centimeter in size, roughly the volume of two nickels stacked back to back.
The program, Neural Engineering System Design (NESD), stands to dramatically enhance research capabilities in neurotechnology and provide a foundation for new therapies.
“Today’s best brain-computer interface systems are like two supercomputers trying to talk to each other using an old 300-baud modem,” said Phillip Alvelda, the NESD program manager. “Imagine what will become possible when we upgrade our tools to really open the channel between the human brain and modern electronics.
Among the program’s potential applications are devices that could compensate for deficits in sight or hearing by feeding digital auditory or visual information into the brain at a resolution and experiential quality far higher than is possible with current technology.
Neural interfaces currently approved for human use squeeze a tremendous amount of information through just 100 channels, with each channel aggregating signals from tens of thousands of neurons at a time. The result is noisy and imprecise. In contrast, the NESD program aims to develop systems that can communicate clearly and individually with any of up to one million neurons in a given region of the brain.
Achieving the program’s ambitious goals and ensuring that the envisioned devices will have the potential to be practical outside of a research setting will require integrated breakthroughs across numerous disciplines including 
  • neuroscience, 
  • synthetic biology, 
  • low-power electronics, 
  • photonics, 
  • medical device packaging and manufacturing, systems engineering, and 
  • clinical testing.
In addition to the program’s hardware challenges, NESD researchers will be required to develop advanced mathematical and neuro-computation techniques to first transcode high-definition sensory information between electronic and cortical neuron representations and then compress and represent those data with minimal loss of fidelity and functionality.
To accelerate that integrative process, the NESD program aims to recruit a diverse roster of leading industry stakeholders willing to offer state-of-the-art prototyping and manufacturing services and intellectual property to NESD researchers on a pre-competitive basis. In later phases of the program, these partners could help transition the resulting technologies into research and commercial application spaces.
To familiarize potential participants with the technical objectives of NESD, DARPA will host a Proposers Day meeting that runs Tuesday and Wednesday, February 2-3, 2016, in Arlington, Va. The Special Notice announcing the Proposers Day meeting is available at https://www.fbo.gov/spg/ODA/DARPA/CMO/DARPA-SN-16-16/listing.html. More details about the Industry Group that will support NESD is available at https://www.fbo.gov/spg/ODA/DARPA/CMO/DARPA-SN-16-17/listing.html. A Broad Agency Announcement describing the specific capabilities sought will be forthcoming on www.fbo.gov.
NESD is part of a broader portfolio of programs within DARPA that support President Obama’s brain initiative. For more information about DARPA’s work in that domain, please visit:http://www.darpa.mil/program/our-research/darpa-and-the-brain-initiative.
ORIGINAL: DARPA
[email protected]
1/19/2016

Microsoft Neural Net Shows Deep Learning can get Way Deeper

By Hugo Angel,

Silicon Wafer by Sonic
PAUL TAYLOR/GETTY IMAGES
COMPUTER VISION IS now a part of everyday life. Facebook recognizes faces in the photos you post to the popular social network. The Google Photos app can find images buried in your collection, identifying everything from dogs to birthday parties to gravestones. Twitter can pinpoint pornographic images without help from human curators.
All of this “seeing” stems from a remarkably effective breed of artificial intelligence called deep learning. But as far as this much-hyped technology has come in recent years, a new experiment from Microsoft Research shows it’s only getting started. Deep learning can go so much deeper.
We’re staring at a huge design space, trying to figure out where to go next.‘ 

 

PETER LEE, MICROSOFT RESEARCH
This revolution in computer vision was a long time coming. A key turning point came in 2012, when artificial intelligence researchers from the University of Toronto won a competition called ImageNet. ImageNet pits machines against each other in an image recognition contest—which computer can identify cats or cars or clouds more accurately?—and that year, the Toronto team, including researcher Alex Krizhevsky and professor Geoff Hinton, topped the contest using deep neural nets, a technology that learns to identify images by examining enormous numbers of them, rather than identifying images according to rules diligently hand-coded by humans.
 
Toronto’s win provided a roadmap for the future of deep learning. In the years since, the biggest names on the ‘net—including Facebook, Google, Twitter, and Microsoft—have used similar tech to build computer vision systems that can match and even surpass humans. “We can’t claim that our system ‘sees’ like a person does,” says Peter Lee, the head of research at Microsoft. “But what we can say is that for very specific, narrowly defined tasks, we can learn to be as good as humans.
Roughly speaking, neural nets use hardware and software to approximate the web of neurons in the human brain. This idea dates to the 1980s, but in 2012, Krizhevsky and Hinton advanced the technology by running their neural nets atop graphics processing units, or GPUs. These specialized chips were originally designed to render images for games and other highly graphical software, but as it turns out, they’re also suited to the kind of math that drives neural nets. Google, Facebook, Twitter, Microsoft, and so many others now use GPU-powered-AI to handle image recognition and so many others tasks, from Internet search to security. Krizhevsky and Hinton joined the staff at Google.
Deep learning can go so much deeper.
Now, the latest ImageNet winner is pointing to what could be another step in the evolution of computer vision—and the wider field of artificial intelligence. Last month, a team of Microsoft researchers took the ImageNet crown using a new approach they call a deep residual network. The name doesn’t quite describe it. They’ve designed a neural net that’s significantly more complex than typical designs—one that spans 152 layers of mathematical operations, compared to the typical six or seven. It shows that, in the years to come, companies like Microsoft will be able to use vast clusters of GPUs and other specialized chips to significantly improve not only image recognition but other AI services, including systems that recognize speech and even understand language as we humans naturally speak it.
In other words, deep learning is nowhere close to reaching its potential. “We’re staring at a huge design space,” Lee says, “trying to figure out where to go next.
Layers of Neurons
Deep neural networks are arranged in layers. Each layer is a different set of mathematical operations—aka algorithms. The output of one layer becomes the input of the next. Loosely speaking, if a neural network is designed for image recognition, one layer will look for a particular set of features in an image—edges or angles or shapes or textures or the like—and the next will look for another set. These layers are what make these neural networks deep. “Generally speaking, if you make these networks deeper, it becomes easier for them to learn,” says Alex Berg, a researcher at the University of North Carolina who helps oversee the ImageNet competition.
Constructing this kind of mega-neural net is flat-out difficult.
Today, a typical neural network includes six or seven layers. Some might extend to 20 or even 30. But the Microsoft team, led by researcher Jian Sun, just expanded that to 152. In essence, this neural net is better at recognizing images because it can examine more features. “There is a lot more subtlety that can be learned,” Lee says.
In the past, according Lee and researchers outside of Microsoft, this sort of very deep neural net wasn’t feasible. Part of the problem was that as your mathematical signal moved from layer to layer, it became diluted and tended to fade. As Lee explains, Microsoft solved this problem by building a neural net that skips certain layers when it doesn’t need them, but uses them when it does. “When you do this kind of skipping, you’re able to preserve the strength of the signal much further,” Lee says, “and this is turning out to have a tremendous, beneficial impact on accuracy.
Berg says that this is an notable departure from previous systems, and he believes that others companies and researchers will follow suit.
Deep Difficulty
The other issue is that constructing this kind of mega-neural net is tremendously difficult. Landing on a particular set of algorithms—determining how each layer should operate and how it should talk to the next layer—is an almost epic task. But Microsoft has a trick here, too. It has designed a computing system that can help build these networks.
As Jian Sun explains it, researchers can identify a promising arrangement for massive neural networks, and then the system can cycle through a range of similar possibilities until it settles on this best one. “In most cases, after a number of tries, the researchers learn [something], reflect, and make a new decision on the next try,” he says. “You can view this as ‘human-assisted search.’”
Microsoft has designed a computing system that can help build these networks.
According to Adam Gibson—the chief researcher at deep learning startup Skymind—this kind of thing is getting more common. It’s called “hyper parameter optimization.” “People can just spin up a cluster [of machines], run 10 models at once, find out which one works best and use that,” Gibson says. “They can input some baseline parameter—based on intuition—and the machines kind of homes in on what the best solution is.” As Gibson notes, last year Twitter acquired a company, Whetlab, that offers similar ways of “optimizing” neural networks.

‘A Hardware Problem’
As Peter Lee and Jian Sun describe it, such an approach isn’t exactly “brute forcing” the problem. “With very very large amounts of compute resources, one could fantasize about a gigantic ‘natural selection’ setup where evolutionary forces help direct a brute-force search through a huge space of possibilities,” Lee says. “The world doesn’t have those computing resources available for such a thing…For now, we will still depend on really smart researchers like Jian.
But Lee does say that, thanks to new techniques and computer data centers filled with GPU machines, the realm of possibilities for deep learning are enormous. A big part of the company’s task is just finding the time and the computing power needed to explore these possibilities. “This work as dramatically exploded the design space. The amount of ground to cover, in terms of scientific investigation, has become exponentially larger,” Lee says. And this extends well beyond image recognition, into speech recognition, natural language understanding, and other tasks.
As Lee explains, that’s one reason Microsoft is not only pushing to improve the power of its GPUs clusters, but exploring the use of other specialized processors, including FPGAs—chips that can programmed for particular tasks, such as deep learning. “There has also been an explosion in demand for much more experimental hardware platforms from our researchers,” he says. And this work is sending ripples across the wider of world of tech and artificial intelligence. This past summer, in its largest ever acquisition deal, Intel agreed to buy Altera, which specializes in FPGAs.
Indeed, Gibson says that deep learning has become more of “a hardware problem.” Yes, we still need top researchers to guide the creation of neural networks, but more and more, finding new paths is a matter of brute-forcing new algorithms across ever more powerful collections of hardware. As Gibson point out, though these deep neural nets work extremely well, we don’t quite know why they work. The trick lies in finding the complex combination of algorithms that work the best. More and better hardware can shorten the path.
The end result is that the companies that can build the most powerful networks of hardware are the companies will come out ahead. That would be Google and Facebook and Microsoft. Those that are good at deep learning today will only get better.
ORIGINAL: Wired

NVIDIA DRIVE PX 2. NVIDIA Accelerates Race to Autonomous Driving at CES 2016

By Hugo Angel,

NVIDIA today shifted its autonomous-driving leadership into high gear.
At a press event kicking off CES 2016, we unveiled artificial-intelligence technology that will let cars sense the world around them and pilot a safe route forward.
Dressed in his trademark black leather jacket, speaking to a crowd of some 400 automakers, media and analysts, NVIDIA CEO Jen-Hsun Huang revealed DRIVE PX 2, an automotive supercomputing platform that processes 24 trillion deep learning operations a second. That’s 10 times the performance of the first-generation DRIVE PX, now being used by more than 50 companies in the automotive world.
The new DRIVE PX 2 delivers 8 teraflops of processing power. It has the processing power of 150 MacBook Pros. And it’s the size of a lunchbox in contrast to earlier autonomous-driving technology being used today, which takes up the entire trunk of a mid-sized sedan.
Self-driving cars will revolutionize society,” Huang said at the beginning of his talk. “And NVIDIA’s vision is to enable them.
 
Volvo to Deploy DRIVE PX in Self-Driving SUVs
As part of its quest to eliminate traffic fatalities, Volvo will be the first automaker to deploy DRIVE PX 2.
Huang announced that Volvo – known worldwide for safety and reliability – will be the first automaker to deploy DRIVE PX 2.
In the world’s first public trial of autonomous driving, the Swedish automaker next year will lease 100 XC90 luxury SUVs outfitted with DRIVE PX 2 technology. The technology will help the vehicles drive autonomously around Volvo’s hometown of Gothenburg, and semi-autonomously elsewhere.
DRIVE PX 2 has the power to harness a host of sensors to get a 360 degree view of the environment around the car.
The rear-view mirror is history,” Jen-Hsun said.
Drive Safely, by Not Driving at All
Not so long ago, pundits had questioned the safety of technology in cars. Now, with Volvo incorporating autonomous vehicles into its plan to end traffic fatalities, that script has been flipped. Autonomous cars may be vastly safer than human-piloted vehicles.
Car crashes – an estimated 93 percent of them caused by human error kill 1.3 million drivers each year. More American teenagers die from texting while driving than any other cause, including drunk driving.
There’s also a productivity issue. Americans waste some 5.5 billion hours of time each year in traffic, costing the U.S. about $121 billion, according to an Urban Mobility Report from Texas A&M. And inefficient use of roads by cars wastes even vaster sums spent on infrastructure.
Deep Learning Hits the Road
Self-driving solutions based on computer vision can provide some answers. But tackling the infinite permutations that a driver needs to react to – stray pets, swerving cars, slashing rain, steady road construction crews – is far too complex a programming challenge.
Deep learning enabled by NVIDIA technology can address these challenges. A highly trained deep neural network – residing on supercomputers in the cloud – captures the experience of many tens of thousands of hours of road time.
Huang noted that a number of automotive companies are already using NVIDIA’s deep learning technology to power their efforts, getting speedup of 30-40X in training their networks compared with other technology. BMW, Daimler and Ford are among them, along with innovative Japanese startups like Preferred Networks and ZMP. And Audi said it was able in four hours to do training that took it two years with a competing solution.
  NVIDIA DRIVE PX 2 is part of an end-to-end platform that brings deep learning to the road.
NVIDIA’s end-to-end solution for deep learning starts with NVIDIA DIGITS, a supercomputer that can be used to train digital neural networks by exposing them to data collected during that time on the road. On the other end is DRIVE PX 2, which draws on this training to make inferences to enable the car to progress safely down the road. In the middle is NVIDIA DriveWorks, a suite of software tools, libraries and modules that accelerates development and testing of autonomous vehicles.
DriveWorks enables sensor calibration, acquisition of surround data, synchronization, recording and then processing streams of sensor data through a complex pipeline of algorithms running on all of the DRIVE PX 2’s specialized and general-purpose processors.
During the event, Huang reminded the audience that machines are already beating humans at tasks once considered impossible for computers, such as image recognition. Systems trained with deep learning can now correctly classify images more than 96 percent of the time, exceeding what humans can do on similar tasks.
He used the event to show what deep learning can do for autonomous vehicles.
A series of demos drove this home, showing in three steps how DRIVE PX 2 harnesses a host of sensors – lidar, radar and cameras and ultrasonic – to understand the world around it, in real time, and plan a safe and efficient path forward.
The World’s Biggest Infotainment System
 
The highlight of the demos was what Huang called the world’s largest car infotainment system — an elegant block the size of a medium-sized bedroom wall mounted with a long horizontal screen and a long vertical one.
While a third larger screen showed the scene that a driver would take in, the wide demo screen showed how the car — using deep learning and sensor fusion — “viewed” the very same scene in real-time, stitched together from its array of sensors. On its right, the huge portrait-oriented screen shows a highly precise map that marked the car’s progress.
It’s a demo that will leave an impression on an audience that’s going to be hear a lot about the future of driving in the week ahead.
Photos from Our CES 2016 Press Event
NVIDIA Drive PX-2
ORIGINAL: Nvidia
By Bob Sherbin on January 3, 2016

Scientists have built a functional ‘hybrid’ logic gate for use in quantum computers

By Hugo Angel,

NIST Quantum Gate
An ion trap used in NIST quantum computing experiments. Credit: Blakestad/NIST
Here’s how to solve the problem of quantum memory.
As conventional computers draw ever closer to their theoretical limit, the race is on to build a machine that can truly harness the unprecedented processing power of quantum computing. And now two research teams have independently demonstrated how entangling atoms from different elements can address the problem of quantum memory errors while functioning within a logic gate framework, and also pass the all-important test of true entanglement. 
Hybrid quantum computers allow the unique advantages of different types of quantum systems to be exploited together in a single platform,said lead author Ting Rei Tan. “Each ion species is unique, and certain ones are better suited for certain tasks such as memory storage, while others are more suited to provide interconnects for data transfer between remote systems.
In the computers we use today, data is processed and stored as binary bits, with each individual bit taking on a state of either 0 or 1. Because these states are set, there’s a finite amount of information that can ultimately be processed, and we’re quickly approaching the point where this isn’t going to be enough.
Quantum computers, on the other hand, store data as qubits, which can be in the state of 0 or 1, or can take on another state called superposition, which allows them to be both 0 and 1 at the same time. If we can figure out how to build a machine that integrates this phenomenon with data-processing capabilities, we’re looking at computers that are hundreds of millions of times faster than the super computers of today.
The qubits used in this set-up are actually atomic ions (atoms with an electron removed), and their states are determined by their spin – spin up is 1, spin down is 0. Each atomic ion is paired off, and if the control ion takes on the state of superposition, it will become entangled with its partner, so anything you do to one ion will affect the other.
This can pose problems, particularly when it comes to memory, and there’s no point storing and processing information if you can’t reliably retain it. If you’ve got an entire system built on pairs of the same atomic ions, you leave yourself open to constant errors, because if one ion is affected by a malfunction, this will also affect its partner. At the same time, using the same atomic ions in a pair makes it very difficult for them to perform separate functions.
So researchers from the University of Oxford in the UK, and a second team from the National Institute of Standards and Technology (NIST) and the University of Washington, have figured out which combinations of different elements can function together as pairs in a quantum set-up.
Each trapped ion is used to represent one ‘quantum bit’ of information. The quantum states of the ions are controlled with laser pulses of precise frequency and duration,says one of the researchers, David Lucas from the University of Oxford. “Two different species of ion are needed in the computer

  • one to store information, a ‘memory qubit’, and 
  • one to link different parts of the computer together via photons, an ‘interface qubit’.
While the Oxford team achieved this using two different isotopes of calcium (the abundant isotope calcium-40 and the rare isotope calcium-43), the second team went even further by pairing up entirely different atoms – magnesium and beryllium. Each one is sensitive to a different wavelength of light, which means zapping one with a laser pulse to control its function won’t affect its partner.
The teams them went on to demonstrate for the first time that these pairs could have their 0,1, or superposition states controlled by two different types of logic gates, called the CNOT gate and the SWAP gate. Logic gates are crucial components of any digital circuit, because they’re able to record two input values and provide a new output based on programmed logic. 
A CNOT gate flips the second (target) qubit if the first (control) qubit is a 1; if it is a 0, the target bit is unchanged,the NIST press release explains. “If the control qubit is in a superposition, the ions become entangled. A SWAP gate interchanges the qubit states, including superpositions.
The Oxford team demonstrated ion pairing in this set-up for about 60 seconds, while the NIST/Washington team managed to keep theirs entangled for 1.5 seconds. That doesn’t sound like much, but that’s relatively stable when it comes to qubits.
Both teams confirm that their two atoms are entangled with a very high probability; 0.998 for one, 0.979 for the other (of a maximum of one),John Timmer reports for Ars Technica. “The NIST team even showed that it could track the beryllium atom as it changed state by observing the state of the magnesium atom.
Further, both teams were able to successfully perform a Bell test by using the logic gate to entangle the pairs of different-species ions, and then manipulating and measuring them independently.
[W]e show that quantum logic gates between different isotopic species are possible, can be driven by a relatively simple laser system, and can work with precision beyond the so-called ‘fault-tolerant threshold’ precision of approximately 99 percent – the precision necessary to implement the techniques of quantum error correction, without which a quantum computer of useful size cannot be built,said Lucas in an Oxford press release.
Of course, we don’t have proper quantum computers to actually test these components in the context of a functioning system – that will have to be the next step, and international teams of scientists and engineers are racing to get us there. We can’t wait to see it when they do.
The papers have been published in Nature here and here.
ORIGINAL: ScienceAlert
BEC CREW
18 DEC 2015

Scaling up synthetic-biology innovation

By Hugo Angel,

.
Gen9’s BioFab platform synthesizes small DNA fragments on silicon chips
and uses other technologies to build longer DNA constructs from those
fragments. Done in a parallel, this produces hundreds to thousands of
DNA constructs simultaneously. Shown here is an automated
liquid-handling instrument that dispenses DNA onto the chips. Courtesy of Gen9
MIT professor’s startup makes synthesizing genes many times more cost effective.
Inside and outside of the classroom, MIT professor Joseph Jacobson has become a prominent figure in — and advocate for — the emerging field of synthetic biology.

As head of the Molecular Machines group at the MIT Media Lab, Jacobson’s work has focused on, among other things, developing technologies for the rapid fabrication of DNA molecules. In 2009, he spun out some of his work into .Gen9, which aims to boost synthetic-biology innovation by offering scientists more cost-effective tools and resources.
Headquartered in Cambridge, Massachusetts, Gen9 has developed a method for synthesizing DNA on silicon chips, which significantly cuts costs and accelerates the creation and testing of genes. Commercially available since 2013, the platform is now being used by dozens of scientists and commercial firms worldwide.
Synthetic biologists synthesize genes by combining strands of DNA. These new genes can be inserted into microorganisms such as yeast and bacteria. Using this approach, scientists can tinker with the cells’ metabolic pathways, enabling the microbes to perform new functions, including testing new antibodies, sensing chemicals in an environment, or creating biofuels.

But conventional gene-synthesizing methods can be time-consuming and costly. Chemical-based processes, for instance, cost roughly 20 cents per base pair — DNA’s key building block — and produce one strand of DNA at a time. This adds up in time and money when synthesizing genes comprising 100,000 base pairs.

Gen9’s chip-based DNA, however, drops the price to roughly 2 cents per base pair, Jacobson says. Additionally, hundreds of thousands of base pairs can be tested and compiled in parallel, as opposed to testing and compiling each pair individually through conventional methods.

This means faster testing and development of new pathways — which usually takes many years — for applications such as advanced therapeutics, and more effective enzymes for detergents, food processing, and biofuels, Jacobson says. “If you can build thousands of pathways on a chip in parallel, and can test them all at once, you get to a working metabolic pathway much faster,” he says.

Over the years, Jacobson and Gen9 have earned many awards and honors. In November, Jacobson was also inducted into the National Inventors Hall of Fame for co-inventing E Ink, the electronic ink used for Amazon’s Kindle e-reader display.

Scaling gene synthesizing Throughout the early-and mid-2000s, a few important pieces of research came together to allow for the scaling up of gene synthesis, which ultimately led to Gen9.

First, Jacobson and his students Chris Emig and Brian Chow began developing chips with thousands of “spots,” which each contained about 100 million copies of a different DNA sequence.

Then, Jacobson and another student, David Kong, created a process that used a certain enzyme as a catalyst to assemble those small DNA fragments into larger DNA strands inside microfluidics devices — “which was the first microfluidics assembly of DNA ever,” Jacobson says.

Despite the novelty, however, the process still wasn’t entirely cost effective. On average, it produced a 99 percent yield, meaning that about 1 percent of the base pairs didn’t match when constructing larger strands. That’s not so bad for making genes with 100 base pairs. “But if you want to make something that’s 10,000 or 100,000 bases long, that’s no good anymore,” Jacobson says.

Around 2004, Jacobson and then-postdoc Peter Carr, along with several other students, found a way to drastically increase yields by taking a cue from a natural error-correcting protein, Mut-S, which recognizes mismatches in DNA base pairing that occur when two DNA strands form a double helix. For synthetic DNA, the protein can detect and extract mismatches arising in base pairs synthesized on the chip, improving yields. In a paper published that year in Nucleic Acids Research, the researchers wrote that this process reduces the frequency of errors, from one in every 100 base pairs to around one in every 10,000.

With these innovations, Jacobson launched Gen9 with two co-founders: George Church of Harvard University, who was also working on synthesizing DNA on microchips, and Drew Endy of Stanford University, a world leader in synthetic-biology innovations.

Together with employees, they created a platform called BioFab and several other tools for synthetic biologists. Today, clients use an online portal to order gene sequences. Then Gen9 designs and fabricates those sequences on chips and delivers them to customers. Recently, the startup updated the portal to allow drag-and-drop capabilities and options for editing and storing gene sequences.

This allows users to “make these very extensive libraries that have been inaccessible previously,” Jacobson says.


Fueling big ideas

Many published studies have already used Gen9’s tools, several of which are posted to the startup’s website. Notable ones, Jacobson says, include designing proteins for therapeutics. In those cases, the researcher needs to make 10 million or 100 million versions of a protein, each comprising maybe 50,000 pieces of DNA, to see which ones work best.

Instead of making and testing DNA sequences one at a time with conventional methods, Gen9 lets researchers test hundreds of thousands of sequences at once on a chip. This should increase chances of finding the right protein, more quickly. “If you just have one shot you’re very unlikely to hit the target,” Jacobson says. “If you have thousands or tens of thousands of shots on a goal, you have a much better chance of success.


Currently, all the world’s synthetic-biology methods produce only about 300 million bases per year. About 10 of the chips Gen9 uses to make DNA can hold the same amount of content, Jacobson says. In principle, he says, the platform used to make Gen9’s chips — based on collaboration with manufacturing firm Agilent — could produce enough chips to cover about 200 billion bases. This is about the equivalent capacity of GenBank, an open-access database of DNA bases and gene sequences that has been constantly updated since the 1980s.

Such technology could soon be worth a pretty penny: According to a study published in November by MarketsandMarkets, a major marketing research firm, the market for synthesizing short DNA strands is expected to reach roughly $1.9 billion by 2020.

Still, Gen9 is pushing to drop costs for synthesis to under 1 cent per base pair, Jacobson says. Additionally, for the past few years, the startup has hosted an annual G-Prize Competition, which awards 1 million base pairs of DNA to researchers with creative synthetic-biology ideas. That’s a prize worth roughly $100,000.

The aim, Jacobson says, is to remove cost barriers for synthetic biologists to boost innovation. “People have lots of ideas but are unable to try out those ideas because of cost,” he says. “This encourages people to think about bigger and bigger ideas.”

ORIGINAL: .MIT News

Rob Matheson | MIT News Office
December 10, 2015

IBM Announces Computer Chips More Powerful Than Any in Existence

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A wafer made up of seven-nanometer chips.
A wafer made up of seven-nanometer chips. IBM said it made the advance by using silicon-germanium instead of pure silicon. CreditDarryl Bautista/IBM
IBM said on Thursday that it had made working versions of ultradense computer chips, with roughly four times the capacity of today’s most powerful chips.
The announcement, made on behalf of an international consortium led by IBM, the giant computer company, is part of an effort to manufacture the most advanced computer chips in New York’s Hudson Valley, where IBM is investing $3 billion in a private-public partnership with New York State, GlobalFoundries, Samsung and equipment vendors.
The development lifts a bit of the cloud that has fallen over the semiconductor industry, which has struggled to maintain its legendary pace of doubling transistor density every two years.
Intel, which for decades has been the industry leader, has faced technical challenges in recent years. Moreover, technologists have begun to question whether the longstanding pace of chip improvement, known as Moore’s Law, would continue past the current 14-nanometer generation of chips.
Each generation of chip technology is defined by the minimum size of fundamental components that switch current at nanosecond intervals. Today the industry is making the commercial transition from what the industry generally describes as 14-nanometer manufacturing to 10-nanometer manufacturing.
Michael Liehr of the SUNY College of Nanoscale Science and Engineering, left, and Bala Haranand of IBM examine a wafer comprised of the new chips. They are not yet ready for commercial manufacturing. CreditDarryl Bautista/IBM
Each generation brings roughly a 50 percent reduction in the area required by a given amount of circuitry. IBM’s new chips, though still in a research phase, suggest that semiconductor technology will continue to shrink at least through 2018.
The company said on Thursday that it had working samples of chips with seven-nanometer transistors. It made the research advance by using silicon-germanium instead of pure silicon in key regions of the molecular-size switches.
The new material makes possible faster transistor switching and lower power requirements. The tiny size of these transistors suggests that further advances will require new materials and new manufacturing techniques.
As points of comparison to the size of the seven-nanometer transistors, a strand of DNA is about 2.5 nanometers in diameter and a red blood cell is roughly 7,500 nanometers in diameter. IBM said that would make it possible to build microprocessors with more than 20 billion transistors.
I’m not surprised, because this is exactly what the road map predicted, but this is fantastic,” said Subhashish Mitra, director of the Robust Systems Group in the Electrical Engineering Department at Stanford University.
Even though IBM has shed much of its computer and semiconductor manufacturing capacity, the announcement indicates that the company remains interested in supporting the nation’s high technology manufacturing base.
This puts IBM in the position of being a gentleman gambler as opposed to being a horse owner,” said Richard Doherty, president of Envisioneering, a Seaford, N.Y., consulting firm, referring to the fact that IBM’s chip manufacturing facility was acquired by GlobalFoundries effective last week.
IBM’s seven-nanometer node transistors. A strand of DNA is about 2.5 nanometers in diameter and a red blood cell is roughly 7,500 nanometers in diameter. CreditIBM Research
They still want to be in the race,” he added.
IBM now licenses the technology it is developing to a number of manufacturers and GlobalFoundries, owned by the Emirate of Abu Dhabi, to make chips for companies including Broadcom, Qualcomm and Advanced Micro Devices.
The semiconductor industry must now decide if IBM’s bet on silicon-germanium is the best way forward.
It must also grapple with the shift to using extreme ultraviolet, or EUV, light to etch patterns on chips at a resolution that approaches the diameter of individual atoms. In the past, Intel said it could see its way toward seven-nanometer manufacturing. But it has not said when that generation of chip making might arrive.
IBM also declined to speculate on when it might begin commercial manufacturing of this technology generation. This year, Taiwan Semiconductor Manufacturing Company said that it planned to begin pilot product of seven-nanometer chips in 2017. Unlike IBM, however, it has not demonstrated working chips to meet that goal.
It is uncertain whether the longer exposure times required by the new generation of EUV photolithographic stepper machines would make high-speed manufacturing operations impossible. Even the slightest vibration can undermine the precision of the optics necessary to etch lines of molecular thicknesses, and the semiconductor industry has been forced to build specialized stabilized buildings to try to isolate equipment from vibration.
An IBM official said that the consortium now sees a way to use EUV light in commercial manufacturing operations.
EUV is another game changer,” said Mukesh Khare, vice president for semiconductor research at IBM. To date, he noted, the demonstration has taken place in a research lab, not in a manufacturing plant. Ultimately the goal is to create circuits that have been reduced in area by another 50 percent over the industry’s 10-nanometer technology generation scheduled to be introduced next year.
ORIGINAL: NYTimes
JULY 9, 2015

Scientists Just Invented the Neural Lace

By admin,

A 3D microscope image of the mesh merging with brain cells.

Images via Charles Lieber

In the Culture novels by Iain M. Banks, futuristic post-humans install devices on their brains called a neural lace.” A mesh that grows with your brain, it’s essentially a wireless brain-computer interface. But it’s also a way to program your neurons to release certain chemicals with a thought. And now, there’s a neural lace prototype in real life.

A group of chemists and engineers who work with nanotechnology published a paper this month in Nature Nanotechnology about an ultra-fine mesh that can merge into the brain to create what appears to be a seamless interface between machine and biological circuitry. Called “mesh electronics,” the device is so thin and supple that it can be injected with a needle — they’ve already tested it on mice, who survived the implantation and are thriving. The researchers describe their device as “syringe-injectable electronics,” and say it has a number of uses, including 

  • monitoring brain activity, 
  • delivering treatment for degenerative disorders like Parkinson’s, and 
  • even enhancing brain capabilities.

Writing about the paper in Smithsonian magazine, Devin Powell says a number of groups are investing in this research, including the military:

[Study researcher Charles Lieber’s] backers include Fidelity Biosciences, a venture capital firm interested in new ways to treat neurodegenerative disorders such as Parkinson’s disease. The military has also taken an interest, providing support through the U.S. Air Force’s Cyborgcell program, which focuses on small-scale electronics for the “performance enhancement” of cells.

For now, the mice with this electronic mesh are connected by a wire to computer — but in the future, this connection could become wireless. The most amazing part about the mesh is that the mouse brain cells grew around it, forming connections with the wires, essentially welcoming a mechanical component into a biochemical system.

A 3D microscope image of the mesh merging with brain cells

Lieber and his colleagues do hope to begin testing it on humans as soon as possible, though realistically that’s many years off. Still, this could be the beginning of the first true human internet, where brain-to-brain interfaces are possible via injectable electronics that pass your mental traffic through the cloud. What could go wrong?

[Read the scientific article in Nature Nanotechnology]

ORIGINAL: Gizmodo
Annalee Newitz
6/15/15

Contact the author at [email protected].
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