Last summer, Congress passed and President Obama signed into law the FOIA Improvement Act of 2016 (Public Law No. 114-185), which adds to and amends the Freedom of Information Act.

The amendments create a “presumption of openness” limiting the federal government’s discretionary power to withhold requested information only when disclosure would result in “foreseeable harm.”

For those that transact business with or even simply communicate with the government (referred to as “submitters” in FOIA parlance), the FOIA changes mean that submitters such as government contractors and grant recipients must proactively respond when a FOIA request potentially targets confidential and/or proprietary data that has been shared with the government.

Importantly, the 2016 FOIA improvement Act did not change FOIA Exemption 4, which protects from disclosure “trade secrets and commercial or financial information obtained from a person [that is] privileged or confidential.” Under Exemption 4, the government is prohibited from disclosing trade secrets or other proprietary/confidential information that any submitter has shared with the government.

Unlike with some of the other FOIA exemptions, in their interpretation of Exemption 4, courts have determined that the government lacks any discretion to disclose trade secret or commercial confidential/proprietary information in response to a FOIA request.

The 2016 FOIA Improvement Act was passed to accelerate the FOIA process and to compel government FOIA officials to provide as much information as soon as possible in response to a FOIA request. The act now imposes a penalty (i.e., the waiver of the statutory FOIA fees) on the agency for failing to provide a timely FOIA response. The act also requires that the FOIA response segregate exempt information from releasable information in the same document, as an agency can no longer simply refuse to produce any document containing exempt information.

In addition, the Act requires the agency to produce electronic copies of documents/data, which can be instantly disseminated by the requesting party, rather than paper documents, in response to a FOIA request.

Furthermore, the act requires the creation of a federal government FOIA portal that allows the same FOIA request to be simultaneously submitted to multiple agencies. As a result, submitters must be poised to respond immediately as soon as the government provides notice that a FOIA request seeks disclosure of the submitter’s data and/or documents.

As an initial step, whenever any person or entity first shares information/data with the government that it does not want disclosed to any third party, the title page and each subsequent page of the confidential document or data should be plainly marked as containing “confidential and proprietary information which is exempt from disclosure under FOIA.”

Next, when the agency contacts the submitter (as FOIA requires) to tell them that a request seeks the disclosure of their information, the submitter should promptly respond by identifying:

1. The specific information within each responsive document that is exempt from disclosure.
2. The particular FOIA exemption (there are nine) that prohibits disclosure (as stated above, Exemption 4 protects trade secrets and confidential/proprietary data)
3. Why that exemption applies to each identified section of data/information that the submitter seeks to protect.

Also, the submitter (or submitter’s counsel) should attempt to maintain an open dialogue with the assigned agency FOIA official throughout the FOIA process to promptly address and resolve any disagreements about what should and should not be disclosed before the agency takes a final disclosure position, which is often difficult to unwind.

Finally, the submitter must be ready to assert a “reverse FOIA” action to prevent the disclosure of trade secrets or other confidential/proprietary information in the event that the agency disregards the submitter’s exemption recommendations before the agency releases the submitter’s trade secrets and confidential information in response to a FOIA request.

Editor’s note: Original Source ‘Washington Technology’

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Doug Proxmire. “New FOIA rules open contractors to more risks of disclosure”

Washington Technology. N.p., Web. 17 February. 2017.

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests.

The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they’re called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world’s best player of the strategy board game Go or classifying cat photos, yet know one fully understood why.

It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

The laws of physics only present this “very special class of problems” — the problems that AI shines at solving. “This tiny fraction of the problems that physics makes us care about and the tiny fraction of problems that neural networks can solve are more or less the same.

Deep learning

Last year, AI accomplished a task many people thought impossible: DeepMind, Google’s deep learning AI system, defeated the world’s best Go player after trouncing the European Go champion. The feat stunned the world because the number of potential Go moves exceeds the number of atoms in the universe, and past Go-playing robots performed only as well as a mediocre human player.

But even more astonishing than DeepMind’s utter rout of its opponents was how it accomplished the task.

“The big mystery behind neural networks is why they work so well,” said study co-author Henry Lin, a physicist at Harvard University. “Almost every problem we throw at them, they crack.”

For instance, DeepMind was not explicitly taught Go strategy and was not trained to recognize classic sequences of moves. Instead, it simply “watched” millions of games, and then played many, many more against itself and other players.

Like newborn babies, these deep-learning algorithms start out “clueless,” yet typically outperform other AI algorithms that are given some of the rules of the game in advance.

Another long-held mystery is why these deep networks are so much better than so-called shallow ones, which contain as little as one layer. Deep networks have a hierarchy and look a bit like connections between neurons in the brain, with lower-level data from many neurons feeding into another “higher” group of neurons, repeated over many layers. In a similar way, deep layers of these neural networks make some calculations, and then feed those results to a higher layer of the program, and so on, he said.

Magical keys or magical locks?

To understand why this process works, Tegmark and Lin decided to flip the question on its head.

“Suppose somebody gave you a key. Every lock you try, it seems to open. One might assume that the key has some magic properties. But another possibility is that all the locks are magical. In the case of neural nets, I suspect it’s a bit of both,” Lin said.

One possibility could be that the “real world” problems have special properties because the real world is very special, Tegmark said.

Take one of the biggest neural-network mysteries: These networks often take what seem to be computationally hairy problems, like the Go game, and somehow find solutions using far fewer calculations than expected.

It turns out that the math employed by neural networks is simplified thanks to a few special properties of the universe. The first is that the equations that govern many laws of physics, from quantum mechanics to gravity to special relativity, are essentially simple math problems, Tegmark said. The equations involve variables raised to a low power (for instance, 4 or less).

What’s more, objects in the universe are governed by locality, meaning they are limited by the speed of light. Practically speaking, that means neighboring objects in the universe are more likely to influence each other than things that are far from each other, Tegmark said.

Many things in the universe also obey what’s called a normal or Gaussian distribution. This is the classic “bell curve” that governs everything from traits such as human height to the speed of gas molecules zooming around in the atmosphere.

Finally, symmetry is woven into the fabric of physics. Think of the veiny pattern on a leaf, or the two arms, eyes and ears of the average human. At the galactic scale, if one travels a light-year to the left or right, or waits a year, the laws of physics are the same, Tegmark said.

Tougher problems to crack

All of these special traits of the universe mean that the problems facing neural networks are actually special math problems that can be radically simplified.

“If you look at the class of data sets that we actually come across in nature, they’re way simpler than the sort of worst-case scenario you might imagine,” Tegmark said.

There are also problems that would be much tougher for neural networks to crack, including encryption schemes that secure information on the web; such schemes just look like random noise.

“If you feed that into a neural network, it’s going to fail just as badly as I am; it’s not going to find any patterns,” Tegmark said.

While the subatomic laws of nature are simple, the equations describing a bumblebee flight are incredibly complicated, while those governing gas molecules remain simple, Lin added. It’s not yet clear whether deep learning will perform just as well describing those complicated bumblebee flights as it will describing gas molecules, he said.

“The point is that some ’emergent’ laws of physics, like those governing an ideal gas, remain quite simple, whereas some become quite complicated. So there is a lot of additional work that needs to be done if one is going to answer in detail why deep learning works so well.” Lin said. “I think the paper raises a lot more questions than it answers!”

 

Editor’s note: Original Source ‘LiveScience’

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Tia Ghose. “The Spooky Secret Behind Artificial Intelligence’s Incredible Power”

LiveScience. N.p., Web. 7 October. 2016.

 

The field of quantum computing is undergoing a rapid shake-up, and engineers at Google have quietly set out a plan to dominate

SOMEWHERE in California, Google is building a device that will usher in a new era for computing. It’s a quantum computer, the largest ever made, designed to prove once and for all that machines exploiting exotic physics can outperform the world’s top supercomputers.

The quantum computing revolution has been a long time coming. In the 1980s, theorists realised that a computer based on quantum mechanics had the potential to vastly outperform ordinary, or classical, computers at certain tasks. But building one was another matter. Only recently has a quantum computer that can beat a classical one gone from a lab curiosity to something that could actually happen. Google wants to create the first.

“They are definitely the world leaders now, there is no doubt about it,” says Simon Devitt at the RIKEN Center for Emergent Matter Science in Japan. “It’s Google’s to lose. If Google’s not the group that does it, then something has gone wrong.”

We have had a glimpse of Google’s intentions. Last month, its engineers quietly published a paper detailing their plans (arxiv.org/abs/1608.00263). Their goal, audaciously named quantum supremacy, is to build the first quantum computer capable of performing a task no classical computer can.

“It’s a blueprint for what they’re planning to do in the next couple of years,” says Scott Aaronson at the University of Texas at Austin, who has discussed the plans with the team.

So how will they do it? Quantum computers process data as quantum bits, or qubits. Unlike classical bits, these can store a mixture of both 0 and 1 at the same time, thanks to the principle of quantum superposition. It’s this potential that gives quantum computers the edge at certain problems, like factoring large numbers. But ordinary computers are also pretty good at such tasks. Showing quantum computers are better would require thousands of qubits, which is far beyond our current technical ability.

Instead, Google wants to claim the prize with just 50 qubits. That’s still an ambitious goal – publicly, they have only announced a 9-qubit computer – but one within reach.

“It’s Google’s to lose. If Google’s not the group that does it, then something has gone wrong”

To help it succeed, Google has brought the fight to quantum’s home turf. It is focusing on a problem that is fiendishly difficult for ordinary computers but that a quantum computer will do naturally: simulating the behavior of a random arrangement of quantum circuits.

Any small variation in the input into those quantum circuits can produce a massively different output, so it’s difficult for the classical computer to cheat with approximations to simplify the problem. “They’re doing a quantum version of chaos,” says Devitt. “The output is essentially random, so you have to compute everything.”

To push classical computing to the limit, Google turned to Edison, one of the most advanced supercomputers in the world, housed at the US National Energy Research Scientific Computing Center. Google had it simulate the behaviour of quantum circuits on increasingly larger grids of qubits, up to a 6 × 7 grid of 42 qubits.

This computation is difficult because as the grid size increases, the amount of memory needed to store everything balloons rapidly. A 6 × 4 grid needed just 268 megabytes, less than found in your average smartphone. The 6 × 7 grid demanded 70 terabytes, roughly 10,000 times that of a high-end PC.

Google stopped there because going to the next size up is currently impossible: a 48-qubit grid would require 2.252 petabytes of memory, almost double that of the top supercomputer in the world. If Google can solve the problem with a 50-qubit quantum computer, it will have beaten every other computer in existence.

Eyes on the prize

By setting out this clear test, Google hopes to avoid the problems that have plagued previous claims of quantum computers outperforming ordinary ones – including some made by Google.

Last year, the firm announced it had solved certain problems 100 million times faster than a classical computer by using a D-Wave quantum computer, a commercially available device with a controversial history. Experts immediately dismissed the results, saying they weren’t a fair comparison.

Google purchased its D-Wave computer in 2013 to figure out whether it could be used to improve search results and artificial intelligence. The following year, the firm hired John Martinis at the University of California, Santa Barbara, to design its own superconducting qubits. “His qubits are way higher quality,” says Aaronson.

It’s Martinis and colleagues who are now attempting to achieve quantum supremacy with 50 qubits, and many believe they will get there soon. “I think this is achievable within two or three years,” says Matthias Troyer at the Swiss Federal Institute of Technology in Zurich. “They’ve showed concrete steps on how they will do it.”

Martinis and colleagues have discussed a number of timelines for reaching this milestone, says Devitt. The earliest is by the end of this year, but that is unlikely. “I’m going to be optimistic and say maybe at the end of next year,” he says. “If they get it done even within the next five years, that will be a tremendous leap forward.”

The first successful quantum supremacy experiment won’t give us computers capable of solving any problem imaginable – based on current theory, those will need to be much larger machines. But having a working, small computer could drive innovation, or augment existing computers, making it the start of a new era.

Aaronson compares it to the first self-sustaining nuclear reaction, achieved by the Manhattan project in Chicago in 1942. “It might be a thing that causes people to say, if we want a full-scalable quantum computer, let’s talk numbers: how many billions of dollars?” he says.

Solving the challenges of building a 50-qubit device will prepare Google to construct something bigger. “It’s absolutely progress to building a fully scalable machine,” says Ian Walmsley at the University of Oxford.

For quantum computers to be truly useful in the long run, we will also need robust quantum error correction, a technique to mitigate the fragility of quantum states. Martinis and others are already working on this, but it will take longer than achieving quantum supremacy.

Still, achieving supremacy won’t be dismissed.

“Once a system hits quantum supremacy and is showing clear scale-up behaviour, it will be a flare in the sky to the private sector,” says Devitt. “It’s ready to move out of the labs.”

“The field is moving much faster than expected,” says Troyer. “It’s time to move quantum computing from science to engineering and really build devices.”

 

Editor’s note: Original Source ‘NewScientist’

This article appeared in print under the headline “Google plans quantum supremacy”

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Jacob Aron. “Revealed: Google’s plan for quantum computer supremacy”

NewScientist. N.p., Web. 31 August. 2016.

Cyber threats are growing in volume, intensity, and sophistication, and they aren’t going away—ever. And recent failures call into question the effectiveness of the billions already sunk into cybersecurity.

How can government agencies reverse the growing gap between security investment and effectiveness? Traditionally, cybersecurity has focused on preventing intrusions, defending firewalls, monitoring ports, and the like. The evolving threat landscape, however, calls for a more dynamic approach.

Whether it’s an inside or external threat, organizations are finding that building firewalls is less effective than anticipating the nature of threats—studying malware in the wild, before it exploits a vulnerability.

The evolving nature of cyber threats calls for a collaborative, networked defense, which means sharing information about vulnerabilities, threats, and remedies among a community of governments, companies, and security vendors. Promoting this kind of exchange between the public and private sectors was a key aim of the US Cyber Security Act of 2012.

Australia has taken a significant lead in working across government and the private sector to shore up collective defenses. The Australian Cyber Security Centre (ACSC) plays many roles, raising awareness of cybersecurity, reporting on the nature and extent of cyber threats, encouraging reporting of incidents, analyzing and investigating specific threats, coordinating national security operations, and heading up the Australian government’s response to hacking incidents. At its core, it’s a hub for information exchange: Private companies, state and territorial governments, and international partners all share discoveries at the ACSC.

The Australian approach begins with good network hygiene: blocking unknown executable files, automatically installing software updates and security patches on all computers, and restricting administrative privileges.

The program then aims to assess adversaries, combining threat data from multiple entities to strengthen collective intelligence. The system uploads results of intrusion attempts to the cloud, giving analysts from multiple agencies a larger pool of attack data to scan for patterns.

Cybersecurity experts have long valued collective intelligence, perhaps first during the 2001 fight against the Li0n worm, which exploited a vulnerability in computer connections.[i] A few analysts noticed a spike in probes to port 53, which supports the Domain Name Service, the system for naming computers and network servers organized around domains. They warned international colleagues, who collaborated on a response. Soon, a system administrator in the Netherlands collected a sample of the worm, which allowed other experts to examine it in a protected testing environment, a “sandbox.” A global community of security practitioners then identified the worm’s mechanism and built a program to detect infections. Within 14 hours, they had publicized their findings widely enough to defend computers worldwide.

A third core security principle is to rethink network security. All too often, leaders think of it as a wall. But a Great Wall can be scaled—a Maginot Line can be avoided. Fixed obstacles are fixed targets, and that’s not optimal cyber defense. Think of cybersecurity like a chess match: Governments need to deploy their advantages and strengths against their opponents’ disadvantages and weaknesses.

Perpetual unpredictability is the best defense. Keep moving. Keep changing. No sitting; no stopping. Plant fake information. Deploy “honeypots” (decoy servers or systems). Move data around. If criminals get in, flood them with bad information

The goal is to modify the defenses so fast that hackers waste money and time probing systems that have already changed. Savvy cybersecurity pros understand this: The more you change the game, the more your opponents’ costs go up, and the more your costs go down. Maybe they’ll move on to an easier target.

Agencies need to learn to love continuous change. New problems will arise. There’ll always be work.

This challenge for governments resembles that facing military strategists as their primary roles shift from war against established nations to continual skirmishes against elusive, unpredictable non-state actors. Your government will inevitably lose some cybersecurity skirmishes, but that doesn’t mean it’s failed. It’s a given that not every encounter will end in victory.

The important test lies in how government officials anticipate and counter moves by an ever-shifting cast of criminal adversaries.

Digital governments will need speed, dexterity, and adaptability to succeed on this new battlefield.

 

Editor’s note: Original Source: ‘Washington Technology’

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William D. Eggers. “Cybersecurity as chess match: A new approach for governments”

Washington Technology. N.p., Web. 12 August. 2016.

Image Credits: DARPA

We all are aware of what submarines are capable of. In the past submarines were the biggest factors which shaped-up the war. Now with technological advancements, the US Military have introduced its very own unmanned submarine hunter. The ocean’s newest predator, a robotic ship designed to help the U.S. military hunt enemy submarines, has completed its first tests at sea.

Called the “Sea Hunter,” the 132-foot (40 meters) unmanned vessel is still getting its figurative sea legs, but the performance tests off the coast of San Diego have steered the project on a course to enter the U.S. Navy’s fleet by 2018, according to the Defense Advanced Research Projects Agency (DARPA), the branch of the U.S. Department of Defense responsible for developing new technologies for the military.

The Sea Hunter “surpassed all performance objectives for speed, maneuverability, stability, sea-keeping, acceleration/deceleration and fuel consumption,” representatives from Leidos, the company developing the Sea Hunter, said in a statement.

The autonomous submarine-hunting ship was christened in April, and is part of a DARPA initiative to expand the use of artificial intelligence in the military. The drone ship’s mission will be to seek out and neutralize enemy submarines, according to the agency.

Initial tests required a pilot on the ship, but the Sea Hunter is designed for autonomous missions.

“When the Sea Hunter is fully operational, it will be able to stay at sea for three months with no crew and very little remote control, which can be done from thousands of miles away,” Leidos officials said in the statement.

Advanced artificial intelligence software will continuously navigate the Sea Hunter safely around other ships and in rough waters, according to DARPA. The technology also allows for remote guidance if a specific mission requires it.

“It will still be sailors who are deciding how, when and where to use this new capability and the technology that has made it possible,” Scott Littlefield, DARPA program manager, said in a statement when the Sea Hunter was christened.

The Sea Hunter still faces a two-year test program, co-sponsored by DARPA and the Office of Naval Research. Leidos said upcoming tests will include assessments of the ship’s sensors, the vessel’s autonomous controls and more.

Other DARPA projects being driven by AI include a potential robot battlefield manager that helps decide the next move in a space war, and an AI technology that could decode enemy messages during air reconnaissance missions.

The world’s first unmanned ship completed its first performance tests, and is set to join the US Navy in 2018 to hunt enemy submarines lurking in the deep.

 

Editor’s note: Original Source: ‘Live Science’

Image Credits: DARPA

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Kacey Deamer. “US Military’s Robotic Submarine Hunter Completes First Tests at Sea”

Live Science. N.p., Web. 4 August. 2016.